NLCPyが提供する関数

NLCPyは、NumPyのAPIのサブセットを提供します。 NLCPyの関数とNumPyの関数の比較表は次のとおりです。"NLCPy"列に - と表示されている関数はまだ提供されていません。スクリプトでNLCPyでサポートされていない関数を使用する必要がある場合は、 NumPyとNLCPyの相互運用性を参照してください。

NumPy / NLCPyのAPI

モジュールレベル

NumPy	NLCPy
`numpy.absolute`	`nlcpy.absolute`
`numpy.add`	`nlcpy.add`
`numpy.all`	`nlcpy.all`
`numpy.allclose`	-
`numpy.amax`	`nlcpy.amax`
`numpy.amin`	`nlcpy.amin`
`numpy.angle`	`nlcpy.angle`
`numpy.any`	`nlcpy.any`
`numpy.append`	`nlcpy.append`
`numpy.apply_along_axis`	-
`numpy.apply_over_axes`	-
`numpy.arange`	`nlcpy.arange`
`numpy.arccos`	`nlcpy.arccos`
`numpy.arccosh`	`nlcpy.arccosh`
`numpy.arcsin`	`nlcpy.arcsin`
`numpy.arcsinh`	`nlcpy.arcsinh`
`numpy.arctan`	`nlcpy.arctan`
`numpy.arctan2`	`nlcpy.arctan2`
`numpy.arctanh`	`nlcpy.arctanh`
`numpy.argmax`	`nlcpy.argmax`
`numpy.argmin`	`nlcpy.argmin`
`numpy.argpartition`	-
`numpy.argsort`	`nlcpy.argsort`
`numpy.argwhere`	`nlcpy.argwhere`
`numpy.around`	-
`numpy.array`	`nlcpy.array`
`numpy.array2string`	-
`numpy.array_equal`	-
`numpy.array_equiv`	-
`numpy.array_repr`	-
`numpy.array_split`	-
`numpy.array_str`	-
`numpy.asanyarray`	`nlcpy.asanyarray`
`numpy.asarray`	`nlcpy.asarray`
`numpy.asarray_chkfinite`	-
`numpy.ascontiguousarray`	-
`numpy.asfarray`	-
`numpy.asfortranarray`	-
`numpy.asmatrix`	-
`numpy.asscalar`	-
`numpy.atleast_1d`	`nlcpy.atleast_1d`
`numpy.atleast_2d`	`nlcpy.atleast_2d`
`numpy.atleast_3d`	`nlcpy.atleast_3d`
`numpy.average`	`nlcpy.average`
`numpy.bartlett`	-
`numpy.base_repr`	-
`numpy.binary_repr`	-
`numpy.bincount`	`nlcpy.bincount`
`numpy.bitwise_and`	`nlcpy.bitwise_and`
`numpy.bitwise_or`	`nlcpy.bitwise_or`
`numpy.bitwise_xor`	`nlcpy.bitwise_xor`
`numpy.blackman`	-
`numpy.block`	`nlcpy.block`
`numpy.bmat`	-
`numpy.broadcast_arrays`	`nlcpy.broadcast_arrays`
`numpy.broadcast_to`	`nlcpy.broadcast_to`
`numpy.busday_count`	-
`numpy.busday_offset`	-
`numpy.byte_bounds`	-
`numpy.can_cast`	-
`numpy.cbrt`	`nlcpy.cbrt`
`numpy.ceil`	`nlcpy.ceil`
`numpy.choose`	-
`numpy.clip`	`nlcpy.clip`
`numpy.column_stack`	-
`numpy.common_type`	-
`numpy.compress`	-
`numpy.concatenate`	`nlcpy.concatenate`
`numpy.conj`	`nlcpy.conj`
`numpy.conjugate`	`nlcpy.conjugate`
`numpy.convolve`	-
`numpy.copy`	`nlcpy.copy`
`numpy.copysign`	`nlcpy.copysign`
`numpy.copyto`	`nlcpy.copyto`
`numpy.corrcoef`	`nlcpy.corrcoef`
`numpy.correlate`	`nlcpy.correlate`
`numpy.cos`	`nlcpy.cos`
`numpy.cosh`	`nlcpy.cosh`
`numpy.count_nonzero`	`nlcpy.count_nonzero`
`numpy.cov`	`nlcpy.cov`
`numpy.cross`	-
`numpy.cumprod`	-
`numpy.cumsum`	`nlcpy.cumsum`
`numpy.datetime_as_string`	-
`numpy.datetime_data`	-
`numpy.deg2rad`	`nlcpy.deg2rad`
`numpy.degrees`	`nlcpy.degrees`
`numpy.delete`	`nlcpy.delete`
`numpy.deprecate`	-
`numpy.deprecate_with_doc`	-
`numpy.diag`	`nlcpy.diag`
`numpy.diag_indices`	`nlcpy.diag_indices`
`numpy.diag_indices_from`	-
`numpy.diagflat`	`nlcpy.diagflat`
`numpy.diagonal`	`nlcpy.diagonal`
`numpy.diff`	`nlcpy.diff`
`numpy.digitize`	`nlcpy.digitize`
`numpy.disp`	-
`numpy.divide`	`nlcpy.divide`
`numpy.divmod`	-
`numpy.dot`	`nlcpy.dot`
`numpy.dsplit`	-
`numpy.dstack`	-
`numpy.ediff1d`	-
`numpy.einsum`	-
`numpy.einsum_path`	-
`numpy.empty`	`nlcpy.empty`
`numpy.empty_like`	`nlcpy.empty_like`
`numpy.equal`	`nlcpy.equal`
`numpy.exp`	`nlcpy.exp`
`numpy.exp2`	`nlcpy.exp2`
`numpy.expand_dims`	`nlcpy.expand_dims`
`numpy.expm1`	`nlcpy.expm1`
`numpy.extract`	-
`numpy.eye`	`nlcpy.eye`
`numpy.fabs`	`nlcpy.fabs`
`numpy.fill_diagonal`	`nlcpy.fill_diagonal`
`numpy.find_common_type`	-
`numpy.fix`	-
`numpy.flatnonzero`	-
`numpy.flip`	`nlcpy.flip`
`numpy.fliplr`	`nlcpy.fliplr`
`numpy.flipud`	`nlcpy.flipud`
`numpy.float_power`	-
`numpy.floor`	`nlcpy.floor`
`numpy.floor_divide`	`nlcpy.floor_divide`
`numpy.fmax`	`nlcpy.fmax`
`numpy.fmin`	`nlcpy.fmin`
`numpy.fmod`	`nlcpy.fmod`
`numpy.format_float_positional`	-
`numpy.format_float_scientific`	-
`numpy.frexp`	-
`numpy.frombuffer`	-
`numpy.fromfile`	`nlcpy.fromfile`
`numpy.fromfunction`	-
`numpy.fromiter`	-
`numpy.frompyfunc`	-
`numpy.fromregex`	-
`numpy.fromstring`	-
`numpy.full`	`nlcpy.full`
`numpy.full_like`	`nlcpy.full_like`
`numpy.gcd`	-
`numpy.genfromtxt`	-
`numpy.geomspace`	-
`numpy.get_include`	`nlcpy.get_include`
`numpy.get_printoptions`	`nlcpy.get_printoptions` (alias of `numpy.get_printoptions`)
`numpy.getbufsize`	-
`numpy.geterr`	`nlcpy.geterr`
`numpy.geterrcall`	-
`numpy.geterrobj`	-
`numpy.gradient`	-
`numpy.greater`	`nlcpy.greater`
`numpy.greater_equal`	`nlcpy.greater_equal`
`numpy.hamming`	-
`numpy.hanning`	-
`numpy.heaviside`	`nlcpy.heaviside`
`numpy.histogram`	`nlcpy.histogram`
`numpy.histogram2d`	`nlcpy.histogram2d`
`numpy.histogram_bin_edges`	`nlcpy.histogram_bin_edges`
`numpy.histogramdd`	`nlcpy.histogramdd`
`numpy.hsplit`	`nlcpy.hsplit`
`numpy.hstack`	`nlcpy.hstack`
`numpy.hypot`	`nlcpy.hypot`
`numpy.i0`	-
`numpy.identity`	`nlcpy.identity`
`numpy.imag`	`nlcpy.imag`
`numpy.in1d`	-
`numpy.indices`	-
`numpy.info`	-
`numpy.inner`	`nlcpy.inner`
`numpy.insert`	`nlcpy.insert`
`numpy.interp`	-
`numpy.intersect1d`	-
`numpy.invert`	`nlcpy.invert`
`numpy.is_busday`	-
`numpy.isclose`	-
`numpy.iscomplex`	-
`numpy.iscomplexobj`	-
`numpy.isfinite`	`nlcpy.isfinite`
`numpy.isfortran`	-
`numpy.isin`	-
`numpy.isinf`	`nlcpy.isinf`
`numpy.isnan`	`nlcpy.isnan`
`numpy.isnat`	-
`numpy.isneginf`	-
`numpy.isposinf`	-
`numpy.isreal`	-
`numpy.isrealobj`	-
`numpy.isscalar`	-
`numpy.issctype`	-
`numpy.issubclass_`	-
`numpy.issubdtype`	-
`numpy.issubsctype`	-
`numpy.ix_`	-
`numpy.kaiser`	-
`numpy.kron`	-
`numpy.lcm`	-
`numpy.ldexp`	`nlcpy.ldexp`
`numpy.left_shift`	`nlcpy.left_shift`
`numpy.less`	`nlcpy.less`
`numpy.less_equal`	`nlcpy.less_equal`
`numpy.lexsort`	-
`numpy.linspace`	`nlcpy.linspace`
`numpy.load`	`nlcpy.load`
`numpy.loadtxt`	`nlcpy.loadtxt`
`numpy.log`	`nlcpy.log`
`numpy.log10`	`nlcpy.log10`
`numpy.log1p`	`nlcpy.log1p`
`numpy.log2`	`nlcpy.log2`
`numpy.logaddexp`	`nlcpy.logaddexp`
`numpy.logaddexp2`	`nlcpy.logaddexp2`
`numpy.logical_and`	`nlcpy.logical_and`
`numpy.logical_not`	`nlcpy.logical_not`
`numpy.logical_or`	`nlcpy.logical_or`
`numpy.logical_xor`	`nlcpy.logical_xor`
`numpy.logspace`	`nlcpy.logspace`
`numpy.lookfor`	-
`numpy.mask_indices`	-
`numpy.mat`	-
`numpy.matmul`	`nlcpy.matmul`
`numpy.maximum`	`nlcpy.maximum`
`numpy.maximum_sctype`	-
`numpy.may_share_memory`	`nlcpy.may_share_memory`
`numpy.mean`	`nlcpy.mean`
`numpy.median`	`nlcpy.median`
`numpy.meshgrid`	`nlcpy.meshgrid`
`numpy.min_scalar_type`	-
`numpy.minimum`	`nlcpy.minimum`
`numpy.mintypecode`	-
`numpy.mod`	`nlcpy.mod`
`numpy.modf`	-
`numpy.moveaxis`	`nlcpy.moveaxis`
`numpy.msort`	-
`numpy.multiply`	`nlcpy.multiply`
`numpy.nan_to_num`	-
`numpy.nanargmax`	`nlcpy.nanargmax`
`numpy.nanargmin`	`nlcpy.nanargmin`
`numpy.nancumprod`	-
`numpy.nancumsum`	-
`numpy.nanmax`	`nlcpy.nanmax`
`numpy.nanmean`	`nlcpy.nanmean`
`numpy.nanmedian`	`nlcpy.nanmedian`
`numpy.nanmin`	`nlcpy.nanmin`
`numpy.nanpercentile`	`nlcpy.nanpercentile`
`numpy.nanprod`	-
`numpy.nanquantile`	`nlcpy.nanquantile`
`numpy.nanstd`	`nlcpy.nanstd`
`numpy.nansum`	-
`numpy.nanvar`	`nlcpy.nanvar`
`numpy.negative`	`nlcpy.negative`
`numpy.nested_iters`	-
`numpy.nextafter`	`nlcpy.nextafter`
`numpy.nonzero`	`nlcpy.nonzero`
`numpy.not_equal`	`nlcpy.not_equal`
`numpy.obj2sctype`	-
`numpy.ones`	`nlcpy.ones`
`numpy.ones_like`	`nlcpy.ones_like`
`numpy.outer`	`nlcpy.outer`
`numpy.packbits`	-
`numpy.pad`	-
`numpy.partition`	-
`numpy.percentile`	`nlcpy.percentile`
`numpy.piecewise`	-
`numpy.place`	-
`numpy.poly`	-
`numpy.polyadd`	-
`numpy.polyder`	-
`numpy.polydiv`	-
`numpy.polyfit`	-
`numpy.polyint`	-
`numpy.polymul`	-
`numpy.polysub`	-
`numpy.polyval`	-
`numpy.positive`	`nlcpy.positive`
`numpy.power`	`nlcpy.power`
`numpy.printoptions`	-
`numpy.prod`	`nlcpy.prod`
`numpy.promote_types`	-
`numpy.ptp`	`nlcpy.ptp`
`numpy.put`	-
`numpy.put_along_axis`	-
`numpy.putmask`	-
`numpy.quantile`	`nlcpy.quantile`
`numpy.rad2deg`	`nlcpy.rad2deg`
`numpy.radians`	`nlcpy.radians`
`numpy.ravel`	`nlcpy.ravel`
`numpy.ravel_multi_index`	-
`numpy.real`	`nlcpy.real`
`numpy.real_if_close`	-
`numpy.reciprocal`	`nlcpy.reciprocal`
`numpy.remainder`	`nlcpy.remainder`
`numpy.repeat`	`nlcpy.repeat`
`numpy.require`	-
`numpy.reshape`	`nlcpy.reshape`
`numpy.resize`	`nlcpy.resize`
`numpy.result_type`	-
`numpy.right_shift`	`nlcpy.right_shift`
`numpy.rint`	`nlcpy.rint`
`numpy.roll`	`nlcpy.roll`
`numpy.rollaxis`	`nlcpy.rollaxis`
`numpy.roots`	-
`numpy.rot90`	-
`numpy.round_`	-
`numpy.save`	`nlcpy.save`
`numpy.savetxt`	`nlcpy.savetxt`
`numpy.savez`	`nlcpy.savez`
`numpy.savez_compressed`	`nlcpy.savez_compressed`
`numpy.sctype2char`	-
`numpy.searchsorted`	-
`numpy.select`	`nlcpy.select`
`numpy.set_printoptions`	`nlcpy.set_printoptions` (alias of `numpy.set_printoptions`)
`numpy.set_string_function`	-
`numpy.setbufsize`	-
`numpy.setdiff1d`	-
`numpy.seterr`	`nlcpy.seterr`
`numpy.seterrcall`	-
`numpy.seterrobj`	-
`numpy.setxor1d`	-
`numpy.shape`	`nlcpy.shape`
`numpy.shares_memory`	-
`numpy.show_config`	`nlcpy.show_config`
`numpy.sign`	`nlcpy.sign`
`numpy.signbit`	`nlcpy.signbit`
`numpy.sin`	`nlcpy.sin`
`numpy.sinc`	-
`numpy.sinh`	`nlcpy.sinh`
`numpy.sort`	`nlcpy.sort`
`numpy.sort_complex`	-
`numpy.source`	-
`numpy.spacing`	`nlcpy.spacing`
`numpy.split`	`nlcpy.split`
`numpy.sqrt`	`nlcpy.sqrt`
`numpy.square`	`nlcpy.square`
`numpy.squeeze`	`nlcpy.squeeze`
`numpy.stack`	`nlcpy.stack`
`numpy.std`	`nlcpy.std`
`numpy.subtract`	`nlcpy.subtract`
`numpy.sum`	`nlcpy.sum`
`numpy.swapaxes`	`nlcpy.swapaxes`
`numpy.take`	`nlcpy.take`
`numpy.take_along_axis`	-
`numpy.tan`	`nlcpy.tan`
`numpy.tanh`	`nlcpy.tanh`
`numpy.tensordot`	-
`numpy.tile`	`nlcpy.tile`
`numpy.trace`	-
`numpy.transpose`	`nlcpy.transpose`
`numpy.trapz`	-
`numpy.tri`	`nlcpy.tri`
`numpy.tril`	`nlcpy.tril`
`numpy.tril_indices`	-
`numpy.tril_indices_from`	-
`numpy.trim_zeros`	-
`numpy.triu`	`nlcpy.triu`
`numpy.triu_indices`	-
`numpy.triu_indices_from`	-
`numpy.true_divide`	`nlcpy.true_divide`
`numpy.trunc`	`nlcpy.trunc`
`numpy.typename`	-
`numpy.union1d`	-
`numpy.unique`	`nlcpy.unique`
`numpy.unpackbits`	-
`numpy.unravel_index`	-
`numpy.unwrap`	-
`numpy.vander`	-
`numpy.var`	`nlcpy.var`
`numpy.vdot`	-
`numpy.vsplit`	`nlcpy.vsplit`
`numpy.vstack`	`nlcpy.vstack`
`numpy.where`	`nlcpy.where`
`numpy.who`	-
`numpy.zeros`	`nlcpy.zeros`
`numpy.zeros_like`	`nlcpy.zeros_like`

多次元配列

NumPy	NLCPy
`numpy.ndarray.all()`	`nlcpy.ndarray.all()`
`numpy.ndarray.any()`	`nlcpy.ndarray.any()`
`numpy.ndarray.argmax()`	`nlcpy.ndarray.argmax()`
`numpy.ndarray.argmin()`	`nlcpy.ndarray.argmin()`
`numpy.ndarray.argpartition()`	-
`numpy.ndarray.argsort()`	`nlcpy.ndarray.argsort()`
`numpy.ndarray.astype()`	`nlcpy.ndarray.astype()`
`numpy.ndarray.byteswap()`	-
`numpy.ndarray.choose()`	-
`numpy.ndarray.clip()`	`nlcpy.ndarray.clip()`
`numpy.ndarray.compress()`	-
`numpy.ndarray.conj()`	`nlcpy.ndarray.conj()`
`numpy.ndarray.conjugate()`	`nlcpy.ndarray.conjugate()`
`numpy.ndarray.copy()`	`nlcpy.ndarray.copy()`
`numpy.ndarray.cumprod()`	-
`numpy.ndarray.cumsum()`	`nlcpy.ndarray.cumsum()`
`numpy.ndarray.diagonal()`	`nlcpy.ndarray.diagonal()`
`numpy.ndarray.dot()`	`nlcpy.ndarray.dot()`
`numpy.ndarray.dump()`	-
`numpy.ndarray.dumps()`	-
`numpy.ndarray.fill()`	`nlcpy.ndarray.fill()`
`numpy.ndarray.flatten()`	`nlcpy.ndarray.flatten()`
`numpy.ndarray.getfield()`	-
`numpy.ndarray.item()`	-
`numpy.ndarray.itemset()`	-
`numpy.ndarray.mean()`	`nlcpy.ndarray.mean()`
`numpy.ndarray.newbyteorder()`	-
`numpy.ndarray.nonzero()`	`nlcpy.ndarray.nonzero()`
`numpy.ndarray.partition()`	-
`numpy.ndarray.prod()`	`nlcpy.ndarray.prod()`
`numpy.ndarray.ptp()`	`nlcpy.ndarray.ptp()`
`numpy.ndarray.put()`	-
`numpy.ndarray.ravel()`	`nlcpy.ndarray.ravel()`
`numpy.ndarray.repeat()`	`nlcpy.ndarray.repeat()`
`numpy.ndarray.reshape()`	`nlcpy.ndarray.reshape()`
`numpy.ndarray.resize()`	`nlcpy.ndarray.resize()`
`numpy.ndarray.searchsorted()`	-
`numpy.ndarray.setfield()`	-
`numpy.ndarray.setflags()`	-
`numpy.ndarray.sort()`	`nlcpy.ndarray.sort()`
`numpy.ndarray.squeeze()`	`nlcpy.ndarray.squeeze()`
`numpy.ndarray.std()`	`nlcpy.ndarray.std()`
`numpy.ndarray.sum()`	`nlcpy.ndarray.sum()`
`numpy.ndarray.swapaxes()`	`nlcpy.ndarray.swapaxes()`
`numpy.ndarray.take()`	`nlcpy.ndarray.take()`
`numpy.ndarray.tobytes()`	`nlcpy.ndarray.tobytes()`
`numpy.ndarray.tofile()`	-
`numpy.ndarray.tolist()`	`nlcpy.ndarray.tolist()`
`numpy.ndarray.tostring()`	-
`numpy.ndarray.trace()`	-
`numpy.ndarray.transpose()`	`nlcpy.ndarray.transpose()`
`numpy.ndarray.var()`	`nlcpy.ndarray.var()`
`numpy.ndarray.view()`	`nlcpy.ndarray.view()`

線形代数

NumPy	NLCPy
`numpy.linalg.cholesky`	`nlcpy.linalg.cholesky`
`numpy.linalg.cond`	-
`numpy.linalg.det`	-
`numpy.linalg.eig`	`nlcpy.linalg.eig`
`numpy.linalg.eigh`	`nlcpy.linalg.eigh`
`numpy.linalg.eigvals`	`nlcpy.linalg.eigvals`
`numpy.linalg.eigvalsh`	`nlcpy.linalg.eigvalsh`
`numpy.linalg.inv`	`nlcpy.linalg.inv`
`numpy.linalg.lstsq`	`nlcpy.linalg.lstsq`
`numpy.linalg.matrix_power`	-
`numpy.linalg.matrix_rank`	-
`numpy.linalg.multi_dot`	-
`numpy.linalg.norm`	`nlcpy.linalg.norm`
`numpy.linalg.pinv`	-
`numpy.linalg.qr`	`nlcpy.linalg.qr`
`numpy.linalg.slogdet`	-
`numpy.linalg.solve`	`nlcpy.linalg.solve`
`numpy.linalg.svd`	`nlcpy.linalg.svd`
`numpy.linalg.tensorinv`	-
`numpy.linalg.tensorsolve`	-

離散フーリエ変換

NumPy	NLCPy
`numpy.fft.fft`	`nlcpy.fft.fft`
`numpy.fft.fft2`	`nlcpy.fft.fft2`
`numpy.fft.fftfreq`	`nlcpy.fft.fftfreq`
`numpy.fft.fftn`	`nlcpy.fft.fftn`
`numpy.fft.fftshift`	`nlcpy.fft.fftshift`
`numpy.fft.hfft`	`nlcpy.fft.hfft`
`numpy.fft.ifft`	`nlcpy.fft.ifft`
`numpy.fft.ifft2`	`nlcpy.fft.ifft2`
`numpy.fft.ifftn`	`nlcpy.fft.ifftn`
`numpy.fft.ifftshift`	`nlcpy.fft.ifftshift`
`numpy.fft.ihfft`	`nlcpy.fft.ihfft`
`numpy.fft.irfft`	`nlcpy.fft.irfft`
`numpy.fft.irfft2`	`nlcpy.fft.irfft2`
`numpy.fft.irfftn`	`nlcpy.fft.irfftn`
`numpy.fft.rfft`	`nlcpy.fft.rfft`
`numpy.fft.rfft2`	`nlcpy.fft.rfft2`
`numpy.fft.rfftfreq`	`nlcpy.fft.rfftfreq`
`numpy.fft.rfftn`	`nlcpy.fft.rfftn`

乱数生成

NumPy	NLCPy
`numpy.random.Generator.beta()`	-
`numpy.random.Generator.binomial()`	`nlcpy.random.Generator.binomial()`
`numpy.random.Generator.bytes()`	`nlcpy.random.Generator.bytes()`
`numpy.random.Generator.chisquare()`	-
`numpy.random.Generator.choice()`	-
`numpy.random.Generator.dirichlet()`	-
`numpy.random.Generator.exponential()`	`nlcpy.random.Generator.exponential()`
`numpy.random.Generator.f()`	-
`numpy.random.Generator.gamma()`	`nlcpy.random.Generator.gamma()`
`numpy.random.Generator.geometric()`	`nlcpy.random.Generator.geometric()`
`numpy.random.Generator.gumbel()`	`nlcpy.random.Generator.gumbel()`
`numpy.random.Generator.hypergeometric()`	-
`numpy.random.Generator.integers()`	`nlcpy.random.Generator.integers()`
`numpy.random.Generator.laplace()`	-
`numpy.random.Generator.logistic()`	`nlcpy.random.Generator.logistic()`
`numpy.random.Generator.lognormal()`	`nlcpy.random.Generator.lognormal()`
`numpy.random.Generator.logseries()`	-
`numpy.random.Generator.multinomial()`	-
`numpy.random.Generator.multivariate_hypergeometric()`	-
`numpy.random.Generator.multivariate_normal()`	-
`numpy.random.Generator.negative_binomial()`	-
`numpy.random.Generator.noncentral_chisquare()`	-
`numpy.random.Generator.noncentral_f()`	-
`numpy.random.Generator.normal()`	`nlcpy.random.Generator.normal()`
`numpy.random.Generator.pareto()`	-
`numpy.random.Generator.permutation()`	`nlcpy.random.Generator.permutation()`
`numpy.random.Generator.poisson()`	`nlcpy.random.Generator.poisson()`
`numpy.random.Generator.power()`	-
`numpy.random.Generator.random()`	`nlcpy.random.Generator.random()`
`numpy.random.Generator.rayleigh()`	-
`numpy.random.Generator.shuffle()`	`nlcpy.random.Generator.shuffle()`
`numpy.random.Generator.standard_cauchy()`	`nlcpy.random.Generator.standard_cauchy()`
`numpy.random.Generator.standard_exponential()`	`nlcpy.random.Generator.standard_exponential()`
`numpy.random.Generator.standard_gamma()`	`nlcpy.random.Generator.standard_gamma()`
`numpy.random.Generator.standard_normal()`	`nlcpy.random.Generator.standard_normal()`
`numpy.random.Generator.standard_t()`	-
`numpy.random.Generator.triangular()`	-
`numpy.random.Generator.uniform()`	`nlcpy.random.Generator.uniform()`
`numpy.random.Generator.vonmises()`	-
`numpy.random.Generator.wald()`	-
`numpy.random.Generator.weibull()`	`nlcpy.random.Generator.weibull()`
`numpy.random.Generator.zipf()`	-