brainpy.math module

Basis for Object-oriented Transformations

BrainPyObject([name])	The BrainPyObject class for whole BrainPy ecosystem.
FunAsObject(target[, child_objs, dyn_vars, name])	Transform a Python function as a BrainPyObject.
NodeList(value)	A list to represent a dynamically changed numerical sequence in which its element can be changed during JIT compilation.
NodeDict(*args, **kwargs)	An object to represent a dict of node in which its element can be changed during JIT compilation.
ListVar(value)	A sequence variable, whose contents can be changed during JIT compilation.
DictVar(*args, **kwargs)	A dict variable, in which its element can be changed during JIT compilation.
Variable(value_or_size[, dtype, batch_axis])	The pointer to specify the dynamical variable.
Parameter(value_or_size[, dtype, batch_axis])	The pointer to specify the parameter.
TrainVar(value_or_size[, dtype, batch_axis])	The pointer to specify the trainable variable.
Partial(fun, *args[, child_objs, dyn_vars])

Object-oriented Transformations

grad([func, grad_vars, dyn_vars, ...])	Automatic gradient computation for functions or class objects.
vector_grad(func[, grad_vars, dyn_vars, ...])	Take vector-valued gradients for function func.
jacobian(func[, grad_vars, dyn_vars, ...])	Extending automatic Jacobian (reverse-mode) of func to classes.
jacrev(func[, grad_vars, dyn_vars, ...])	Extending automatic Jacobian (reverse-mode) of func to classes.
jacfwd(func[, grad_vars, dyn_vars, ...])	Extending automatic Jacobian (forward-mode) of func to classes.
hessian(func[, grad_vars, dyn_vars, ...])	Hessian of func as a dense array.
make_loop(body_fun, dyn_vars[, out_vars, ...])	Make a for-loop function, which iterate over inputs.
make_while(cond_fun, body_fun, dyn_vars)	Make a while-loop function.
make_cond(true_fun, false_fun[, dyn_vars])	Make a condition (if-else) function.
cond(pred, true_fun, false_fun, operands[, ...])	Simple conditional statement (if-else) with instance of Variable.
ifelse(conditions, branches[, operands, ...])	If-else control flows looks like native Pythonic programming.
for_loop(body_fun, operands[, dyn_vars, ...])	for-loop control flow with Variable.
while_loop(body_fun, cond_fun, operands[, ...])	while-loop control flow with Variable.
to_object([f, child_objs, dyn_vars, name])	Transform a Python function to BrainPyObject.
function([f, nodes, dyn_vars, name])	Transform a Python function into a BrainPyObject.
jit([func, dyn_vars, child_objs, ...])	JIT (Just-In-Time) compilation for class objects.
ObjectTransform([name])	Object-oriented JAX transformation for BrainPy computation.

Brain Dynamics Dedicated Operators

event_csr_matvec(data, indices, indptr, ...)	Product of a sparse CSR matrix and a dense event vector.
event_info(events)	Collect event information, including event indices, and event number.
event_matvec_prob_conn_homo_weight(events, ...)	Perform the \(y=M@v\) operation, where \(M\) is just-in-time randomly generated with a scalar weight at each position.
event_matvec_prob_conn_uniform_weight(...[, ...])	Perform the \(y=M@v\) operation, where \(M\) is just-in-time randomly generated with a uniform distribution for its value.
event_matvec_prob_conn_normal_weight(events, ...)	Perform the \(y=M@v\) operation, where \(M\) is just-in-time randomly generated with a normal distribution for its value.
matmat_prob_conn_homo_weight(vector, weight, ...)	Perform the \(Y=X@M\) operation, where \(X\), \(Y\) and \(M\) are matrices, and \(M\) is just-in-time randomly generated with a scalar weight at each position.
matmat_prob_conn_uniform_weight(matrix, *, ...)	Perform the \(y=M@v\) operation, where \(M\) is just-in-time randomly generated with a uniform distribution for its value.
matmat_prob_conn_normal_weight(matrix, *, ...)	Perform the \(Y=X@M\) operation, where \(X\), \(Y\) and \(M\) are matrices, and \(M\) is just-in-time randomly generated with a normal distribution for its value.
matvec_prob_conn_homo_weight(vector, weight, ...)	Perform the \(y=M@v\) operation, where \(M\) is just-in-time randomly generated with a scalar weight at each position.
matvec_prob_conn_uniform_weight(vector, *, ...)	Perform the \(y=M@v\) operation, where \(M\) is just-in-time randomly generated with a uniform distribution for its value.
matvec_prob_conn_normal_weight(vector, *, ...)	Perform the \(y=M@v\) operation, where \(M\) is just-in-time randomly generated with a normal distribution for its value.
compile_cpu_signature_with_numba(c, func, ...)
pre2post_sum(pre_values, post_num, post_ids)	The pre-to-post synaptic summation.
pre2post_prod(pre_values, post_num, post_ids)	The pre-to-post synaptic production.
pre2post_max(pre_values, post_num, post_ids)	The pre-to-post synaptic maximization.
pre2post_min(pre_values, post_num, post_ids)	The pre-to-post synaptic minimization.
pre2post_mean(pre_values, post_num, post_ids)	The pre-to-post synaptic mean computation.
pre2post_event_sum(events, pre2post, post_num)	The pre-to-post event-driven synaptic summation with CSR synapse structure.
pre2post_coo_event_sum(events, pre_ids, ...)	The pre-to-post synaptic computation with event-driven summation.
pre2post_event_prod(events, pre2post, post_num)	The pre-to-post synaptic computation with event-driven production.
pre2syn(pre_values, pre_ids)	The pre-to-syn computation.
syn2post_sum(syn_values, post_ids, post_num)	The syn-to-post summation computation.
syn2post(syn_values, post_ids, post_num[, ...])	The syn-to-post summation computation.
syn2post_prod(syn_values, post_ids, post_num)	The syn-to-post product computation.
syn2post_max(syn_values, post_ids, post_num)	The syn-to-post maximum computation.
syn2post_min(syn_values, post_ids, post_num)	The syn-to-post minimization computation.
syn2post_mean(syn_values, post_ids, post_num)	The syn-to-post mean computation.
syn2post_softmax(syn_values, post_ids, post_num)	The syn-to-post softmax computation.
cusparse_csr_matvec(data, indices, indptr, ...)	Product of CSR sparse matrix and a dense vector using cuSPARSE algorithm.
cusparse_coo_matvec(data, row, col, vector, ...)	Product of COO sparse matrix and a dense vector using cuSPARSE algorithm.
csr_matvec(data, indices, indptr, vector, *, ...)	CSR sparse matrix product with a dense vector, which outperforms the cuSPARSE algorithm.
sparse_matmul(A, B)	Sparse matrix multiplication.
coo_to_csr(pre_ids, post_ids, *, num_row)	convert pre_ids, post_ids to (indices, indptr).
csr_to_coo(indices, indptr)	Given CSR (indices, indptr) return COO (row, col)
csr_to_dense(data, indices, indptr, *, shape)	rtype: Array
XLACustomOp([eval_shape, con_compute, ...])	Creating a XLA custom call operator.

Activation Functions

celu(x[, alpha])	Continuously-differentiable exponential linear unit activation.
elu(x[, alpha])	Exponential linear unit activation function.
gelu(x[, approximate])	Gaussian error linear unit activation function.
glu(x[, axis])	Gated linear unit activation function.
hard_tanh(x)	Hard \(\mathrm{tanh}\) activation function.
hard_sigmoid(x)	Hard Sigmoid activation function.
hard_silu(x)	Hard SiLU activation function
hard_swish(x)	Hard SiLU activation function
leaky_relu(x[, negative_slope])	Leaky rectified linear unit activation function.
log_sigmoid(x)	Log-sigmoid activation function.
log_softmax(x[, axis])	Log-Softmax function.
one_hot(x, num_classes, *[, dtype, axis])	One-hot encodes the given indicies.
normalize(x[, axis, mean, variance, epsilon])	Normalizes an array by subtracting mean and dividing by sqrt(var).
relu(x)
relu6(x)	Rectified Linear Unit 6 activation function.
sigmoid(x)	Sigmoid activation function.
soft_sign(x)	Soft-sign activation function.
softmax(x[, axis])	Softmax function.
softplus(x)	Softplus activation function.
silu(x)	SiLU activation function.
swish(x)	SiLU activation function.
selu(x)	Scaled exponential linear unit activation.
identity(x)
tanh	Similar to jax.numpy.tanh function, while it is compatible with brainpy Array/Variable.

Delay Variables

TimeDelay(delay_target, delay_len[, ...])	Delay variable which has a fixed delay time length.
LengthDelay(delay_target, delay_len[, ...])	Delay variable which has a fixed delay length.
NeuTimeDelay(delay_target, delay_len[, ...])	Neutral Time Delay.
NeuLenDelay(delay_target, delay_len[, ...])	Neutral Length Delay.
ROTATE_UPDATE	str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str
CONCAT_UPDATE	str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str

Environment Settings

set_float(dtype)	Set global default float type.
get_float()	Get the default float data type.
set_int(dtype)	Set global default integer type.
get_int()	Get the default int data type.
set_bool(dtype)	Set global default boolean type.
get_bool()	Get the default boolean data type.
set_complex(dtype)	Set global default complex type.
get_complex()	Get the default complex data type.
set_dt(dt)	Set the default numerical integrator precision.
get_dt()	Get the numerical integrator precision.
set_mode(mode)	Set the default computing mode.
get_mode()	Get the default computing mode.
set([mode, dt, x64, complex_, float_, int_, ...])	Set the default computation environment.
set_environment([mode, dt, x64, complex_, ...])	Set the default computation environment.
enable_x64()
disable_x64()
set_platform(platform)	Changes platform to CPU, GPU, or TPU.
get_platform()	Get the computing platform.
set_host_device_count(n)	By default, XLA considers all CPU cores as one device.
clear_buffer_memory([platform])	Clear all on-device buffers.
enable_gpu_memory_preallocation()	Disable pre-allocating the GPU memory.
disable_gpu_memory_preallocation()	Disable pre-allocating the GPU memory.
ditype()	Default int type.
dftype()	Default float type.
environment([mode, dt, x64, complex_, ...])	Context-manager that sets a computing environment for brain dynamics computation.
batching_environment([dt, x64, complex_, ...])	Environment with the batching mode.
training_environment([dt, x64, complex_, ...])	Environment with the training mode.

Computing Modes

Mode()	Base class for computation Mode
NonBatchingMode()	Normal non-batching mode.
BatchingMode([batch_size])	Batching mode.
TrainingMode([batch_size])	Training mode requires data batching.
nonbatching_mode	Normal non-batching mode.
batching_mode	Batching mode.
training_mode	Training mode requires data batching.

Array Interoperability

as_device_array(tensor[, dtype])	Convert the input to a jax.numpy.DeviceArray.
as_jax(tensor[, dtype])	Convert the input to a jax.numpy.DeviceArray.
as_ndarray(tensor[, dtype])	Convert the input to a numpy.ndarray.
as_numpy(tensor[, dtype])	Convert the input to a numpy.ndarray.
as_variable(tensor[, dtype])	Convert the input to a brainpy.math.Variable.

Array Operators with NumPy Syntax

fill_diagonal(a, val[, inplace])
empty(shape[, dtype])
empty_like(a[, dtype, shape])
ones(shape[, dtype])
ones_like(a[, dtype, shape])
zeros(shape[, dtype])
zeros_like(a[, dtype, shape])
array(a[, dtype, copy, order, ndmin])	rtype: Array
asarray(a[, dtype, order])
arange(*args, **kwargs)
linspace(*args, **kwargs)
logspace(*args, **kwargs)
full(shape, fill_value[, dtype, order, like])	Similar to jax.numpy.full function, while it is compatible with brainpy Array/Variable.
full_like(a, fill_value[, dtype, order, ...])	Similar to jax.numpy.full_like function, while it is compatible with brainpy Array/Variable.
eye(N[, M, k, dtype, order, like])	Similar to jax.numpy.eye function, while it is compatible with brainpy Array/Variable.
identity(x)
diag(v[, k])	Similar to jax.numpy.diag function, while it is compatible with brainpy Array/Variable.
tri(N[, M, k, dtype, like])	Similar to jax.numpy.tri function, while it is compatible with brainpy Array/Variable.
tril(m[, k])	Similar to jax.numpy.tril function, while it is compatible with brainpy Array/Variable.
triu(m[, k])	Similar to jax.numpy.triu function, while it is compatible with brainpy Array/Variable.
real(val)	Similar to jax.numpy.real function, while it is compatible with brainpy Array/Variable.
imag(val)	Similar to jax.numpy.imag function, while it is compatible with brainpy Array/Variable.
conj	Similar to jax.numpy.conjugate function, while it is compatible with brainpy Array/Variable.
conjugate	Similar to jax.numpy.conjugate function, while it is compatible with brainpy Array/Variable.
ndim(a)	Similar to jax.numpy.ndim function, while it is compatible with brainpy Array/Variable.
isreal(x)	Similar to jax.numpy.isreal function, while it is compatible with brainpy Array/Variable.
isscalar(element)	Similar to jax.numpy.isscalar function, while it is compatible with brainpy Array/Variable.
add(input, other, *[, alpha, out])	rtype: Optional[Array]
reciprocal	Similar to jax.numpy.reciprocal function, while it is compatible with brainpy Array/Variable.
negative	Similar to jax.numpy.negative function, while it is compatible with brainpy Array/Variable.
positive	Similar to jax.numpy.positive function, while it is compatible with brainpy Array/Variable.
multiply	Similar to jax.numpy.multiply function, while it is compatible with brainpy Array/Variable.
divide	Similar to jax.numpy.true_divide function, while it is compatible with brainpy Array/Variable.
power	Similar to jax.numpy.power function, while it is compatible with brainpy Array/Variable.
subtract	Similar to jax.numpy.subtract function, while it is compatible with brainpy Array/Variable.
true_divide	Similar to jax.numpy.true_divide function, while it is compatible with brainpy Array/Variable.
floor_divide	Similar to jax.numpy.floor_divide function, while it is compatible with brainpy Array/Variable.
float_power	Similar to jax.numpy.float_power function, while it is compatible with brainpy Array/Variable.
fmod	Similar to jax.numpy.fmod function, while it is compatible with brainpy Array/Variable.
mod	Similar to jax.numpy.remainder function, while it is compatible with brainpy Array/Variable.
modf	Similar to jax.numpy.modf function, while it is compatible with brainpy Array/Variable.
divmod	Similar to jax.numpy.divmod function, while it is compatible with brainpy Array/Variable.
remainder	Similar to jax.numpy.remainder function, while it is compatible with brainpy Array/Variable.
abs(input, *[, out])	rtype: Optional[Array]
exp	Similar to jax.numpy.exp function, while it is compatible with brainpy Array/Variable.
exp2	Similar to jax.numpy.exp2 function, while it is compatible with brainpy Array/Variable.
expm1	Similar to jax.numpy.expm1 function, while it is compatible with brainpy Array/Variable.
log	Similar to jax.numpy.log function, while it is compatible with brainpy Array/Variable.
log10	Similar to jax.numpy.log10 function, while it is compatible with brainpy Array/Variable.
log1p	Similar to jax.numpy.log1p function, while it is compatible with brainpy Array/Variable.
log2	Similar to jax.numpy.log2 function, while it is compatible with brainpy Array/Variable.
logaddexp	Similar to jax.numpy.logaddexp function, while it is compatible with brainpy Array/Variable.
logaddexp2	Similar to jax.numpy.logaddexp2 function, while it is compatible with brainpy Array/Variable.
lcm	Similar to jax.numpy.lcm function, while it is compatible with brainpy Array/Variable.
gcd	Similar to jax.numpy.gcd function, while it is compatible with brainpy Array/Variable.
arccos(input, *[, out])	rtype: Optional[Array]
arccosh(input, *[, out])	rtype: Optional[Array]
arcsin(input, *[, out])	rtype: Optional[Array]
arcsinh	Similar to jax.numpy.arcsinh function, while it is compatible with brainpy Array/Variable.
arctan(input, *[, out])	rtype: Optional[Array]
arctan2	Similar to jax.numpy.arctan2 function, while it is compatible with brainpy Array/Variable.
arctanh	Similar to jax.numpy.arctanh function, while it is compatible with brainpy Array/Variable.
cos	Similar to jax.numpy.cos function, while it is compatible with brainpy Array/Variable.
cosh	Similar to jax.numpy.cosh function, while it is compatible with brainpy Array/Variable.
sin	Similar to jax.numpy.sin function, while it is compatible with brainpy Array/Variable.
sinc(x)	Similar to jax.numpy.sinc function, while it is compatible with brainpy Array/Variable.
sinh	Similar to jax.numpy.sinh function, while it is compatible with brainpy Array/Variable.
tan	Similar to jax.numpy.tan function, while it is compatible with brainpy Array/Variable.
tanh	Similar to jax.numpy.tanh function, while it is compatible with brainpy Array/Variable.
deg2rad	Similar to jax.numpy.deg2rad function, while it is compatible with brainpy Array/Variable.
hypot	Similar to jax.numpy.hypot function, while it is compatible with brainpy Array/Variable.
rad2deg	Similar to jax.numpy.rad2deg function, while it is compatible with brainpy Array/Variable.
degrees	Similar to jax.numpy.rad2deg function, while it is compatible with brainpy Array/Variable.
radians	Similar to jax.numpy.deg2rad function, while it is compatible with brainpy Array/Variable.
round(a[, decimals, out])	Similar to jax.numpy.around function, while it is compatible with brainpy Array/Variable.
around(a[, decimals, out])	Similar to jax.numpy.around function, while it is compatible with brainpy Array/Variable.
round_(a[, decimals, out])	Similar to jax.numpy.around function, while it is compatible with brainpy Array/Variable.
rint	Similar to jax.numpy.rint function, while it is compatible with brainpy Array/Variable.
floor	Similar to jax.numpy.floor function, while it is compatible with brainpy Array/Variable.
ceil	Similar to jax.numpy.ceil function, while it is compatible with brainpy Array/Variable.
trunc	Similar to jax.numpy.trunc function, while it is compatible with brainpy Array/Variable.
fix(x[, out])	Similar to jax.numpy.fix function, while it is compatible with brainpy Array/Variable.
prod(a[, axis, dtype, out, keepdims, ...])	Similar to jax.numpy.prod function, while it is compatible with brainpy Array/Variable.
sum(a[, axis, dtype, out, keepdims, ...])	Similar to jax.numpy.sum function, while it is compatible with brainpy Array/Variable.
diff(a[, n, axis, prepend, append])	Similar to jax.numpy.diff function, while it is compatible with brainpy Array/Variable.
median(a[, axis, out, overwrite_input, keepdims])	Similar to jax.numpy.median function, while it is compatible with brainpy Array/Variable.
nancumprod(a[, axis, dtype, out])	Similar to jax.numpy.nancumprod function, while it is compatible with brainpy Array/Variable.
nancumsum(a[, axis, dtype, out])	Similar to jax.numpy.nancumsum function, while it is compatible with brainpy Array/Variable.
nanprod(a[, axis, dtype, out, keepdims])	Similar to jax.numpy.nanprod function, while it is compatible with brainpy Array/Variable.
nansum(a[, axis, dtype, out, keepdims])	Similar to jax.numpy.nansum function, while it is compatible with brainpy Array/Variable.
cumprod(a[, axis, dtype, out])	Similar to jax.numpy.cumprod function, while it is compatible with brainpy Array/Variable.
cumsum(a[, axis, dtype, out])	Similar to jax.numpy.cumsum function, while it is compatible with brainpy Array/Variable.
ediff1d(ary[, to_end, to_begin])	Similar to jax.numpy.ediff1d function, while it is compatible with brainpy Array/Variable.
cross(a, b[, axisa, axisb, axisc, axis])	Similar to jax.numpy.cross function, while it is compatible with brainpy Array/Variable.
trapz(y[, x, dx, axis])	Similar to jax.numpy.trapz function, while it is compatible with brainpy Array/Variable.
isfinite	Similar to jax.numpy.isfinite function, while it is compatible with brainpy Array/Variable.
isinf	Similar to jax.numpy.isinf function, while it is compatible with brainpy Array/Variable.
isnan	Similar to jax.numpy.isnan function, while it is compatible with brainpy Array/Variable.
signbit	Similar to jax.numpy.signbit function, while it is compatible with brainpy Array/Variable.
copysign	Similar to jax.numpy.copysign function, while it is compatible with brainpy Array/Variable.
nextafter	Similar to jax.numpy.nextafter function, while it is compatible with brainpy Array/Variable.
ldexp	Similar to jax.numpy.ldexp function, while it is compatible with brainpy Array/Variable.
frexp	Similar to jax.numpy.frexp function, while it is compatible with brainpy Array/Variable.
convolve(a, v[, mode])	Similar to jax.numpy.convolve function, while it is compatible with brainpy Array/Variable.
sqrt	Similar to jax.numpy.sqrt function, while it is compatible with brainpy Array/Variable.
cbrt	Similar to jax.numpy.cbrt function, while it is compatible with brainpy Array/Variable.
square	Similar to jax.numpy.square function, while it is compatible with brainpy Array/Variable.
absolute(input, *[, out])	rtype: Optional[Array]
fabs	Similar to jax.numpy.fabs function, while it is compatible with brainpy Array/Variable.
sign	Similar to jax.numpy.sign function, while it is compatible with brainpy Array/Variable.
heaviside	Similar to jax.numpy.heaviside function, while it is compatible with brainpy Array/Variable.
maximum	Similar to jax.numpy.maximum function, while it is compatible with brainpy Array/Variable.
minimum	Similar to jax.numpy.minimum function, while it is compatible with brainpy Array/Variable.
fmax	Similar to jax.numpy.fmax function, while it is compatible with brainpy Array/Variable.
fmin	Similar to jax.numpy.fmin function, while it is compatible with brainpy Array/Variable.
interp(x, xp, fp[, left, right, period])	Similar to jax.numpy.interp function, while it is compatible with brainpy Array/Variable.
clip(a, a_min, a_max[, out])	Similar to jax.numpy.clip function, while it is compatible with brainpy Array/Variable.
angle(input, *[, out])	rtype: Optional[Array]
bitwise_and	Similar to jax.numpy.bitwise_and function, while it is compatible with brainpy Array/Variable.
bitwise_not	Similar to jax.numpy.invert function, while it is compatible with brainpy Array/Variable.
bitwise_or	Similar to jax.numpy.bitwise_or function, while it is compatible with brainpy Array/Variable.
bitwise_xor	Similar to jax.numpy.bitwise_xor function, while it is compatible with brainpy Array/Variable.
invert	Similar to jax.numpy.invert function, while it is compatible with brainpy Array/Variable.
left_shift	Similar to jax.numpy.left_shift function, while it is compatible with brainpy Array/Variable.
right_shift	Similar to jax.numpy.right_shift function, while it is compatible with brainpy Array/Variable.
equal	Similar to jax.numpy.equal function, while it is compatible with brainpy Array/Variable.
not_equal	Similar to jax.numpy.not_equal function, while it is compatible with brainpy Array/Variable.
greater	Similar to jax.numpy.greater function, while it is compatible with brainpy Array/Variable.
greater_equal	Similar to jax.numpy.greater_equal function, while it is compatible with brainpy Array/Variable.
less	Similar to jax.numpy.less function, while it is compatible with brainpy Array/Variable.
less_equal	Similar to jax.numpy.less_equal function, while it is compatible with brainpy Array/Variable.
array_equal(a1, a2[, equal_nan])	Similar to jax.numpy.array_equal function, while it is compatible with brainpy Array/Variable.
isclose(a, b[, rtol, atol, equal_nan])	Similar to jax.numpy.isclose function, while it is compatible with brainpy Array/Variable.
allclose(a, b[, rtol, atol, equal_nan])	Similar to jax.numpy.allclose function, while it is compatible with brainpy Array/Variable.
logical_and	Similar to jax.numpy.logical_and function, while it is compatible with brainpy Array/Variable.
logical_not	Similar to jax.numpy.logical_not function, while it is compatible with brainpy Array/Variable.
logical_or	Similar to jax.numpy.logical_or function, while it is compatible with brainpy Array/Variable.
logical_xor	Similar to jax.numpy.logical_xor function, while it is compatible with brainpy Array/Variable.
all(a[, axis, out, keepdims, where])	Similar to jax.numpy.all function, while it is compatible with brainpy Array/Variable.
any(a[, axis, out, keepdims, where])	Similar to jax.numpy.any function, while it is compatible with brainpy Array/Variable.
alltrue(a[, axis, out, keepdims, where])	Similar to jax.numpy.all function, while it is compatible with brainpy Array/Variable.
sometrue(a[, axis, out, keepdims, where])	Similar to jax.numpy.any function, while it is compatible with brainpy Array/Variable.
shape(a)	Return the shape of an array.
size(a[, axis])	Return the number of elements along a given axis.
reshape(a, newshape[, order])	Similar to jax.numpy.reshape function, while it is compatible with brainpy Array/Variable.
ravel(a[, order])	Similar to jax.numpy.ravel function, while it is compatible with brainpy Array/Variable.
moveaxis(a, source, destination)	Similar to jax.numpy.moveaxis function, while it is compatible with brainpy Array/Variable.
transpose(a[, axes])	Similar to jax.numpy.transpose function, while it is compatible with brainpy Array/Variable.
swapaxes(a, axis1, axis2)	Similar to jax.numpy.swapaxes function, while it is compatible with brainpy Array/Variable.
concatenate	Similar to jax.numpy.concatenate function, while it is compatible with brainpy Array/Variable.
stack(arrays[, axis, out])	Similar to jax.numpy.stack function, while it is compatible with brainpy Array/Variable.
vstack(tup)	Similar to jax.numpy.vstack function, while it is compatible with brainpy Array/Variable.
hstack(tup)	Similar to jax.numpy.hstack function, while it is compatible with brainpy Array/Variable.
dstack(tup)	Similar to jax.numpy.dstack function, while it is compatible with brainpy Array/Variable.
column_stack(tup)	Similar to jax.numpy.column_stack function, while it is compatible with brainpy Array/Variable.
split(ary, indices_or_sections[, axis])	Similar to jax.numpy.split function, while it is compatible with brainpy Array/Variable.
dsplit(ary, indices_or_sections)	Similar to jax.numpy.dsplit function, while it is compatible with brainpy Array/Variable.
hsplit(ary, indices_or_sections)	Similar to jax.numpy.hsplit function, while it is compatible with brainpy Array/Variable.
vsplit(ary, indices_or_sections)	Similar to jax.numpy.vsplit function, while it is compatible with brainpy Array/Variable.
tile(A, reps)	Similar to jax.numpy.tile function, while it is compatible with brainpy Array/Variable.
repeat(a, repeats[, axis])	Similar to jax.numpy.repeat function, while it is compatible with brainpy Array/Variable.
unique(ar[, return_index, return_inverse, ...])	Similar to jax.numpy.unique function, while it is compatible with brainpy Array/Variable.
append(arr, values[, axis])	Similar to jax.numpy.append function, while it is compatible with brainpy Array/Variable.
flip(m[, axis])	Similar to jax.numpy.flip function, while it is compatible with brainpy Array/Variable.
fliplr(m)	Similar to jax.numpy.fliplr function, while it is compatible with brainpy Array/Variable.
flipud(m)	Similar to jax.numpy.flipud function, while it is compatible with brainpy Array/Variable.
roll(a, shift[, axis])	Similar to jax.numpy.roll function, while it is compatible with brainpy Array/Variable.
atleast_1d(*arys)	Similar to jax.numpy.atleast_1d function, while it is compatible with brainpy Array/Variable.
atleast_2d(*arys)	Similar to jax.numpy.atleast_2d function, while it is compatible with brainpy Array/Variable.
atleast_3d(*arys)	Similar to jax.numpy.atleast_3d function, while it is compatible with brainpy Array/Variable.
expand_dims(a, axis)	Similar to jax.numpy.expand_dims function, while it is compatible with brainpy Array/Variable.
squeeze(a[, axis])	Similar to jax.numpy.squeeze function, while it is compatible with brainpy Array/Variable.
sort(a[, axis, kind, order])	Similar to jax.numpy.sort function, while it is compatible with brainpy Array/Variable.
argsort(a[, axis, kind, order])	Similar to jax.numpy.argsort function, while it is compatible with brainpy Array/Variable.
argmax(a[, axis, out])	Similar to jax.numpy.argmax function, while it is compatible with brainpy Array/Variable.
argmin(a[, axis, out])	Similar to jax.numpy.argmin function, while it is compatible with brainpy Array/Variable.
argwhere(a)	Similar to jax.numpy.argwhere function, while it is compatible with brainpy Array/Variable.
nonzero(a)	Similar to jax.numpy.nonzero function, while it is compatible with brainpy Array/Variable.
flatnonzero(a)	Similar to jax.numpy.flatnonzero function, while it is compatible with brainpy Array/Variable.
where	Similar to jax.numpy.where function, while it is compatible with brainpy Array/Variable.
searchsorted(a, v[, side, sorter])	Similar to jax.numpy.searchsorted function, while it is compatible with brainpy Array/Variable.
extract(condition, arr)	Similar to jax.numpy.extract function, while it is compatible with brainpy Array/Variable.
count_nonzero(a[, axis, keepdims])	Similar to jax.numpy.count_nonzero function, while it is compatible with brainpy Array/Variable.
max(a[, axis, out, keepdims, initial, where])	Similar to jax.numpy.amax function, while it is compatible with brainpy Array/Variable.
min(a[, axis, out, keepdims, initial, where])	Similar to jax.numpy.amin function, while it is compatible with brainpy Array/Variable.
amax(a[, axis, out, keepdims, initial, where])	Similar to jax.numpy.amax function, while it is compatible with brainpy Array/Variable.
amin(a[, axis, out, keepdims, initial, where])	Similar to jax.numpy.amin function, while it is compatible with brainpy Array/Variable.
array_split(ary, indices_or_sections[, axis])	Similar to jax.numpy.array_split function, while it is compatible with brainpy Array/Variable.
meshgrid(*xi[, copy, sparse, indexing])	Similar to jax.numpy.meshgrid function, while it is compatible with brainpy Array/Variable.
vander(x[, N, increasing])	Similar to jax.numpy.vander function, while it is compatible with brainpy Array/Variable.
tril_indices(*args, **kwargs)	Return the indices for the lower-triangle of an (n, m) array.
tril_indices_from(arr[, k])	Similar to jax.numpy.tril_indices_from function, while it is compatible with brainpy Array/Variable.
triu_indices(*args, **kwargs)	Return the indices for the upper-triangle of an (n, m) array.
triu_indices_from(arr[, k])	Similar to jax.numpy.triu_indices_from function, while it is compatible with brainpy Array/Variable.
take(a, indices[, axis, out, mode])	Similar to jax.numpy.take function, while it is compatible with brainpy Array/Variable.
select(condlist, choicelist[, default])	Similar to jax.numpy.select function, while it is compatible with brainpy Array/Variable.
nanmin(a[, axis, out, keepdims])	Similar to jax.numpy.nanmin function, while it is compatible with brainpy Array/Variable.
nanmax(a[, axis, out, keepdims])	Similar to jax.numpy.nanmax function, while it is compatible with brainpy Array/Variable.
ptp(a[, axis, out, keepdims])	Similar to jax.numpy.ptp function, while it is compatible with brainpy Array/Variable.
percentile(a, q[, axis, out, ...])	Similar to jax.numpy.percentile function, while it is compatible with brainpy Array/Variable.
nanpercentile(a, q[, axis, out, ...])	Similar to jax.numpy.nanpercentile function, while it is compatible with brainpy Array/Variable.
quantile(a, q[, axis, out, overwrite_input, ...])	Similar to jax.numpy.quantile function, while it is compatible with brainpy Array/Variable.
nanquantile(a, q[, axis, out, ...])	Similar to jax.numpy.nanquantile function, while it is compatible with brainpy Array/Variable.
average(a[, axis, weights, returned])	Similar to jax.numpy.average function, while it is compatible with brainpy Array/Variable.
mean(a[, axis, dtype, out, keepdims, where])	Similar to jax.numpy.mean function, while it is compatible with brainpy Array/Variable.
std(a[, axis, dtype, out, ddof, keepdims, where])	Similar to jax.numpy.std function, while it is compatible with brainpy Array/Variable.
var(a[, axis, dtype, out, ddof, keepdims, where])	Similar to jax.numpy.var function, while it is compatible with brainpy Array/Variable.
nanmedian(a[, axis, out, overwrite_input, ...])	Similar to jax.numpy.nanmedian function, while it is compatible with brainpy Array/Variable.
nanmean(a[, axis, dtype, out, keepdims])	Similar to jax.numpy.nanmean function, while it is compatible with brainpy Array/Variable.
nanstd(a[, axis, dtype, out, ddof, keepdims])	Similar to jax.numpy.nanstd function, while it is compatible with brainpy Array/Variable.
nanvar(a[, axis, dtype, out, ddof, keepdims])	Similar to jax.numpy.nanvar function, while it is compatible with brainpy Array/Variable.
corrcoef(x[, y, rowvar, bias, ddof, dtype])	Similar to jax.numpy.corrcoef function, while it is compatible with brainpy Array/Variable.
correlate(a, v[, mode])	Similar to jax.numpy.correlate function, while it is compatible with brainpy Array/Variable.
cov(m[, y, rowvar, bias, ddof, fweights, ...])	Similar to jax.numpy.cov function, while it is compatible with brainpy Array/Variable.
histogram(a[, bins, range, normed, weights, ...])	Similar to jax.numpy.histogram function, while it is compatible with brainpy Array/Variable.
bincount	Similar to jax.numpy.bincount function, while it is compatible with brainpy Array/Variable.
digitize(x, bins[, right])	Similar to jax.numpy.digitize function, while it is compatible with brainpy Array/Variable.
bartlett(M)	Similar to jax.numpy.bartlett function, while it is compatible with brainpy Array/Variable.
blackman(M)	Similar to jax.numpy.blackman function, while it is compatible with brainpy Array/Variable.
hamming(M)	Similar to jax.numpy.hamming function, while it is compatible with brainpy Array/Variable.
hanning(M)	Similar to jax.numpy.hanning function, while it is compatible with brainpy Array/Variable.
kaiser(M, beta)	Similar to jax.numpy.kaiser function, while it is compatible with brainpy Array/Variable.
dot	Similar to jax.numpy.dot function, while it is compatible with brainpy Array/Variable.
vdot	Similar to jax.numpy.vdot function, while it is compatible with brainpy Array/Variable.
inner	Similar to jax.numpy.inner function, while it is compatible with brainpy Array/Variable.
outer(a, b[, out])	Similar to jax.numpy.outer function, while it is compatible with brainpy Array/Variable.
kron(a, b)	Similar to jax.numpy.kron function, while it is compatible with brainpy Array/Variable.
matmul	Similar to jax.numpy.matmul function, while it is compatible with brainpy Array/Variable.
trace(a[, offset, axis1, axis2, dtype, out])	Similar to jax.numpy.trace function, while it is compatible with brainpy Array/Variable.
product(a[, axis, dtype, out, keepdims, ...])	Similar to jax.numpy.prod function, while it is compatible with brainpy Array/Variable.
row_stack(tup)	Similar to jax.numpy.vstack function, while it is compatible with brainpy Array/Variable.
apply_over_axes(func, a, axes)	Similar to jax.numpy.apply_over_axes function, while it is compatible with brainpy Array/Variable.
apply_along_axis(func1d, axis, arr, *args, ...)	Similar to jax.numpy.apply_along_axis function, while it is compatible with brainpy Array/Variable.
array_equiv(a1, a2)	Similar to jax.numpy.array_equiv function, while it is compatible with brainpy Array/Variable.
array_repr(arr[, max_line_width, precision, ...])	Similar to jax.numpy.array_repr function, while it is compatible with brainpy Array/Variable.
array_str(a[, max_line_width, precision, ...])	Similar to jax.numpy.array_str function, while it is compatible with brainpy Array/Variable.
block(arrays)	Similar to jax.numpy.block function, while it is compatible with brainpy Array/Variable.
broadcast_arrays(*args[, subok])	Similar to jax.numpy.broadcast_arrays function, while it is compatible with brainpy Array/Variable.
broadcast_shapes(*args)	Similar to jax.numpy.broadcast_shapes function, while it is compatible with brainpy Array/Variable.
broadcast_to(array, shape[, subok])	Similar to jax.numpy.broadcast_to function, while it is compatible with brainpy Array/Variable.
compress(condition, a[, axis, out])	Similar to jax.numpy.compress function, while it is compatible with brainpy Array/Variable.
cumproduct(a[, axis, dtype, out])	Similar to jax.numpy.cumprod function, while it is compatible with brainpy Array/Variable.
diag_indices(n[, ndim])	Similar to jax.numpy.diag_indices function, while it is compatible with brainpy Array/Variable.
diag_indices_from(arr)	Similar to jax.numpy.diag_indices_from function, while it is compatible with brainpy Array/Variable.
diagflat(v[, k])	Similar to jax.numpy.diagflat function, while it is compatible with brainpy Array/Variable.
diagonal(a[, offset, axis1, axis2])	Similar to jax.numpy.diagonal function, while it is compatible with brainpy Array/Variable.
einsum(*operands[, out, optimize])	Similar to jax.numpy.einsum function, while it is compatible with brainpy Array/Variable.
einsum_path(*operands[, optimize, einsum_call])	Similar to jax.numpy.einsum_path function, while it is compatible with brainpy Array/Variable.
geomspace(start, stop[, num, endpoint, ...])	Similar to jax.numpy.geomspace function, while it is compatible with brainpy Array/Variable.
gradient(f, *varargs[, axis, edge_order])	Similar to jax.numpy.gradient function, while it is compatible with brainpy Array/Variable.
histogram2d(x, y[, bins, range, normed, ...])	Similar to jax.numpy.histogram2d function, while it is compatible with brainpy Array/Variable.
histogram_bin_edges(a[, bins, range, weights])	Similar to jax.numpy.histogram_bin_edges function, while it is compatible with brainpy Array/Variable.
histogramdd(sample[, bins, range, normed, ...])	Similar to jax.numpy.histogramdd function, while it is compatible with brainpy Array/Variable.
i0(x)	Similar to jax.numpy.i0 function, while it is compatible with brainpy Array/Variable.
in1d(ar1, ar2[, assume_unique, invert])	Similar to jax.numpy.in1d function, while it is compatible with brainpy Array/Variable.
indices(dimensions[, dtype, sparse])	Similar to jax.numpy.indices function, while it is compatible with brainpy Array/Variable.
insert(arr, obj, values[, axis])	Similar to jax.numpy.insert function, while it is compatible with brainpy Array/Variable.
intersect1d(ar1, ar2[, assume_unique, ...])	Similar to jax.numpy.intersect1d function, while it is compatible with brainpy Array/Variable.
iscomplex(x)	Similar to jax.numpy.iscomplex function, while it is compatible with brainpy Array/Variable.
isin(element, test_elements[, ...])	Similar to jax.numpy.isin function, while it is compatible with brainpy Array/Variable.
ix_(*args)	Similar to jax.numpy.ix_ function, while it is compatible with brainpy Array/Variable.
lexsort	Similar to jax.numpy.lexsort function, while it is compatible with brainpy Array/Variable.
load(file[, mmap_mode, allow_pickle, ...])	Similar to jax.numpy.load function, while it is compatible with brainpy Array/Variable.
save(file, arr[, allow_pickle, fix_imports])	Similar to jax.numpy.save function, while it is compatible with brainpy Array/Variable.
savez(file, *args, **kwds)	Similar to jax.numpy.savez function, while it is compatible with brainpy Array/Variable.
mask_indices(n, mask_func[, k])	Similar to jax.numpy.mask_indices function, while it is compatible with brainpy Array/Variable.
msort(a)	Return a copy of an array sorted along the first axis.
nan_to_num(x[, copy, nan, posinf, neginf])	Similar to jax.numpy.nan_to_num function, while it is compatible with brainpy Array/Variable.
nanargmax(a[, axis])	Similar to jax.numpy.nanargmax function, while it is compatible with brainpy Array/Variable.
setdiff1d(ar1, ar2[, assume_unique])	Similar to jax.numpy.setdiff1d function, while it is compatible with brainpy Array/Variable.
nanargmin(a[, axis])	Similar to jax.numpy.nanargmin function, while it is compatible with brainpy Array/Variable.
pad(array, pad_width[, mode])	Similar to jax.numpy.pad function, while it is compatible with brainpy Array/Variable.
poly(seq_of_zeros)	Similar to jax.numpy.poly function, while it is compatible with brainpy Array/Variable.
polyadd(a1, a2)	Similar to jax.numpy.polyadd function, while it is compatible with brainpy Array/Variable.
polyder(p[, m])	Similar to jax.numpy.polyder function, while it is compatible with brainpy Array/Variable.
polyfit(x, y, deg[, rcond, full, w, cov])	Similar to jax.numpy.polyfit function, while it is compatible with brainpy Array/Variable.
polyint(p[, m, k])	Similar to jax.numpy.polyint function, while it is compatible with brainpy Array/Variable.
polymul(a1, a2)	Similar to jax.numpy.polymul function, while it is compatible with brainpy Array/Variable.
polysub(a1, a2)	Similar to jax.numpy.polysub function, while it is compatible with brainpy Array/Variable.
polyval(p, x)	Similar to jax.numpy.polyval function, while it is compatible with brainpy Array/Variable.
resize(a, new_shape)	Similar to jax.numpy.resize function, while it is compatible with brainpy Array/Variable.
rollaxis(a, axis[, start])	Similar to jax.numpy.rollaxis function, while it is compatible with brainpy Array/Variable.
roots(p)	Similar to jax.numpy.roots function, while it is compatible with brainpy Array/Variable.
rot90(m[, k, axes])	Similar to jax.numpy.rot90 function, while it is compatible with brainpy Array/Variable.
setxor1d(ar1, ar2[, assume_unique])	Similar to jax.numpy.setxor1d function, while it is compatible with brainpy Array/Variable.
tensordot(a, b[, axes])	Similar to jax.numpy.tensordot function, while it is compatible with brainpy Array/Variable.
trim_zeros(filt[, trim])	Similar to jax.numpy.trim_zeros function, while it is compatible with brainpy Array/Variable.
union1d(ar1, ar2)	Similar to jax.numpy.union1d function, while it is compatible with brainpy Array/Variable.
unravel_index	Similar to jax.numpy.unravel_index function, while it is compatible with brainpy Array/Variable.
unwrap(p[, discont, axis, period])	Similar to jax.numpy.unwrap function, while it is compatible with brainpy Array/Variable.
take_along_axis(arr, indices, axis)	Similar to jax.numpy.take_along_axis function, while it is compatible with brainpy Array/Variable.
can_cast	Similar to jax.numpy.can_cast function, while it is compatible with brainpy Array/Variable.
choose(a, choices[, out, mode])	Similar to jax.numpy.choose function, while it is compatible with brainpy Array/Variable.
copy(a[, order, subok])	Similar to jax.numpy.copy function, while it is compatible with brainpy Array/Variable.
frombuffer	Similar to jax.numpy.frombuffer function, while it is compatible with brainpy Array/Variable.
fromfile(*args, **kwargs)	Similar to jax.numpy.fromfile function, while it is compatible with brainpy Array/Variable.
fromfunction(function, shape, *[, dtype, like])	Similar to jax.numpy.fromfunction function, while it is compatible with brainpy Array/Variable.
fromiter(*args, **kwargs)	Similar to jax.numpy.fromiter function, while it is compatible with brainpy Array/Variable.
fromstring	Similar to jax.numpy.fromstring function, while it is compatible with brainpy Array/Variable.
get_printoptions()	Return the current print options.
iscomplexobj(x)	Similar to jax.numpy.iscomplexobj function, while it is compatible with brainpy Array/Variable.
isneginf(x[, out])	Similar to jax.numpy.isneginf function, while it is compatible with brainpy Array/Variable.
isposinf(x[, out])	Similar to jax.numpy.isposinf function, while it is compatible with brainpy Array/Variable.
isrealobj(x)	Similar to jax.numpy.isrealobj function, while it is compatible with brainpy Array/Variable.
issubdtype(arg1, arg2)	Returns True if first argument is a typecode lower/equal in type hierarchy.
issubsctype(arg1, arg2)	Determine if the first argument is a subclass of the second argument.
iterable(y)	Similar to jax.numpy.iterable function, while it is compatible with brainpy Array/Variable.
packbits	Similar to jax.numpy.packbits function, while it is compatible with brainpy Array/Variable.
piecewise(x, condlist, funclist, *args, **kw)	Similar to jax.numpy.piecewise function, while it is compatible with brainpy Array/Variable.
printoptions(*args, **kwargs)	Context manager for setting print options.
set_printoptions([precision, threshold, ...])	Set printing options.
promote_types(a, b)	Similar to jax.numpy.promote_types function, while it is compatible with brainpy Array/Variable.
ravel_multi_index	Similar to jax.numpy.ravel_multi_index function, while it is compatible with brainpy Array/Variable.
result_type	Similar to jax.numpy.result_type function, while it is compatible with brainpy Array/Variable.
sort_complex(a)	Similar to jax.numpy.sort_complex function, while it is compatible with brainpy Array/Variable.
unpackbits	Similar to jax.numpy.unpackbits function, while it is compatible with brainpy Array/Variable.
delete(arr, obj[, axis])	Similar to jax.numpy.delete function, while it is compatible with brainpy Array/Variable.
add_newdoc(place, obj, doc[, warn_on_python])	Add documentation to an existing object, typically one defined in C
array2string(a[, max_line_width, precision, ...])
asanyarray(a[, dtype, order])
ascontiguousarray(a[, dtype, order])
asfarray(a[, dtype])
asscalar(a)
common_type(*arrays)
disp(mesg[, device, linefeed])	Display a message on a device.
genfromtxt(*args, **kwargs)
loadtxt(*args, **kwargs)
info([object, maxwidth, output, toplevel])	Get help information for a function, class, or module.
issubclass_(arg1, arg2)	Determine if a class is a subclass of a second class.
place(arr, mask, vals)
polydiv(u, v)	Similar to jax.numpy.polydiv function, while it is compatible with brainpy Array/Variable.
put(a, ind, v)
putmask(a, mask, values)
safe_eval(source)
savetxt(fname, X[, fmt, delimiter, newline, ...])
savez_compressed(file, *args, **kwds)
show_config()	Show libraries in the system on which NumPy was built.
typename(char)	Return a description for the given data type code.
copyto(dst, src)
matrix(data[, dtype])
asmatrix(data[, dtype])
mat(data[, dtype])
dtype(dtype[, align, copy])	Create a data type object.
finfo(dtype)	Machine limits for floating point types.
iinfo(type)	Machine limits for integer types.
e	Convert a string or number to a floating point number, if possible.
pi	Convert a string or number to a floating point number, if possible.
inf	Convert a string or number to a floating point number, if possible.
add_docstring(obj, docstring)	Add a docstring to a built-in obj if possible.
add_newdoc_ufunc	add_ufunc_docstring(ufunc, new_docstring)

Array Operators with PyTorch Syntax

flatten(input[, start_dim, end_dim])	Flattens input by reshaping it into a one-dimensional tensor.
cat	Similar to jax.numpy.concatenate function, while it is compatible with brainpy Array/Variable.
unsqueeze(input, dim)	Returns a new tensor with a dimension of size one inserted at the specified position.
abs(input, *[, out])	rtype: Optional[Array]
absolute(input, *[, out])	rtype: Optional[Array]
acos(input, *[, out])	rtype: Optional[Array]
arccos(input, *[, out])	rtype: Optional[Array]
acosh(input, *[, out])	rtype: Optional[Array]
arccosh(input, *[, out])	rtype: Optional[Array]
add(input, other, *[, alpha, out])	rtype: Optional[Array]
addcdiv(input, tensor1, tensor2, *[, value, out])	rtype: Optional[Array]
addcmul(input, tensor1, tensor2, *[, value, out])	rtype: Optional[Array]
angle(input, *[, out])	rtype: Optional[Array]
asin(input, *[, out])	rtype: Optional[Array]
arcsin(input, *[, out])	rtype: Optional[Array]
asinh(input, *[, out])	rtype: Optional[Array]
atan(input, *[, out])	rtype: Optional[Array]
arctan(input, *[, out])	rtype: Optional[Array]
atan2(input, *[, out])	rtype: Optional[Array]
atanh(input, *[, out])	rtype: Optional[Array]
Tensor	alias of Array

Array Operators with TensorFlow Syntax

concat	Similar to jax.numpy.concatenate function, while it is compatible with brainpy Array/Variable.
reduce_sum(a[, axis, dtype, out, keepdims, ...])	Similar to jax.numpy.sum function, while it is compatible with brainpy Array/Variable.
reduce_max(input_tensor[, axis, keepdims])	Computes maximum of elements across dimensions of a tensor.
reduce_min(a[, axis, out, keepdims, ...])	Similar to jax.numpy.amin function, while it is compatible with brainpy Array/Variable.
reduce_mean(a[, axis, dtype, out, keepdims, ...])	Similar to jax.numpy.mean function, while it is compatible with brainpy Array/Variable.
reduce_all(a[, axis, out, keepdims, where])	Similar to jax.numpy.all function, while it is compatible with brainpy Array/Variable.
reduce_any(a[, axis, out, keepdims, where])	Similar to jax.numpy.any function, while it is compatible with brainpy Array/Variable.
reduce_logsumexp(input_tensor[, axis, keepdims])	Computes log(sum(exp(elements across dimensions of a tensor))).
reduce_prod(a[, axis, dtype, out, keepdims, ...])	Similar to jax.numpy.prod function, while it is compatible with brainpy Array/Variable.
reduce_std(a[, axis, dtype, out, ddof, ...])	Similar to jax.numpy.std function, while it is compatible with brainpy Array/Variable.
reduce_variance(a[, axis, dtype, out, ddof, ...])	Similar to jax.numpy.var function, while it is compatible with brainpy Array/Variable.
reduce_euclidean_norm(input_tensor[, axis, ...])	Computes the Euclidean norm of elements across dimensions of a tensor.
unsorted_segment_sqrt_n(data, segment_ids, ...)	Computes the sum along segments of a tensor divided by the sqrt(N).
segment_mean(data, segment_ids)	Computes the average along segments of a tensor.
unsorted_segment_sum(data, segment_ids, ...)	Computes the sum along segments of a tensor.
unsorted_segment_prod(data, segment_ids, ...)	Computes the product along segments of a tensor.
unsorted_segment_max(data, segment_ids, ...)	Computes the maximum along segments of a tensor.
unsorted_segment_min(data, segment_ids, ...)	Computes the minimum along segments of a tensor.
unsorted_segment_mean(data, segment_ids, ...)	Computes the average along segments of a tensor.
segment_sum(data, segment_ids[, ...])	segment_sum operator for brainpy Array and Variable.
segment_prod(data, segment_ids[, ...])	segment_prod operator for brainpy Array and Variable.
segment_max(data, segment_ids[, ...])	segment_max operator for brainpy Array and Variable.
segment_min(data, segment_ids[, ...])	segment_min operator for brainpy Array and Variable.
clip_by_value(a, a_min, a_max[, out])	Similar to jax.numpy.clip function, while it is compatible with brainpy Array/Variable.
cast(x, dtype)	Casts a tensor to a new type.

brainpy.math.surrogate module: Surrogate Gradient Functions

Sigmoid([alpha, orgin])
PiecewiseQuadratic([alpha, origin])
PiecewiseExp([alpha, origin])
SoftSign([alpha, origin])
Arctan([alpha, origin])
NonzeroSignLog([alpha, origin])
ERF([alpha, origin])
PiecewiseLeakyRelu([c, w, origin])
SquarewaveFourierSeries([n, t_period, origin])
S2NN([alpha, beta, epsilon, origin])
QPseudoSpike([alpha, origin])
LeakyRelu([alpha, beta, origin])
LogTailedRelu([alpha, origin])
ReluGrad([alpha, width])
GaussianGrad([sigma, alpha])
InvSquareGrad([alpha])
MultiGaussianGrad([h, s, sigma, scale])
SlayerGrad([alpha])
sigmoid	Spike function with the sigmoid-shaped surrogate gradient.
piecewise_quadratic	Judge spiking state with a piecewise quadratic function [1]_ [2]_ [3]_ [4]_ [5]_.
piecewise_exp	Judge spiking state with a piecewise exponential function [1]_.
soft_sign	Judge spiking state with a soft sign function.
arctan	Judge spiking state with an arctan function.
nonzero_sign_log	Judge spiking state with a nonzero sign log function.
erf	Judge spiking state with an erf function [1]_ [2]_ [3]_.
piecewise_leaky_relu	Judge spiking state with a piecewise leaky relu function [1]_ [2]_ [3]_ [4]_ [5]_ [6]_ [7]_ [8]_.
squarewave_fourier_series	Judge spiking state with a squarewave fourier series.
s2nn	Judge spiking state with the S2NN surrogate spiking function [1]_.
q_pseudo_spike	Judge spiking state with the q-PseudoSpike surrogate function [1]_.
leaky_relu	Judge spiking state with the Leaky ReLU function.
log_tailed_relu	Judge spiking state with the Log-tailed ReLU function [1]_.
relu_grad	Spike function with the ReLU gradient function [1]_.
gaussian_grad	Spike function with the Gaussian gradient function [1]_.
inv_square_grad	Spike function with the inverse-square surrogate gradient.
multi_gaussian_grad	Spike function with the multi-Gaussian gradient function [1]_.
slayer_grad	Spike function with the slayer surrogate gradient function.
inv_square_grad2
relu_grad2

brainpy.math.random module: Random Number Generations

seed([seed])	Sets a new random seed.
split_key()
default_rng([seed_or_key, clone])	rtype: RandomState
rand(*dn[, key])	Random values in a given shape.
randint(low[, high, size, dtype, key])	Return random integers from low (inclusive) to high (exclusive).
random_integers(low[, high, size, key])	Random integers of type np.int_ between low and high, inclusive.
randn(*dn[, key])	Return a sample (or samples) from the "standard normal" distribution.
random([size, key])	Return random floats in the half-open interval [0.0, 1.0).
random_sample([size, key])	Return random floats in the half-open interval [0.0, 1.0).
ranf([size, key])	This is an alias of random_sample. See random_sample for the complete
sample([size, key])	This is an alias of random_sample. See random_sample for the complete
choice(a[, size, replace, p, key])	Generates a random sample from a given 1-D array
permutation(x[, axis, independent, key])	Randomly permute a sequence, or return a permuted range.
shuffle(x[, axis, key])	Modify a sequence in-place by shuffling its contents.
beta(a, b[, size, key])	Draw samples from a Beta distribution.
exponential([scale, size, key])
gamma(shape[, scale, size, key])
gumbel([loc, scale, size, key])
laplace([loc, scale, size, key])
logistic([loc, scale, size, key])
normal([loc, scale, size, key])
pareto(a[, size, key])
poisson([lam, size, key])
standard_cauchy([size, key])
standard_exponential([size, key])
standard_gamma(shape[, size, key])
standard_normal([size, key])
standard_t(df[, size, key])
uniform([low, high, size, key])
truncated_normal(lower, upper[, size, ...])	Sample truncated standard normal random values with given shape and dtype.
bernoulli([p, size, key])	Sample Bernoulli random values with given shape and mean.
lognormal([mean, sigma, size, key])
binomial(n, p[, size, key])
chisquare(df[, size, key])
dirichlet(alpha[, size, key])
geometric(p[, size, key])
f(dfnum, dfden[, size, key])
hypergeometric(ngood, nbad, nsample[, size, key])
logseries(p[, size, key])
multinomial(n, pvals[, size, key])
multivariate_normal(mean, cov[, size, ...])
negative_binomial(n, p[, size, key])
noncentral_chisquare(df, nonc[, size, key])
noncentral_f(dfnum, dfden, nonc[, size, key])
power(a[, size, key])
rayleigh([scale, size, key])
triangular([size, key])
vonmises(mu, kappa[, size, key])
wald(mean, scale[, size, key])
weibull(a[, size, key])	Draw samples from a Weibull distribution.
weibull_min(a[, scale, size, key])	Sample from a Weibull distribution.
zipf(a[, size, key])	Draw samples from a Zipf distribution.
maxwell([size, key])	Sample from a one sided Maxwell distribution.
t(df[, size, key])	Sample Student’s t random values.
orthogonal(n[, size, key])	Sample uniformly from the orthogonal group O(n).
loggamma(a[, size, key])	Sample log-gamma random values.
categorical(logits[, axis, size, key])	Sample random values from categorical distributions.
rand_like(input, *[, dtype, key])	Similar to rand_like in torch.
randint_like(input[, low, high, dtype, key])	Similar to randint_like in torch.
randn_like(input, *[, dtype, key])	Similar to randn_like in torch.
RandomState([seed_or_key, seed])	RandomState that track the random generator state.
Generator	alias of RandomState
DEFAULT	RandomState that track the random generator state.

brainpy.math.linalg module: Linear algebra

cholesky(a)	Similar to jax.numpy.linalg.cholesky function, while it is compatible with brainpy Array/Variable.
cond(x[, p])	Similar to jax.numpy.linalg.cond function, while it is compatible with brainpy Array/Variable.
det(a)	Similar to jax.numpy.linalg.det function, while it is compatible with brainpy Array/Variable.
eig(a)	Similar to jax.numpy.linalg.eig function, while it is compatible with brainpy Array/Variable.
eigh(a[, UPLO])	Similar to jax.numpy.linalg.eigh function, while it is compatible with brainpy Array/Variable.
eigvals(a)	Similar to jax.numpy.linalg.eigvals function, while it is compatible with brainpy Array/Variable.
eigvalsh(a[, UPLO])	Similar to jax.numpy.linalg.eigvalsh function, while it is compatible with brainpy Array/Variable.
inv(a)	Similar to jax.numpy.linalg.inv function, while it is compatible with brainpy Array/Variable.
svd(a[, full_matrices, compute_uv, hermitian])	Similar to jax.numpy.linalg.svd function, while it is compatible with brainpy Array/Variable.
lstsq(a, b[, rcond])	Similar to jax.numpy.linalg.lstsq function, while it is compatible with brainpy Array/Variable.
matrix_power(a, n)	Similar to jax.numpy.linalg.matrix_power function, while it is compatible with brainpy Array/Variable.
matrix_rank(M[, tol, hermitian])	Similar to jax.numpy.linalg.matrix_rank function, while it is compatible with brainpy Array/Variable.
norm(x[, ord, axis, keepdims])	Similar to jax.numpy.linalg.norm function, while it is compatible with brainpy Array/Variable.
pinv(a[, rcond, hermitian])	Similar to jax.numpy.linalg.pinv function, while it is compatible with brainpy Array/Variable.
qr(a[, mode])	Similar to jax.numpy.linalg.qr function, while it is compatible with brainpy Array/Variable.
solve(a, b)	Similar to jax.numpy.linalg.solve function, while it is compatible with brainpy Array/Variable.
slogdet(a)	Similar to jax.numpy.linalg.slogdet function, while it is compatible with brainpy Array/Variable.
tensorinv(a[, ind])	Similar to jax.numpy.linalg.tensorinv function, while it is compatible with brainpy Array/Variable.
tensorsolve(a, b[, axes])	Similar to jax.numpy.linalg.tensorsolve function, while it is compatible with brainpy Array/Variable.
multi_dot(arrays, *[, out])	Similar to jax.numpy.linalg.multi_dot function, while it is compatible with brainpy Array/Variable.

brainpy.math.fft module: Discrete Fourier Transform

fft(a[, n, axis, norm])	Similar to jax.numpy.fft.fft function, while it is compatible with brainpy Array/Variable.
fft2(a[, s, axes, norm])	Similar to jax.numpy.fft.fft2 function, while it is compatible with brainpy Array/Variable.
fftfreq(n[, d])	Similar to jax.numpy.fft.fftfreq function, while it is compatible with brainpy Array/Variable.
fftn(a[, s, axes, norm])	Similar to jax.numpy.fft.fftn function, while it is compatible with brainpy Array/Variable.
fftshift(x[, axes])	Similar to jax.numpy.fft.fftshift function, while it is compatible with brainpy Array/Variable.
hfft(a[, n, axis, norm])	Similar to jax.numpy.fft.hfft function, while it is compatible with brainpy Array/Variable.
ifft(a[, n, axis, norm])	Similar to jax.numpy.fft.ifft function, while it is compatible with brainpy Array/Variable.
ifft2(a[, s, axes, norm])	Similar to jax.numpy.fft.ifft2 function, while it is compatible with brainpy Array/Variable.
ifftn(a[, s, axes, norm])	Similar to jax.numpy.fft.ifftn function, while it is compatible with brainpy Array/Variable.
ifftshift(x[, axes])	Similar to jax.numpy.fft.ifftshift function, while it is compatible with brainpy Array/Variable.
ihfft(a[, n, axis, norm])	Similar to jax.numpy.fft.ihfft function, while it is compatible with brainpy Array/Variable.
irfft(a[, n, axis, norm])	Similar to jax.numpy.fft.irfft function, while it is compatible with brainpy Array/Variable.
irfft2(a[, s, axes, norm])	Similar to jax.numpy.fft.irfft2 function, while it is compatible with brainpy Array/Variable.
irfftn(a[, s, axes, norm])	Similar to jax.numpy.fft.irfftn function, while it is compatible with brainpy Array/Variable.
rfft(a[, n, axis, norm])	Similar to jax.numpy.fft.rfft function, while it is compatible with brainpy Array/Variable.
rfft2(a[, s, axes, norm])	Similar to jax.numpy.fft.rfft2 function, while it is compatible with brainpy Array/Variable.
rfftfreq(n[, d])	Similar to jax.numpy.fft.rfftfreq function, while it is compatible with brainpy Array/Variable.
rfftn(a[, s, axes, norm])	Similar to jax.numpy.fft.rfftn function, while it is compatible with brainpy Array/Variable.