Changes in some perturbations from the math to numpy library, and added a ClassInterchange perturbation

.github/workflows/build.yml

@@ -19,9 +19,9 @@ jobs:
                     - { os: windows-latest, py: "3.8" }
                     - { os: windows-latest, py: "3.9" }
                     - { os: windows-latest, py: "3.10" }
-                    - { os: macOS-latest, py: "3.7" }
-                    - { os: macOS-latest, py: "3.8" }
-                    - { os: macOS-latest, py: "3.9" }
+                    - { os: macOS-12, py: "3.7" }
+                    - { os: macOS-12, py: "3.8" }
+                    - { os: macOS-12, py: "3.9" }
                     - { os: ubuntu-latest, py: "3.7" }
                     - { os: ubuntu-latest, py: "3.8" }
                     - { os: ubuntu-latest, py: "3.9" }

pudu/perturbation.py

@@ -68,7 +68,7 @@ class Log:
     def __init__(self, base=10):
         self.base = base
     def apply(self, x, row, col, window, bias):
-        x[0, row:row+window[0], col:col+window[1], 0] = math.log(x[0, row:row+window[0], col:col+window[1], 0] + bias, self.base)
+        x[0, row:row+window[0], col:col+window[1], 0] = np.log(x[0, row:row+window[0], col:col+window[1], 0] + bias) / np.log(self.base)
         return x, None
 
 
@@ -151,7 +151,7 @@ def __init__(self, freq=30, amp=1):
         self.freq = freq
         self.amp = amp
     def apply(self, x, row, col, window, bias):
-        x[0, row:row+window[0], col:col+window[1], 0] = (x[0, row:row+window[0], col:col+window[1], 0] + bias) * math.sin(self.freq * x[0, row:row+window[0], col:col+window[1], 0]) + self.amp
+        x[0, row:row+window[0], col:col+window[1], 0] = (x[0, row:row+window[0], col:col+window[1], 0] + bias) * np.sin(self.freq * x[0, row:row+window[0], col:col+window[1], 0]) + self.amp
         return x, None
 
 
@@ -172,7 +172,7 @@ def __init__(self, mean=1, stdv=1):
         self.mean = mean
         self.stdv = stdv
     def apply(self, x, row, col, window, bias):
-        x[0, row:row+window[0], col:col+window[1], 0] = (x[0, row:row+window[0], col:col+window[1], 0] + bias) * math.exp(-((x[0, row:row+window[0], col:col+window[1], 0] - self.mean) ** 2) / (2 * self.stdv ** 2))
+        x[0, row:row+window[0], col:col+window[1], 0] = (x[0, row:row+window[0], col:col+window[1], 0] + bias) * np.exp(-((x[0, row:row+window[0], col:col+window[1], 0] - self.mean) ** 2) / (2 * self.stdv ** 2))
         return x, None
 
 
@@ -189,7 +189,7 @@ class Tanh:
     def __init__(self, sf=1):
         self.sf = sf # scale factor
     def apply(self, x, row, col, window, bias):
-        x[0, row:row+window[0], col:col+window[1], 0] = math.tanh(self.sf * (x[0, row:row+window[0], col:col+window[1], 0] + bias))
+        x[0, row:row+window[0], col:col+window[1], 0] = np.tanh(self.sf * (x[0, row:row+window[0], col:col+window[1], 0] + bias))
         return x, None
 
 
@@ -206,7 +206,7 @@ class Sigmoid:
     def __init__(self, sf=1):
         self.sf = sf
     def apply(self, x, row, col, window, bias):
-        x[0, row:row+window[0], col:col+window[1], 0] = 1 / (1 + math.exp(-self.sf * (x[0, row:row+window[0], col:col+window[1], 0] + bias)))
+        x[0, row:row+window[0], col:col+window[1], 0] = 1 / (1 + np.exp(-self.sf * (x[0, row:row+window[0], col:col+window[1], 0] + bias)))
         return x, None
 
 
@@ -253,7 +253,7 @@ class Softplus:
     def __init__(self, sf=1):
         self.sf = sf
     def apply(self, x, row, col, window, bias):
-        x[0, row:row+window[0], col:col+window[1], 0] = math.log(1 + math.exp(self.sf * (x[0, row:row+window[0], col:col+window[1], 0] + bias)))
+        x[0, row:row+window[0], col:col+window[1], 0] = np.log(1 + np.exp(self.sf * (x[0, row:row+window[0], col:col+window[1], 0] + bias)))
         return x, None
 
 
@@ -419,3 +419,22 @@ def apply(self, x, row, col, window, bias):
         if x[0, row, col, 0] >= self.th:
             x[0, row:row+window[0], col:col+window[1], 0] = self.c
         return x, None
+
+
+class ClassInterchange:
+    """
+    Sets the values to a combination between the spectrum and another spectrum
+    (generally another class spectrum)
+
+    :type change: float
+    :param change: The percentage of change (0=no perturbation, 1=spectrum2)
+
+    :rtype: 4d array
+    :return: Custom perturbated array
+    """
+    def __init__(self, spectrum2, change=0):
+        self.spectrum2 = spectrum2
+        self.change = change
+    def apply(self, x, row, col, window, bias):
+        x[0, row:row+window[0], col:col+window[1], 0] = x[0, row:row+window[0], col:col+window[1], 0]*(1-self.change)+self.spectrum2[0, row:row+window[0], col:col+window[1], 0]*(self.change)
+        return x, None

requirements.txt

@@ -1,4 +1,4 @@
-numpy
+numpy<2.0
 pandas
 matplotlib
 spectrapepper