Source code for WrightTools.kit._array

"""Array interaction tools."""

# --- import --------------------------------------------------------------------------------------

import numpy as np

from .. import exceptions as wt_exceptions

# --- define --------------------------------------------------------------------------------------

__all__ = [

# --- functions -----------------------------------------------------------------------------------

[docs]def closest_pair(arr, give="indicies"): """Find the pair of indices corresponding to the closest elements in an array. If multiple pairs are equally close, both pairs of indicies are returned. Optionally returns the closest distance itself. I am sure that this could be written as a cheaper operation. I wrote this as a quick and dirty method because I need it now to use on some relatively small arrays. Feel free to refactor if you need this operation done as fast as possible. - Blaise 2016-02-07 Parameters ---------- arr : numpy.ndarray The array to search. give : {'indicies', 'distance'} (optional) Toggle return behavior. If 'distance', returns a single float - the closest distance itself. Default is indicies. Returns ------- list of lists of two tuples List containing lists of two tuples: indicies the nearest pair in the array. >>> arr = np.array([0, 1, 2, 3, 3, 4, 5, 6, 1]) >>> closest_pair(arr) [[(1,), (8,)], [(3,), (4,)]] """ idxs = [idx for idx in np.ndindex(arr.shape)] outs = [] min_dist = arr.max() - arr.min() for idxa in idxs: for idxb in idxs: if idxa == idxb: continue dist = abs(arr[idxa] - arr[idxb]) if dist == min_dist: if not [idxb, idxa] in outs: outs.append([idxa, idxb]) elif dist < min_dist: min_dist = dist outs = [[idxa, idxb]] if give == "indicies": return outs elif give == "distance": return min_dist else: raise KeyError("give not recognized in closest_pair")
[docs]def diff(xi, yi, order=1) -> np.ndarray: """Take the numerical derivative of a 1D array. Output is mapped onto the original coordinates using linear interpolation. Expects monotonic xi values. Parameters ---------- xi : 1D array-like Coordinates. yi : 1D array-like Values. order : positive integer (optional) Order of differentiation. Returns ------- 1D numpy array Numerical derivative. Has the same shape as the input arrays. """ yi = np.array(yi).copy() flip = False if xi[-1] < xi[0]: xi = np.flipud(xi.copy()) yi = np.flipud(yi) flip = True midpoints = (xi[1:] + xi[:-1]) / 2 for _ in range(order): d = np.diff(yi) d /= np.diff(xi) yi = np.interp(xi, midpoints, d) if flip: yi = np.flipud(yi) return yi
[docs]def fft(xi, yi, axis=0) -> tuple: """Take the 1D FFT of an N-dimensional array and return "sensible" properly shifted arrays. Parameters ---------- xi : numpy.ndarray 1D array over which the points to be FFT'ed are defined yi : numpy.ndarray ND array with values to FFT axis : int axis of yi to perform FFT over Returns ------- xi : 1D numpy.ndarray 1D array. Conjugate to input xi. Example: if input xi is in the time domain, output xi is in frequency domain. yi : ND numpy.ndarray FFT. Has the same shape as the input array (yi). """ # xi must be 1D if xi.ndim != 1: raise wt_exceptions.DimensionalityError(1, xi.ndim) # xi must be evenly spaced spacing = np.diff(xi) if not np.allclose(spacing, spacing.mean()): raise RuntimeError("WrightTools.kit.fft: argument xi must be evenly spaced") # fft yi = np.fft.fft(yi, axis=axis) d = (xi.max() - xi.min()) / (xi.size - 1) xi = np.fft.fftfreq(xi.size, d=d) # shift xi = np.fft.fftshift(xi) yi = np.fft.fftshift(yi, axes=axis) return xi, yi
[docs]def joint_shape(*args) -> tuple: """Given a set of arrays, return the joint shape. Parameters ---------- args : array-likes Returns ------- tuple of int Joint shape. """ if len(args) == 0: return () shape = [] shapes = [a.shape for a in args] ndim = args[0].ndim for i in range(ndim): shape.append(max([s[i] for s in shapes])) return tuple(shape)
[docs]def orthogonal(*args) -> bool: """Determine if a set of arrays are orthogonal. Parameters ---------- args : array-likes or array shapes Returns ------- bool Array orthogonality condition. """ for i, arg in enumerate(args): if hasattr(arg, "shape"): args[i] = arg.shape for s in zip(*args): if np.product(s) != max(s): return False return True
[docs]def remove_nans_1D(*args) -> tuple: """Remove nans in a set of 1D arrays. Removes indicies in all arrays if any array is nan at that index. All input arrays must have the same size. Parameters ---------- args : 1D arrays Returns ------- tuple Tuple of 1D arrays in same order as given, with nan indicies removed. """ vals = np.isnan(args[0]) for a in args: vals |= np.isnan(a) return tuple(np.array(a)[~vals] for a in args)
[docs]def share_nans(*arrs) -> tuple: """Take a list of nD arrays and return a new list of nD arrays. The new list is in the same order as the old list. If one indexed element in an old array is nan then every element for that index in all new arrays in the list is then nan. Parameters ---------- *arrs : nD arrays. Returns ------- list List of nD arrays in same order as given, with nan indicies syncronized. """ nans = np.zeros(joint_shape(*arrs)) for arr in arrs: nans *= arr return tuple([a + nans for a in arrs])
[docs]def smooth_1D(arr, n=10, smooth_type="flat") -> np.ndarray: """Smooth 1D data using a window function. Edge effects will be present. Parameters ---------- arr : array_like Input array, 1D. n : int (optional) Window length. smooth_type : {'flat', 'hanning', 'hamming', 'bartlett', 'blackman'} (optional) Type of window function to convolve data with. 'flat' window will produce a moving average smoothing. Returns ------- array_like Smoothed 1D array. """ # check array input if arr.ndim != 1: raise wt_exceptions.DimensionalityError(1, arr.ndim) if arr.size < n: message = "Input array size must be larger than window size." raise wt_exceptions.ValueError(message) if n < 3: return arr # construct window array if smooth_type == "flat": w = np.ones(n, dtype=arr.dtype) elif smooth_type == "hanning": w = np.hanning(n) elif smooth_type == "hamming": w = np.hamming(n) elif smooth_type == "bartlett": w = np.bartlett(n) elif smooth_type == "blackman": w = np.blackman(n) else: message = "Given smooth_type, {0}, not available.".format(str(smooth_type)) raise wt_exceptions.ValueError(message) # convolve reflected array with window function out = np.convolve(w / w.sum(), arr, mode="same") return out
[docs]def svd(a, i=None) -> tuple: """Singular Value Decomposition. Factors the matrix `a` as ``u * np.diag(s) * v``, where `u` and `v` are unitary and `s` is a 1D array of `a`'s singular values. Parameters ---------- a : array_like Input array. i : int or slice (optional) What singular value "slice" to return. Default is None which returns unitary 2D arrays. Returns ------- tuple Decomposed arrays in order `u`, `v`, `s` """ u, s, v = np.linalg.svd(a, full_matrices=False, compute_uv=True) u = u.T if i is None: return u, v, s else: return u[i], v[i], s[i]
[docs]def unique(arr, tolerance=1e-6) -> np.ndarray: """Return unique elements in 1D array, within tolerance. Parameters ---------- arr : array_like Input array. This will be flattened if it is not already 1D. tolerance : number (optional) The tolerance for uniqueness. Returns ------- array The sorted unique values. """ arr = sorted(arr.flatten()) unique = [] while len(arr) > 0: current = arr[0] lis = [xi for xi in arr if np.abs(current - xi) < tolerance] arr = [xi for xi in arr if not np.abs(lis[0] - xi) < tolerance] xi_lis_average = sum(lis) / len(lis) unique.append(xi_lis_average) return np.array(unique)
[docs]def valid_index(index, shape) -> tuple: """Get a valid index for a broadcastable shape. Parameters ---------- index : tuple Given index. shape : tuple of int Shape. Returns ------- tuple Valid index. """ # append slices to index index = list(index) while len(index) < len(shape): index.append(slice(None)) # fill out, in reverse out = [] for i, s in zip(index[::-1], shape[::-1]): if s == 1: if isinstance(i, slice): out.append(slice(None)) else: out.append(0) else: out.append(i) return tuple(out[::-1])
[docs]def mask_reduce(mask): """Reduce a boolean mask, removing all false slices in any dimension. Parameters ---------- mask : ndarray with bool dtype The mask which is to be reduced Returns ------- A boolean mask with no all False slices. """ mask = mask.copy() for i in range(len(mask.shape)): a = mask.copy() j = list(range(len(mask.shape))) j.remove(i) j = tuple(j) a = a.max(axis=j, keepdims=True) idx = [slice(None)] * len(mask.shape) a = a.flatten() idx[i] = [k for k in range(len(a)) if a[k]] mask = mask[tuple(idx)] return mask
[docs]def enforce_mask_shape(mask, shape): """Reduce a boolean mask to fit a given shape. Parameters ---------- mask : ndarray with bool dtype The mask which is to be reduced shape : tuple of int Shape which broadcasts to the mask shape. Returns ------- A boolean mask, collapsed along axes where the shape given has one element. """ red = tuple([i for i in range(len(shape)) if shape[i] == 1]) return mask.max(axis=red, keepdims=True)