Last data-point. Sorry for the delay

.gitignore

+*.npy
+data/data_converted/
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]

calibrations.py

@@ -3,7 +3,7 @@ import matplotlib.pyplot as plt
 
 #%% Measurements
 voltage_1 = np.array([1900, 1975, 2000, 2010, 2015, 2020, 2030, 2050, 2075, 2100]) # We define 2020 as end of the plateau
-count_1   = np.array([ 224, 685, 1200, 1938, 2322, 2388, 4788, 6588, 21124, 51420])
+count_1   = np.array([224, 685, 1200, 1938, 2322, 2388, 4788, 6588, 21124, 51420])
 count_frequency_1 = count_1/120
 
 voltage_2 = np.array([1975, 2015, 2010, 2020, 2030, 2050, 2100])

decode.py

@@ -207,6 +207,7 @@ class ArrayLoader:
         for f in files:
             if fname_fuzzy in f:
                 matches.append(f)
+
         if len(matches) > 1:
             print("Warning: The measurement file was not unambiguous. Please specify more arguments")
             return
@@ -214,7 +215,10 @@ class ArrayLoader:
             print("Warning: No data found.")
             return
         else:
-            fname = self.base_path + matches[0]
+            f = matches[0]
+
+            delay = int(f[f.find("_d") + 2 : f.find(".dat")])
+            fname = self.base_path + f
             fname_converted = fname.replace(self.base_path, self.base_path + "data_converted/").replace(".dat", ".npy")
             # change /data/... to /data/data_converted and change .dat to .npy
 

models.py

-from os import device_encoding
 import numpy as np
 from scipy import signal
-import decode
-DECODER = decode.ArrayLoader("data/")
+from . import decode
+DECODER = decode.ArrayLoader("driftchamber_computations/data/")
 
 
 
@@ -96,9 +95,18 @@ class Event:
         return (good >= self.min_good_channels)# and ends_good
 
 
-    def recreate_muon_path(self, drift_velocity):
-        pass
+    def recreate_muon_path(self, drift_velocity, delta_v, minimum_times):
+        x_pos = []
+        x_err = []
+        for i,ch in enumerate(self.channels):
+            delta_t = ch.peak_time - minimum_times[i] # each channel has a different minimum time
+            x_pos.append(drift_velocity * delta_t)
+            if not ch.is_usable:
+                x_err.append(0.3)
+            else:
+                x_err.append(delta_v * delta_t) 
 
+        return np.array(x_pos), np.array(x_err)
 
 
 class Measurement:
@@ -114,6 +122,7 @@ class Measurement:
             self.events.append(Event(loaded[i,...], nbins, min_good_channels))
         del loaded
         self.n_events = len(self.events)
+        self.n_channels = self.events[0].n_channels
 
 
     def filter_events(self):
@@ -134,4 +143,37 @@ class Measurement:
                 else:
                     timings[j,i] = -1
 
-        return timings
\ No newline at end of file
+        return timings
+
+
+    def get_per_channel_histogram(self, nbins):
+        """Returns the binned histograms as well as their bin-centers"""
+        timings = self.get_peak_timings()
+    
+        hists = []
+        bin_centers = []
+        raw_datas = []
+        self._min_times = []
+        for i in range(timings.shape[0]): # usually 8
+            hist, bin_edges = np.histogram(timings[i,...], bins=nbins, density=True)#, stacked=True)
+            bin_center = (bin_edges[:-1] + bin_edges[1:]) / 2
+            self._min_times.append(bin_center[1]) # 0th element is 0 
+
+            hists.append(hist)
+            bin_centers.append(bin_center)
+            raw_datas.append(timings[i,...])
+
+        return raw_datas, hists, bin_centers
+
+    @property
+    def per_channel_min_times(self):
+        try:
+            return self._min_times
+        except:
+            raise Exception("Please call get_per_channel_histogram first.")
+    def get_total_histogram(self, nbins):
+        hists, bin_centers = self.get_per_channel_histogram(nbins)
+        avg_hist = np.mean(hists, axis=0)
+        avg_bin_centers = np.mean(bin_centers, axis=0)
+
+        return avg_hist, avg_bin_centers
\ No newline at end of file