Refactoring for More Pythonic Code

yoshi_seals/eigen/eigen.py

@@ -19,46 +19,43 @@
 
 import numpy as np
 
-def eigen(a: np.ndarray) -> np.ndarray:
 
-    k = 0
+def eigen(a: np.ndarray) -> np.ndarray:
     l = np.ones((a.shape[0]))
 
-    at = a #variavel temporaria para A
-    b = np.random.rand(a.shape[0],a.shape[1])
+    at = a  # variavel temporaria para A
+    b = np.random.rand(a.shape[0], a.shape[1])
 
-    while (k < at.shape[0]):
+    for k in range(at.shape[0]):
 
-        u = np.random.rand(at.shape[0],1)
-        u = u/max(u.min(), u.max(), key=abs)
+        u = np.random.rand(at.shape[0], 1)
+        u = u / max(u.min(), u.max(), key=abs)
 
         ctrl = 0
 
-        while (ctrl != l[k]):
-            
+        while ctrl != l[k]:
             ctrl = l[k]
             u = at.dot(u)
             l[k] = max(u.min(), u.max(), key=abs)
-            u = u/l[k]
+            u = u / l[k]
 
-        alpha = 0.999*l[k]
+        alpha = 0.999 * l[k]
 
-        t = np.random.rand(a.shape[0],1)
+        t = np.random.rand(a.shape[0], 1)
 
-        b[k] = b[k]/max(b[k].min(), b[k].max(), key=abs)
-        t = l/max(l.min(), l.max(), key=abs)
+        b[k] = b[k] / max(b[k].min(), b[k].max(), key=abs)
+        t = l / max(l.min(), l.max(), key=abs)
 
-        while not (np.allclose(b[k],t,atol=10**(-17))):
+        while not (np.allclose(b[k], t, atol=10 ** (-17))):
             t = b[k].copy()
-            b[k] = np.linalg.solve((a - alpha*np.identity(a.shape[0])),((l[k]-alpha)*t))
-            b[k] = b[k]/max(b[k].min(), b[k].max(), key=abs)
+            b[k] = np.linalg.solve((a - alpha * np.identity(a.shape[0])), ((l[k] - alpha) * t))
+            b[k] = b[k] / max(b[k].min(), b[k].max(), key=abs)
 
         i = 0
 
-        while (u[i] == 0):
+        while u[i] == 0:
             i += 1
 
-        at = at - (1/u[i])*u*at[i]
-        k += 1
+        at = at - (1 / u[i]) * u * at[i]
 
-    return l, b
+    return l, b

yoshi_seals/insert/insert.py

@@ -19,30 +19,19 @@
 
 import numpy as np
 
-def matrix(matrix: np.ndarray) -> np.ndarray:
 
-    i = 0
-    
-    while (i < matrix.shape[0]):
-        
-        j = 0
-        
-        while (j < matrix.shape[1]):
-            
-            matrix[i][j] = float(input('Insira o elemento {}x{}: '.format((i+1),(j+1))))
-            j += 1
-        
-        i += 1
-        
-    return matrix
+def matrix(a: np.ndarray) -> np.ndarray:
 
-def vector(vector: np.ndarray) -> np.ndarray:
-    
-    j=0
-    
-    while (j < vector.shape[0]):
-            
-        vector[j] = float(input('Insira o elemento b{}: '.format((j+1))))
-        j += 1
-        
-    return vector
+    for i in range(a.shape[0]):
+        for j in range(a.shape[1]):
+            a[i][j] = float(input(f"Insert the element a{i+1}x{j+1}: "))
+
+    return a
+
+
+def vector(v: np.ndarray) -> np.ndarray:
+
+    for j in range(v.shape[0]):
+        v[j] = float(input(f"Insert the element b{j+1}: "))
+
+    return v

yoshi_seals/scan/scan.py

@@ -20,26 +20,13 @@
 import numpy as np
 import pandas as pd
 
-def numpy(path:str, sep: str = None, decimal: str = None) -> np.ndarray:
 
-    if sep is None:
-        sep = ","
-
-    if decimal is None:
-        decimal = "."
-
-    df=pd.read_csv(path, sep=sep, decimal=decimal, header=None)
+def numpy(path: str, sep: str = ",", decimal: str = ".") -> np.ndarray:
+    df = pd.read_csv(path, sep=sep, decimal=decimal, header=None)
     array = df.to_numpy()
 
     return array
 
-def pandas(path: str, sep: str = None, decimal: str = None) -> pd.DataFrame:
-
-    if sep is None:
-        sep = ","
-
-    if decimal is None:
-        decimal = "."
 
+def pandas(path: str, sep: str = ",", decimal: str = ".") -> pd.DataFrame:
     return pd.read_csv(path, sep=sep, decimal=decimal)
-

yoshi_seals/write/write.py

@@ -21,15 +21,14 @@ import pandas as pd
 import numpy as np
 import csv
 
+
 def numpy(array: np.ndarray, path: str) -> np.ndarray:
-
-    with open(path, mode='w') as sistema_linear:
-
-        solution_writer = csv.writer(sistema_linear, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)
+    with open(path, mode='w') as linear_system:
+        solution_writer = csv.writer(linear_system, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)
         solution_writer.writerows(array)
-        
+
     return array
 
-def pandas(df: pd.DataFrame, path:str) -> None:
-    
-    df.to_csv(path)
+
+def pandas(df: pd.DataFrame, path: str) -> None:
+    df.to_csv(path)