def sig_f1():
def f1(t, t0):
return (t > 0) * t
qi, mo = -10, 10 # 坐标轴的起始位置
num = 1000 # 样本数量
x = np.linspace(qi, mo)
image_list = []
t1 = 6
t0 = 4
for i in range(0, t0 + 1):
plt.title('点动，函数不变', fontsize=24)
plt.xlim(qi, mo)
x1 = t1 - i
y1 = f1(x1, 0)
plt.plot(x, f1(x, 0), lw=2, label="y=r(t)")
plt.legend()
plt.plot(x1, y1, 'bo')
if (i == t0):
plt.annotate('y值为%d' % y1,
xy=(x1, y1),
xytext=(x1 - 3, y1 + 1),
arrowprops=dict(facecolor='red', shrink=0.1))
plt.annotate('(%d,%d)' % (x1, y1),
xy=(x1 + 2, y1),
xytext=(x1, y1 + 0.4))
plt.savefig('single1.png')
plt.close() # 这样会清楚旧图
image_list.append(imageio.imread('single1.png'))
img = cv2.imread('single1.png')
deputy.read("single1.png")
imageio.mimsave('single1.gif', image_list, duration=1)
return image_list
def step_rope(spe=1):
# 画dirac
ge = 0.5
x = np.arange(-10, 10, ge)
image_list = []
make_rope_func(0)
for i in range(5):
image_list.append(imageio.imread('step.png'))
for i in range(len(x)):
title = "阶跃函数" + r"$\Rightarrow$" + "斜坡函数"
plt.title(title, c="c", fontsize=20)
plt.text(-9,
5,
r"$ r(t)=\int_{-∞}^{∞}\delta(t)\,dx$",
c="c",
fontsize=18,
alpha=0.6)
plt.axhline(y=0, ls="-", color="c", alpha=0.3) # 添加垂直直线
if x[i] < 0: continue
plt.xlim(-10, 10)
plt.ylim(0, 10)
for d in range(40):
if x[i] >= ge * d:
plt.vlines(ge * d, 0, ge * d, linestyles='-', colors="c")
if d * ge > x[i]: break
plt.plot()
name = "step_rope"
plt.savefig(name + ".png")
deputy.read(name + ".png", -1)
plt.close()
image_list.append(imageio.imread(name + '.png'))
for i in range(5):
image_list.append(imageio.imread('rope.png'))
imageio.mimsave('step_rope.gif', image_list, duration=0.5)
def dirac_step():
# 画dirac
ge = 0.5
x = np.arange(-10, 10, ge)
image_list = []
make_step_func(0)
for i in range(5):
image_list.append(imageio.imread('dirac.png'))
for i in range(len(x)):
title = "冲激函数" + r"$\Rightarrow$" + "阶跃函数"
plt.title(title, c="b", fontsize=18)
plt.text(-9,
0.8,
r"$\delta(t)=\int_{-∞}^{∞} \varepsilon(t) \, dx$",
c="b",
fontsize=18,
alpha=0.6)
plt.axhline(y=0, ls="-", color="b", alpha=0.3) # 添加垂直直线
if x[i] < 0: continue
plt.xlim(-10, 10)
for d in range(40):
if x[i] >= ge * d:
plt.vlines(ge * d, 0, 1, linestyles='-', colors="b")
if d * ge > x[i]: break
plt.plot()
plt.savefig("dirac_step.png")
deputy.read("dirac_step.png", -1)
plt.close()
image_list.append(imageio.imread('dirac_step.png'))
for i in range(5):
image_list.append(imageio.imread('step.png'))
imageio.mimsave('dirac_step.gif', image_list, duration=0.5)
def make_rope_func(spe=1):
qi = -10 # 坐标轴的起始位置
mo = -qi
num = 1000 # 样本数量
x = np.linspace(qi, mo, num)
y = (x > 0) * x
plt.title("斜坡函数", fontdict={"size": "xx-large", "color": "deepskyblue"})
plt.plot(x, y, lw=2, label="r(t)", c="deepskyblue")
plt.legend()
plt.savefig("rope.png")
if spe == 1: deputy.read("rope.png", 3)
plt.show()
def make_step_func(spe=1):
qi = -10 # 坐标轴的起始位置
mo = -qi
num = 1000 # 样本数量
x = np.linspace(qi, mo, num)
y = x > 0
plt.title("画阶跃函数", fontdict={"size": "xx-large", "color": "c"})
plt.plot(x, y, lw=2, label=r"$\varepsilon(t)$", c="c")
plt.legend()
plt.savefig("step.png")
if spe == 1: deputy.read("step.png", 3)
plt.show()
def make_dirac():
# 画dirac
scope = 0.01 #冲激范围
xie = 100
x = np.arange(-10, 10, 0.01, np.float)
y = np.zeros(x.shape, np.float32)
for i in range(len(x)):
if x[i] > scope: break
if x[i] < -scope: continue
if x[i] >= 0:
y[i] = (x[i] - scope) * (-xie)
elif x[i] < 0:
y[i] = (x[i] + scope) * xie
plt.text(0.2, 0.6, "(1)", weight="bold", color="b")
plt.title("画冲激函数", fontdict={"size": "xx-large", "color": "b"})
plt.plot(x, y, lw=2, label=r"$\delta(t)$", c="b")
plt.legend()
plt.savefig("dirac.png")
deputy.read("dirac.png", 3)
plt.show()
def sig_f2():
def f1(t, t0):
t = t - t0
return (t > 0) * t
qi, mo = -10, 10 # 坐标轴的起始位置
num = 1000 # 样本数量
x = np.linspace(qi, mo)
image_list = []
t1 = 6
t0 = 4
for i in range(0, t0 + 1):
plt.title('函数右移，点不变', fontsize=24)
plt.xlim(qi, mo)
plt.ylim(-2, 10)
x1 = t1
y1 = f1(x1, i)
plt.plot(x, f1(x, i), lw=2, label="y=u(t-%d)" % i)
plt.legend(loc=2)
plt.plot(x1, y1, 'bo')
if (i == t0):
plt.annotate('y值为%d!' % y1,
xy=(x1, y1),
xytext=(x1 - 3, y1 + 1),
arrowprops=dict(facecolor='red', shrink=0.1))
plt.annotate('(%d,%d)' % (x1, y1),
xy=(x1 + 0.3, y1),
xytext=(x1, y1 + 0.4))
plt.savefig('single2.png')
plt.close() # 这样会清楚旧图
image_list.append(imageio.imread('single2.png'))
deputy.read("single2.png") #把这个照片存储到视频里去
imageio.mimsave('single2.gif', image_list, duration=1)
def func1(t=3):
image_list = []
ge = 0.1 #time的时间间隔,不能取1
time = np.arange(-0.5, 3.5, ge)
tt = 0
x = np.arange(-6, 10, ge)
data_list = np.zeros(len(time))
yes = np.zeros(6)
print(yes)
for i in range(len(time)):
# plt.text(0,0,"hello",fontsize=20,color="gray",alpha=0.5)
plt.title("hello")
y1 = h(x)
y2 = f(x, time[i])
y_int = np.sum(y1 * y2) * ge
t = np.arange(-5, time[i], ge)
data_list[i] = y_int
# print(time[i],":",y_int)
ax1 = plt.subplot(121)
ax1.plot(x, y1, lw=2) #函数曲线)
ax1.plot(x, y2, lw=2)
ax1.fill(x, y1, 'r', alpha=0.5, label='y1')
ax1.fill(x, y2, 'b', alpha=0.5, label='y2')
ax1.legend(loc=("upper left"))
plt.title("卷积过程")
ax2 = plt.subplot(122)
plt.title("h(t)*f(t)")
#画分隔线
if time[i] >= 1.00:
plt.axvline(x=1, ls="--", c="dimgray", alpha=0.3) # 添加垂直直线
if time[i] >= 2.00:
plt.axvline(x=2, ls="--", c="dimgray", alpha=0.4) # 添加垂直直线
# 画分段函数
### 画各个阶段的函数式
plt.text(-4.7, 0.9, "0<t:y=0", weight="bold", color="b", alpha=0.8)
if time[i] > 0:
plt.text(-4.7,
0.8,
r"$0\leq t\leq 1:y=\frac{1}{4}t^2$",
weight="bold",
color="b",
alpha=0.8)
if time[i] > 1:
plt.text(-4.7,
0.7,
r"$1<t\leq2:y=\frac{1}{2}t-\frac{1}{4}$",
weight="bold",
color="b",
alpha=0.8)
if time[i] > 2:
plt.text(-4.7,
0.6,
r"$2<t\leq3:$",
weight="bold",
color="b",
alpha=0.8)
plt.text(-4.7,
0.5,
r"$y=\frac{1}{4}t^2+\frac{1}{2}t+\frac{3}{4}$",
weight="bold",
color="b",
alpha=0.8)
if time[i] > 3:
plt.text(-4.7,
0.4,
"$3<t:y=0$",
weight="bold",
color="b",
alpha=0.8)
###
x_major_locator = MultipleLocator(1)
ax = plt.gca()
# ax为两条坐标轴的实例
ax.xaxis.set_major_locator(x_major_locator)
ax2.plot(time, data_list, 'b')
plt.suptitle("卷积演示", fontsize=25)
plt.xlim(-5, 5)
plt.ylim(0, 1)
# plt.plot(y1,y3)
# plt.show()
plt.savefig('tem.png')
image_list.append((imageio.imread('tem.png')))
plt.close()
deputy.read("tem.png")
image_list.append(imageio.imread('tem.png'))
imageio.mimsave('卷积.gif', image_list, duration=1)
def combin1():
name="对比图1"
def x0(t,i=0):
t=t
t=t-i
return t
#表示激励
#原函數为y=u(-t)
def f1(t,i):
return x0(t,i)
qi,mo=-10,10 # 坐标轴的起始位置
ylim_min,ylim_max=-4,200 #y轴的范围
num = 1000 # 样本数量
image_list = []
t1 = 6
t0 = 5
x = np.linspace(qi, mo)
for i in range(0, t0 + 1):
plt.title("hello")
#picture 1
x1=t1-i # 点动
ax1=plt.subplot(1,2,1)
ax1.set_title('函数不变，t变')
ax1.plot(x, f1(x,0), lw=2, label="y(t)")
ax1.legend()
ax1.set(ylim=(-10,15))
plt.plot(x1, f1(x1,0), 'bo') #标出移动的点
ax1.annotate('(%d,%d)' % (x1,f1(x1,0)), xy=(x1 ,f1(x1,0)))
if (i == t0):
y1=f1(x1,0)
ax1.annotate("y值为%lf"%y1, xy=(x1-2, y1+2), xytext=(x1-3, y1 + 1),color='b')
ax1.annotate('y=f(t)', xy=(1,1),xytext=(t1-5, t1-7)) #曲线的注释
#picture 2
x2=t1 #点不动
y2 = f1(x2, i)
ax2 = plt.subplot(1,2,2)
ax2.set_title('函数变，t不变')
ax2.plot(x, f1(x, i), lw=2, label="y=y(t-t0)",color="darkviolet") #函数曲线
ax2.legend() #显示图标
plt.annotate('y=f(t-%d)' % i, xy=(1,1),xytext=(t1-3, t1-7)) #给函数图像做注释
ax2.set(ylim=(-10,15))
ax2.plot(x2, y2, 'p',color="purple") # 标出移动的点
ax2.annotate('(%d,%d)' % (x2,y2), xy=(x2, y2))
if i == t0:
ax2.annotate('y值为%lf'%(y2), xy=(x2-1, y2-1), xytext=(x2-5, y2 + 1),color='darkorchid')
plt.savefig('comb1.png')
plt.close() # 这样会清楚旧图
image_list.append(imageio.imread('comb1.png'))
deputy.read("comb1.png",1)
imageio.mimsave('对比图1.gif', image_list, duration=1)