binning.py 3.01 KB
 Matthijs committed Mar 27, 2020 1 2 ``````import numpy as np `````` Matthijs committed Apr 23, 2020 3 4 ``````__all__ = ['bin_array'] `````` Matthijs committed Mar 27, 2020 5 `````` `````` Matthijs committed Apr 27, 2020 6 ``````def bin_array(arr: np.ndarray, new_shape: any, pad_zeros:bool=True) -> np.ndarray: `````` Matthijs committed Mar 27, 2020 7 8 9 10 11 `````` """ Reduce the size of an array by binning :param arr: original :param new_shape: tuple which must be an integer divisor of the original shape, or integer to bin by that factor `````` Matthijs committed Apr 27, 2020 12 `````` :param pad_zeros: pad array with zeros to enable binning by the given factor `````` Matthijs committed Mar 27, 2020 13 14 15 16 17 18 19 20 21 22 23 24 25 `````` :return: new array """ # make tuple with new shape if type(new_shape) == int: # binning factor is given _shape = tuple([i // new_shape for i in arr.shape]) binfactor = tuple([new_shape for i in _shape]) else: _shape = new_shape binfactor = tuple([s // _shape[i] for i, s in enumerate(arr.shape)]) # determine if padding is needed padding = tuple([(0, (binfactor[i] - s % binfactor[i]) % binfactor[i]) for i, s in enumerate(arr.shape)]) if pad_zeros and np.any(np.array(padding) != 0): _arr = np.pad(arr, padding, mode='constant', constant_values=0) # pad array `````` Matthijs committed Apr 23, 2020 26 `````` _shape = tuple([s // binfactor[i] for i, s in enumerate(_arr.shape)]) # update binned size due to padding `````` Matthijs committed Mar 27, 2020 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 `````` else: _arr = arr # expected to fail if padding has non-zeros # send to 2d or 3d padding functions try: if len(arr.shape) == 2: out = bin_2d_array(_arr, _shape) elif len(arr.shape) == 3: out = bin_3d_array(_arr, _shape) else: raise NotImplementedError('Cannot only bin 3d or 2d arrays') return out except ValueError: raise ValueError("Cannot bin data with this shape. Try setting pad_zeros=True, or change the binning.") def bin_2d_array(arr: np.ndarray, new_shape: tuple) -> np.ndarray: """ bins a 2D numpy array Args: arr: input array to be binned new_shape: shape after binning, must be an integer divisor of the original shape Returns: binned np array """ shape = (new_shape[0], arr.shape[0] // new_shape[0], new_shape[1], arr.shape[1] // new_shape[1]) if np.any(np.isnan(arr)): binfactor = 1 for i, s in enumerate(arr.shape): binfactor *= new_shape[i] / s return np.nanmean(arr.reshape(shape), axis=(3, 1)) * binfactor else: return arr.reshape(shape).sum(-1).sum(1) def bin_3d_array(arr: np.ndarray, new_shape: tuple) -> np.ndarray: """" bins a 3D numpy array Args: arr: input array to be binned new_shape: shape after binning, must be an integer divisor of the original shape Returns: binned np array """ shape = (new_shape[0], arr.shape[0] // new_shape[0], new_shape[1], arr.shape[1] // new_shape[1], new_shape[2], arr.shape[2] // new_shape[2]) if np.any(np.isnan(arr)): binfactor = 1 for i, s in enumerate(arr.shape): binfactor *= new_shape[i] / s return np.nanmean(arr.reshape(shape), axis=(5, 3, 1)) * binfactor else: return np.sum(arr.reshape(shape), axis=(5, 3, 1))``````