Convolve and Correlate#

NumPy	cupynumeric	single-GPU/CPU	multi-GPU/CPU
`numpy.convolve`	`cupynumeric.convolve`	✓	✓
`numpy.correlate`

Summary

Number of NumPy functions: 2

Number of functions covered by cupynumeric: 1

Ufuncs#

NumPy	cupynumeric	single-GPU/CPU	multi-GPU/CPU
`numpy.DataSource`
`numpy.RankWarning`
`numpy.absolute`	`cupynumeric.absolute`	✓	✓
`numpy.add`	`cupynumeric.add`	✓	✓
`numpy.all`	`cupynumeric.all`	✓	✓
`numpy.allclose`	`cupynumeric.allclose`	✓	✓
`numpy.amax`	`cupynumeric.amax`	✓	✓
`numpy.amin`	`cupynumeric.amin`	✓	✓
`numpy.angle`	`cupynumeric.angle`	✓	✓
`numpy.any`	`cupynumeric.any`	✓	✓
`numpy.append`	`cupynumeric.append`	✓	✓
`numpy.apply_along_axis`
`numpy.apply_over_axes`
`numpy.arange`	`cupynumeric.arange`	✓	✓
`numpy.arccos`	`cupynumeric.arccos`	✓	✓
`numpy.arccosh`	`cupynumeric.arccosh`	✓	✓
`numpy.arcsin`	`cupynumeric.arcsin`	✓	✓
`numpy.arcsinh`	`cupynumeric.arcsinh`	✓	✓
`numpy.arctan`	`cupynumeric.arctan`	✓	✓
`numpy.arctan2`	`cupynumeric.arctan2`	✓	✓
`numpy.arctanh`	`cupynumeric.arctanh`	✓	✓
`numpy.argmax`	`cupynumeric.argmax`	✓	✓
`numpy.argmin`	`cupynumeric.argmin`	✓	✓
`numpy.argpartition`	`cupynumeric.argpartition`	✓	✓
`numpy.argsort`	`cupynumeric.argsort`	✓	✓
`numpy.argwhere`	`cupynumeric.argwhere`	✓	✓
`numpy.around`
`numpy.array`	`cupynumeric.array`	✓	✓
`numpy.array2string`
`numpy.array_equal`	`cupynumeric.array_equal`	✓	✓
`numpy.array_equiv`
`numpy.array_repr`
`numpy.array_split`	`cupynumeric.array_split`	✓	✓
`numpy.array_str`
`numpy.asarray`	`cupynumeric.asarray`	✓	✓
`numpy.asarray_chkfinite`
`numpy.asfarray`
`numpy.asmatrix`
`numpy.atleast_1d`	`cupynumeric.atleast_1d`	✓	✓
`numpy.atleast_2d`	`cupynumeric.atleast_2d`	✓	✓
`numpy.atleast_3d`	`cupynumeric.atleast_3d`	✓	✓
`numpy.average`	`cupynumeric.average`	✓	✓
`numpy.bartlett`	`cupynumeric.bartlett`	✓	✓
`numpy.base_repr`
`numpy.binary_repr`
`numpy.bincount`	`cupynumeric.bincount`	✓	✓
`numpy.bitwise_and`	`cupynumeric.bitwise_and`	✓	✓
`numpy.bitwise_or`	`cupynumeric.bitwise_or`	✓	✓
`numpy.bitwise_xor`	`cupynumeric.bitwise_xor`	✓	✓
`numpy.blackman`	`cupynumeric.blackman`	✓	✓
`numpy.block`	`cupynumeric.block`	✓	✓
`numpy.bmat`
`numpy.bool_`
`numpy.broadcast`
`numpy.broadcast_arrays`	`cupynumeric.broadcast_arrays`	✓	✓
`numpy.broadcast_shapes`	`cupynumeric.broadcast_shapes`	✓	✓
`numpy.broadcast_to`	`cupynumeric.broadcast_to`	✓	✓
`numpy.busday_count`
`numpy.busday_offset`
`numpy.busdaycalendar`
`numpy.byte`
`numpy.byte_bounds`
`numpy.bytes_`
`numpy.can_cast`
`numpy.cast`	`cupynumeric.cast`	❌	❌
`numpy.cbrt`	`cupynumeric.cbrt`	✓	✓
`numpy.cdouble`
`numpy.ceil`	`cupynumeric.ceil`	✓	✓
`numpy.cfloat`
`numpy.character`
`numpy.chararray`
`numpy.choose`	`cupynumeric.choose`	✓	✓
`numpy.clip`	`cupynumeric.clip`	✓	✓
`numpy.clongdouble`
`numpy.clongfloat`
`numpy.column_stack`	`cupynumeric.column_stack`	✓	✓
`numpy.common_type`
`numpy.complex128`
`numpy.complex256`
`numpy.complex64`
`numpy.complex_`
`numpy.complexfloating`
`numpy.compress`	`cupynumeric.compress`	✓	✓
`numpy.concatenate`	`cupynumeric.concatenate`	✓	✓
`numpy.conj`	`cupynumeric.conj`	✓	✓
`numpy.conjugate`	`cupynumeric.conjugate`	✓	✓
`numpy.convolve`	`cupynumeric.convolve`	✓	✓
`numpy.copy`	`cupynumeric.copy`	✓	✓
`numpy.copysign`	`cupynumeric.copysign`	✓	✓
`numpy.copyto`
`numpy.corrcoef`
`numpy.correlate`
`numpy.cos`	`cupynumeric.cos`	✓	✓
`numpy.cosh`	`cupynumeric.cosh`	✓	✓
`numpy.count_nonzero`	`cupynumeric.count_nonzero`	✓	✓
`numpy.cov`	`cupynumeric.cov`	✓	✓
`numpy.cross`
`numpy.csingle`
`numpy.cumprod`	`cupynumeric.cumprod`	✓	✓
`numpy.cumsum`	`cupynumeric.cumsum`	✓	✓
`numpy.datetime64`
`numpy.datetime_as_string`
`numpy.deg2rad`	`cupynumeric.deg2rad`	✓	✓
`numpy.degrees`	`cupynumeric.degrees`	✓	✓
`numpy.delete`
`numpy.deprecate`
`numpy.deprecate_with_doc`
`numpy.diag`	`cupynumeric.diag`	✓	✓
`numpy.diag_indices`	`cupynumeric.diag_indices`	✓	✓
`numpy.diag_indices_from`	`cupynumeric.diag_indices_from`	✓	✓
`numpy.diagflat`
`numpy.diagonal`	`cupynumeric.diagonal`	✓	✓
`numpy.diff`	`cupynumeric.diff`	✓	✓
`numpy.digitize`	`cupynumeric.digitize`	✓	✓
`numpy.disp`
`numpy.divide`	`cupynumeric.divide`	✓	✓
`numpy.divmod`
`numpy.dot`	`cupynumeric.dot`	✓	✓
`numpy.double`
`numpy.dsplit`	`cupynumeric.dsplit`	✓	✓
`numpy.dstack`	`cupynumeric.dstack`	✓	✓
`numpy.dtype`
`numpy.ediff1d`
`numpy.einsum`	`cupynumeric.einsum`	✓	✓
`numpy.einsum_path`	`cupynumeric.einsum_path`	✓	✓
`numpy.empty`	`cupynumeric.empty`	✓	✓
`numpy.empty_like`	`cupynumeric.empty_like`	✓	✓
`numpy.equal`	`cupynumeric.equal`	✓	✓
`numpy.errstate`
`numpy.exp`	`cupynumeric.exp`	✓	✓
`numpy.exp2`	`cupynumeric.exp2`	✓	✓
`numpy.expand_dims`	`cupynumeric.expand_dims`	✓	✓
`numpy.expm1`	`cupynumeric.expm1`	✓	✓
`numpy.extract`	`cupynumeric.extract`	✓	✓
`numpy.eye`	`cupynumeric.eye`	✓	✓
`numpy.fabs`	`cupynumeric.fabs`	✓	✓
`numpy.fill_diagonal`	`cupynumeric.fill_diagonal`	✓	✓
`numpy.find_common_type`	`cupynumeric.find_common_type`	❌	❌
`numpy.finfo`
`numpy.fix`
`numpy.flatiter`
`numpy.flatnonzero`	`cupynumeric.flatnonzero`	✓	✓
`numpy.flexible`
`numpy.flip`	`cupynumeric.flip`	✓	❌
`numpy.fliplr`	`cupynumeric.fliplr`	✓	❌
`numpy.flipud`	`cupynumeric.flipud`	✓	❌
`numpy.float128`
`numpy.float16`
`numpy.float32`
`numpy.float64`
`numpy.float_`
`numpy.float_power`	`cupynumeric.float_power`	✓	✓
`numpy.floor`	`cupynumeric.floor`	✓	✓
`numpy.floor_divide`	`cupynumeric.floor_divide`	✓	✓
`numpy.fmax`	`cupynumeric.fmax`	✓	✓
`numpy.fmin`	`cupynumeric.fmin`	✓	✓
`numpy.fmod`	`cupynumeric.fmod`	✓	✓
`numpy.format_float_positional`
`numpy.format_float_scientific`
`numpy.format_parser`
`numpy.frexp`	`cupynumeric.frexp`	✓	✓
`numpy.fromfunction`
`numpy.fromregex`
`numpy.full`	`cupynumeric.full`	✓	✓
`numpy.full_like`	`cupynumeric.full_like`	✓	✓
`numpy.gcd`	`cupynumeric.gcd`	✓	✓
`numpy.generic`
`numpy.genfromtxt`
`numpy.geomspace`
`numpy.get_include`
`numpy.get_printoptions`
`numpy.getbufsize`
`numpy.geterr`
`numpy.geterrcall`
`numpy.gradient`	`cupynumeric.gradient`	✓	✓
`numpy.greater`	`cupynumeric.greater`	✓	✓
`numpy.greater_equal`	`cupynumeric.greater_equal`	✓	✓
`numpy.half`
`numpy.hamming`	`cupynumeric.hamming`	✓	✓
`numpy.hanning`	`cupynumeric.hanning`	✓	✓
`numpy.heaviside`
`numpy.histogram`	`cupynumeric.histogram`	✓	✓
`numpy.histogram2d`
`numpy.histogram_bin_edges`
`numpy.histogramdd`
`numpy.hsplit`	`cupynumeric.hsplit`	✓	✓
`numpy.hstack`	`cupynumeric.hstack`	✓	✓
`numpy.hypot`	`cupynumeric.hypot`	✓	✓
`numpy.i0`
`numpy.identity`	`cupynumeric.identity`	✓	✓
`numpy.iinfo`
`numpy.imag`	`cupynumeric.imag`	✓	✓
`numpy.in1d`
`numpy.indices`	`cupynumeric.indices`	✓	✓
`numpy.inexact`
`numpy.info`
`numpy.inner`	`cupynumeric.inner`	✓	✓
`numpy.insert`
`numpy.int16`
`numpy.int32`
`numpy.int64`
`numpy.int8`
`numpy.int_`
`numpy.intc`
`numpy.integer`
`numpy.interp`
`numpy.intersect1d`
`numpy.intp`
`numpy.invert`	`cupynumeric.invert`	✓	✓
`numpy.is_busday`
`numpy.isclose`	`cupynumeric.isclose`	✓	✓
`numpy.iscomplex`	`cupynumeric.iscomplex`	✓	✓
`numpy.iscomplexobj`	`cupynumeric.iscomplexobj`	✓	✓
`numpy.isfinite`	`cupynumeric.isfinite`	✓	✓
`numpy.isfortran`
`numpy.isin`
`numpy.isinf`	`cupynumeric.isinf`	✓	✓
`numpy.isnan`	`cupynumeric.isnan`	✓	✓
`numpy.isnat`
`numpy.isneginf`	`cupynumeric.isneginf`	✓	✓
`numpy.isposinf`	`cupynumeric.isposinf`	✓	✓
`numpy.isreal`	`cupynumeric.isreal`	✓	✓
`numpy.isrealobj`	`cupynumeric.isrealobj`	✓	✓
`numpy.isscalar`	`cupynumeric.isscalar`	✓	✓
`numpy.issctype`
`numpy.issubclass_`
`numpy.issubdtype`
`numpy.issubsctype`
`numpy.ix_`	`cupynumeric.ix_`	✓	✓
`numpy.kaiser`	`cupynumeric.kaiser`	✓	✓
`numpy.kron`
`numpy.lcm`	`cupynumeric.lcm`	✓	✓
`numpy.ldexp`	`cupynumeric.ldexp`	✓	✓
`numpy.left_shift`	`cupynumeric.left_shift`	✓	✓
`numpy.less`	`cupynumeric.less`	✓	✓
`numpy.less_equal`	`cupynumeric.less_equal`	✓	✓
`numpy.lexsort`
`numpy.linspace`	`cupynumeric.linspace`	✓	✓
`numpy.load`	`cupynumeric.load`	❌	❌
`numpy.loadtxt`
`numpy.log`	`cupynumeric.log`	✓	✓
`numpy.log10`	`cupynumeric.log10`	✓	✓
`numpy.log1p`	`cupynumeric.log1p`	✓	✓
`numpy.log2`	`cupynumeric.log2`	✓	✓
`numpy.logaddexp`	`cupynumeric.logaddexp`	✓	✓
`numpy.logaddexp2`	`cupynumeric.logaddexp2`	✓	✓
`numpy.logical_and`	`cupynumeric.logical_and`	✓	✓
`numpy.logical_not`	`cupynumeric.logical_not`	✓	✓
`numpy.logical_or`	`cupynumeric.logical_or`	✓	✓
`numpy.logical_xor`	`cupynumeric.logical_xor`	✓	✓
`numpy.logspace`
`numpy.longcomplex`
`numpy.longdouble`
`numpy.longfloat`
`numpy.longlong`
`numpy.lookfor`
`numpy.mask_indices`	`cupynumeric.mask_indices`	✓	✓
`numpy.mat`
`numpy.matmul`	`cupynumeric.matmul`	✓	✓
`numpy.matrix`
`numpy.maximum`	`cupynumeric.maximum`	✓	✓
`numpy.maximum_sctype`
`numpy.may_share_memory`
`numpy.mean`	`cupynumeric.mean`	✓	✓
`numpy.median`	`cupynumeric.median`	✓	✓
`numpy.memmap`
`numpy.meshgrid`	`cupynumeric.meshgrid`	✓	✓
`numpy.min_scalar_type`
`numpy.minimum`	`cupynumeric.minimum`	✓	✓
`numpy.mintypecode`
`numpy.mod`	`cupynumeric.mod`	✓	✓
`numpy.modf`	`cupynumeric.modf`	✓	✓
`numpy.moveaxis`	`cupynumeric.moveaxis`	✓	✓
`numpy.msort`	`cupynumeric.msort`	✓	✓
`numpy.multiply`	`cupynumeric.multiply`	✓	✓
`numpy.nan_to_num`
`numpy.nanargmax`	`cupynumeric.nanargmax`	✓	✓
`numpy.nanargmin`	`cupynumeric.nanargmin`	✓	✓
`numpy.nancumprod`	`cupynumeric.nancumprod`	✓	✓
`numpy.nancumsum`	`cupynumeric.nancumsum`	✓	✓
`numpy.nanmax`	`cupynumeric.nanmax`	✓	✓
`numpy.nanmean`	`cupynumeric.nanmean`	✓	✓
`numpy.nanmedian`	`cupynumeric.nanmedian`	✓	✓
`numpy.nanmin`	`cupynumeric.nanmin`	✓	✓
`numpy.nanpercentile`	`cupynumeric.nanpercentile`	✓	✓
`numpy.nanprod`	`cupynumeric.nanprod`	✓	✓
`numpy.nanquantile`	`cupynumeric.nanquantile`	✓	✓
`numpy.nanstd`
`numpy.nansum`	`cupynumeric.nansum`	✓	✓
`numpy.nanvar`
`numpy.ndarray()`
`numpy.ndenumerate`
`numpy.ndim`	`cupynumeric.ndim`	✓	✓
`numpy.ndindex`
`numpy.nditer`
`numpy.negative`	`cupynumeric.negative`	✓	✓
`numpy.nextafter`	`cupynumeric.nextafter`	✓	✓
`numpy.nonzero`	`cupynumeric.nonzero`	✓	✓
`numpy.not_equal`	`cupynumeric.not_equal`	✓	✓
`numpy.number`
`numpy.obj2sctype`
`numpy.object_`
`numpy.ones`	`cupynumeric.ones`	✓	✓
`numpy.ones_like`	`cupynumeric.ones_like`	✓	✓
`numpy.outer`	`cupynumeric.outer`	✓	✓
`numpy.packbits`	`cupynumeric.packbits`	✓	✓
`numpy.pad`
`numpy.partition`	`cupynumeric.partition`	✓	✓
`numpy.percentile`	`cupynumeric.percentile`	✓	✓
`numpy.piecewise`
`numpy.place`	`cupynumeric.place`	✓	✓
`numpy.poly`
`numpy.poly1d`
`numpy.polyadd`
`numpy.polyder`
`numpy.polydiv`
`numpy.polyfit`
`numpy.polyint`
`numpy.polymul`
`numpy.polysub`
`numpy.polyval`
`numpy.positive`	`cupynumeric.positive`	✓	✓
`numpy.power`	`cupynumeric.power`	✓	✓
`numpy.printoptions`
`numpy.prod`	`cupynumeric.prod`	✓	✓
`numpy.ptp`
`numpy.put`	`cupynumeric.put`	✓	✓
`numpy.put_along_axis`	`cupynumeric.put_along_axis`	✓	✓
`numpy.putmask`	`cupynumeric.putmask`	✓	✓
`numpy.quantile`	`cupynumeric.quantile`	✓	✓
`numpy.rad2deg`	`cupynumeric.rad2deg`	✓	✓
`numpy.radians`	`cupynumeric.radians`	✓	✓
`numpy.ravel`	`cupynumeric.ravel`	✓	✓
`numpy.ravel_multi_index`
`numpy.real`	`cupynumeric.real`	✓	✓
`numpy.real_if_close`
`numpy.recarray`
`numpy.reciprocal`	`cupynumeric.reciprocal`	✓	✓
`numpy.record`
`numpy.remainder`	`cupynumeric.remainder`	✓	✓
`numpy.repeat`	`cupynumeric.repeat`	✓	✓
`numpy.require`
`numpy.reshape`	`cupynumeric.reshape`	✓	✓
`numpy.resize`
`numpy.result_type`
`numpy.right_shift`	`cupynumeric.right_shift`	✓	✓
`numpy.rint`	`cupynumeric.rint`	✓	✓
`numpy.roll`	`cupynumeric.roll`	✓	✓
`numpy.rollaxis`
`numpy.roots`
`numpy.rot90`	`cupynumeric.rot90`	✓	❌
`numpy.round_`
`numpy.row_stack`	`cupynumeric.row_stack`	✓	✓
`numpy.save`
`numpy.savetxt`
`numpy.savez`
`numpy.savez_compressed`
`numpy.sctype2char`
`numpy.searchsorted`	`cupynumeric.searchsorted`	✓	✓
`numpy.select`	`cupynumeric.select`	✓	✓
`numpy.set_printoptions`
`numpy.set_string_function`
`numpy.setbufsize`
`numpy.setdiff1d`
`numpy.seterr`
`numpy.seterrcall`
`numpy.setxor1d`
`numpy.shape`	`cupynumeric.shape`	✓	✓
`numpy.shares_memory`
`numpy.short`
`numpy.show_config`
`numpy.show_runtime`
`numpy.sign`	`cupynumeric.sign`	✓	✓
`numpy.signbit`	`cupynumeric.signbit`	✓	✓
`numpy.signedinteger`
`numpy.sin`	`cupynumeric.sin`	✓	✓
`numpy.sinc`
`numpy.single`
`numpy.singlecomplex`
`numpy.sinh`	`cupynumeric.sinh`	✓	✓
`numpy.sort`	`cupynumeric.sort`	✓	✓
`numpy.sort_complex`	`cupynumeric.sort_complex`	✓	✓
`numpy.source`
`numpy.spacing`
`numpy.split`	`cupynumeric.split`	✓	✓
`numpy.sqrt`	`cupynumeric.sqrt`	✓	✓
`numpy.square`	`cupynumeric.square`	✓	✓
`numpy.squeeze`	`cupynumeric.squeeze`	✓	✓
`numpy.stack`	`cupynumeric.stack`	✓	✓
`numpy.std`
`numpy.str_`
`numpy.string_`
`numpy.subtract`	`cupynumeric.subtract`	✓	✓
`numpy.sum`	`cupynumeric.sum`	✓	✓
`numpy.swapaxes`	`cupynumeric.swapaxes`	✓	✓
`numpy.take`	`cupynumeric.take`	✓	✓
`numpy.take_along_axis`	`cupynumeric.take_along_axis`	✓	✓
`numpy.tan`	`cupynumeric.tan`	✓	✓
`numpy.tanh`	`cupynumeric.tanh`	✓	✓
`numpy.tensordot`	`cupynumeric.tensordot`	✓	✓
`numpy.tile`	`cupynumeric.tile`	✓	✓
`numpy.timedelta64`
`numpy.trace`	`cupynumeric.trace`	✓	✓
`numpy.transpose`	`cupynumeric.transpose`	✓	✓
`numpy.trapz`
`numpy.tri`	`cupynumeric.tri`	✓	✓
`numpy.tril`	`cupynumeric.tril`	✓	✓
`numpy.tril_indices`	`cupynumeric.tril_indices`	✓	✓
`numpy.tril_indices_from`	`cupynumeric.tril_indices_from`	✓	✓
`numpy.trim_zeros`
`numpy.triu`	`cupynumeric.triu`	✓	✓
`numpy.triu_indices`	`cupynumeric.triu_indices`	✓	✓
`numpy.triu_indices_from`	`cupynumeric.triu_indices_from`	✓	✓
`numpy.true_divide`	`cupynumeric.true_divide`	✓	✓
`numpy.trunc`	`cupynumeric.trunc`	✓	✓
`numpy.typename`
`numpy.ubyte`
`numpy.ufunc`
`numpy.uint`
`numpy.uint16`
`numpy.uint32`
`numpy.uint64`
`numpy.uint8`
`numpy.uintc`
`numpy.uintp`
`numpy.ulonglong`
`numpy.unicode_`
`numpy.union1d`
`numpy.unique`	`cupynumeric.unique`	✓	✓
`numpy.unpackbits`	`cupynumeric.unpackbits`	✓	✓
`numpy.unravel_index`	`cupynumeric.unravel_index`	✓	✓
`numpy.unsignedinteger`
`numpy.unwrap`
`numpy.ushort`
`numpy.vander`
`numpy.var`	`cupynumeric.var`	✓	✓
`numpy.vdot`	`cupynumeric.vdot`	✓	✓
`numpy.vectorize`
`numpy.void`
`numpy.vsplit`	`cupynumeric.vsplit`	✓	✓
`numpy.vstack`	`cupynumeric.vstack`	✓	✓
`numpy.where`	`cupynumeric.where`	✓	✓
`numpy.who`
`numpy.zeros`	`cupynumeric.zeros`	✓	✓
`numpy.zeros_like`	`cupynumeric.zeros_like`	✓	✓

Summary

Number of NumPy functions: 445

Number of functions covered by cupynumeric: 243

Logical operations#

NumPy	cupynumeric	single-GPU/CPU	multi-GPU/CPU
`numpy.all`	`cupynumeric.all`	✓	✓
`numpy.allclose`	`cupynumeric.allclose`	✓	✓
`numpy.any`	`cupynumeric.any`	✓	✓
`numpy.array_equal`	`cupynumeric.array_equal`	✓	✓
`numpy.array_equiv`
`numpy.isclose`	`cupynumeric.isclose`	✓	✓
`numpy.iscomplex`	`cupynumeric.iscomplex`	✓	✓
`numpy.iscomplexobj`	`cupynumeric.iscomplexobj`	✓	✓
`numpy.isfortran`
`numpy.isneginf`	`cupynumeric.isneginf`	✓	✓
`numpy.isposinf`	`cupynumeric.isposinf`	✓	✓
`numpy.isreal`	`cupynumeric.isreal`	✓	✓
`numpy.isrealobj`	`cupynumeric.isrealobj`	✓	✓
`numpy.isscalar`	`cupynumeric.isscalar`	✓	✓

Summary

Number of NumPy functions: 14

Number of functions covered by cupynumeric: 12

Einsum and related#

NumPy	cupynumeric	single-GPU/CPU	multi-GPU/CPU
`numpy.dot`	`cupynumeric.dot`	✓	✓
`numpy.einsum`	`cupynumeric.einsum`	✓	✓
`numpy.inner`	`cupynumeric.inner`	✓	✓
`numpy.outer`	`cupynumeric.outer`	✓	✓
`numpy.tensordot`	`cupynumeric.tensordot`	✓	✓
`numpy.trace`	`cupynumeric.trace`	✓	✓

Summary

Number of NumPy functions: 6

Number of functions covered by cupynumeric: 6

Discrete Fourier transform#

NumPy	cupynumeric	single-GPU/CPU	multi-GPU/CPU
`numpy.fft.fft`	`cupynumeric.fft.fft`	✓	✓
`numpy.fft.fft2`	`cupynumeric.fft.fft2`	✓	✓
`numpy.fft.fftfreq`
`numpy.fft.fftn`	`cupynumeric.fft.fftn`	✓	✓
`numpy.fft.fftshift`	`cupynumeric.fft.fftshift`	✓	✓
`numpy.fft.hfft`	`cupynumeric.fft.hfft`	✓	✓
`numpy.fft.ifft`	`cupynumeric.fft.ifft`	✓	✓
`numpy.fft.ifft2`	`cupynumeric.fft.ifft2`	✓	✓
`numpy.fft.ifftn`	`cupynumeric.fft.ifftn`	✓	✓
`numpy.fft.ifftshift`	`cupynumeric.fft.ifftshift`	✓	✓
`numpy.fft.ihfft`	`cupynumeric.fft.ihfft`	✓	✓
`numpy.fft.irfft`	`cupynumeric.fft.irfft`	✓	✓
`numpy.fft.irfft2`	`cupynumeric.fft.irfft2`	✓	✓
`numpy.fft.irfftn`	`cupynumeric.fft.irfftn`	✓	✓
`numpy.fft.rfft`	`cupynumeric.fft.rfft`	✓	✓
`numpy.fft.rfft2`	`cupynumeric.fft.rfft2`	✓	✓
`numpy.fft.rfftfreq`
`numpy.fft.rfftn`	`cupynumeric.fft.rfftn`	✓	✓

Summary

Number of NumPy functions: 18

Number of functions covered by cupynumeric: 16

Set operations#

NumPy	cupynumeric	single-GPU/CPU	multi-GPU/CPU
`numpy.in1d`
`numpy.intersect1d`
`numpy.isin`
`numpy.setdiff1d`
`numpy.setxor1d`
`numpy.union1d`
`numpy.unique`	`cupynumeric.unique`	✓	✓

Summary

Number of NumPy functions: 7

Number of functions covered by cupynumeric: 1

Array manipulation#

NumPy	cupynumeric	single-GPU/CPU	multi-GPU/CPU
`numpy.append`	`cupynumeric.append`	✓	✓
`numpy.array_split`	`cupynumeric.array_split`	✓	✓
`numpy.asanyarray`
`numpy.asarray`	`cupynumeric.asarray`	✓	✓
`numpy.asarray_chkfinite`
`numpy.ascontiguousarray`
`numpy.asfortranarray`
`numpy.asmatrix`
`numpy.atleast_1d`	`cupynumeric.atleast_1d`	✓	✓
`numpy.atleast_2d`	`cupynumeric.atleast_2d`	✓	✓
`numpy.atleast_3d`	`cupynumeric.atleast_3d`	✓	✓
`numpy.block`	`cupynumeric.block`	✓	✓
`numpy.broadcast_arrays`	`cupynumeric.broadcast_arrays`	✓	✓
`numpy.broadcast_to`	`cupynumeric.broadcast_to`	✓	✓
`numpy.column_stack`	`cupynumeric.column_stack`	✓	✓
`numpy.concatenate`	`cupynumeric.concatenate`	✓	✓
`numpy.copyto`
`numpy.delete`
`numpy.dsplit`	`cupynumeric.dsplit`	✓	✓
`numpy.dstack`	`cupynumeric.dstack`	✓	✓
`numpy.expand_dims`	`cupynumeric.expand_dims`	✓	✓
`numpy.flip`	`cupynumeric.flip`	✓	❌
`numpy.fliplr`	`cupynumeric.fliplr`	✓	❌
`numpy.flipud`	`cupynumeric.flipud`	✓	❌
`numpy.hsplit`	`cupynumeric.hsplit`	✓	✓
`numpy.hstack`	`cupynumeric.hstack`	✓	✓
`numpy.insert`
`numpy.moveaxis`	`cupynumeric.moveaxis`	✓	✓
`numpy.ravel`	`cupynumeric.ravel`	✓	✓
`numpy.repeat`	`cupynumeric.repeat`	✓	✓
`numpy.require`
`numpy.reshape`	`cupynumeric.reshape`	✓	✓
`numpy.resize`
`numpy.roll`	`cupynumeric.roll`	✓	✓
`numpy.rollaxis`
`numpy.rot90`	`cupynumeric.rot90`	✓	❌
`numpy.row_stack`	`cupynumeric.row_stack`	✓	✓
`numpy.shape`	`cupynumeric.shape`	✓	✓
`numpy.split`	`cupynumeric.split`	✓	✓
`numpy.squeeze`	`cupynumeric.squeeze`	✓	✓
`numpy.stack`	`cupynumeric.stack`	✓	✓
`numpy.swapaxes`	`cupynumeric.swapaxes`	✓	✓
`numpy.tile`	`cupynumeric.tile`	✓	✓
`numpy.transpose`	`cupynumeric.transpose`	✓	✓
`numpy.trim_zeros`
`numpy.vsplit`	`cupynumeric.vsplit`	✓	✓
`numpy.vstack`	`cupynumeric.vstack`	✓	✓

Summary

Number of NumPy functions: 47

Number of functions covered by cupynumeric: 35

Array manipulation (ndarray)#

NumPy	cupynumeric	single-GPU/CPU	multi-GPU/CPU
`numpy.ndarray.flatten()`	`cupynumeric.ndarray.flatten()`	✓	✓

Summary

Number of NumPy functions: 1

Number of functions covered by cupynumeric: 1

Factorizations#

NumPy	cupynumeric	single-GPU/CPU	multi-GPU/CPU
`numpy.linalg.cholesky`	`cupynumeric.linalg.cholesky`	✓	✓
`numpy.linalg.qr`	`cupynumeric.linalg.qr`	✓	❌

Summary

Number of NumPy functions: 2

Number of functions covered by cupynumeric: 2

SVD and related#

NumPy	cupynumeric	single-GPU/CPU	multi-GPU/CPU
`numpy.linalg.lstsq`
`numpy.linalg.matrix_rank`
`numpy.linalg.pinv`
`numpy.linalg.svd`	`cupynumeric.linalg.svd`	✓	❌

Summary

Number of NumPy functions: 4

Number of functions covered by cupynumeric: 1

Eigenvalues#

NumPy	cupynumeric	single-GPU/CPU	multi-GPU/CPU
`numpy.linalg.eig`	`cupynumeric.linalg.eig`	❌	❌
`numpy.linalg.eigh`
`numpy.linalg.eigvals`	`cupynumeric.linalg.eigvals`	❌	❌
`numpy.linalg.eigvalsh`

Summary

Number of NumPy functions: 4

Number of functions covered by cupynumeric: 2

LU factorization and related#

NumPy	cupynumeric	single-GPU/CPU	multi-GPU/CPU
`numpy.linalg.det`
`numpy.linalg.inv`
`numpy.linalg.slogdet`
`numpy.linalg.solve`	`cupynumeric.linalg.solve`	✓	✓
`numpy.linalg.tensorinv`
`numpy.linalg.tensorsolve`

Summary

Number of NumPy functions: 6

Number of functions covered by cupynumeric: 1

Input and output#

NumPy	cupynumeric	single-GPU/CPU	multi-GPU/CPU
`numpy.array2string`
`numpy.array_repr`
`numpy.array_str`
`numpy.base_repr`
`numpy.binary_repr`
`numpy.format_float_positional`
`numpy.format_float_scientific`
`numpy.fromregex`
`numpy.fromstring`
`numpy.genfromtxt`
`numpy.get_printoptions`
`numpy.load`	`cupynumeric.load`	❌	❌
`numpy.loadtxt`
`numpy.printoptions`
`numpy.save`
`numpy.savetxt`
`numpy.savez`
`numpy.savez_compressed`
`numpy.set_printoptions`

Summary

Number of NumPy functions: 19

Number of functions covered by cupynumeric: 1

Input and output (ndarray)#

NumPy	cupynumeric	single-GPU/CPU	multi-GPU/CPU
`numpy.ndarray.tofile()`	`cupynumeric.ndarray.tofile()`	❌	❌
`numpy.ndarray.tolist()`	`cupynumeric.ndarray.tolist()`	✓	✓

Summary

Number of NumPy functions: 2

Number of functions covered by cupynumeric: 2

Array creation#

NumPy	cupynumeric	single-GPU/CPU	multi-GPU/CPU
`numpy.arange`	`cupynumeric.arange`	✓	✓
`numpy.array`	`cupynumeric.array`	✓	✓
`numpy.bmat`
`numpy.copy`	`cupynumeric.copy`	✓	✓
`numpy.diag`	`cupynumeric.diag`	✓	✓
`numpy.diagflat`
`numpy.empty`	`cupynumeric.empty`	✓	✓
`numpy.empty_like`	`cupynumeric.empty_like`	✓	✓
`numpy.eye`	`cupynumeric.eye`	✓	✓
`numpy.frombuffer`
`numpy.fromfile`
`numpy.fromfunction`
`numpy.fromiter`
`numpy.full`	`cupynumeric.full`	✓	✓
`numpy.full_like`	`cupynumeric.full_like`	✓	✓
`numpy.geomspace`
`numpy.identity`	`cupynumeric.identity`	✓	✓
`numpy.linspace`	`cupynumeric.linspace`	✓	✓
`numpy.logspace`
`numpy.meshgrid`	`cupynumeric.meshgrid`	✓	✓
`numpy.ones`	`cupynumeric.ones`	✓	✓
`numpy.ones_like`	`cupynumeric.ones_like`	✓	✓
`numpy.tri`	`cupynumeric.tri`	✓	✓
`numpy.tril`	`cupynumeric.tril`	✓	✓
`numpy.triu`	`cupynumeric.triu`	✓	✓
`numpy.vander`
`numpy.zeros`	`cupynumeric.zeros`	✓	✓
`numpy.zeros_like`	`cupynumeric.zeros_like`	✓	✓

Summary

Number of NumPy functions: 28

Number of functions covered by cupynumeric: 19

Array creation (ndarray)#

NumPy	cupynumeric	single-GPU/CPU	multi-GPU/CPU
`numpy.ndarray.copy()`	`cupynumeric.ndarray.copy()`	✓	✓

Summary

Number of NumPy functions: 1

Number of functions covered by cupynumeric: 1

Mathematical functions#

NumPy	cupynumeric	single-GPU/CPU	multi-GPU/CPU
`numpy.amax`	`cupynumeric.amax`	✓	✓
`numpy.amin`	`cupynumeric.amin`	✓	✓
`numpy.angle`	`cupynumeric.angle`	✓	✓
`numpy.around`
`numpy.clip`	`cupynumeric.clip`	✓	✓
`numpy.cross`
`numpy.cumprod`	`cupynumeric.cumprod`	✓	✓
`numpy.cumsum`	`cupynumeric.cumsum`	✓	✓
`numpy.diff`	`cupynumeric.diff`	✓	✓
`numpy.ediff1d`
`numpy.fix`
`numpy.gradient`	`cupynumeric.gradient`	✓	✓
`numpy.i0`
`numpy.imag`	`cupynumeric.imag`	✓	✓
`numpy.interp`
`numpy.nan_to_num`
`numpy.nancumprod`	`cupynumeric.nancumprod`	✓	✓
`numpy.nancumsum`	`cupynumeric.nancumsum`	✓	✓
`numpy.nanmax`	`cupynumeric.nanmax`	✓	✓
`numpy.nanmin`	`cupynumeric.nanmin`	✓	✓
`numpy.nanprod`	`cupynumeric.nanprod`	✓	✓
`numpy.nansum`	`cupynumeric.nansum`	✓	✓
`numpy.prod`	`cupynumeric.prod`	✓	✓
`numpy.real`	`cupynumeric.real`	✓	✓
`numpy.real_if_close`
`numpy.sinc`
`numpy.sum`	`cupynumeric.sum`	✓	✓
`numpy.trapz`
`numpy.unwrap`

Summary

Number of NumPy functions: 29

Number of functions covered by cupynumeric: 18

Searching, sorting, and counting#

NumPy	cupynumeric	single-GPU/CPU	multi-GPU/CPU
`numpy.argmax`	`cupynumeric.argmax`	✓	✓
`numpy.argmin`	`cupynumeric.argmin`	✓	✓
`numpy.argpartition`	`cupynumeric.argpartition`	✓	✓
`numpy.argsort`	`cupynumeric.argsort`	✓	✓
`numpy.argwhere`	`cupynumeric.argwhere`	✓	✓
`numpy.count_nonzero`	`cupynumeric.count_nonzero`	✓	✓
`numpy.extract`	`cupynumeric.extract`	✓	✓
`numpy.flatnonzero`	`cupynumeric.flatnonzero`	✓	✓
`numpy.lexsort`
`numpy.nanargmax`	`cupynumeric.nanargmax`	✓	✓
`numpy.nanargmin`	`cupynumeric.nanargmin`	✓	✓
`numpy.nonzero`	`cupynumeric.nonzero`	✓	✓
`numpy.partition`	`cupynumeric.partition`	✓	✓
`numpy.searchsorted`	`cupynumeric.searchsorted`	✓	✓
`numpy.sort`	`cupynumeric.sort`	✓	✓
`numpy.sort_complex`	`cupynumeric.sort_complex`	✓	✓
`numpy.where`	`cupynumeric.where`	✓	✓

Summary

Number of NumPy functions: 17

Number of functions covered by cupynumeric: 16

Advanced statistics#

NumPy	cupynumeric	single-GPU/CPU	multi-GPU/CPU
`numpy.average`	`cupynumeric.average`	✓	✓
`numpy.bincount`	`cupynumeric.bincount`	✓	✓
`numpy.corrcoef`
`numpy.cov`	`cupynumeric.cov`	✓	✓
`numpy.digitize`	`cupynumeric.digitize`	✓	✓
`numpy.histogram`	`cupynumeric.histogram`	✓	✓
`numpy.histogram2d`
`numpy.histogram_bin_edges`
`numpy.histogramdd`
`numpy.mean`	`cupynumeric.mean`	✓	✓
`numpy.median`	`cupynumeric.median`	✓	✓
`numpy.nanmean`	`cupynumeric.nanmean`	✓	✓
`numpy.nanmedian`	`cupynumeric.nanmedian`	✓	✓
`numpy.nanpercentile`	`cupynumeric.nanpercentile`	✓	✓
`numpy.nanquantile`	`cupynumeric.nanquantile`	✓	✓
`numpy.nanstd`
`numpy.nanvar`
`numpy.percentile`	`cupynumeric.percentile`	✓	✓
`numpy.ptp`
`numpy.quantile`	`cupynumeric.quantile`	✓	✓
`numpy.std`
`numpy.var`	`cupynumeric.var`	✓	✓

Summary

Number of NumPy functions: 22

Number of functions covered by cupynumeric: 14

Miscellaneous matrix routines#

NumPy	cupynumeric	single-GPU/CPU	multi-GPU/CPU
`numpy.kron`
`numpy.ndim`	`cupynumeric.ndim`	✓	✓

Summary

Number of NumPy functions: 2

Number of functions covered by cupynumeric: 1

Packing and unpacking bits#

NumPy	cupynumeric	single-GPU/CPU	multi-GPU/CPU
`numpy.packbits`	`cupynumeric.packbits`	✓	✓
`numpy.unpackbits`	`cupynumeric.unpackbits`	✓	✓

Summary

Number of NumPy functions: 2

Number of functions covered by cupynumeric: 2

Indexing#

NumPy	cupynumeric	single-GPU/CPU	multi-GPU/CPU
`numpy.choose`	`cupynumeric.choose`	✓	✓
`numpy.compress`	`cupynumeric.compress`	✓	✓
`numpy.diag_indices`	`cupynumeric.diag_indices`	✓	✓
`numpy.diag_indices_from`	`cupynumeric.diag_indices_from`	✓	✓
`numpy.diagonal`	`cupynumeric.diagonal`	✓	✓
`numpy.fill_diagonal`	`cupynumeric.fill_diagonal`	✓	✓
`numpy.indices`	`cupynumeric.indices`	✓	✓
`numpy.ix_`	`cupynumeric.ix_`	✓	✓
`numpy.mask_indices`	`cupynumeric.mask_indices`	✓	✓
`numpy.nested_iters`
`numpy.place`	`cupynumeric.place`	✓	✓
`numpy.put`	`cupynumeric.put`	✓	✓
`numpy.put_along_axis`	`cupynumeric.put_along_axis`	✓	✓
`numpy.putmask`	`cupynumeric.putmask`	✓	✓
`numpy.ravel_multi_index`
`numpy.select`	`cupynumeric.select`	✓	✓
`numpy.take`	`cupynumeric.take`	✓	✓
`numpy.take_along_axis`	`cupynumeric.take_along_axis`	✓	✓
`numpy.tril_indices`	`cupynumeric.tril_indices`	✓	✓
`numpy.tril_indices_from`	`cupynumeric.tril_indices_from`	✓	✓
`numpy.triu_indices`	`cupynumeric.triu_indices`	✓	✓
`numpy.triu_indices_from`	`cupynumeric.triu_indices_from`	✓	✓
`numpy.unravel_index`	`cupynumeric.unravel_index`	✓	✓

Summary

Number of NumPy functions: 23

Number of functions covered by cupynumeric: 21

Padding arrays#

NumPy	cupynumeric	single-GPU/CPU	multi-GPU/CPU
`numpy.pad`

Summary

Number of NumPy functions: 1

Number of functions covered by cupynumeric: 0

Random sampling#

NumPy	cupynumeric	single-GPU/CPU	multi-GPU/CPU
`numpy.random.BitGenerator`
`numpy.random.Generator`
`numpy.random.MT19937`
`numpy.random.PCG64`
`numpy.random.PCG64DXSM`
`numpy.random.Philox`
`numpy.random.RandomState`
`numpy.random.SFC64`
`numpy.random.SeedSequence`
`numpy.random.beta`	`cupynumeric.random.beta`	✓	✓
`numpy.random.binomial`	`cupynumeric.random.binomial`	✓	✓
`numpy.random.bytes`	`cupynumeric.random.bytes`	✓	✓
`numpy.random.chisquare`	`cupynumeric.random.chisquare`	✓	✓
`numpy.random.choice`
`numpy.random.default_rng`	`cupynumeric.random.default_rng`	✓	✓
`numpy.random.dirichlet`
`numpy.random.exponential`	`cupynumeric.random.exponential`	✓	✓
`numpy.random.f`	`cupynumeric.random.f`	✓	✓
`numpy.random.gamma`	`cupynumeric.random.gamma`	✓	✓
`numpy.random.geometric`	`cupynumeric.random.geometric`	✓	✓
`numpy.random.get_bit_generator`
`numpy.random.get_state`
`numpy.random.gumbel`	`cupynumeric.random.gumbel`	✓	✓
`numpy.random.hypergeometric`	`cupynumeric.random.hypergeometric`	✓	✓
`numpy.random.laplace`	`cupynumeric.random.laplace`	✓	✓
`numpy.random.logistic`	`cupynumeric.random.logistic`	✓	✓
`numpy.random.lognormal`	`cupynumeric.random.lognormal`	✓	✓
`numpy.random.logseries`	`cupynumeric.random.logseries`	✓	✓
`numpy.random.multinomial`
`numpy.random.multivariate_normal`
`numpy.random.negative_binomial`	`cupynumeric.random.negative_binomial`	✓	✓
`numpy.random.noncentral_chisquare`	`cupynumeric.random.noncentral_chisquare`	✓	✓
`numpy.random.noncentral_f`	`cupynumeric.random.noncentral_f`	✓	✓
`numpy.random.normal`	`cupynumeric.random.normal`	✓	✓
`numpy.random.pareto`	`cupynumeric.random.pareto`	✓	✓
`numpy.random.permutation`
`numpy.random.poisson`	`cupynumeric.random.poisson`	✓	✓
`numpy.random.power`	`cupynumeric.random.power`	✓	✓
`numpy.random.rand`	`cupynumeric.random.rand`	✓	✓
`numpy.random.randint`	`cupynumeric.random.randint`	✓	✓
`numpy.random.randn`	`cupynumeric.random.randn`	✓	✓
`numpy.random.random`	`cupynumeric.random.random`	✓	✓
`numpy.random.random_integers`	`cupynumeric.random.random_integers`	✓	✓
`numpy.random.random_sample`	`cupynumeric.random.random_sample`	✓	✓
`numpy.random.ranf`	`cupynumeric.random.ranf`	✓	✓
`numpy.random.rayleigh`	`cupynumeric.random.rayleigh`	✓	✓
`numpy.random.sample`	`cupynumeric.random.sample`	✓	✓
`numpy.random.seed`	`cupynumeric.random.seed`	✓	✓
`numpy.random.set_bit_generator`
`numpy.random.set_state`
`numpy.random.shuffle`
`numpy.random.standard_cauchy`	`cupynumeric.random.standard_cauchy`	✓	✓
`numpy.random.standard_exponential`	`cupynumeric.random.standard_exponential`	✓	✓
`numpy.random.standard_gamma`	`cupynumeric.random.standard_gamma`	✓	✓
`numpy.random.standard_normal`
`numpy.random.standard_t`	`cupynumeric.random.standard_t`	✓	✓
`numpy.random.triangular`	`cupynumeric.random.triangular`	✓	✓
`numpy.random.uniform`	`cupynumeric.random.uniform`	✓	✓
`numpy.random.vonmises`	`cupynumeric.random.vonmises`	✓	✓
`numpy.random.wald`	`cupynumeric.random.wald`	✓	✓
`numpy.random.weibull`	`cupynumeric.random.weibull`	✓	✓
`numpy.random.zipf`	`cupynumeric.random.zipf`	✓	✓

Summary

Number of NumPy functions: 62

Number of functions covered by cupynumeric: 42

Functional programming#

NumPy	cupynumeric	single-GPU/CPU	multi-GPU/CPU
`numpy.apply_along_axis`
`numpy.apply_over_axes`
`numpy.frompyfunc`
`numpy.piecewise`

Summary

Number of NumPy functions: 4

Number of functions covered by cupynumeric: 0