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

@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.