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timcornwelltimcornwell
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Revert array_functions to non-numba version
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Lines changed: 27 additions & 116 deletions

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‎processing_library/util/array_functions.py‎

Lines changed: 27 additions & 116 deletions
Original file line numberDiff line numberDiff line change
@@ -1,11 +1,8 @@
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"""Useful array functions.
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"""
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from numba import jit, types as nbtypes, c16, f8, i8,float64, int64, int32,int8
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# import numba
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import numpy
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import warnings
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from numba import NumbaPerformanceWarning
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warnings.simplefilter('ignore', NumbaPerformanceWarning)
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# @numba.jit([numba.types.Tuple((numba.float64[:], numba.float64[:]))
@@ -49,40 +46,6 @@ def average_chunks_jit(arr, wts, chunksize):
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return chunks, weights
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@jit([
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nbtypes.Tuple((c16[:], f8[:]))(c16[:], f8[:], i8),
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], nopython=True)
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def average_chunks_complex(arr, wts, chunksize):
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""" Average the array arr with weights by chunks
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Array len does not have to be multiple of chunksize
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The function is used for processing arr with complex data type.
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:param arr: 1D array of values
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:param wts: 1D array of weights
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:param chunksize: averaging size
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:return: 1D array of averaged data_models, 1d array of weights
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"""
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if chunksize <= 1:
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return arr, wts
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mask = numpy.zeros(((len(arr) - 1) // chunksize + 1, arr.shape[0]), dtype=arr.dtype)
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maskwts = numpy.zeros_like(mask, dtype=wts.dtype)
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for enumerate_id, i in enumerate(range(0, len(arr), chunksize)):
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mask[enumerate_id, i:i + chunksize] = 1+1j
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maskwts[enumerate_id, i:i + chunksize] = 1
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tmp = wts * arr
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chunks = mask.dot(tmp)
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weights = maskwts.dot(wts)
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chunks[weights > 0.0] = chunks[weights > 0.0] / weights[weights > 0.0]
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return chunks, weights
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@jit([nbtypes.Tuple((float64[:], float64[:]))(float64[:], float64[:], int64),
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nbtypes.Tuple((f8[:],float64[:]))(f8[:], float64[:], int64),
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nbtypes.Tuple((f8[:],float64[:]))(f8[:], float64[:], int32),
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nbtypes.Tuple((f8[:],float64[:]))(f8[:], float64[:], int8)
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], nopython=True)
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def average_chunks(arr, wts, chunksize):
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""" Average the array arr with weights by chunks
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@@ -108,24 +71,13 @@ def average_chunks(arr, wts, chunksize):
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# Codes optimized
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# mask = numpy.zeros(((len(arr)-1)//chunksize + 1, arr.shape[0]), dtype=bool)
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# for enumerate_id, i in enumerate(range(0, len(arr), chunksize)):
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# mask[enumerate_id,i:i+chunksize]= 1
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# chunks = mask.dot(wts*arr)
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# weights = mask.dot(wts)
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# # chunks[weights > 0.0] = chunks[weights > 0.0] / weights[weights > 0.0]
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# numpy.putmask(chunks, weights>0.0, chunks/weights)
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# Numba
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mask = numpy.zeros(((len(arr)-1)//chunksize + 1, arr.shape[0]), dtype=arr.dtype)
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maskwts = numpy.zeros_like(mask,dtype=wts.dtype)
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for enumerate_id, i in enumerate(range(0, len(arr), chunksize)):
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mask[enumerate_id,i:i+chunksize]= 1
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maskwts[enumerate_id,i:i+chunksize]= 1
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tmp = wts*arr
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chunks = mask.dot(tmp)
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weights = maskwts.dot(wts)
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chunks[weights > 0.0] = chunks[weights > 0.0] / weights[weights > 0.0]
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mask = numpy.zeros(((len(arr)-1)//chunksize + 1, arr.shape[0]), dtype=bool)
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for enumerate_id,i in enumerate(range(0, len(arr), chunksize)):
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mask[enumerate_id,i:i+chunksize]=1
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chunks = mask.dot(wts*arr)
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weights = mask.dot(wts)
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# chunks[weights > 0.0] = chunks[weights > 0.0] / weights[weights > 0.0]
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numpy.putmask(chunks, weights>0.0, chunks/weights)
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return chunks, weights
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@@ -144,66 +96,25 @@ def average_chunks2(arr, wts, chunksize):
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# assert arr.shape == wts.shape, "Shapes of arrays must be the same"
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# It is possible that there is a dangling null axis on wts
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wts = wts.reshape(arr.shape)
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#
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# For numba
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if arr.dtype=='c16':
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l0 = len(average_chunks_complex(arr[:, 0].flatten(), wts[:, 0].flatten(), chunksize[0])[0])
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l1 = len(average_chunks_complex(arr[0, :].flatten(), wts[0, :].flatten(), chunksize[1])[0])
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tempchunks = numpy.zeros([arr.shape[0], l1], dtype=arr.dtype)
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tempwt = numpy.zeros([arr.shape[0], l1])
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tempchunks *= tempwt
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for i in range(arr.shape[0]):
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result = average_chunks_complex(arr[i, :], wts[i, :], chunksize[1])
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tempchunks[i, :], tempwt[i, :] = result[0].flatten(), result[1].flatten()
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chunks = numpy.zeros([l0, l1], dtype=arr.dtype)
162-
weights = numpy.zeros([l0, l1])
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for i in range(l1):
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result = average_chunks_complex(tempchunks[:, i], tempwt[:, i], chunksize[0])
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chunks[:, i], weights[:, i] = result[0].flatten(), result[1].flatten()
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else:
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l0 = len(average_chunks(arr[:, 0].flatten(), wts[:, 0].flatten(), chunksize[0])[0])
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l1 = len(average_chunks(arr[0, :].flatten(), wts[0, :].flatten(), chunksize[1])[0])
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tempchunks = numpy.zeros([arr.shape[0], l1], dtype=arr.dtype)
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tempwt = numpy.zeros([arr.shape[0], l1])
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tempchunks *= tempwt
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for i in range(arr.shape[0]):
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result = average_chunks(arr[i, :], wts[i, :], chunksize[1])
178-
tempchunks[i, :], tempwt[i, :] = result[0].flatten(), result[1].flatten()
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chunks = numpy.zeros([l0, l1], dtype=arr.dtype)
181-
weights = numpy.zeros([l0, l1])
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for i in range(l1):
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result = average_chunks(tempchunks[:, i], tempwt[:, i], chunksize[0])
185-
chunks[:, i], weights[:, i] = result[0].flatten(), result[1].flatten()
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# Original
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# l0 = len(average_chunks(arr[:, 0].flatten(), wts[:, 0].flatten(), chunksize[0])[0])
190-
# l1 = len(average_chunks(arr[0, :].flatten(), wts[0, :].flatten(), chunksize[1])[0])
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#
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# tempchunks = numpy.zeros([arr.shape[0], l1], dtype=arr.dtype)
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# tempwt = numpy.zeros([arr.shape[0], l1])
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#
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# tempchunks *= tempwt
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# for i in range(arr.shape[0]):
197-
# result = average_chunks(arr[i, :], wts[i, :], chunksize[1])
198-
# tempchunks[i, :], tempwt[i, :] = result[0].flatten(), result[1].flatten()
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#
200-
# chunks = numpy.zeros([l0, l1], dtype=arr.dtype)
201-
# weights = numpy.zeros([l0, l1])
202-
#
203-
# for i in range(l1):
204-
# result = average_chunks(tempchunks[:, i], tempwt[:, i], chunksize[0])
205-
# chunks[:, i], weights[:, i] = result[0].flatten(), result[1].flatten()
206-
99+
100+
l0 = len(average_chunks(arr[:, 0], wts[:, 0], chunksize[0])[0])
101+
l1 = len(average_chunks(arr[0, :], wts[0, :], chunksize[1])[0])
102+
103+
tempchunks = numpy.zeros([arr.shape[0], l1], dtype=arr.dtype)
104+
tempwt = numpy.zeros([arr.shape[0], l1])
105+
106+
tempchunks *= tempwt
107+
for i in range(arr.shape[0]):
108+
result = average_chunks(arr[i, :], wts[i, :], chunksize[1])
109+
tempchunks[i, :], tempwt[i, :] = result[0].flatten(), result[1].flatten()
110+
111+
chunks = numpy.zeros([l0, l1], dtype=arr.dtype)
112+
weights = numpy.zeros([l0, l1])
113+
114+
for i in range(l1):
115+
result = average_chunks(tempchunks[:, i], tempwt[:, i], chunksize[0])
116+
chunks[:, i], weights[:, i] = result[0].flatten(), result[1].flatten()
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return chunks, weights
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