M: mod plot scripts about with-as

skyleaworlder · Oct 28, 2020 · 02a480a · 02a480a
1 parent 8b6d504
commit 02a480a
Show file tree

Hide file tree

Showing 3 changed files with 79 additions and 80 deletions.
diff --git a/scripts/plot1_1.py b/scripts/plot1_1.py
@@ -5,32 +5,32 @@
 with open("../data/lab_1_1_data.txt", "r") as f:
     arr = np.loadtxt(f, dtype=float)
 
-mean1_1_target = arr[0]
-var1_1_target = arr[1]
-num1_1_target = int(arr[2])
-mean1_1_eval_mle = arr[-4]
-var1_1_eval_mle = arr[-3]
-mean1_1_eval_moe = arr[-2]
-var1_1_eval_moe = arr[-1]
-arr = arr[3:-4]
+    mean1_1_target = arr[0]
+    var1_1_target = arr[1]
+    num1_1_target = int(arr[2])
+    mean1_1_eval_mle = arr[-4]
+    var1_1_eval_mle = arr[-3]
+    mean1_1_eval_moe = arr[-2]
+    var1_1_eval_moe = arr[-1]
+    arr = arr[3:-4]
 
-min1_1, max1_1 = np.min(arr), np.max(arr)
-arr = np.sort(arr)
+    min1_1, max1_1 = np.min(arr), np.max(arr)
+    arr = np.sort(arr)
 
-x_arr = np.zeros(num1_1_target)
-y_arr = np.zeros(num1_1_target)
-yac_arr = np.zeros(num1_1_target)
-stride = (max1_1 - min1_1) / num1_1_target
-for i in range(num1_1_target):
-    x = min1_1 + i * stride
-    y = len(np.where(arr < x)[0]) / num1_1_target
-    yac = norm.cdf(x-mean1_1_target, scale=var1_1_target)
-    x_arr[i], y_arr[i], yac_arr[i] = x, y, yac
+    x_arr = np.zeros(num1_1_target)
+    y_arr = np.zeros(num1_1_target)
+    yac_arr = np.zeros(num1_1_target)
+    stride = (max1_1 - min1_1) / num1_1_target
+    for i in range(num1_1_target):
+        x = min1_1 + i * stride
+        y = len(np.where(arr < x)[0]) / num1_1_target
+        yac = norm.cdf(x-mean1_1_target, scale=var1_1_target)
+        x_arr[i], y_arr[i], yac_arr[i] = x, y, yac
 
-plt.plot(x_arr, y_arr, "b-", label="Experimental Value")
-plt.plot(x_arr, yac_arr, "r-", label="Theoretical Value")
-plt.axis([min1_1-1, max1_1+1, 0, 1])
-plt.xlabel("x", horizontalalignment="right")
-plt.ylabel("y", rotation=0, verticalalignment="top")
-plt.legend()
-plt.show()
+    plt.plot(x_arr, y_arr, "b-", label="Experimental Value")
+    plt.plot(x_arr, yac_arr, "r-", label="Theoretical Value")
+    plt.axis([min1_1-1, max1_1+1, 0, 1])
+    plt.xlabel("x", horizontalalignment="right")
+    plt.ylabel("y", rotation=0, verticalalignment="top")
+    plt.legend()
+    plt.show()
diff --git a/scripts/plot1_2.py b/scripts/plot1_2.py
@@ -5,31 +5,31 @@
 with open("../data/lab_1_2_data.txt", "r") as f:
     arr = np.loadtxt(f, dtype=float)
 
-''' it has to be noticed that beta is scale parameter,
-    while sigma is scale parameter in normal distribution.
-'''
-beta1_2_target = arr[0]
-num1_2_target = int(arr[1])
-arr = arr[2:-1]
+    ''' it has to be noticed that beta is scale parameter,
+        while sigma is scale parameter in normal distribution.
+    '''
+    beta1_2_target = arr[0]
+    num1_2_target = int(arr[1])
+    arr = arr[2:-1]
 
-min1_2, max1_2 = np.min(arr), np.max(arr)
-mean1_2_target = np.mean(arr)
-arr = np.sort(arr)
+    min1_2, max1_2 = np.min(arr), np.max(arr)
+    mean1_2_target = np.mean(arr)
+    arr = np.sort(arr)
 
-x_arr = np.zeros(num1_2_target)
-y_arr = np.zeros(num1_2_target)
-yac_arr = np.zeros(num1_2_target)
-stride = (max1_2 - min1_2) / num1_2_target
-for i in range(num1_2_target):
-    x = min1_2 + i * stride
-    y = len(np.where(arr < x)[0]) / num1_2_target
-    yac = expon.cdf(x, scale=beta1_2_target)
-    x_arr[i], y_arr[i], yac_arr[i] = x, y, yac
+    x_arr = np.zeros(num1_2_target)
+    y_arr = np.zeros(num1_2_target)
+    yac_arr = np.zeros(num1_2_target)
+    stride = (max1_2 - min1_2) / num1_2_target
+    for i in range(num1_2_target):
+        x = min1_2 + i * stride
+        y = len(np.where(arr < x)[0]) / num1_2_target
+        yac = expon.cdf(x, scale=beta1_2_target)
+        x_arr[i], y_arr[i], yac_arr[i] = x, y, yac
 
-plt.plot(x_arr, y_arr, "b-", label="Experimental Value")
-plt.plot(x_arr, yac_arr, "r-", label="Theoretical Value")
-plt.axis([min1_2-1, max1_2+1, 0, 1])
-plt.xlabel("x", horizontalalignment="right")
-plt.ylabel("y", rotation=0, verticalalignment="top")
-plt.legend()
-plt.show()
+    plt.plot(x_arr, y_arr, "b-", label="Experimental Value")
+    plt.plot(x_arr, yac_arr, "r-", label="Theoretical Value")
+    plt.axis([min1_2-1, max1_2+1, 0, 1])
+    plt.xlabel("x", horizontalalignment="right")
+    plt.ylabel("y", rotation=0, verticalalignment="top")
+    plt.legend()
+    plt.show()
diff --git a/scripts/plot1_3.py b/scripts/plot1_3.py
@@ -3,39 +3,38 @@
 
 with open("../data/lab_1_3_data1.txt", "r") as f:
     arr_c1 = np.loadtxt(f, dtype=float)
-with open("../data/lab_1_3_data2.txt", "r") as f:
-    arr_c05 = np.loadtxt(f, dtype=float)
-with open("../data/lab_1_3_data3.txt", "r") as f:
-    arr_c05t = np.loadtxt(f, dtype=float)
-
-num1_3_target = int(arr_c1[1])
-arr_c1 = arr_c1[2:]
+    num1_3_target = int(arr_c1[1])
+    arr_c1 = arr_c1[2:]
 
-min1_3_c1, max1_3_c1 = np.min(arr_c1), np.max(arr_c1)
-x_c1arr = np.zeros(num1_3_target)
-y_c1arr = np.zeros(num1_3_target)
-stride = (max1_3_c1 - min1_3_c1) / num1_3_target
-for i in range(num1_3_target):
-    x = min1_3_c1 + i * stride
-    y = len(np.where(arr_c1 < x)[0]) / num1_3_target
-    x_c1arr[i], y_c1arr[i] = x, y
+    min1_3_c1, max1_3_c1 = np.min(arr_c1), np.max(arr_c1)
+    x_c1arr = np.zeros(num1_3_target)
+    y_c1arr = np.zeros(num1_3_target)
+    stride = (max1_3_c1 - min1_3_c1) / num1_3_target
+    for i in range(num1_3_target):
+        x = min1_3_c1 + i * stride
+        y = len(np.where(arr_c1 < x)[0]) / num1_3_target
+        x_c1arr[i], y_c1arr[i] = x, y
 
-num1_3_target = int(arr_c05[1])
-arr_c05 = arr_c05[2:]
+with open("../data/lab_1_3_data2.txt", "r") as f:
+    arr_c05 = np.loadtxt(f, dtype=float)
+    num1_3_target = int(arr_c05[1])
+    arr_c05 = arr_c05[2:]
 
-min1_3_c05, max1_3_c05 = np.min(arr_c05), np.max(arr_c05)
-x_c05arr = np.zeros(num1_3_target)
-y_c05arr = np.zeros(num1_3_target)
-stride = (max1_3_c05 - min1_3_c05) / num1_3_target
-for i in range(num1_3_target):
-    x = min1_3_c05 + i * stride
-    y = len(np.where(arr_c05 < x)[0]) / num1_3_target
-    x_c05arr[i], y_c05arr[i] = x, y
+    min1_3_c05, max1_3_c05 = np.min(arr_c05), np.max(arr_c05)
+    x_c05arr = np.zeros(num1_3_target)
+    y_c05arr = np.zeros(num1_3_target)
+    stride = (max1_3_c05 - min1_3_c05) / num1_3_target
+    for i in range(num1_3_target):
+        x = min1_3_c05 + i * stride
+        y = len(np.where(arr_c05 < x)[0]) / num1_3_target
+        x_c05arr[i], y_c05arr[i] = x, y
 
-num1_3_target = int(arr_c05t[2])
-x_c05tarr = np.array(arr_c05t[5:5+num1_3_target])
-y_c05tarr = np.array(arr_c05t[5+num1_3_target:5+num1_3_target*2])
-print(arr_c05t[5+num1_3_target:5+num1_3_target*2])
+with open("../data/lab_1_3_data3.txt", "r") as f:
+    arr_c05t = np.loadtxt(f, dtype=float)
+    num1_3_target = int(arr_c05t[2])
+    x_c05tarr = np.array(arr_c05t[5:5+num1_3_target])
+    y_c05tarr = np.array(arr_c05t[5+num1_3_target:5+num1_3_target*2])
+    print(arr_c05t[5+num1_3_target:5+num1_3_target*2])
 
 plt.plot(x_c1arr, y_c1arr, "b-", label="Experimental Value")
 plt.plot(x_c05arr, y_c05arr, "r+", label="Experimental Value")