########################################################################################### # Copyright INAOS GmbH, Thalwil, 2018. # Copyright Francesc Alted, 2018. # # All rights reserved. # # This software is the confidential and proprietary information of INAOS GmbH # and Francesc Alted ("Confidential Information"). You shall not disclose such Confidential # Information and shall use it only in accordance with the terms of the license agreement. ########################################################################################### import iarray as ia from iarray import iarray_ext as ext from itertools import zip_longest import numpy as np from typing import Union import ndindex import sys from .info import InfoReporter def is_documented_by(original): def wrapper(target): target.__doc__ = original.__doc__ return target return wrapper # For avoiding a warning in PyCharm in method signatures IArray = None class IArray(ext.Container): """The ironArray data container. This is not meant to be called from user space. """ @property def info(self): """ Print information about this array. """ return InfoReporter(self) @property def info_items(self): items = [] items += [("type", self.__class__.__name__)] items += [("shape", self.shape)] items += [("chunks", self.chunks)] items += [("blocks", self.blocks)] items += [("cratio", f"{self.cratio:.2f}")] return items @property def data(self): """ Get a ndarray with array data. Returns ------- np.ndarray """ return ia.iarray2numpy(self) def copy(self, view=False, cfg=None, **kwargs) -> IArray: """Return a copy of the array. Parameters ---------- view : bool If True, return a view; else an actual copy. Default is False. cfg : Config The configuration for this operation. If None (default), the current configuration will be used. kwargs : dict A dictionary for setting some or all of the fields in the Config dataclass that should override the current configuration. Returns ------- IArray The copy. """ if cfg is None: cfg = ia.get_config() # Workaround until https://github.com/inaos/iron-array/issues/418 is fixed chunks = self.chunks if cfg.chunks is None else cfg.chunks blocks = self.blocks if cfg.blocks is None else cfg.blocks with ia.config(shape=self.shape, chunks=chunks, blocks=blocks, cfg=cfg, **kwargs) as cfg: return ext.copy(cfg, self, view) def copyto(self, dest): """Copy array contents to `dest`. Parameters ---------- dest : Any The destination container. It can be any object that supports multidimensional assignment (NumPy, Zarr, HDF5...). It should have the same shape than `self`. """ if tuple(dest.shape) != self.shape: raise IndexError("Incompatible destination shape") for info, block in self.iter_read_block(): dest[info.slice] = block[:] def iter_read_block(self, iterblock: tuple = None): if iterblock is None: if self.chunks is not None: iterblock = self.chunks else: iterblock, _ = ia.partition_advice(self.shape) return ext.ReadBlockIter(self, iterblock) def iter_write_block(self, iterblock=None): if iterblock is None: if self.chunks: iterblock = self.chunks else: iterblock, _ = ia.partition_advice(self.shape) return ext.WriteBlockIter(self, iterblock) def __getitem__(self, key): # Massage the key a bit so that it is compatible with self.shape key = list(ndindex.ndindex(key).expand(self.shape).raw) squeeze_mask = tuple(True if isinstance(k, int) else False for k in key) for i, k in enumerate(key): if isinstance(k, np.ndarray): raise AttributeError("Advance indexing is not supported yet") elif isinstance(k, int): key[i] = slice(k, k + 1, None) elif isinstance(k, slice): if k.step is not None and k.step != 1: raise AttributeError("Step indexing is not supported yet") else: raise AttributeError(f"Type {type(k)} is not supported") start = [sl.start for sl in key] stop = [sl.stop for sl in key] return super().__getitem__([start, stop, squeeze_mask]) def __iter__(self): return self.iter_read_block() def __str__(self): return f"" def __repr__(self): return str(self) def __matmul__(self, value): a = self return ia.matmul(a, value) def __add__(self, value): return ia.LazyExpr(new_op=(self, "+", value)) def __radd__(self, value): return ia.LazyExpr(new_op=(value, "+", self)) def __sub__(self, value): return ia.LazyExpr(new_op=(self, "-", value)) def __rsub__(self, value): return ia.LazyExpr(new_op=(value, "-", self)) def __mul__(self, value): return ia.LazyExpr(new_op=(self, "*", value)) def __rmul__(self, value): return ia.LazyExpr(new_op=(value, "*", self)) def __truediv__(self, value): return ia.LazyExpr(new_op=(self, "/", value)) def __rtruediv__(self, value): return ia.LazyExpr(new_op=(value, "/", self)) # def __array_function__(self, func, types, args, kwargs): # if not all(issubclass(t, np.ndarray) for t in types): # # Defer to any non-subclasses that implement __array_function__ # return NotImplemented # # # Use NumPy's private implementation without __array_function__ # # dispatching # return func._implementation(*args, **kwargs) # def __array_ufunc__(self, ufunc, method, *inputs, **kwargs): # print("method:", method) @property def T(self): return self.transpose() def transpose(self, **kwargs): return ia.transpose(self, **kwargs) def abs(self): """ Absolute value, element-wise. Parameters ---------- iarr: IArray Input array. Returns ------- abs: IArray An array containing the absolute value of each element in x. References ---------- https://numpy.org/doc/stable/reference/generated/numpy.abs.html """ return ia.LazyExpr(new_op=(self, "abs", None)) def arccos(self): """ Trigonometric inverse cosine, element-wise. The inverse of :py:obj:`cos` so that, if $y = \\\\cos(x)$, then $x = \\\\arccos(y)$. Parameters ---------- iarr: IArray x-coordinate on the unit circle. For real arguments, the domain is $[-1, 1]$. Returns ------- angle: IArray The angle of the ray intersecting the unit circle at the given x-coordinate in radians $[0, \\\\pi]$. Notes ----- :py:obj:`arccos` is a multivalued function: for each x there are infinitely many numbers z such that $\\\\cos(z) = x$. The convention is to return the angle z whose real part lies in &[0, \\\\pi]$. References ---------- https://numpy.org/doc/stable/reference/generated/numpy.arccos.html """ return ia.LazyExpr(new_op=(self, "acos", None)) def arcsin(self): """ Trigonometric inverse sine, element-wise. The inverse of :py:obj:`sin` so that, if $y = \\\\sin(x)$, then $x = \\\\arcsin(y)$. Parameters ---------- iarr: IArray y-coordinate on the unit circle. Returns ------- angle: IArray The inverse sine of each element in x, in radians and in the closed interval $\\\\left[-\\\\frac{\\\\pi}{2}, \\\\frac{\\\\pi}{2}\\\\right]$. Notes ----- :py:obj:`arcsin` is a multivalued function: for each x there are infinitely many numbers z such that $\\\\sin(z) = x$. The convention is to return the angle z whose real part lies in $\\\\left[-\\\\frac{\\\\pi}{2}, \\\\frac{\\\\pi}{2}\\\\right]$. References ---------- https://numpy.org/doc/stable/reference/generated/numpy.arcsin.html """ return ia.LazyExpr(new_op=(self, "asin", None)) def arctan(self): """ Trigonometric inverse tangent, element-wise. The inverse of :py:obj:`tan` so that, if $y = \\\\tan(x)$, then $x = \\\\arctan(y)$. Parameters ---------- iarr: IArray Input array. Returns ------- angle: IArray Array of angles in radians, in the range $\\\\left[-\\\\frac{\\\\pi}{2}, \\\\frac{\\\\pi}{2}\\\\right]$. Notes ----- :py:obj:`arctan` is a multi-valued function: for each x there are infinitely many numbers z such that $\\\\tan(z) = x$. The convention is to return the angle z whose real part lies in $\\\\left[-\\\\frac{\\\\pi}{2}, \\\\frac{\\\\pi}{2}\\\\right]$. References ---------- https://numpy.org/doc/stable/reference/generated/numpy.arctan.html """ return ia.LazyExpr(new_op=(self, "atan", None)) def arctan2(self, op2): """ Element-wise arc tangent of $\\\\frac{iarr_1}{iarr_2}$ choosing the quadrant correctly. Parameters ---------- iarr1: IArray y-coordinates. iarr2: IArray x-coordinates. Returns ------- angle: IArray Array of angles in radians, in the range $[-\\\\pi, \\\\pi]$. References ---------- https://numpy.org/doc/stable/reference/generated/numpy.arctan2.html """ return ia.LazyExpr(new_op=(self, "atan2", op2)) def acos(self): """See :py:obj:`IArray.arccos`.""" return ia.LazyExpr(new_op=(self, "acos", None)) def asin(self): """See :py:obj:`IArray.arcsin`.""" return ia.LazyExpr(new_op=(self, "asin", None)) def atan(self): """See :py:obj:`IArray.arctan`.""" return ia.LazyExpr(new_op=(self, "atan", None)) def atan2(self, op2): """See :py:obj:`IArray.arctan2`.""" return ia.LazyExpr(new_op=(self, "atan2", op2)) def ceil(self): """ Return the ceiling of the input, element-wise. It is often denoted as $\\\\lceil x \\\\rceil$. Parameters ---------- iarr: IArray Input array. Returns ------- out: IArray The ceiling of each element in x. References ---------- https://numpy.org/doc/stable/reference/generated/numpy.ceil.html """ return ia.LazyExpr(new_op=(self, "ceil", None)) def cos(self): """ Trigonometric cosine, element-wise. Parameters ---------- iarr: IArray Angle, in radians. Returns ------- out: IArray The corresponding cosine values. References ---------- https://numpy.org/doc/stable/reference/generated/numpy.cos.html """ return ia.LazyExpr(new_op=(self, "cos", None)) def cosh(self): """ Hyperbolic cosine, element-wise. Equivalent to ``1/2 * (ia.exp(x) + ia.exp(-x))``. Parameters ---------- iarr: IArray Input data. Returns ------- out: IArray The corresponding hyperbolic cosine values. References ---------- https://numpy.org/doc/stable/reference/generated/numpy.cosh.html """ return ia.LazyExpr(new_op=(self, "cosh", None)) def exp(self): """ Calculate the exponential of all elements in the input array. Parameters ---------- iarr: IArray Input array. Returns ------- out: IArray Element-wise exponential of input data. References ---------- See https://numpy.org/doc/stable/reference/generated/numpy.exp.html """ return ia.LazyExpr(new_op=(self, "exp", None)) def floor(self): """ Return the floor of the input, element-wise. It is often denoted as $\\\\lfloor x \\\\rfloor$. Parameters ---------- iarr: IArray Input array. Returns ------- out: IArray The floor of each element in input data. References ---------- https://numpy.org/doc/stable/reference/generated/numpy.floor.html """ return ia.LazyExpr(new_op=(self, "floor", None)) def log(self): """ Natural logarithm, element-wise. The natural logarithm log is the inverse of the exponential function, so that $\\\\log(\\\\exp(x)) = x$. The natural logarithm is logarithm in base $e$. Parameters ---------- iarr: IArray Input array. Returns ------- out: IArray The natural logarithm of input data, element-wise. References ---------- https://numpy.org/doc/stable/reference/generated/numpy.log.html """ return ia.LazyExpr(new_op=(self, "log", None)) def log10(self): """ Return the base 10 logarithm of the input array, element-wise. Parameters ---------- iarr: IArray Input array. Returns ------- out: IArray The logarithm to the base 10 of input data, element-wise. References ---------- https://numpy.org/doc/stable/reference/generated/numpy.log10.html """ return ia.LazyExpr(new_op=(self, "log10", None)) def negative(self): """ Numerical negative, element-wise. Parameters ---------- iarr: IArray Input array. Returns ------- out: IArray Returned array $out = -iarr$. References ---------- https://numpy.org/doc/stable/reference/generated/numpy.log10.html """ return ia.LazyExpr(new_op=(self, "negate", None)) def power(self, op2): """ First array elements raised to powers from second array, element-wise. Parameters ---------- iarr1: IArray The bases. iarr1: IArray The exponents. Returns ------- out: IArray The bases raised to the exponents. References ---------- https://numpy.org/doc/stable/reference/generated/numpy.log10.html """ return ia.LazyExpr(new_op=(self, "pow", op2)) def sin(self): """ Trigonometric sine, element-wise. Parameters ---------- iarr: IArray Angle, in radians. Returns ------- out: IArray The corresponding sine values. References ---------- https://numpy.org/doc/stable/reference/generated/numpy.sin.html """ return ia.LazyExpr(new_op=(self, "sin", None)) def sinh(self): """ Hyperbolic sine, element-wise. Equivalent to ``1/2 * (ia.exp(x) - ia.exp(-x))``. Parameters ---------- iarr: IArray Input data. Returns ------- out: IArray The corresponding hyperbolic sine values. References ---------- https://numpy.org/doc/stable/reference/generated/numpy.sinh.html """ return ia.LazyExpr(new_op=(self, "sinh", None)) def sqrt(self): """ Return the non-negative square-root of an array, element-wise. Parameters ---------- iarr: IArray The values whose square-roots are required. Returns ------- out: IArray An array containing the positive square-root of each element in input data. References ---------- https://numpy.org/doc/stable/reference/generated/numpy.sqrt.html """ return ia.LazyExpr(new_op=(self, "sqrt", None)) def tan(self): """ Compute tangent element-wise. Equivalent to ``ia.sin(x)/ia.cos(x)`` element-wise. Parameters ---------- iarr: IArray Input data. Returns ------- out: IArray The corresponding tangent values. References ---------- https://numpy.org/doc/stable/reference/generated/numpy.tan.html """ return ia.LazyExpr(new_op=(self, "tan", None)) def tanh(self): """ Compute hyperbolic tangent element-wise. Equivalent to ``ia.sinh(x)/ia.cosh(x)``. Parameters ---------- iarr: IArray Input data. Returns ------- out: IArray The corresponding hyperbolic tangent values. References ---------- https://numpy.org/doc/stable/reference/generated/numpy.tanh.html """ return ia.LazyExpr(new_op=(self, "tanh", None)) @is_documented_by(IArray.abs) def abs(iarr: IArray): return iarr.abs() @is_documented_by(IArray.arccos) def arccos(iarr: IArray): return iarr.arccos() @is_documented_by(IArray.arcsin) def arcsin(iarr: IArray): return iarr.arcsin() @is_documented_by(IArray.arctan) def arctan(iarr: IArray): return iarr.arctan() @is_documented_by(IArray.arctan2) def arctan2(iarr1: IArray, iarr2: IArray): return iarr1.arctan2(iarr2) @is_documented_by(IArray.ceil) def ceil(iarr: IArray): return iarr.ceil() @is_documented_by(IArray.cos) def cos(iarr: IArray): return iarr.cos() @is_documented_by(IArray.cosh) def cosh(iarr: IArray): return iarr.cosh() @is_documented_by(IArray.exp) def exp(iarr: IArray): return iarr.exp() @is_documented_by(IArray.floor) def floor(iarr: IArray): return iarr.floor() @is_documented_by(IArray.log) def log(iarr: IArray): return iarr.log() @is_documented_by(IArray.log10) def log10(iarr: IArray): return iarr.log10() @is_documented_by(IArray.negative) def negative(iarr: IArray): return iarr.negative() @is_documented_by(IArray.power) def power(iarr1: IArray, iarr2: IArray): return iarr1.power(iarr2) @is_documented_by(IArray.sin) def sin(iarr: IArray): return iarr.sin() @is_documented_by(IArray.sinh) def sinh(iarr: IArray): return iarr.sinh() @is_documented_by(IArray.sqrt) def sqrt(iarr: IArray): return iarr.sqrt() @is_documented_by(IArray.tan) def tan(iarr: IArray): return iarr.tan() @is_documented_by(IArray.tanh) def tanh(iarr: IArray): return iarr.tanh() # Reductions def reduce( a: IArray, method: ia.Reduce, axis: Union[int, tuple] = None, cfg: ia.Config = None, **kwargs ): if axis is None: axis = range(a.ndim) if isinstance(axis, int): axis = (axis,) shape = tuple([s for i, s in enumerate(a.shape) if i not in axis]) if cfg is None: cfg = ia.get_config() with ia.config(shape=shape, cfg=cfg, **kwargs) as cfg: c = ext.reduce_multi(cfg, a, method, axis) if c.ndim == 0: c = float(ia.iarray2numpy(c)) return c def max(a: IArray, axis: Union[int, tuple] = None, cfg: ia.Config = None, **kwargs): """ Return the maximum of an array or maximum along an axis. Parameters ---------- a : IArray Input data. axis : None, int, tuple of ints, optional Axis or axes along which the reduction is performed. The default (axis = None) is perform the reduction over all dimensions of the input array. If this is a tuple of ints, a reduction is performed on multiple axes, instead of a single axis or all the axes as default. cfg : Config or None The configuration for this operation. If None (default), the current configuration will be used. kwargs : dict A dictionary for setting some or all of the fields in the Config dataclass that should override the current configuration. Returns ------- max : IArray or float Maximum of a. If axis is None, the result is a float value. If axis is given, the result is an array of dimension a.ndim - len(axis). """ return reduce(a, ia.Reduce.MAX, axis, cfg, **kwargs) def min(a: IArray, axis: Union[int, tuple] = None, cfg: ia.Config = None, **kwargs): """ Return the minimum of an array or minimum along an axis. Parameters ---------- a : IArray Input data. axis : None, int, tuple of ints, optional Axis or axes along which the reduction is performed. The default (axis = None) is perform the reduction over all dimensions of the input array. If this is a tuple of ints, a reduction is performed on multiple axes, instead of a single axis or all the axes as default. cfg : Config or None The configuration for this operation. If None (default), the current configuration will be used. kwargs : dict A dictionary for setting some or all of the fields in the Config dataclass that should override the current configuration. Returns ------- min : IArray or float Minimum of a. If axis is None, the result is a float value. If axis is given, the result is an array of dimension a.ndim - len(axis). """ return reduce(a, ia.Reduce.MIN, axis, cfg, **kwargs) def sum(a: IArray, axis: Union[int, tuple] = None, cfg: ia.Config = None, **kwargs): """ Return the sum of array elements over a given axis. Parameters ---------- a : IArray Input data. axis : None, int, tuple of ints, optional Axis or axes along which the reduction is performed. The default (axis = None) is perform the reduction over all dimensions of the input array. If this is a tuple of ints, a reduction is performed on multiple axes, instead of a single axis or all the axes as default. cfg : Config or None The configuration for this operation. If None (default), the current configuration will be used. kwargs : dict A dictionary for setting some or all of the fields in the Config dataclass that should override the current configuration. Returns ------- sum : IArray or float Sum of a. If axis is None, the result is a float value. If axis is given, the result is an array of dimension a.ndim - len(axis). """ return reduce(a, ia.Reduce.SUM, axis, cfg, **kwargs) def prod(a: IArray, axis: Union[int, tuple] = None, cfg: ia.Config = None, **kwargs): """ Return the product of array elements over a given axis. Parameters ---------- a : IArray Input data. axis : None, int, tuple of ints, optional Axis or axes along which the reduction is performed. The default (axis = None) is perform the reduction over all dimensions of the input array. If this is a tuple of ints, a reduction is performed on multiple axes, instead of a single axis or all the axes as default. cfg : Config or None The configuration for this operation. If None (default), the current configuration will be used. kwargs : dict A dictionary for setting some or all of the fields in the Config dataclass that should override the current configuration. Returns ------- prod : IArray or float Product of a. If axis is None, the result is a float value. If axis is given, the result is an array of dimension a.ndim - len(axis). """ return reduce(a, ia.Reduce.PROD, axis, cfg, **kwargs) def mean(a: IArray, axis: Union[int, tuple] = None, cfg: ia.Config = None, **kwargs): """ Compute the arithmetic mean along the specified axis. Returns the average of the array elements. Parameters ---------- a : IArray Input data. axis : None, int, tuple of ints, optional Axis or axes along which the reduction is performed. The default (axis = None) is perform the reduction over all dimensions of the input array. If this is a tuple of ints, a reduction is performed on multiple axes, instead of a single axis or all the axes as default. cfg : Config or None The configuration for this operation. If None (default), the current configuration will be used. kwargs : dict A dictionary for setting some or all of the fields in the Config dataclass that should override the current configuration. Returns ------- mean : IArray or float Mean of a. If axis is None, the result is a float value. If axis is given, the result is an array of dimension a.ndim - len(axis). """ return reduce(a, ia.Reduce.MEAN, axis, cfg, **kwargs) # Linear Algebra def matmul(a: IArray, b: IArray, cfg=None, **kwargs): """Multiply two matrices. Parameters ---------- a : IArray First array. b : IArray Second array. cfg : Config The configuration for running the expression. If None (default), global defaults are used. kwargs : dict A dictionary for setting some or all of the fields in the Config dataclass that should override the current configuration. Returns ------- IArray The resulting array. """ shape = (a.shape[0], b.shape[1]) if b.ndim == 2 else (a.shape[0],) if cfg is None: cfg = ia.get_config() with ia.config(shape=shape, cfg=cfg, **kwargs) as cfg: return ext.matmul(cfg, a, b) def transpose(a: IArray, cfg=None, **kwargs): """Transpose an array. Parameters ---------- a : IArray The array to transpose. cfg : Config The configuration for running the expression. If None (default), global defaults are used. kwargs : dict A dictionary for setting some or all of the fields in the Config dataclass that should override the current configuration. Returns ------- IArray The transposed array. """ if a.ndim != 2: raise AttributeError("Array dimension must be 2") if cfg is None: cfg = ia.get_config() with ia.config(cfg=cfg, **kwargs) as cfg: return ext.transpose(cfg, a)