如何使用python获取正确的阳性区域？

如何解决如何使用python获取正确的阳性区域？

我无法获得正区域（y = 0以上）。数学上该区域应为1.125。

x=np.arange(1,5,1)

y=np.array([-1.5,-0.5,0.5,1.5])

但是下面的函数都没有给我答案。第一个为0。第二个为我1.25，它不会进行插值来计算面积。我需要得到答案1.125。有人可以帮忙吗？谢谢！

np.trapz(y,x)

np.trapz(y.clip(min=0),x)

解决方法

要执行此操作，必须找到数据的线性插值与x轴交叉的位置。此函数是我在另一个答案（Digitizing an analog signal）中包含的函数的变体：

def find_zero_crossings(t,y):
    """
    Given the input signal `y` with samples at times `t`,find the times where the linearly interpolated graph
    crosses 0.

    `t` and `y` must be 1-D numpy arrays.
    """
    transition_indices = np.where((np.sign(y[:-1]) * np.sign(y[1:])) == -1)[0]

    # Linearly interpolate the time values where the transition occurs.
    t0 = t[transition_indices]
    t1 = t[transition_indices + 1]
    y0 = y[transition_indices]
    y1 = y[transition_indices + 1]
    slope = (y1 - y0) / (t1 - t0)
    transition_times = t0 - y0 / slope

    return transition_times

您可以在以下脚本中将其用作示例：

xx = np.arange(1,5,1)
yy = np.array([-1.5,-0.5,0.5,1.5])

xz = find_zero_crossings(xx,yy)
print("xz:",xz)

# Insert the x intercepts into the data.
xx2 = np.append(xx,xz)
yy2 = np.append(yy,np.zeros_like(xz))

# Restore the order of xx2,and order yy2 in the same way.
k = xx2.argsort()
xx2 = xx2[k]
yy2 = yy2[k]

print("xx2:",xx2)
print("yy2:",yy2)

# pos_yy2 is the clipped version of yy2.
pos_yy2 = np.maximum(yy2,0.0)

print("pos_yy2:",pos_yy2)

# Compute the area with `trapz`.
pos_area = np.trapz(pos_yy2,xx2)
print("pos_area:",pos_area)

输出：

xz: [2.5]
xx2: [1.  2.  2.5 3.  4. ]
yy2: [-1.5 -0.5  0.   0.5  1.5]
pos_yy2: [0.  0.  0.  0.5 1.5]
pos_area: 1.125

一个可以完成所有任务的函数：

def pos_trapz(y,x=None,dx=1.0):
    if x is None:
        x = np.arange(len(y))*dx
    xz = find_zero_crossings(x,y)

    # Insert the x intercepts into the data.
    x2 = np.append(x,xz)
    y2 = np.append(y,np.zeros_like(xz))

    # Restore the order of x2,and order y2 in the same way.
    # (The function assumes that the input `x` is in ascending order.)
    k = x2.argsort()
    x2 = x2[k]
    y2 = y2[k]

    # pos_y2 is the clipped version of y2.
    pos_y2 = np.maximum(y2,0.0)

    # Compute the area with `trapz`.
    return np.trapz(pos_y2,x2)

在ipython会话中：

In [107]: xx = np.arange(1,1)

In [108]: yy = np.array([-1.5,1.5])

In [109]: pos_trapz(yy,xx)                        
Out[109]: 1.125