我需要使用 linspace 函式創建更高解析度的繪圖，但我不知道如何在我的代碼中實作它。也許有人對此有更好的理解并可以幫助我。
import numpy as np
import matplotlib.pyplot as plt
N = np.array([1, 2, 3, 4])
c = np.array([1359,2136.6,2617.74,2630.16])
ct = c/1000
ct0 = 103.8348/1000
cmax = 2630.16/1000
n = N.size
A = np.zeros(n)
k = np.zeros(n)
for j in range(0,n):
A[j] = (ct[j] - ct0)/(cmax - ct0)
for j in range(0, n):
if j < 3:
k[j] = -(np.log(1 - A[j])) / N[j]
else:
k[j] = 1
MWk = np.mean(k)
Amod = np.zeros(n)
for j in range(0,n):
Amod[j] = 1 - np.exp((-N[j]) * MWk)
print(ct)
print(A)
print(k)
plt.xlabel("N")
plt.ylabel("Aufschlussgrad ")
plt.plot(N, A, "g", label = "Aufschlussgrad")
plt.plot(N, Amod, "k", label = "Modelfunktion")
plt.title("Hochdruckhomogenisator")
plt.legend()
plt.show()
uj5u.com熱心網友回復：
無需 interpolate Amod，因為它是您定義的函式。另一方面，有必要執行插值（A在原始資料上或在原始資料上c）以便向圖中添加更多點。只有 4 或 5 個點，插值不會很有意義。在這種情況下，我選擇插值 A。
代碼沒有從numpy's 陣列中獲利，所以我對其進行了一點 Python 化（它看起來像 C）
import numpy as np
import matplotlib.pyplot as plt
def Amod(x, MWk):
return 1 - np.exp((-x) * MWk)
def k(x, A):
rv = -np.log(1 - A) / x
rv[np.nonzero(A==1)] = 1
return rv
# No real changes here: only a little 'pythonization'
N = np.array([1, 2, 3, 4])
c = np.array([1359,2136.6,2617.74,2630.16])
ct = c/1000
ct0 = 103.8348/1000
cmax = 2630.16/1000
n = N.size
A = (ct - ct0) / (cmax-ct0)
MWk = np.mean(k(N, A))
print(ct)
print(A)
print(k)
# we now interpolate A
# numpy's interpolation is linear... it is not useful for this case
from scipy.interpolate import interp1d
# interp1d returns a function that interpolates the data we provide
# in this case, i choose quadratic interpolation
A_interp_fun = interp1d(N, A, 'quadratic')
# Let's increase the number of points
new_x = np.linspace(N[0], N[-1])
A_xtra = A_interp_fun(new_x)
plt.xlabel("N")
plt.ylabel("Aufschlussgrad ")
plt.scatter(N, A, label = "Aufschlussgrad - data")
plt.plot(new_x, A_xtra, "g", label="Aufschlussgrad - interpolation")
plt.scatter(N, Amod(N, MWk), label="Modelfunktion - data")
plt.plot(new_x, Amod(new_x, MWk), "k", label="Modelfunktion - function")
plt.title("Hochdruckhomogenisator")
plt.legend()
plt.show()
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