get first paper and add meta-review

trust/EMSOFT24/mmlab_checker.py

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+import numpy as np
+from datetime import datetime, timedelta
+from glob import glob
+
+# PARAMETERS
+#trace_time_length = timedelta(hours=4)
+#sampling_rate     = 20 # Hz
+#chunk_time_length = timedelta(minutes=10) # chunk of time to get one check value
+
+#print(f"Considering {trace_time_length/chunk_time_length} chunks of {chunk_time_length} over a {trace_time_length} trace.")
+
+data_folder       = "./data"
+data_selector     = "/ARTHUR-103/*.npy"
+
+# load data
+data_filenames    = sorted(glob(data_folder+data_selector))
+print(f"Listed {len(data_filenames)} traces.")
+
+# define the policy checking function
+def checker_long_high(labels):
+    """Check a policy on a trace (array of state label).
+    Produce a single ternary value 1=OK, 0=Unsure, -1=Not OK
+    Policy: No continuous High load (label=2) for more than 3m
+    """
+
+    req_L = int(timedelta(minutes=3).total_seconds()*sampling_rate)
+    #inneficient non-numpy shit, tempormanent solution
+    for i in range(labels.shape[0]):
+        if labels[i] == 2 or labels[i] == -1 :
+            k=1
+            while i+k < labels.shape[0] and (labels[i+k] == 2 or labels[i+k] == -1):
+                k+=1
+                if k == req_L:
+                    if -1 in labels[i:i+k]:
+                        return 0
+                    else:
+                        return -1
+    return 1
+
+def checker_off(trace):
+    chunk_sample_length = 30
+    thresh = 1000
+    N = trace.shape[0]
+    nbr_windows = int(N/chunk_sample_length)
+    windows = trace[:chunk_sample_length*nbr_windows].reshape(nbr_windows,chunk_sample_length)
+    res = np.zeros(windows.shape[0],dtype=int)
+    for i in range(windows.shape[0]):
+        if np.any(windows[i]<thresh):
+            res[i] = -1
+        elif np.all(windows[i]>=thresh):
+            res[i] = 1
+        else:
+            pass
+            #value is already 0 by default
+    return res
+
+total_counts = {"-1":0,"0":0,"1":0}
+all_res = []
+for f in data_filenames:
+    trace = np.load(f)
+    res = checker_off(trace)
+    print(res.shape)
+    unique, counts = np.unique(res,return_counts=True)
+    counts = dict(zip(unique,counts))
+    for key in counts.keys():
+        total_counts[str(key)]+=counts[key]
+
+    all_res.append(res.reshape(1,-1))
+
+    print(f"{f.split('/')[-1]}: {res.shape[0]} evaluations, {total_counts}")
+
+final_res = np.concatenate(all_res,axis=0)
+print(final_res.shape)
+np.save(f"generated/{data_filenames[0].split('/')[-1].split('--')[0]}.npy",final_res)