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from math import floor
from scipy.signal import resample
from scipy.interpolate import interp1d
import numpy as np
def distribute_tasks(tasks, rank, size):
num_tasks = len(tasks)
num_tasks_per_job = num_tasks//size
tasks = list(range(rank*num_tasks_per_job,(rank+1)*num_tasks_per_job))
if rank < num_tasks%size:
tasks.append(size*num_tasks_per_job+rank)
return tasks
def ResampleLinear1D(original, targetLen):
index_arr = np.linspace(0, len(original)-1, num=targetLen, dtype=np.float)
index_floor = np.array(index_arr, dtype=np.int) #Round down
index_ceil = index_floor + 1
index_rem = index_arr - index_floor #Remain
val1 = original[index_floor]
val2 = original[index_ceil % len(original)]
interp = val1 * np.expand_dims(1.0-index_rem,1) + val2 * np.expand_dims(index_rem,1)
assert(len(interp) == targetLen)
return interp
def downsample_signal(original,ratio):
# ratio is the ratio between the time step in the original and in the new one (so it should be > 1 for downsampling)
old_indices = np.arange(len(original), dtype=np.int32)
new_indices = (old_indices // ratio).astype(np.int32)
M = np.zeros((int(len(original)//ratio)+1,len(original)), dtype=np.float32)
M[new_indices,old_indices] = 1
w = np.sum(M,1)
w = np.expand_dims(w,1)
# print(M.shape)
return np.matmul(M,original)/w
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