reflections successfully parsed

scripts/helper.py

@@ -0,0 +1,17 @@
+# {'TIME': [data], 'CH1': [data], 'CH2': [data]}
+def cut_time(data : dict, start : float, end : float) -> dict:
+    idx_start = 0
+    idx_end = 0
+
+    for idx, t in enumerate(data['TIME']):
+        if t > start and idx_start == 0:
+            idx_start = idx
+        if t > end and idx_end == 0:
+            idx_end = idx
+        if idx_end != 0 and idx_start != 0:
+            break
+    
+    if idx_end != 0 and idx_start != 0: 
+        return {key: value[idx_start:idx_end] for key, value in data.items()}
+    else:
+        return None

scripts/reader.py

@@ -4,14 +4,13 @@ class Reader():
     def __init__(self) -> None:
         pass
     
-    def readFile(self, filename) -> dict:
+    def read_file(self, filename) -> dict:
         with open(filename, 'r') as file:
             data = list(csv.reader(file, delimiter=','))
             
             # Delete metadata for now
-            print("Removing metadata")
             for i in range(15):
-                print(data.pop(0))
+                data.pop(0)
 
             # {'TIME': [data], 'CH1': [data], 'CH2': [data]}
             channels = []
@@ -31,5 +30,6 @@ class Reader():
 
 if __name__ == '__main__':
     reader = Reader()
-    data = reader.readFile(r".\data\T0007ALL.csv")
-    print(data)
+    data = reader.read_file(r".\data\T0007ALL.csv")
+    print(data)
+    

scripts/reflection.py

@@ -1,10 +1,45 @@
 from matplotlib import pyplot as plt
+from scipy import signal
+import numpy as np
 import reader
+import helper
 
+# load data
 data_reader = reader.Reader()
-data = data_reader.readFile(r".\data\T0001CH1.csv")
+data = data_reader.read_file(r".\data\T0004CH1.csv")
 
+# slice empty space, start and end time is in seconds
+data = helper.cut_time(data, -1e-7, 5e-7)
 
+# find peaks, use max to find the highest, or a threshold
+# adjust width to find the middle of a peak (width is in indices)
+# peaks, _ = signal.find_peaks(data['CH1'], max(data['CH1']))
+peaks, _ = signal.find_peaks(data['CH1'], 2, width=10)
 
+# find throughs, comment away it finds too many throughs
+# invert = [-x for x in data['CH1']]
+# throughs, _ = signal.find_peaks(invert, max(invert))
+# peaks = np.concatenate([peaks, throughs])
+
+# store time step
+step = data['TIME'][1] - data['TIME'][0]
+
+# do some calcs
+idx_diff = peaks[-1] - peaks[0]
+tim_diff = idx_diff*step
+print(f"Time between peaks: {tim_diff*1e9:.1f} ns")
+c = 299792458 
+k = 0.66
+c_coax = c * k
+print(f"Measured distance: {(tim_diff/2)*c_coax:.2f} m")
+
+# Plot data
 plt.plot(data['TIME'], data['CH1'])
-plt.show()
+if len(peaks) > 0:
+    for peak in peaks:
+        plt.vlines(
+            peak*step+data['TIME'][0], 
+            min(data['CH1']), 
+            max(data['CH1']), 'red', 'dashed')
+plt.grid()
+plt.show()