下面是一個數據集。每一行都是一個時間片。第一列是讀數。第二個是多少個時間段前的讀數是它的50%。我通過盯著它來手工計算它，所以數字并不完全正確。197 218 256 328 4413 4525 4646 4777 51159 41838 22417 23240 2.54257 34955 45752 5.56620 57738 5.58966 4.510402 5因此，假設我有一個數據幀，如下所示：df = pd.DataFrame({'val': [197,218,256,328,413,525,646,777,1159,1838,2417,3240,4257,4955,5752,6620,7738,8966,10402]})如何計算 df.倍增？我可以想象從最后開始，向后工作，每次掃描一個值的50%。但是有更好的方法。我認為這與Log2有關，但不知道該怎么做！
3 回答
MM們
TA貢獻1886條經驗 獲得超2個贊
您是否正在將Covid-19感染時間加倍？
請仔細檢查結果。
我忘了你正在使用熊貓，所以你可能需要這個：
y = df['val'].to_numpy()
這是第一次拍攝：
import numpy as np
from scipy.interpolate import interp1d
y = np.array([197, 218, 256, 328, 413,525, 646, 646, 777,
1159, 1838, 2417, 3240, 4257, 4955, 4955,
5752, 6620, 7738, 8966, 10402],
dtype=float)
# get the deltas to check if there was no increase
# between two consecutive data points
dy = np.diff(y)
# these are the places without increase
idx = np.argwhere(dy) #could also be np.where(dy == 0.0)[0]
y_fixed = y.copy()
# create the x axis, probably days
x = np.arange(y.shape[0])
# Hack: increase the second identical value be a
# small amount so the interpolation works
# increase the indices by one to increment the second value
y_fixed[idx + 1] += 0.001
# you need scipy > 0.17 for extrapolation to work
f = interp1d(y_fixed, x, fill_value="extrapolate")
# there are the values you need?
y_half = y / 2.0
# get the according x values by interpolation
x_interp = f(y_half)
# delta between the current day and the date when
# the value was half
dbl = x - x_interp
# this already looks quite good, but double check!
print(dbl)
也許 x 軸需要移動。或者也許它畢竟是正確的。我明天會用新鮮的大腦來思考這個問題。
下圖顯示了兩種算法，其中包含計算出的指數數據，其中兩個位置設置為非遞增值。
不負相思意
TA貢獻1777條經驗 獲得超10個贊
可能最終會看起來像這樣。
ACCURACY = 0
cases = [197, 218, 256, 328, 413,525, 646, 646, 777,
1159, 1838, 2417, 3240, 4257, 4955, 4955,
5752, 6620, 7738, 8966, 10402]
doubling = []
for t in range(len(cases)):
found = False
for t_2 in range(t):
if cases[t_2] - (cases[t] // 2) > ACCURACY:
doubling.append(t - t_2)
found = True
break
# Append nothing if value not found
if not found:
doubling.append(None)
拉丁的傳說
TA貢獻1789條經驗 獲得超8個贊
我正在處理這些數據。這是一個老派的解決方案。我跟蹤了您的解決方案，但無法完全遵循它。但是我的不是那么優雅，但我認為這是正確的...而且圖表看起來與您的圖表非常相似...！
import numpy as np
readings = np.array([197, 218, 256, 328, 413,525, 646, 646, 777,
1159, 1838, 2417, 3240, 4257, 4955, 4955,
5752, 6620, 7738, 8966, 10402],
dtype=float)
readingsLength = len(readings)
double = np.zeros(readingsLength)
for i in range( readingsLength - 1, -1, -1):
target = readings[i]
count = 0
for j in range(i, -1, -1):
diffsofar = target-readings[j]
exact = target / 2
if diffsofar > exact:
f = (exact - readings[j]) / (readings[j]-readings[j+1]) + count
double[i] = f
break
else:
count = count+1
print(double)
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