Skripte/STRATO3_auswertung.py

@@ -164,7 +164,7 @@ def main():
     #Data_preparation for altitude plot
     t, data = clear_data(t, altitude, args.uptime)
     if args.onset:
-        onset_correction(t, data, 300)
+        onset_correction(t, data, 150)
 
 
     fig = plt.figure()

Skripte/STRATO4_altitude_mean.py

@@ -13,55 +13,24 @@ def layout(fig):
     fig.legend()
     plt.grid()
     
-
 def read_data(files, uptime):
     t = []
     data = []
+    for fp in files:
+        t.append(np.loadtxt(fp, usecols=(0),unpack=True,skiprows=7,dtype='str',delimiter=';'))
     for i in range(len(files)):
-        if files[i][-3:] == 'csv':
-            t.append(np.loadtxt(files[i], usecols=(0),unpack=True,skiprows=7,dtype='str',delimiter=';'))
         data.append(np.loadtxt(files[i], usecols=(7),unpack=True,skiprows=7,dtype='str',delimiter=';'))
-        elif files[i][-3:] == 'LOG':
-            t.append(np.loadtxt(files[i], usecols=(1),unpack=True,skiprows=3,dtype='str',delimiter=';'))
-            data.append(np.loadtxt(files[i], usecols=(11),unpack=True,skiprows=3,dtype='str',delimiter=';'))
     return t, data
 
-def time_to_seconds(time_str):
-    hh, mm, ss = map(int, time_str.split(':'))
-    return hh * 3600 + mm * 60 + ss
-
 def clear_data(t, data, uptime):
     #Data preparation: cut-off everything before long time differences (before initialization of STRATO4)
-    #print(t, data)
     for i in range(len(t)):
-        #print(t[i])
-        for j in range(len(t[i])):
-            try:
-                #time = datetime.datetime.strptime(t[i][j], "%H:%M:%S")
-                #t[i][j] = int(time.hour * 3600 + time.minute * 60 + time.second)
-                t[i][j] = time_to_seconds(t[i][j])
-            except:
-                t[i][j] = float(t[i][j])
-        #for string in data[i]:
-        #    if string == "Inval.":
-        #        t[i] = t[i][1:]
-        #        for j in range(len(data[i])):
-        #            data[i] = data[i][1:]
-                #data[i] = data[i][1:]
-        k = 0
-        while k < len(data[i]):
-            #for k in range(len(data[i][j]))
-            if data[i][k] == "Err"  or data[i][k] == 'NA' or data[i][k] == "Inval.":
-                #print(data[i][j][k])
-                data[i] = np.delete(data[i], k)
-                t[i] = np.delete(t[i], k)
-            else:
-                k+=1
-        data[i] = data[i].astype(float)
-                #print(data[i][j])
-            #else:
-            #    altitude_float.append(float(string))
         t[i] = t[i].astype(float)
+        for string in data[i]:
+            if string == "Inval.":
+                t[i] = t[i][1:]
+                data[i] = data[i][1:]
+        data[i] = data[i].astype(float)
     return t, data
 
 
@@ -70,7 +39,7 @@ def plot_data(t, data, altitude, speed, temperature, ax):
     cmap = plt.get_cmap("viridis")
     if altitude:
         for i in range(len(t)):
-            ax.plot(t[i][::50],data[i][::50], '--', color=cmap(i/len(t)),label="Altitude "+str(i))
+            ax.plot(t[i][::50],data[i][0][::50], '--', color=cmap(i/len(t)),label="Altitude "+str(i))
         ax.set_xlabel("Time/s")
         ax.set_ylabel("Altitude/m", color='r')
         ax.tick_params(axis='y', labelcolor='r')
@@ -90,7 +59,6 @@ def onset_correction(t, data, thresh):
     for i in range(len(t)):
         start=0
         start_found = False
-        print(data[i])
         while not start_found:
             if data[i][start] > thresh:
                 start_found = True
@@ -106,27 +74,17 @@ def onset_correction(t, data, thresh):
         t0 = -b/a
         for j in range(len(t[i])):
             t[i][j]-=t0
-        print(t[i])
-
-def index_of_first_positive(lst):
-    return next((i for i, x in enumerate(lst) if x > 0), -1)
 
 #Mean function to compute mean altitude from various flights: current problem: flights have different durations
-def mean(data, t):
+def mean(data):
-    offset = []
-    for i in range(len(data)):
-        offset.append(np.argmax(t[i]>0))
     length = []
     for part in data:
         length.append(len(part))
     mean = []
-    min_run = []
+    for i in range(min(length)):
-    for i in range(len(data)):
-        min_run.append(length[i]-offset[i])
-    for i in range(min(min_run)):
         sum = 0
         for j in range(len(data)):
-            sum += data[j][i+offset[j]]
+            sum += data[j][i]
         mean.append(sum/len(data))
     return mean
 
@@ -151,7 +109,7 @@ def main():
 
     #Data_preparation for altitude plot
     t, data = clear_data(t, altitude, args.uptime)
-    #if args.onset:
+    if args.onset:
         onset_correction(t, data, 150)
 
 
@@ -160,9 +118,8 @@ def main():
     ax.set_xlabel("Time/s")
     ax.set_ylabel("Altitude/m")
     plot_data(t, data, args.altitude, args.speed, args.temperature, ax)
-    mean_data = mean(data, t)
+    mean_data = mean(data)
-    min_idx = min(enumerate(t), key=lambda x: len(x[1]))[0]
+    plot_mean(t[0], mean_data, ax)
-    plot_mean(t[2], mean_data, ax)
     #plt.gcf().autofmt_xdate()
 
     layout(fig)

Skripte/STRATO4_auswertung.py

@@ -103,7 +103,7 @@ def onset_correction(t, data, thresh):
         start=0
         start_found = False
         while not start_found:
-            if data[i][0][start] > thresh:
+            if data[i][start] > thresh:
                 start_found = True
             else:
                 start+=1