Here you go B

signalprocess.py

@@ -0,0 +1,44 @@
+import numpy as np
+from scipy import signal
+from numpy.fft import fft, ifft
+import matplotlib.pyplot as plt
+
+
+# Filtering things I have mainly stolen from the internet. I reallt need to learn more so I can do something compitent.
+class SignalProcess:
+    def __init__(self, path):
+        self.data = np.loadtxt(path, delimiter="\t", dtype=np.float64)
+        self.times = self.data[:, 0]
+        self.counts = self.data[:, 1]
+        self.ave_counts = self.data[:, 2]
+
+    def band_pass(self):
+        fs = 30.0
+        lowcut = 1.5 * 1.0 / 30.0
+        highcut = 2.5 * 1.0 / 30.0
+        nyqs = 0.5 * fs
+        low = lowcut / nyqs
+        high = highcut / nyqs
+        b, a = signal.butter(2, [low, high], "bandpass", analog=False)
+        filtered_counts = signal.filtfilt(b, a, self.ave_counts, axis=0, padlen=150)
+        return filtered_counts
+
+    def butter_filter(self):
+
+        b, a = signal.butter(2, 0.02)
+        filtered_counts = signal.filtfilt(b, a, self.ave_counts, padlen=150)
+        return filtered_counts
+
+    def fft(self):
+        sr = 30
+        ts = 1.0 / sr
+        X = fft(self.counts)
+        N = len(X)
+        n = np.arange(N)
+        T = N / sr
+        freq = n / T
+        plt.stem(freq, np.abs(X), "b", markerfmt=" ", basefmt="-b")
+        plt.xlabel("Freq (Hz)")
+        plt.ylabel("FFT Amplitude |X(freq)|")
+        plt.xlim(0, 10)
+        plt.show()