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Ask questionsUnimplemented NumPy core functions

If you implement a feature with a PR, you get to ring the bell! 🔔 (list generated with np.UNIMPLEMENTED_FUNCS with #69)

High level categories:

  • [x] np.linalg: https://github.com/google/jax/issues/1999
  • [x] np.fft: https://github.com/google/jax/issues/1877
  • [x] searching: https://github.com/google/jax/issues/2080
  • [ ] sorting: https://github.com/google/jax/issues/2079
  • [ ] sets: https://github.com/google/jax/issues/2078
  • [x] NaN functions: https://github.com/google/jax/issues/2077

Other stuff:

  • [ ] np.alen
  • [x] np.alltrue
  • [x] np.percentile
  • [x] np.deg2rad
  • [x] np.quantile
  • [x] np.degrees
  • [x] np.empty_like
  • [x] np.trapz
  • [x] np.digitize
  • [x] np.empty
  • [x] np.meshgrid
  • [x] np.exp2
  • [ ] np.delete
  • [ ] np.insert
  • [x] np.append
  • [x] np.fabs
  • [x] np.arange
  • [x] np.float_power
  • [ ] np.find_common_type
  • [x] np.take
  • [x] np.fmax
  • [ ] np.choose
  • [x] np.fmin
  • [x] np.fmod
  • [x] np.frexp
  • [x] np.inner
  • [x] np.gcd
  • [x] np.heaviside
  • [ ] np.putmask (note: this modifies array in-place)
  • [x] np.hypot
  • [x] np.isinf
  • [x] np.isnan
  • [x] np.lcm
  • [x] np.isposinf
  • [x] np.ldexp
  • [x] np.einsum
  • [x] np.histogram_bin_edges
  • [x] np.isneginf
  • [x] np.histogram
  • [x] np.fix
  • [ ] np.histogramdd
  • [x] np.full_like
  • [x] np.flip
  • [x] np.count_nonzero
  • [x] np.log10
  • [x] np.asarray
  • [x] np.iscomplex
  • [x] np.average
  • [ ] np.asanyarray
  • [x] np.isreal
  • [x] np.bincount
  • [ ] np.ascontiguousarray
  • [x] np.iscomplexobj
  • [x] np.log2
  • [x] np.piecewise
  • [x] np.isrealobj
  • [x] np.select
  • [x] np.logaddexp
  • [ ] np.require (deals with C/F contiguity not relevant for jax)
  • [ ] np.copy
  • [x] np.nan_to_num
  • [x] np.logaddexp2
  • [x] np.gradient
  • [x] np.diff
  • [x] np.unpackbits
  • [x] np.take_along_axis
  • [ ] np.interp
  • [ ] np.common_type
  • [x] np.correlate
  • [x] np.unwrap
  • [ ] np.apply_over_axes
  • [x] np.convolve
  • [x] np.ix_
  • [x] np.outer
  • [ ] np.trim_zeros
  • [x] np.tensordot
  • [x] np.dstack
  • [x] np.roll
  • [x] np.rollaxis
  • [x] np.modf
  • [ ] np.array_split
  • [x] np.pad
  • [ ] np.min_scalar_type
  • [x] np.hsplit
  • [x] np.result_type
  • [x] np.array_str
  • [x] np.unravel_index
  • [x] np.vsplit
  • [x] np.dsplit
  • [x] np.broadcast_to
  • [x] np.promote_types
  • [x] np.positive
  • [x] np.broadcast_arrays
  • [x] np.kron
  • [x] np.can_cast
  • [x] np.cov
  • [x] np.tile
  • [x] np.rad2deg
  • [x] np.radians
  • [x] np.reciprocal
  • [x] np.linspace
  • [x] np.logspace
  • [x] np.corrcoef
  • [x] np.geomspace
  • [x] np.blackman
  • [x] np.rint
  • [x] np.hanning
  • [x] np.hamming
  • [x] np.signbit
  • [x] np.atleast_3d
  • [ ] np.i0
  • [x] np.kaiser
  • [ ] np.spacing
  • [x] np.issubsctype
  • [ ] np.poly
  • [x] np.sinc
  • [x] np.sqrt
  • [x] np.roots
  • [x] np.issubdtype
  • [x] np.block
  • [x] np.diag_indices
  • [ ] np.polyint
  • [x] np.square
  • [x] np.diag_indices_from
  • [x] np.median
  • [x] np.polyder
  • [ ] np.polyfit
  • [x] np.arccos
  • [x] np.tan
  • [x] np.arccosh
  • [ ] np.resize
  • [x] np.polyval
  • [x] np.arcsin
  • [x] np.polyadd
  • [x] np.diagonal
  • [x] np.polysub
  • [x] np.trace
  • [x] np.trunc
  • [x] np.cross
  • [x] np.polymul
  • [ ] np.indices
  • [ ] np.polydiv
  • [x] np.shape
  • [x] np.sum
  • [x] np.any
  • [x] np.rot90
  • [x] np.all
  • [x] np.identity
  • [x] np.cumsum
  • [x] np.fliplr
  • [x] np.flipud
  • [x] np.ptp
  • [x] np.amax
  • [x] np.eye
  • [x] np.array_equal
  • [ ] np.array_equiv
  • [x] np.cbrt
  • [x] np.amin
  • [x] np.diag
  • [x] np.diagflat
  • [ ] np.ravel_multi_index
  • [x] np.prod
  • [x] np.tri
  • [x] np.arcsinh
  • [x] np.cumprod
  • [x] np.tril
  • [x] np.ndim
  • [x] np.triu
  • [x] np.arctan
  • [x] np.size
  • [x] np.vander
  • [x] np.arctan2
  • [x] np.around
  • [ ] np.histogram2d
  • [x] np.arctanh
  • [x] np.mask_indices
  • [x] np.tril_indices
  • [x] np.tril_indices_from
  • [x] np.triu_indices
  • [x] np.product
  • [x] np.triu_indices_from
  • [x] np.copysign
  • [x] np.cumproduct
  • [x] np.sometrue
google/jax

Answer questions navneet-nmk

@mattjj since this would be my first hand at this, I should probably look at numpy functions that are less tricky compared to unique?

Any suggestions would be really helpful.

useful!

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