python用在化学上_在Python上使用savgol_过滤器平滑在线数据化学信号图书馆
谢谢你更新问题！在
问题是，对于您的online_res方法，您缺少部分数据。边缘值由scipy的savgol_filter处理，但不适用于手工编码的版本。在
对于您的示例，请看两个结果：
“联机分辨率”：数组([3.93，3.17，0.73，0.2，1.11，5.87，6.37]))
“脱机分辨率”：数组([1.84，3.52，3.93，3.17，0.73，0.2，1.11，5.87，6.37，5.3，1.84]))
它们是相同的，但是offline res处理了data[0:2]和{}的值。在您的例子中，如果指定了不特定的mode，则将其设置为默认值interpolate：When the ‘interp’ mode is selected (the default), no extension is
used. Instead, a degree polyorder polynomial is fit to the last
window_length values of the edges, and this polynomial is used to
evaluate the last window_length // 2 output values.
这不是你为你的online res所做的。在
我为两边实现了一个简单的polynomial fit，得到了完全相同的结果：from queue import Queue, Empty
import numpy as np
from scipy.signal import savgol_filter
window_size = 5
data = list()
q = Queue()
d = [2.22, 2.22, 5.55, 2.22, 1.11, 0.01, 1.11, 4.44, 9.99, 1.11, 3.33]
for i in d:
q.put(i)
res = list()
while not q.empty():
element = q.get()
data.append(element)
length = len(data)
npd = np.array(data[length - window_size:])
if length >= window_size:
res.append(savgol_filter(npd, window_size, 2)[window_size//2])
# calculate the polynomial fit for elements 0,1,2,3,4
poly = np.polyfit(range(window_size), d[0:window_size], deg=2)
p = np.poly1d(poly)
res.insert(0, p(0)) # insert the polynomial fits at index 0 and 1
res.insert(1, p(1))
# calculate the polynomial fit for the 5 last elements (range runs like [4,3,2,1,0])
poly = np.polyfit(range(window_size-1, -1, -1), d[-window_size:], deg=2)
p = np.poly1d(poly)
res.append(p(1))
res.append(p(0))
npd = np.array(data)
res2 = savgol_filter(npd, window_size, 2)
diff = res - res2 # in your example you were calculating the wrong diff btw
np.set_printoptions(precision=2)
print('source data ', npd)
print('online res ', np.array(res))
print('offline res ', res2)
print('error ', diff.sum())
结果：
^{pr2}$
编辑：
这个版本独立于d-list，这意味着它可以消化从源代码获取的任何数据。在window_size = 5
half_window_size = window_size // 2 # this variable is used often
data = list()
q = Queue()
d = [2.22, 2.22, 5.55, 2.22, 1.11, 0.01, 1.11, 4.44, 9.99, 1.11, 3.33]
for i in d:
q.put(i)
res = [None]*window_size # create list of correct size instead of appending
while not q.empty():
element = q.get()
data.append(element)
length = len(data)
npd = np.array(data[length - window_size:])
if length == window_size: # this is called only once, when reaching the filter-center
# calculate the polynomial fit for elements 0,1,2,3,4
poly = np.polyfit(range(window_size), data, deg=2)
p = np.poly1d(poly)
for poly_i in range(half_window_size): # independent from window_size
res[poly_i] = p(poly_i)
# insert the sav_gol-value at index 2
res[(length-1)-half_window_size] = savgol_filter(npd, window_size, 2)[half_window_size]
poly = np.polyfit(range(window_size - 1, -1, -1), data[-window_size:], deg=2)
p = np.poly1d(poly)
for poly_i_end in range(half_window_size):
res[(window_size-1)-poly_i_end] = p(poly_i_end)
elif length > window_size:
res.append(None) # add another slot in the res-list
# overwrite poly-value with savgol
res[(length-1)-half_window_size] = savgol_filter(npd, window_size, 2)[half_window_size]
# extrapolate again into the future
poly = np.polyfit(range(window_size - 1, -1, -1), data[-window_size:], deg=2)
p = np.poly1d(poly)
for poly_i_end in range(half_window_size):
res[-poly_i_end-1] = p(poly_i_end)
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