diff --git a/examples/02105/instructor/week2/StoneReport_handin_10_of_10.token b/examples/02105/instructor/week2/StoneReport_handin_10_of_10.token
new file mode 100644
index 0000000000000000000000000000000000000000..14ff1bbd847dc4ba97e969f86d3429a1c4295f84
--- /dev/null
+++ b/examples/02105/instructor/week2/StoneReport_handin_10_of_10.token
@@ -0,0 +1,180 @@
+# This file contains your results. Do not edit its content. Simply upload it as it is.
+---------------------------------------------------------------------- ..ooO0Ooo.. ----------------------------------------------------------------------
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+---------------------------------------------------------------------- ..ooO0Ooo.. ----------------------------------------------------------------------
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+Hjp3p/tcHWAAAAAANoEHqim9V1AABzrgB0IsCNZpMy7HEZ/sCAAAAAARZWg==.
\ No newline at end of file
diff --git a/examples/02105/instructor/week2/deploy.py b/examples/02105/instructor/week2/deploy.py
new file mode 100644
index 0000000000000000000000000000000000000000..a59af502aada3549c16dedd0d03a11f8b215c3cf
--- /dev/null
+++ b/examples/02105/instructor/week2/deploy.py
@@ -0,0 +1,9 @@
+from stones_tests import StoneReport
+from unitgrade_private.hidden_create_files import setup_grade_file_report
+from snipper import snip_dir
+
+if __name__ == "__main__":
+    setup_grade_file_report(StoneReport, minify=False, obfuscate=False, execute=False, with_coverage=True)
+
+    # Deploy the files using snipper: https://gitlab.compute.dtu.dk/tuhe/snipper
+    snip_dir(source_dir="", dest_dir="../../students/week2", exclude=['*.token', 'deploy.py'])
diff --git a/examples/02105/instructor/week2/stones.py b/examples/02105/instructor/week2/stones.py
new file mode 100644
index 0000000000000000000000000000000000000000..96c9cf5eaeba7da59ecc9db55ada75b06cd093b0
--- /dev/null
+++ b/examples/02105/instructor/week2/stones.py
@@ -0,0 +1,15 @@
+def maximum_stones(W, stone_weights):
+    stone_weights.sort()
+    T = 0
+    s = 0
+    for k, we in enumerate(stone_weights):
+        T += we
+        if T <= W:
+            s = s + 1
+        else:
+            break
+    return s
+
+if __name__ == "__main__":
+    print("The following call using maximum weight of W=15 should return 5.")
+    print(maximum_stones(15, [2, 5, 3, 1, 8, 4, 5, 7]))
diff --git a/examples/02105/instructor/week2/stones_tests.py b/examples/02105/instructor/week2/stones_tests.py
new file mode 100644
index 0000000000000000000000000000000000000000..9f61cfd3822b0d2f526c318e6b380e8d0c5d6ef1
--- /dev/null
+++ b/examples/02105/instructor/week2/stones_tests.py
@@ -0,0 +1,44 @@
+from unitgrade.framework import Report, UTestCase
+from unitgrade.evaluate import evaluate_report_student
+import stones
+from stones import maximum_stones
+
+# A fancy helper function to generate nicer-looking titles.
+def trlist(x):
+    s = str(list(x))
+    if len(s) > 30:
+        s = s[:30] + "...]"
+    return s
+
+
+class Stones(UTestCase):
+    """ Test of the Stones function """
+    def stest(self, W, stone_weights): # Helper function.
+        N = maximum_stones(W, stone_weights)
+        self.title = f"stones({W}, {trlist(stone_weights)}) = {N} ?"
+        self.assertEqualC(N)
+
+    def test_basecase(self):
+        """ Test the stones-example given in the homework """
+        N = maximum_stones(15, [2, 5, 3, 1, 8, 4, 5, 7])
+        self.assertEqual(N, 5) # Test that we can collect 5 stones.
+
+    def test_stones1(self):
+        self.stest(4, [4]) # One stone weighing 4 kg.
+
+    def test_stones2(self):
+        self.stest(4, [1, 4]) # should also give 1
+
+    def test_stones3(self):
+        self.stest(4, [4, 1]) # should also give 1
+
+    def test_stones4(self):
+        self.stest(13, [2, 5, 3, 1, 8, 4, 5, 7])
+
+class StoneReport(Report):
+    title = "02105 week 2: Stone collection"
+    questions = [(Stones, 10),]
+    pack_imports = [stones]
+
+if __name__ == "__main__":
+    evaluate_report_student(StoneReport())
diff --git a/examples/02105/instructor/week2/stones_tests_grade.py b/examples/02105/instructor/week2/stones_tests_grade.py
new file mode 100644
index 0000000000000000000000000000000000000000..50a90757f62400fc8f96e174999c81ce2d52a1bb
--- /dev/null
+++ b/examples/02105/instructor/week2/stones_tests_grade.py
@@ -0,0 +1,4 @@
+# stones_tests.py
+''' WARNING: Modifying, decompiling or otherwise tampering with this script, it's data or the resulting .token file will be investigated as a cheating attempt. '''
+import bz2, base64
+exec(bz2.decompress(base64.b64decode('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')))
\ No newline at end of file
diff --git a/examples/02105/instructor/week2/unitgrade_data/Stones.pkl b/examples/02105/instructor/week2/unitgrade_data/Stones.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..070b083e581b9052718352ac872d777848661905
Binary files /dev/null and b/examples/02105/instructor/week2/unitgrade_data/Stones.pkl differ
diff --git a/examples/02105/students/week2/stones.py b/examples/02105/students/week2/stones.py
new file mode 100644
index 0000000000000000000000000000000000000000..96c9cf5eaeba7da59ecc9db55ada75b06cd093b0
--- /dev/null
+++ b/examples/02105/students/week2/stones.py
@@ -0,0 +1,15 @@
+def maximum_stones(W, stone_weights):
+    stone_weights.sort()
+    T = 0
+    s = 0
+    for k, we in enumerate(stone_weights):
+        T += we
+        if T <= W:
+            s = s + 1
+        else:
+            break
+    return s
+
+if __name__ == "__main__":
+    print("The following call using maximum weight of W=15 should return 5.")
+    print(maximum_stones(15, [2, 5, 3, 1, 8, 4, 5, 7]))
diff --git a/examples/02105/students/week2/stones_tests.py b/examples/02105/students/week2/stones_tests.py
new file mode 100644
index 0000000000000000000000000000000000000000..9f61cfd3822b0d2f526c318e6b380e8d0c5d6ef1
--- /dev/null
+++ b/examples/02105/students/week2/stones_tests.py
@@ -0,0 +1,44 @@
+from unitgrade.framework import Report, UTestCase
+from unitgrade.evaluate import evaluate_report_student
+import stones
+from stones import maximum_stones
+
+# A fancy helper function to generate nicer-looking titles.
+def trlist(x):
+    s = str(list(x))
+    if len(s) > 30:
+        s = s[:30] + "...]"
+    return s
+
+
+class Stones(UTestCase):
+    """ Test of the Stones function """
+    def stest(self, W, stone_weights): # Helper function.
+        N = maximum_stones(W, stone_weights)
+        self.title = f"stones({W}, {trlist(stone_weights)}) = {N} ?"
+        self.assertEqualC(N)
+
+    def test_basecase(self):
+        """ Test the stones-example given in the homework """
+        N = maximum_stones(15, [2, 5, 3, 1, 8, 4, 5, 7])
+        self.assertEqual(N, 5) # Test that we can collect 5 stones.
+
+    def test_stones1(self):
+        self.stest(4, [4]) # One stone weighing 4 kg.
+
+    def test_stones2(self):
+        self.stest(4, [1, 4]) # should also give 1
+
+    def test_stones3(self):
+        self.stest(4, [4, 1]) # should also give 1
+
+    def test_stones4(self):
+        self.stest(13, [2, 5, 3, 1, 8, 4, 5, 7])
+
+class StoneReport(Report):
+    title = "02105 week 2: Stone collection"
+    questions = [(Stones, 10),]
+    pack_imports = [stones]
+
+if __name__ == "__main__":
+    evaluate_report_student(StoneReport())
diff --git a/examples/02105/students/week2/unitgrade_data/Stones.pkl b/examples/02105/students/week2/unitgrade_data/Stones.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..070b083e581b9052718352ac872d777848661905
Binary files /dev/null and b/examples/02105/students/week2/unitgrade_data/Stones.pkl differ
diff --git a/examples/02631/instructor/week5/__pycache__/looping.cpython-38.pyc b/examples/02631/instructor/week5/__pycache__/looping.cpython-38.pyc
index fa302e5a102c505ba4b62aa5eab3205ca2683298..7b24c7c64e73acf8c9b9726d7c99d052ac701171 100644
Binary files a/examples/02631/instructor/week5/__pycache__/looping.cpython-38.pyc and b/examples/02631/instructor/week5/__pycache__/looping.cpython-38.pyc differ
diff --git a/examples/02631/instructor/week5/__pycache__/report1intro.cpython-38.pyc b/examples/02631/instructor/week5/__pycache__/report1intro.cpython-38.pyc
index fbaa15d315d2c8ab276a8238abeca56a9e792bb4..ba16d7002f51dee562fcff1bb147d4600de3fb07 100644
Binary files a/examples/02631/instructor/week5/__pycache__/report1intro.cpython-38.pyc and b/examples/02631/instructor/week5/__pycache__/report1intro.cpython-38.pyc differ
diff --git a/examples/02631/instructor/week5/looping.py b/examples/02631/instructor/week5/looping.py
index 59a1485204b3c5fbf7c8e7e5d86531fa71c39d21..b88afd5f2e95b860ba838f8ce1492bc851ff54ab 100644
--- a/examples/02631/instructor/week5/looping.py
+++ b/examples/02631/instructor/week5/looping.py
@@ -5,13 +5,7 @@ def bacteriaGrowth(n0, alpha, K, N): #!f
     """
     Calculate time until bacteria growth exceed N starting from a population of n0 bacteria.
     hints:
-        * consider n0
-        * alpha > 0
-    :param n0:
-    :param alpha:
-    :param K:
-    :param N:
-    :return:
+        * You need to update the number of bacteria n0 within a loop
     """
     if n0 > N:
         return 0
@@ -19,7 +13,8 @@ def bacteriaGrowth(n0, alpha, K, N): #!f
         n0 = (1 + alpha * (1-n0 / K) ) * n0
         if n0 > N:
             break
-    return t+1
+    t += 1
+    return t
 
 def clusterAnalysis(reflectance):
     reflectance = np.asarray(reflectance)
@@ -33,15 +28,17 @@ def clusterAnalysis(reflectance):
     return I1 + 1
 
 def fermentationRate(measuredRate, lowerBound, upperBound):
-    # Insert your code here
-    return np.mean( [r for r in measuredRate if lowerBound < r < upperBound] )
-
-
-
+    """
+    Compute and return the mean value of the rates in 'measuredRate'
+    which falls within lowerBound and upperBound.
+    """
+    mean_value = np.mean( [r for r in measuredRate if lowerBound < r < upperBound] )
+    return mean_value
 
 def removeIncomplete(id):
-    """ Hints:
-    * Take a look at the example in the exercise.
+    """
+    Hints:
+        * Take a look at the example in the exercise.
     """
     id = np.asarray(id)
     id2 = []
@@ -50,15 +47,10 @@ def removeIncomplete(id):
             id2.append(v)
     return np.asarray(id2)
 
-
 if __name__ == "__main__":
     # I = clusterAnalysis([1.7, 1.6, 1.3, 1.3, 2.8, 1.4, 2.8, 2.6, 1.6, 2.7])
     # print(I)
-
     print(fermentationRate(np.array([20.1, 19.3, 1.1, 18.2, 19.7, 121.1, 20.3, 20.0]), 15, 25))
-
-
     # print(removeIncomplete(np.array([1.3, 2.2, 2.3, 4.2, 5.1, 3.2, 5.3, 3.3, 2.1, 1.1, 5.2, 3.1])))
-
     # Problem 1: Write a function which add two numbers
     # clusterAnalysis([2, 1, 2, 4, 5])
\ No newline at end of file
diff --git a/examples/02631/instructor/week5/report1intro.py b/examples/02631/instructor/week5/report1intro.py
index d4abb8b192d3604e165b6cb6cafe77933001eeae..f8076c26210356404b46a61e660ba7ee4617a51b 100644
--- a/examples/02631/instructor/week5/report1intro.py
+++ b/examples/02631/instructor/week5/report1intro.py
@@ -130,7 +130,7 @@ class FermentationRate(UTestCase):
 
 
 class Report1Flat(Report):
-    title = "Week 4: Looping"
+    title = "02531 week 5: Looping"
     questions = [(ClusterAnalysis, 10), (RemoveIncomplete, 10), (Bacteria, 10),  (FermentationRate, 10),]
     pack_imports = [looping]
 
diff --git a/examples/02631/instructor/week5/report1intro_grade.py b/examples/02631/instructor/week5/report1intro_grade.py
index b57b0fdbeef0c3b899a410dae5dde7b6fa050162..335212aa926b93e9c43ae8dbbeb52007d296cab4 100644
--- a/examples/02631/instructor/week5/report1intro_grade.py
+++ b/examples/02631/instructor/week5/report1intro_grade.py
@@ -1,3 +1,4 @@
+# report1intro.py
 ''' WARNING: Modifying, decompiling or otherwise tampering with this script, it's data or the resulting .token file will be investigated as a cheating attempt. '''
 import bz2, base64
-exec(bz2.decompress(base64.b64decode('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')))
\ No newline at end of file
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')))
\ No newline at end of file
diff --git a/examples/02631/instructor/week5/unitgrade_data/Bacteria.pkl b/examples/02631/instructor/week5/unitgrade_data/Bacteria.pkl
index 158944a2063b2c7a5980afbebd1dfdd93bada6bb..703d7a9b626c810bf555748f8a5de1c5886575c6 100644
Binary files a/examples/02631/instructor/week5/unitgrade_data/Bacteria.pkl and b/examples/02631/instructor/week5/unitgrade_data/Bacteria.pkl differ
diff --git a/examples/02631/instructor/week5/unitgrade_data/ClusterAnalysis.pkl b/examples/02631/instructor/week5/unitgrade_data/ClusterAnalysis.pkl
index 6769431fae0db63cb19979f2f83158dc97da4c02..9069a502a63c321f995ded8c9a84212b92cdeb5d 100644
Binary files a/examples/02631/instructor/week5/unitgrade_data/ClusterAnalysis.pkl and b/examples/02631/instructor/week5/unitgrade_data/ClusterAnalysis.pkl differ
diff --git a/examples/02631/instructor/week5/unitgrade_data/FermentationRate.pkl b/examples/02631/instructor/week5/unitgrade_data/FermentationRate.pkl
index 65842dc98428444ad7a0e6a3b677a291a0350dc0..3d0646ebc545b5e45a3adf481ab44ff0231b59bf 100644
Binary files a/examples/02631/instructor/week5/unitgrade_data/FermentationRate.pkl and b/examples/02631/instructor/week5/unitgrade_data/FermentationRate.pkl differ
diff --git a/examples/02631/instructor/week5/unitgrade_data/RemoveIncomplete.pkl b/examples/02631/instructor/week5/unitgrade_data/RemoveIncomplete.pkl
index 51a6605910f84c2a930851fb0a440a8c3d923bb8..3e0852344f326a5cd96be0db968882489bc67637 100644
Binary files a/examples/02631/instructor/week5/unitgrade_data/RemoveIncomplete.pkl and b/examples/02631/instructor/week5/unitgrade_data/RemoveIncomplete.pkl differ
diff --git a/examples/02631/instructor/week6/week6_tests.py b/examples/02631/instructor/week6/week6_tests.py
new file mode 100644
index 0000000000000000000000000000000000000000..5d44ef0ddc922844caba51caf60a8f4cfa8ddfc2
--- /dev/null
+++ b/examples/02631/instructor/week6/week6_tests.py
@@ -0,0 +1,140 @@
+from unitgrade.framework import Report, UTestCase
+from unitgrade import cache
+from unitgrade.evaluate import evaluate_report_student
+import numpy as np
+import looping
+from looping import bacteriaGrowth, clusterAnalysis, removeIncomplete, fermentationRate
+from week6programs import thermoEquilibrium
+
+def trlist(x):
+    s = str(list(x))
+    if len(s) > 30:
+        s = s[:30] + "...]"
+    return s
+
+class Thermodynamics(UTestCase):
+    """ Thermodynamical equilibrium """
+    random_numbers1 = [0.16, 0.04, 0.72,
+                   0.09, 0.17, 0.60, 0.26, 0.65, 0.69, 0.74, 0.45, 0.61,
+                   0.23, 0.37, 0.15, 0.83, 0.61, 1.00, 0.08, 0.44]
+
+    random_numbers2 = [0.9, 0.1, 0.7, 0.3, 0.16, 0.04, 0.72,
+                   0.09, 0.17, 0.60, 0.26, 0.65, 0.69, 0.74, 0.45, 0.61,
+                   0.23, 0.37, 0.15, 0.83, 0.61, 1.00, 0.08, 0.44]
+
+    def test_basecase(self):
+        """ Example from problem sheet """
+        # Standard unittest:
+        t = thermoEquilibrium(2.0, np.array([0.16, 0.04, 0.72,
+                                               0.09, 0.17, 0.60, 0.26, 0.65, 0.69, 0.74, 0.45, 0.61,
+                                               0.23, 0.37, 0.15, 0.83, 0.61, 1.00, 0.08, 0.44]))
+        self.assertEqual(t, 1)
+
+    def test_1(self): # Using the cache system.
+        """ Alternative test case 1 """
+        self.assertEqualC(thermoEquilibrium(2, np.array(self.random_numbers1)))
+
+    def test_2(self):
+        """ Alternative test case 2 """
+        self.assertEqualC(thermoEquilibrium(10, np.array(self.random_numbers2)))
+
+    def test_3(self):
+        """ Alternative test case 3 """
+        self.assertEqualC(thermoEquilibrium(5, np.array(self.random_numbers1)))
+
+class ClusterAnalysis(UTestCase):
+    """ Cluster analysis """
+
+    def stest(self, n, seed):
+        np.random.seed(seed)
+        x = np.round(np.random.rand(n), 1)
+        I = clusterAnalysis(x)
+        self.title = f"clusterAnalysis({list(x)}) = {list(I)} ?"
+        self.assertEqualC(list(I))
+
+    def test_cluster1(self):
+        """ Hints:
+        * Make sure to frobulate the frobulator.
+        * Just try harder
+        """
+        self.stest(3, 10)
+
+    def test_cluster2(self):
+        self.stest(4, 146)
+
+    def test_cluster3(self):
+        self.stest(5, 12)
+
+    def test_cluster4(self):
+        """
+        Cluster analysis for tied lists
+        Hints:
+        * It may be that an observations has the same distance to the two clusters. Where do you assign it in this case?
+        """
+        x = np.array([10.0, 12.0, 10.0, 12.0, 9.0, 11.0, 11.0, 13.0])
+        self.assertEqualC(list(clusterAnalysis(x) ) )
+
+
+class RemoveIncomplete(UTestCase):
+    """ Remove incomplete IDs """
+
+    def stest(self, x):
+        I = list( removeIncomplete(x) )
+        self.title = f"removeId({trlist(x)}) = {trlist(I)} ?"
+        self.assertEqualC(I)
+
+    @cache
+    def rseq(self, max, n):
+        np.random.seed(42)
+        return np.random.randint(max, size=(n,) ) + (np.random.randint(2, size=(n,) )+1)/10
+
+    def test_incomplete1(self):
+        self.stest( np.array([1.3, 2.2, 2.3, 4.2, 5.1, 3.2, 5.3, 3.3, 2.1, 1.1, 5.2, 3.1]) )
+
+    def test_incomplete2(self):
+        self.stest( np.array([1.1, 1.2, 1.3, 2.1, 2.2, 2.3]) )
+
+    def test_incomplete3(self):
+        self.stest(np.array([5.1, 5.2, 4.1, 4.3, 4.2, 8.1, 8.2, 8.3]) )
+
+    def test_incomplete4(self):
+        self.stest(np.array([1.1, 1.3, 2.1, 2.2, 3.1, 3.3, 4.1, 4.2, 4.3]) )
+
+    def test_incomplete5(self):
+        self.stest(self.rseq(10, 40))
+
+
+class FermentationRate(UTestCase):
+    """ Fermentation rate """
+
+    def stest(self, x, lower, upper):
+        I =  fermentationRate(x, lower, upper)
+        s = trlist(x)
+        self.title = f"fermentationRate({s}, {lower}, {upper}) = {I:.3f} ?"
+        self.assertEqualC(I)
+
+    @cache
+    def rseq(self, max, n):
+        np.random.seed(42)
+        return np.random.randint(max, size=(n,) ) + (np.random.randint(3, size=(n,) )+1)/n
+
+    def test_rate1(self):
+        self.stest(np.array([20.1, 19.3, 1.1, 18.2, 19.7, 121.1, 20.3, 20.0]), 15, 25)
+
+    def test_rate2(self):
+        self.stest(np.array([20.1, 19.3, 1.1, 18.2, 19.7, 121.1, 20.3, 20.0]), 1, 200)
+
+    def test_rate3(self):
+        self.stest(np.array([1.75]), 1, 2)
+
+    def test_rate4(self):
+        self.stest(np.array([20.1, 19.3, 1.1, 18.2, 19.7, 121.1, 20.3, 20.0]), 18.2, 20)
+
+
+class Report1Flat(Report):
+    title = "Week 6: Programs"
+    questions = [(ClusterAnalysis, 10), (RemoveIncomplete, 10), (Bacteria, 10),  (FermentationRate, 10),]
+    pack_imports = [looping]
+
+if __name__ == "__main__":
+    evaluate_report_student(Report1Flat())
diff --git a/examples/02631/instructor/week6/week6programs.py b/examples/02631/instructor/week6/week6programs.py
new file mode 100644
index 0000000000000000000000000000000000000000..bda82a517b6a3bd8599e2aa89a9325e40f894384
--- /dev/null
+++ b/examples/02631/instructor/week6/week6programs.py
@@ -0,0 +1,42 @@
+import numpy as np
+
+def thermoEquilibrium(N, r): # ok
+    Nl = N
+    for t, rand in enumerate(r):
+        Plr = Nl / N
+        Nl += (-1 if rand < Plr else 1)
+        if Nl == N//2:
+            break
+    t = t + 1 if N//2 == Nl else 0
+    return t
+
+def circleAreaMC(xvals, yvals):
+    X = np.stack([np.asarray(xvals), np.asarray(yvals)], 1)
+    A = (np.sum(X ** 2, 1) < 1.0 ).sum() / len(xvals) * 4
+    return A
+
+def convertTemperature(T, unitFrom, unitTo):
+    C, F, K = 'Celsius', 'Fahrenheit', 'Kelvin'
+    def x2K(temp, unit):
+        if unit == F: return (temp + 459.67) / 1.8
+        if unit == C: return temp + 273.15
+        return temp
+
+    def K2x(temp, unit):
+        if unit == C: return temp - 273.15
+        if unit == F: return 1.8 * temp - 459.67
+        return temp
+    T = K2x(x2K(T, unitFrom), unitTo)
+    return T
+
+if __name__ == "__main__":
+    print("convertTemperature should return 10. Your output:")
+    print(convertTemperature(50.0, "Fahrenheit", "Celsius"))
+
+    print("circleAreaMC should return 3.2. Your output:")
+    print(circleAreaMC(np.array([-0.1, 0.7, 0.8, 0.5, -0.4]), np.array([0.3, -0.1, 0.9, 0.6, -0.3])))
+
+    print("thermoEquilibrium should return 1. Your output:")
+    print(thermoEquilibrium(2.0, np.array([0.16, 0.04, 0.72,
+                                           0.09, 0.17, 0.60, 0.26, 0.65, 0.69, 0.74, 0.45, 0.61,
+                                           0.23, 0.37, 0.15, 0.83, 0.61, 1.00, 0.08, 0.44])))
diff --git a/examples/02631/students/week5/looping.py b/examples/02631/students/week5/looping.py
index 3d7e4c00dab24105de998e74d8814e97ae520ca0..64db4f21b1aa35baa2ad7650bebee649c2581309 100644
--- a/examples/02631/students/week5/looping.py
+++ b/examples/02631/students/week5/looping.py
@@ -5,17 +5,11 @@ def bacteriaGrowth(n0, alpha, K, N):
     """
     Calculate time until bacteria growth exceed N starting from a population of n0 bacteria.
     hints:
-        * consider n0
-        * alpha > 0
-    :param n0:
-    :param alpha:
-    :param K:
-    :param N:
-    :return:
+        * You need to update the number of bacteria n0 within a loop
     """
-    # TODO: 6 lines missing.
+    # TODO: 7 lines missing.
     raise NotImplementedError("Implement function body")
-    return t+1
+    return t
 
 def clusterAnalysis(reflectance):
     reflectance = np.asarray(reflectance)
@@ -29,15 +23,17 @@ def clusterAnalysis(reflectance):
     return I1 + 1
 
 def fermentationRate(measuredRate, lowerBound, upperBound):
-    # Insert your code here
-    return np.mean( [r for r in measuredRate if lowerBound < r < upperBound] )
-
-
-
+    """
+    Compute and return the mean value of the rates in 'measuredRate'
+    which falls within lowerBound and upperBound.
+    """
+    mean_value = np.mean( [r for r in measuredRate if lowerBound < r < upperBound] )
+    return mean_value
 
 def removeIncomplete(id):
-    """ Hints:
-    * Take a look at the example in the exercise.
+    """
+    Hints:
+        * Take a look at the example in the exercise.
     """
     id = np.asarray(id)
     id2 = []
@@ -46,15 +42,10 @@ def removeIncomplete(id):
             id2.append(v)
     return np.asarray(id2)
 
-
 if __name__ == "__main__":
     # I = clusterAnalysis([1.7, 1.6, 1.3, 1.3, 2.8, 1.4, 2.8, 2.6, 1.6, 2.7])
     # print(I)
-
     print(fermentationRate(np.array([20.1, 19.3, 1.1, 18.2, 19.7, 121.1, 20.3, 20.0]), 15, 25))
-
-
     # print(removeIncomplete(np.array([1.3, 2.2, 2.3, 4.2, 5.1, 3.2, 5.3, 3.3, 2.1, 1.1, 5.2, 3.1])))
-
     # Problem 1: Write a function which add two numbers
     # clusterAnalysis([2, 1, 2, 4, 5])
diff --git a/examples/02631/students/week5/report1intro.py b/examples/02631/students/week5/report1intro.py
index d4abb8b192d3604e165b6cb6cafe77933001eeae..f8076c26210356404b46a61e660ba7ee4617a51b 100644
--- a/examples/02631/students/week5/report1intro.py
+++ b/examples/02631/students/week5/report1intro.py
@@ -130,7 +130,7 @@ class FermentationRate(UTestCase):
 
 
 class Report1Flat(Report):
-    title = "Week 4: Looping"
+    title = "02531 week 5: Looping"
     questions = [(ClusterAnalysis, 10), (RemoveIncomplete, 10), (Bacteria, 10),  (FermentationRate, 10),]
     pack_imports = [looping]
 
diff --git a/examples/02631/students/week5/report1intro_grade.py b/examples/02631/students/week5/report1intro_grade.py
deleted file mode 100644
index b57b0fdbeef0c3b899a410dae5dde7b6fa050162..0000000000000000000000000000000000000000
--- a/examples/02631/students/week5/report1intro_grade.py
+++ /dev/null
@@ -1,3 +0,0 @@
-''' WARNING: Modifying, decompiling or otherwise tampering with this script, it's data or the resulting .token file will be investigated as a cheating attempt. '''
-import bz2, base64
-exec(bz2.decompress(base64.b64decode('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\ No newline at end of file
diff --git a/examples/02631/students/week5/unitgrade_data/Bacteria.pkl b/examples/02631/students/week5/unitgrade_data/Bacteria.pkl
index 158944a2063b2c7a5980afbebd1dfdd93bada6bb..703d7a9b626c810bf555748f8a5de1c5886575c6 100644
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diff --git a/examples/02631/students/week5/unitgrade_data/ClusterAnalysis.pkl b/examples/02631/students/week5/unitgrade_data/ClusterAnalysis.pkl
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diff --git a/examples/02631/students/week5/unitgrade_data/FermentationRate.pkl b/examples/02631/students/week5/unitgrade_data/FermentationRate.pkl
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diff --git a/examples/02631/students/week5/unitgrade_data/RemoveIncomplete.pkl b/examples/02631/students/week5/unitgrade_data/RemoveIncomplete.pkl
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diff --git a/examples/algorithms_and_datastructures.py b/examples/algorithms_and_datastructures.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/examples/autolab_example_py_upload/instructor/cs102_autolab/Report2_handin_16_of_16.token b/examples/autolab_example_py_upload/instructor/cs102_autolab/Report2_handin_16_of_16.token
deleted file mode 100644
index f046391c8732e92e92c7e7fb4a706c7a6fa559ec..0000000000000000000000000000000000000000
--- a/examples/autolab_example_py_upload/instructor/cs102_autolab/Report2_handin_16_of_16.token
+++ /dev/null
@@ -1,178 +0,0 @@
-# This file contains your results. Do not edit its content. Simply upload it as it is.
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----------------------------------------------------------------------- ..ooO0Ooo.. ----------------------------------------------------------------------
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\ No newline at end of file
diff --git a/examples/autolab_example_py_upload/instructor/cs102_autolab/Report2_handin_19_of_26.token b/examples/autolab_example_py_upload/instructor/cs102_autolab/Report2_handin_19_of_26.token
deleted file mode 100644
index 379766ca8efbf72b3380fa54fa7150d2ecd7839f..0000000000000000000000000000000000000000
--- a/examples/autolab_example_py_upload/instructor/cs102_autolab/Report2_handin_19_of_26.token
+++ /dev/null
@@ -1,327 +0,0 @@
-# This file contains your results. Do not edit its content. Simply upload it as it is.
----------------------------------------------------------------------- ..ooO0Ooo.. ----------------------------------------------------------------------
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----------------------------------------------------------------------- ..ooO0Ooo.. ----------------------------------------------------------------------
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\ No newline at end of file
diff --git a/examples/autolab_example_py_upload/instructor/cs102_autolab/Report2_handin_26_of_26.token b/examples/autolab_example_py_upload/instructor/cs102_autolab/Report2_handin_26_of_26.token
deleted file mode 100644
index 1d90ee18a59359a862d6aaafb545706ba2377464..0000000000000000000000000000000000000000
--- a/examples/autolab_example_py_upload/instructor/cs102_autolab/Report2_handin_26_of_26.token
+++ /dev/null
@@ -1,329 +0,0 @@
-# This file contains your results. Do not edit its content. Simply upload it as it is.
----------------------------------------------------------------------- ..ooO0Ooo.. ----------------------------------------------------------------------
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----------------------------------------------------------------------- ..ooO0Ooo.. ----------------------------------------------------------------------
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\ No newline at end of file
diff --git a/examples/autolab_example_py_upload/instructor/cs102_autolab/Report2_handin_26_of_26_v1.token b/examples/autolab_example_py_upload/instructor/cs102_autolab/Report2_handin_26_of_26_v1.token
new file mode 100644
index 0000000000000000000000000000000000000000..f22db828690c2bb4a863327a29629522dba15365
--- /dev/null
+++ b/examples/autolab_example_py_upload/instructor/cs102_autolab/Report2_handin_26_of_26_v1.token
@@ -0,0 +1,345 @@
+# This file contains your results. Do not edit its content. Simply upload it as it is.
+---------------------------------------------------------------------- ..ooO0Ooo.. ----------------------------------------------------------------------
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+---------------------------------------------------------------------- ..ooO0Ooo.. ----------------------------------------------------------------------
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\ No newline at end of file
diff --git a/examples/autolab_example_py_upload/instructor/cs102_autolab/cs105d.tar b/examples/autolab_example_py_upload/instructor/cs102_autolab/cs105d.tar
deleted file mode 100644
index 9077d9fb28ce6e63d85d22be6882956b87c2ba74..0000000000000000000000000000000000000000
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diff --git a/examples/autolab_example_py_upload/instructor/cs102_autolab/cs105e.tar b/examples/autolab_example_py_upload/instructor/cs102_autolab/cs105e.tar
deleted file mode 100644
index 8b7bd3cb81db9da0274f2bf22d0604179bae2ef8..0000000000000000000000000000000000000000
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diff --git a/examples/autolab_example_py_upload/instructor/cs102_autolab/cs105f.tar b/examples/autolab_example_py_upload/instructor/cs102_autolab/cs105f.tar
deleted file mode 100644
index f4f8000f1e5952c935ced0f56ca4244fb2b6126e..0000000000000000000000000000000000000000
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diff --git a/examples/autolab_example_py_upload/instructor/cs102_autolab/cs105g.tar b/examples/autolab_example_py_upload/instructor/cs102_autolab/cs105g.tar
deleted file mode 100644
index 0160c8b73f9c87102e43266bddf1e07f0c9be796..0000000000000000000000000000000000000000
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diff --git a/examples/autolab_example_py_upload/instructor/cs102_autolab/deploy.py b/examples/autolab_example_py_upload/instructor/cs102_autolab/deploy.py
deleted file mode 100644
index ce4fcd93ab73cf761dedd339da3ddfb6a75dabf6..0000000000000000000000000000000000000000
--- a/examples/autolab_example_py_upload/instructor/cs102_autolab/deploy.py
+++ /dev/null
@@ -1,9 +0,0 @@
-from cs102_autolab.report2_test import Report2
-from unitgrade_private.hidden_create_files import setup_grade_file_report
-from snipper.snip_dir import snip_dir
-
-if __name__ == "__main__":
-
-    setup_grade_file_report(Report2)
-    snip_dir("./", "../../students/cs102", clean_destination_dir=True, exclude=['*.token', 'deploy.py'])
-    pass
\ No newline at end of file
diff --git a/examples/autolab_example_py_upload/instructor/cs102_autolab/deploy_autolab.py b/examples/autolab_example_py_upload/instructor/cs102_autolab/deploy_autolab.py
index 63db95ee34954055fd71e136d22ddcc678bdf78c..7ee7087f857a776f15f3785ccc5aef925f5e4f5d 100644
--- a/examples/autolab_example_py_upload/instructor/cs102_autolab/deploy_autolab.py
+++ b/examples/autolab_example_py_upload/instructor/cs102_autolab/deploy_autolab.py
@@ -10,6 +10,7 @@ if __name__ == "__main__":
     from unitgrade import version
     print("version", version.__version__)
     # Set up the instructor _grade script and all files needed for the tests.
+
     setup_grade_file_report(Report2, with_coverage=False, bzip=True)
     snip_dir("./", "../../students/cs102_autolab", clean_destination_dir=True, exclude=['*.token', 'deploy*.py', '*_grade.py', 'tmp', '*.tar'])
 
@@ -26,13 +27,11 @@ if __name__ == "__main__":
     from report2_test import Report2
     # INSTRUCTOR_GRADE_FILE =
     output_tar = new_deploy_assignment("cs105h",  # Autolab name of assignment (and name of .tar file)
-                                   INSTRUCTOR_REPORT_CLASS=Report2,
                                    INSTRUCTOR_BASE=instructor_base,
                                    INSTRUCTOR_GRADE_FILE=f"{instructor_base}/report2_test_grade.py",
                                    STUDENT_BASE=student_base,
                                    STUDENT_GRADE_FILE=f"{instructor_base}/report2_test.py",
                                    autograde_image_tag=autograde_image,
-                                   student_should_upload_token=False,
                                    homework_file="homework1.py") #!s
 
     # What can you do? Get a report class from the .token file?
diff --git a/examples/autolab_example_py_upload/instructor/cs102_autolab/handout.tar b/examples/autolab_example_py_upload/instructor/cs102_autolab/handout.tar
deleted file mode 100644
index 75f2f0f49ebf7f1df224ef7628697989ad3ab581..0000000000000000000000000000000000000000
Binary files a/examples/autolab_example_py_upload/instructor/cs102_autolab/handout.tar and /dev/null differ
diff --git a/examples/autolab_example_py_upload/instructor/cs102_autolab/homework1.py b/examples/autolab_example_py_upload/instructor/cs102_autolab/homework1.py
index c774405dc693d104ed9b1f48df5554a12e3c9ae3..839b6a5fa5eeef8f8093d7d8827bf0f552c6e0a4 100644
--- a/examples/autolab_example_py_upload/instructor/cs102_autolab/homework1.py
+++ b/examples/autolab_example_py_upload/instructor/cs102_autolab/homework1.py
@@ -10,7 +10,6 @@ def add(a,b): #!f
     > add(a,b) = a+b """
     return a+b*2
 
-if __name__ == "__main__":
-    # Example usage:
+if __name__ == "__main__": # Example usage:
     print(f"Your result of 2 + 2 = {add(2,2)}")
     print(f"Reversing a small list", reverse_list([2,3,5,7])) #!s
diff --git a/examples/autolab_example_py_upload/instructor/cs102_autolab/report2_test.py b/examples/autolab_example_py_upload/instructor/cs102_autolab/report2_test.py
index 14d21fa679ac5f1d6cb5c5c6f4f0259c2737e449..fb2bcd6d77c75419517f3306dc40a4d275d067f9 100644
--- a/examples/autolab_example_py_upload/instructor/cs102_autolab/report2_test.py
+++ b/examples/autolab_example_py_upload/instructor/cs102_autolab/report2_test.py
@@ -59,10 +59,9 @@ class Question2(UTestCase): #!s=c
         self.assertEqualC(reverse_list(ls))                 # This will actually test the students code.
         return "Buy world!"                                 # This value will be stored in the .token file  #!s=c
 
-
 class Report2(Report):
-    version = 1.0
-    url = "https://gitlab.compute.dtu.dk/tuhe/unitgrade_private/-/tree/master/examples/autolab_example_py_upload/instructor/cs102_autolab"
+    version = 1
+    # url = "https://gitlab.compute.dtu.dk/tuhe/unitgrade_private/-/tree/master/examples/autolab_example_py_upload/instructor/cs102_autolab"
     title = "CS 106a"
     questions = [(Week1, 10), (Week1Titles, 6), (Question2, 10)]
     pack_imports = [homework1]
diff --git a/examples/autolab_example_py_upload/instructor/cs102_autolab/report2_test_grade.py b/examples/autolab_example_py_upload/instructor/cs102_autolab/report2_test_grade.py
index d35d464081aea7fee536c44ebc291e882d67be65..9a316756a4459a4363bdc0d0e5f7a63270fdee40 100644
--- a/examples/autolab_example_py_upload/instructor/cs102_autolab/report2_test_grade.py
+++ b/examples/autolab_example_py_upload/instructor/cs102_autolab/report2_test_grade.py
@@ -1,4 +1,4 @@
 # report2_test.py
 ''' WARNING: Modifying, decompiling or otherwise tampering with this script, it's data or the resulting .token file will be investigated as a cheating attempt. '''
 import bz2, base64
-exec(bz2.decompress(base64.b64decode('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')))
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')))
\ No newline at end of file
diff --git a/examples/autolab_example_py_upload/instructor/cs102_autolab/unitgrade_data/.snapshot b/examples/autolab_example_py_upload/instructor/cs102_autolab/unitgrade_data/.snapshot
new file mode 100644
index 0000000000000000000000000000000000000000..75f6e4a21c1f7e9b8e0c3786b1d3913d0c880975
--- /dev/null
+++ b/examples/autolab_example_py_upload/instructor/cs102_autolab/unitgrade_data/.snapshot
@@ -0,0 +1 @@
+1653778007.6430795
\ No newline at end of file
diff --git a/examples/autolab_example_py_upload/instructor/cs102_autolab/unitgrade_data/Question2.pkl b/examples/autolab_example_py_upload/instructor/cs102_autolab/unitgrade_data/Question2.pkl
index bcfd4d4c0ec3b7fa90b9d5945812dfd05623770b..ef4518dbefc6c385af36259c48b5dbab65a6b247 100644
Binary files a/examples/autolab_example_py_upload/instructor/cs102_autolab/unitgrade_data/Question2.pkl and b/examples/autolab_example_py_upload/instructor/cs102_autolab/unitgrade_data/Question2.pkl differ
diff --git a/examples/autolab_example_py_upload/instructor/cs102_autolab/unitgrade_data/Week1.pkl b/examples/autolab_example_py_upload/instructor/cs102_autolab/unitgrade_data/Week1.pkl
index d7e897ace5ae661bbe6e4d3d29bff2c3dbbea55a..83f5d359d9a96b2c6fe78b826134cde5460b440a 100644
Binary files a/examples/autolab_example_py_upload/instructor/cs102_autolab/unitgrade_data/Week1.pkl and b/examples/autolab_example_py_upload/instructor/cs102_autolab/unitgrade_data/Week1.pkl differ
diff --git a/examples/autolab_example_py_upload/instructor/cs102_autolab/unitgrade_data/Week1Titles.pkl b/examples/autolab_example_py_upload/instructor/cs102_autolab/unitgrade_data/Week1Titles.pkl
index 3f846dd3e070d58ba594674b8ac74ebd84837781..debd5ec970dec23673b35159817678d8685a0df9 100644
Binary files a/examples/autolab_example_py_upload/instructor/cs102_autolab/unitgrade_data/Week1Titles.pkl and b/examples/autolab_example_py_upload/instructor/cs102_autolab/unitgrade_data/Week1Titles.pkl differ
diff --git a/examples/autolab_example_py_upload/students/cs102_autolab/report2_test.py b/examples/autolab_example_py_upload/students/cs102_autolab/report2_test.py
index 039ade86a6d8886ed0a17f87637047907a4f3fbb..4b81059683226a860c725d8653c81e058cac3d5f 100644
--- a/examples/autolab_example_py_upload/students/cs102_autolab/report2_test.py
+++ b/examples/autolab_example_py_upload/students/cs102_autolab/report2_test.py
@@ -61,6 +61,8 @@ class Question2(UTestCase):
 
 
 class Report2(Report):
+    version = 1
+    # url = "https://gitlab.compute.dtu.dk/tuhe/unitgrade_private/-/tree/master/examples/autolab_example_py_upload/instructor/cs102_autolab"
     title = "CS 106a"
     questions = [(Week1, 10), (Week1Titles, 6), (Question2, 10)]
     pack_imports = [homework1]
diff --git a/examples/autolab_example_py_upload/students/cs102_autolab/unitgrade_data/.snapshot b/examples/autolab_example_py_upload/students/cs102_autolab/unitgrade_data/.snapshot
new file mode 100644
index 0000000000000000000000000000000000000000..75f6e4a21c1f7e9b8e0c3786b1d3913d0c880975
--- /dev/null
+++ b/examples/autolab_example_py_upload/students/cs102_autolab/unitgrade_data/.snapshot
@@ -0,0 +1 @@
+1653778007.6430795
\ No newline at end of file
diff --git a/examples/autolab_example_py_upload/students/cs102_autolab/unitgrade_data/Question2.pkl b/examples/autolab_example_py_upload/students/cs102_autolab/unitgrade_data/Question2.pkl
index bcfd4d4c0ec3b7fa90b9d5945812dfd05623770b..ef4518dbefc6c385af36259c48b5dbab65a6b247 100644
Binary files a/examples/autolab_example_py_upload/students/cs102_autolab/unitgrade_data/Question2.pkl and b/examples/autolab_example_py_upload/students/cs102_autolab/unitgrade_data/Question2.pkl differ
diff --git a/examples/autolab_example_py_upload/students/cs102_autolab/unitgrade_data/Week1.pkl b/examples/autolab_example_py_upload/students/cs102_autolab/unitgrade_data/Week1.pkl
index d7e897ace5ae661bbe6e4d3d29bff2c3dbbea55a..83f5d359d9a96b2c6fe78b826134cde5460b440a 100644
Binary files a/examples/autolab_example_py_upload/students/cs102_autolab/unitgrade_data/Week1.pkl and b/examples/autolab_example_py_upload/students/cs102_autolab/unitgrade_data/Week1.pkl differ
diff --git a/examples/autolab_example_py_upload/students/cs102_autolab/unitgrade_data/Week1Titles.pkl b/examples/autolab_example_py_upload/students/cs102_autolab/unitgrade_data/Week1Titles.pkl
index 3f846dd3e070d58ba594674b8ac74ebd84837781..debd5ec970dec23673b35159817678d8685a0df9 100644
Binary files a/examples/autolab_example_py_upload/students/cs102_autolab/unitgrade_data/Week1Titles.pkl and b/examples/autolab_example_py_upload/students/cs102_autolab/unitgrade_data/Week1Titles.pkl differ
diff --git a/examples/autolab_example/autolab_example.py b/examples/autolab_token_upload/autolab_example.py
similarity index 85%
rename from examples/autolab_example/autolab_example.py
rename to examples/autolab_token_upload/autolab_example.py
index 738abe66bddd5a9e87c2c571c959fb1a560b2cb2..a5783b52e41f4653102461ba8d8e22bd606068f3 100644
--- a/examples/autolab_example/autolab_example.py
+++ b/examples/autolab_token_upload/autolab_example.py
@@ -12,8 +12,8 @@ if __name__ == "__main__":
 
     from unitgrade_private import load_token
     # data, _ = load_token("../example_framework/instructor/cs102/Report2_handin_18_of_18.token")
-    data, _ = load_token("../example_framework/students/cs102/Report2_handin_3_of_16.token")
-    format_autolab_json(data, indent=2)
+    # data, _ = load_token("../example_framework/students/cs102/Report2_handin_3_of_16.token")
+    # format_autolab_json(data, indent=2)
 
     download_docker_images("./docker")
     autograde_image = 'tango_python_tue'
@@ -30,4 +30,4 @@ if __name__ == "__main__":
     from unitgrade_private.docker_helpers import compile_docker_image
 
     compile_docker_image(Dockerfile=dockerfile, tag=autograde_image)  # Make sure docker grading image is up-to-date.
-    compile_docker_image(Dockerfile=dockerfile, tag=autograde_image)  # Make sure docker grading image is up-to-date.
\ No newline at end of file
+    # compile_docker_image(Dockerfile=dockerfile, tag=autograde_image)  # Make sure docker grading image is up-to-date.
\ No newline at end of file
diff --git a/examples/autolab_example/cs101.tar b/examples/autolab_token_upload/cs101.tar
similarity index 100%
rename from examples/autolab_example/cs101.tar
rename to examples/autolab_token_upload/cs101.tar
diff --git a/examples/autolab_example/cs102.tar b/examples/autolab_token_upload/cs102.tar
similarity index 100%
rename from examples/autolab_example/cs102.tar
rename to examples/autolab_token_upload/cs102.tar
diff --git a/examples/autolab_example/cs103.tar b/examples/autolab_token_upload/cs103.tar
similarity index 100%
rename from examples/autolab_example/cs103.tar
rename to examples/autolab_token_upload/cs103.tar
diff --git a/examples/autolab_example/deploy_autolab.py b/examples/autolab_token_upload/deploy_autolab.py
similarity index 100%
rename from examples/autolab_example/deploy_autolab.py
rename to examples/autolab_token_upload/deploy_autolab.py
diff --git a/examples/autolab_example/docker/docker_tango_python/Dockerfile b/examples/autolab_token_upload/docker/docker_tango_python/Dockerfile
similarity index 100%
rename from examples/autolab_example/docker/docker_tango_python/Dockerfile
rename to examples/autolab_token_upload/docker/docker_tango_python/Dockerfile
diff --git a/examples/autolab_example/docker/docker_tango_python/requirements.txt b/examples/autolab_token_upload/docker/docker_tango_python/requirements.txt
similarity index 100%
rename from examples/autolab_example/docker/docker_tango_python/requirements.txt
rename to examples/autolab_token_upload/docker/docker_tango_python/requirements.txt
diff --git a/examples/autolab_example/docker/unitgrade-docker/Dockerfile b/examples/autolab_token_upload/docker/unitgrade-docker/Dockerfile
similarity index 100%
rename from examples/autolab_example/docker/unitgrade-docker/Dockerfile
rename to examples/autolab_token_upload/docker/unitgrade-docker/Dockerfile
diff --git a/examples/autolab_example/docker/unitgrade-docker/requirements.txt b/examples/autolab_token_upload/docker/unitgrade-docker/requirements.txt
similarity index 100%
rename from examples/autolab_example/docker/unitgrade-docker/requirements.txt
rename to examples/autolab_token_upload/docker/unitgrade-docker/requirements.txt
diff --git a/examples/autolab_example/readme.md b/examples/autolab_token_upload/readme.md
similarity index 100%
rename from examples/autolab_example/readme.md
rename to examples/autolab_token_upload/readme.md
diff --git a/examples/autolab_example/tmp/cs101/Makefile b/examples/autolab_token_upload/tmp/cs101/Makefile
similarity index 100%
rename from examples/autolab_example/tmp/cs101/Makefile
rename to examples/autolab_token_upload/tmp/cs101/Makefile
diff --git a/examples/autolab_example/tmp/cs101/autograde-Makefile b/examples/autolab_token_upload/tmp/cs101/autograde-Makefile
similarity index 100%
rename from examples/autolab_example/tmp/cs101/autograde-Makefile
rename to examples/autolab_token_upload/tmp/cs101/autograde-Makefile
diff --git a/examples/autolab_example/tmp/cs101/cs101.rb b/examples/autolab_token_upload/tmp/cs101/cs101.rb
similarity index 100%
rename from examples/autolab_example/tmp/cs101/cs101.rb
rename to examples/autolab_token_upload/tmp/cs101/cs101.rb
diff --git a/examples/autolab_example/tmp/cs101/cs101.yml b/examples/autolab_token_upload/tmp/cs101/cs101.yml
similarity index 100%
rename from examples/autolab_example/tmp/cs101/cs101.yml
rename to examples/autolab_token_upload/tmp/cs101/cs101.yml
diff --git a/examples/autolab_example/tmp/cs101/src/Makefile b/examples/autolab_token_upload/tmp/cs101/src/Makefile
similarity index 100%
rename from examples/autolab_example/tmp/cs101/src/Makefile
rename to examples/autolab_token_upload/tmp/cs101/src/Makefile
diff --git a/examples/autolab_example/tmp/cs101/src/Makefile-handout b/examples/autolab_token_upload/tmp/cs101/src/Makefile-handout
similarity index 100%
rename from examples/autolab_example/tmp/cs101/src/Makefile-handout
rename to examples/autolab_token_upload/tmp/cs101/src/Makefile-handout
diff --git a/examples/autolab_example/tmp/cs101/src/README b/examples/autolab_token_upload/tmp/cs101/src/README
similarity index 100%
rename from examples/autolab_example/tmp/cs101/src/README
rename to examples/autolab_token_upload/tmp/cs101/src/README
diff --git a/examples/autolab_example/tmp/cs101/src/README-handout b/examples/autolab_token_upload/tmp/cs101/src/README-handout
similarity index 100%
rename from examples/autolab_example/tmp/cs101/src/README-handout
rename to examples/autolab_token_upload/tmp/cs101/src/README-handout
diff --git a/examples/autolab_example/tmp/cs101/src/Report1_handin.token b/examples/autolab_token_upload/tmp/cs101/src/Report1_handin.token
similarity index 100%
rename from examples/autolab_example/tmp/cs101/src/Report1_handin.token
rename to examples/autolab_token_upload/tmp/cs101/src/Report1_handin.token
diff --git a/examples/autolab_example/tmp/cs101/src/docker_helpers.py b/examples/autolab_token_upload/tmp/cs101/src/docker_helpers.py
similarity index 100%
rename from examples/autolab_example/tmp/cs101/src/docker_helpers.py
rename to examples/autolab_token_upload/tmp/cs101/src/docker_helpers.py
diff --git a/examples/autolab_example/tmp/cs101/src/driver.sh b/examples/autolab_token_upload/tmp/cs101/src/driver.sh
similarity index 100%
rename from examples/autolab_example/tmp/cs101/src/driver.sh
rename to examples/autolab_token_upload/tmp/cs101/src/driver.sh
diff --git a/examples/autolab_example/tmp/cs101/src/driver.sh-handout b/examples/autolab_token_upload/tmp/cs101/src/driver.sh-handout
similarity index 100%
rename from examples/autolab_example/tmp/cs101/src/driver.sh-handout
rename to examples/autolab_token_upload/tmp/cs101/src/driver.sh-handout
diff --git a/examples/autolab_example/tmp/cs101/src/driver_python.py b/examples/autolab_token_upload/tmp/cs101/src/driver_python.py
similarity index 100%
rename from examples/autolab_example/tmp/cs101/src/driver_python.py
rename to examples/autolab_token_upload/tmp/cs101/src/driver_python.py
diff --git a/examples/autolab_example/tmp/cs101/src/driver_python.py-handout b/examples/autolab_token_upload/tmp/cs101/src/driver_python.py-handout
similarity index 100%
rename from examples/autolab_example/tmp/cs101/src/driver_python.py-handout
rename to examples/autolab_token_upload/tmp/cs101/src/driver_python.py-handout
diff --git a/examples/autolab_example/tmp/cs101/src/report1_grade.py b/examples/autolab_token_upload/tmp/cs101/src/report1_grade.py
similarity index 100%
rename from examples/autolab_example/tmp/cs101/src/report1_grade.py
rename to examples/autolab_token_upload/tmp/cs101/src/report1_grade.py
diff --git a/examples/autolab_example/tmp/cs101/src/student_sources.zip b/examples/autolab_token_upload/tmp/cs101/src/student_sources.zip
similarity index 100%
rename from examples/autolab_example/tmp/cs101/src/student_sources.zip
rename to examples/autolab_token_upload/tmp/cs101/src/student_sources.zip
diff --git a/examples/autolab_example/tmp/cs102/Makefile b/examples/autolab_token_upload/tmp/cs102/Makefile
similarity index 100%
rename from examples/autolab_example/tmp/cs102/Makefile
rename to examples/autolab_token_upload/tmp/cs102/Makefile
diff --git a/examples/autolab_example/tmp/cs102/autograde-Makefile b/examples/autolab_token_upload/tmp/cs102/autograde-Makefile
similarity index 100%
rename from examples/autolab_example/tmp/cs102/autograde-Makefile
rename to examples/autolab_token_upload/tmp/cs102/autograde-Makefile
diff --git a/examples/autolab_example/tmp/cs102/autograde.tar b/examples/autolab_token_upload/tmp/cs102/autograde.tar
similarity index 100%
rename from examples/autolab_example/tmp/cs102/autograde.tar
rename to examples/autolab_token_upload/tmp/cs102/autograde.tar
diff --git a/examples/autolab_example/tmp/cs102/cs102-handout.tar b/examples/autolab_token_upload/tmp/cs102/cs102-handout.tar
similarity index 100%
rename from examples/autolab_example/tmp/cs102/cs102-handout.tar
rename to examples/autolab_token_upload/tmp/cs102/cs102-handout.tar
diff --git a/examples/autolab_example/tmp/cs102/cs102-handout/Makefile b/examples/autolab_token_upload/tmp/cs102/cs102-handout/Makefile
similarity index 100%
rename from examples/autolab_example/tmp/cs102/cs102-handout/Makefile
rename to examples/autolab_token_upload/tmp/cs102/cs102-handout/Makefile
diff --git a/examples/autolab_example/tmp/cs102/cs102-handout/README b/examples/autolab_token_upload/tmp/cs102/cs102-handout/README
similarity index 100%
rename from examples/autolab_example/tmp/cs102/cs102-handout/README
rename to examples/autolab_token_upload/tmp/cs102/cs102-handout/README
diff --git a/examples/autolab_example/tmp/cs102/cs102-handout/Report2_handin.token b/examples/autolab_token_upload/tmp/cs102/cs102-handout/Report2_handin.token
similarity index 100%
rename from examples/autolab_example/tmp/cs102/cs102-handout/Report2_handin.token
rename to examples/autolab_token_upload/tmp/cs102/cs102-handout/Report2_handin.token
diff --git a/examples/autolab_example/tmp/cs102/cs102-handout/docker_helpers.py b/examples/autolab_token_upload/tmp/cs102/cs102-handout/docker_helpers.py
similarity index 100%
rename from examples/autolab_example/tmp/cs102/cs102-handout/docker_helpers.py
rename to examples/autolab_token_upload/tmp/cs102/cs102-handout/docker_helpers.py
diff --git a/examples/autolab_example/tmp/cs102/cs102-handout/driver_python.py b/examples/autolab_token_upload/tmp/cs102/cs102-handout/driver_python.py
similarity index 100%
rename from examples/autolab_example/tmp/cs102/cs102-handout/driver_python.py
rename to examples/autolab_token_upload/tmp/cs102/cs102-handout/driver_python.py
diff --git a/examples/autolab_example/tmp/cs102/cs102-handout/report2_grade.py b/examples/autolab_token_upload/tmp/cs102/cs102-handout/report2_grade.py
similarity index 100%
rename from examples/autolab_example/tmp/cs102/cs102-handout/report2_grade.py
rename to examples/autolab_token_upload/tmp/cs102/cs102-handout/report2_grade.py
diff --git a/examples/autolab_example/tmp/cs102/cs102-handout/student_sources.zip b/examples/autolab_token_upload/tmp/cs102/cs102-handout/student_sources.zip
similarity index 100%
rename from examples/autolab_example/tmp/cs102/cs102-handout/student_sources.zip
rename to examples/autolab_token_upload/tmp/cs102/cs102-handout/student_sources.zip
diff --git a/examples/autolab_example/tmp/cs102/cs102.rb b/examples/autolab_token_upload/tmp/cs102/cs102.rb
similarity index 100%
rename from examples/autolab_example/tmp/cs102/cs102.rb
rename to examples/autolab_token_upload/tmp/cs102/cs102.rb
diff --git a/examples/autolab_example/tmp/cs102/cs102.yml b/examples/autolab_token_upload/tmp/cs102/cs102.yml
similarity index 100%
rename from examples/autolab_example/tmp/cs102/cs102.yml
rename to examples/autolab_token_upload/tmp/cs102/cs102.yml
diff --git a/examples/autolab_example/tmp/cs102/src/Makefile b/examples/autolab_token_upload/tmp/cs102/src/Makefile
similarity index 100%
rename from examples/autolab_example/tmp/cs102/src/Makefile
rename to examples/autolab_token_upload/tmp/cs102/src/Makefile
diff --git a/examples/autolab_example/tmp/cs102/src/Makefile-handout b/examples/autolab_token_upload/tmp/cs102/src/Makefile-handout
similarity index 100%
rename from examples/autolab_example/tmp/cs102/src/Makefile-handout
rename to examples/autolab_token_upload/tmp/cs102/src/Makefile-handout
diff --git a/examples/autolab_example/tmp/cs102/src/README b/examples/autolab_token_upload/tmp/cs102/src/README
similarity index 100%
rename from examples/autolab_example/tmp/cs102/src/README
rename to examples/autolab_token_upload/tmp/cs102/src/README
diff --git a/examples/autolab_example/tmp/cs102/src/README-handout b/examples/autolab_token_upload/tmp/cs102/src/README-handout
similarity index 100%
rename from examples/autolab_example/tmp/cs102/src/README-handout
rename to examples/autolab_token_upload/tmp/cs102/src/README-handout
diff --git a/examples/autolab_example/tmp/cs102/src/Report2_handin.token b/examples/autolab_token_upload/tmp/cs102/src/Report2_handin.token
similarity index 100%
rename from examples/autolab_example/tmp/cs102/src/Report2_handin.token
rename to examples/autolab_token_upload/tmp/cs102/src/Report2_handin.token
diff --git a/examples/autolab_example/tmp/cs102/src/docker_helpers.py b/examples/autolab_token_upload/tmp/cs102/src/docker_helpers.py
similarity index 100%
rename from examples/autolab_example/tmp/cs102/src/docker_helpers.py
rename to examples/autolab_token_upload/tmp/cs102/src/docker_helpers.py
diff --git a/examples/autolab_example/tmp/cs102/src/driver.sh b/examples/autolab_token_upload/tmp/cs102/src/driver.sh
similarity index 100%
rename from examples/autolab_example/tmp/cs102/src/driver.sh
rename to examples/autolab_token_upload/tmp/cs102/src/driver.sh
diff --git a/examples/autolab_example/tmp/cs102/src/driver.sh-handout b/examples/autolab_token_upload/tmp/cs102/src/driver.sh-handout
similarity index 100%
rename from examples/autolab_example/tmp/cs102/src/driver.sh-handout
rename to examples/autolab_token_upload/tmp/cs102/src/driver.sh-handout
diff --git a/examples/autolab_example/tmp/cs102/src/driver_python.py b/examples/autolab_token_upload/tmp/cs102/src/driver_python.py
similarity index 100%
rename from examples/autolab_example/tmp/cs102/src/driver_python.py
rename to examples/autolab_token_upload/tmp/cs102/src/driver_python.py
diff --git a/examples/autolab_example/tmp/cs102/src/driver_python.py-handout b/examples/autolab_token_upload/tmp/cs102/src/driver_python.py-handout
similarity index 100%
rename from examples/autolab_example/tmp/cs102/src/driver_python.py-handout
rename to examples/autolab_token_upload/tmp/cs102/src/driver_python.py-handout
diff --git a/examples/autolab_example/tmp/cs102/src/report2_grade.py b/examples/autolab_token_upload/tmp/cs102/src/report2_grade.py
similarity index 100%
rename from examples/autolab_example/tmp/cs102/src/report2_grade.py
rename to examples/autolab_token_upload/tmp/cs102/src/report2_grade.py
diff --git a/examples/autolab_example/tmp/cs102/src/student_sources.zip b/examples/autolab_token_upload/tmp/cs102/src/student_sources.zip
similarity index 100%
rename from examples/autolab_example/tmp/cs102/src/student_sources.zip
rename to examples/autolab_token_upload/tmp/cs102/src/student_sources.zip
diff --git a/examples/autolab_example/tmp/cs103/Makefile b/examples/autolab_token_upload/tmp/cs103/Makefile
similarity index 100%
rename from examples/autolab_example/tmp/cs103/Makefile
rename to examples/autolab_token_upload/tmp/cs103/Makefile
diff --git a/examples/autolab_example/tmp/cs103/autograde-Makefile b/examples/autolab_token_upload/tmp/cs103/autograde-Makefile
similarity index 100%
rename from examples/autolab_example/tmp/cs103/autograde-Makefile
rename to examples/autolab_token_upload/tmp/cs103/autograde-Makefile
diff --git a/examples/autolab_example/tmp/cs103/cs103.rb b/examples/autolab_token_upload/tmp/cs103/cs103.rb
similarity index 100%
rename from examples/autolab_example/tmp/cs103/cs103.rb
rename to examples/autolab_token_upload/tmp/cs103/cs103.rb
diff --git a/examples/autolab_example/tmp/cs103/cs103.yml b/examples/autolab_token_upload/tmp/cs103/cs103.yml
similarity index 100%
rename from examples/autolab_example/tmp/cs103/cs103.yml
rename to examples/autolab_token_upload/tmp/cs103/cs103.yml
diff --git a/examples/autolab_example/tmp/cs103/src/Makefile b/examples/autolab_token_upload/tmp/cs103/src/Makefile
similarity index 100%
rename from examples/autolab_example/tmp/cs103/src/Makefile
rename to examples/autolab_token_upload/tmp/cs103/src/Makefile
diff --git a/examples/autolab_example/tmp/cs103/src/Makefile-handout b/examples/autolab_token_upload/tmp/cs103/src/Makefile-handout
similarity index 100%
rename from examples/autolab_example/tmp/cs103/src/Makefile-handout
rename to examples/autolab_token_upload/tmp/cs103/src/Makefile-handout
diff --git a/examples/autolab_example/tmp/cs103/src/README b/examples/autolab_token_upload/tmp/cs103/src/README
similarity index 100%
rename from examples/autolab_example/tmp/cs103/src/README
rename to examples/autolab_token_upload/tmp/cs103/src/README
diff --git a/examples/autolab_example/tmp/cs103/src/README-handout b/examples/autolab_token_upload/tmp/cs103/src/README-handout
similarity index 100%
rename from examples/autolab_example/tmp/cs103/src/README-handout
rename to examples/autolab_token_upload/tmp/cs103/src/README-handout
diff --git a/examples/autolab_example/tmp/cs103/src/Report3_handin.token b/examples/autolab_token_upload/tmp/cs103/src/Report3_handin.token
similarity index 100%
rename from examples/autolab_example/tmp/cs103/src/Report3_handin.token
rename to examples/autolab_token_upload/tmp/cs103/src/Report3_handin.token
diff --git a/examples/autolab_example/tmp/cs103/src/docker_helpers.py b/examples/autolab_token_upload/tmp/cs103/src/docker_helpers.py
similarity index 100%
rename from examples/autolab_example/tmp/cs103/src/docker_helpers.py
rename to examples/autolab_token_upload/tmp/cs103/src/docker_helpers.py
diff --git a/examples/autolab_example/tmp/cs103/src/driver.sh b/examples/autolab_token_upload/tmp/cs103/src/driver.sh
similarity index 100%
rename from examples/autolab_example/tmp/cs103/src/driver.sh
rename to examples/autolab_token_upload/tmp/cs103/src/driver.sh
diff --git a/examples/autolab_example/tmp/cs103/src/driver.sh-handout b/examples/autolab_token_upload/tmp/cs103/src/driver.sh-handout
similarity index 100%
rename from examples/autolab_example/tmp/cs103/src/driver.sh-handout
rename to examples/autolab_token_upload/tmp/cs103/src/driver.sh-handout
diff --git a/examples/autolab_example/tmp/cs103/src/driver_python.py b/examples/autolab_token_upload/tmp/cs103/src/driver_python.py
similarity index 100%
rename from examples/autolab_example/tmp/cs103/src/driver_python.py
rename to examples/autolab_token_upload/tmp/cs103/src/driver_python.py
diff --git a/examples/autolab_example/tmp/cs103/src/driver_python.py-handout b/examples/autolab_token_upload/tmp/cs103/src/driver_python.py-handout
similarity index 100%
rename from examples/autolab_example/tmp/cs103/src/driver_python.py-handout
rename to examples/autolab_token_upload/tmp/cs103/src/driver_python.py-handout
diff --git a/examples/autolab_example/tmp/cs103/src/report3_complete_grade.py b/examples/autolab_token_upload/tmp/cs103/src/report3_complete_grade.py
similarity index 100%
rename from examples/autolab_example/tmp/cs103/src/report3_complete_grade.py
rename to examples/autolab_token_upload/tmp/cs103/src/report3_complete_grade.py
diff --git a/examples/autolab_example/tmp/cs103/src/student_sources.zip b/examples/autolab_token_upload/tmp/cs103/src/student_sources.zip
similarity index 100%
rename from examples/autolab_example/tmp/cs103/src/student_sources.zip
rename to examples/autolab_token_upload/tmp/cs103/src/student_sources.zip
diff --git a/examples/example_autolab_deploy/autolab_courses.py b/examples/example_autolab_deploy/autolab_courses.py
new file mode 100644
index 0000000000000000000000000000000000000000..b73df36951980c5b4445bc74d4361409d8a1b3c7
--- /dev/null
+++ b/examples/example_autolab_deploy/autolab_courses.py
@@ -0,0 +1,46 @@
+from unitgrade_private.autolab.autolab import new_deploy_assignment
+from unitgrade_private.docker_helpers import download_docker_images
+from unitgrade_private.docker_helpers import compile_docker_image
+
+if __name__ == "__main__":
+    ## Step 1. Deploy the report file.
+    # from report2_test import Report2
+    from report1intro import Report1Flat
+    from stones_tests import StoneReport
+    from unitgrade_private.hidden_create_files import setup_grade_file_report
+    from snipper.snip_dir import snip_dir
+    from unitgrade import version
+    # print("version", version.__version__)
+    # Set up the instructor _grade script and all files needed for the tests.
+
+    setup_grade_file_report(Report1Flat, with_coverage=False, bzip=True)
+    setup_grade_file_report(StoneReport, with_coverage=False, bzip=True)
+    snip_dir("../02105/instructor/week2", "../02105/students/week2", clean_destination_dir=True, exclude=['*.token', 'deploy*.py', '*_grade.py', 'tmp', '*.tar'])
+    snip_dir("../02631/instructor/week5", "../02631/students/week5", clean_destination_dir=True, exclude=['*.token', 'deploy*.py', '*_grade.py', 'tmp', '*.tar'])
+
+    # Step 1: Download and compile docker grading image. You only need to do this once.  #!s=a
+    download_docker_images("./docker") # Download docker images from gitlab (only do this once).
+    dockerfile = f"./docker/docker_tango_python/Dockerfile"
+    autograde_image = 'tango_python_tue2'  # Tag given to the image in case you have multiple images.
+    compile_docker_image(Dockerfile=dockerfile, tag=autograde_image, no_cache=False)  # Compile docker image. #!s
+
+    # Step 2: Create the cs102.tar file from the grade scripts. #!s=b
+    instructor_base = f"../02105/instructor/week2"
+    student_base = "../02631/instructor/week5"
+
+    from report2_test import Report2
+    # INSTRUCTOR_GRADE_FILE =
+    description = """ Hand in the file 'stones.py'. You can find the full example, including solution, here <a href="https://gitlab.compute.dtu.dk/tuhe/unitgrade_private/-/tree/master/examples/02631/instructor/week5">here</a>"""
+    output_tar = new_deploy_assignment("02105week2",  # Autolab name of assignment (and name of .tar file)
+                                   INSTRUCTOR_BASE=instructor_base,
+                                   INSTRUCTOR_GRADE_FILE=f"{instructor_base}/stones_tests_grade.py",
+                                   STUDENT_BASE=student_base,
+                                   autograde_image_tag=autograde_image,
+                                   homework_file="stones.py",
+                                   description=description)
+
+
+    # STUDENT_GRADE_FILE=f"{instructor_base}/stones_tests_grade.py",
+    # What can you do? Get a report class from the .token file?
+
+
diff --git a/examples/example_autolab_deploy/docker/docker_tango_python/Dockerfile b/examples/example_autolab_deploy/docker/docker_tango_python/Dockerfile
new file mode 100644
index 0000000000000000000000000000000000000000..e081c74465e0b3f3a0e933f16f5a1f180c8740c3
--- /dev/null
+++ b/examples/example_autolab_deploy/docker/docker_tango_python/Dockerfile
@@ -0,0 +1,40 @@
+# syntax=docker/dockerfile:1
+
+FROM python:3.8-slim-buster
+MAINTAINER Autolab Team <autolab-dev@andrew.cmu.edu>
+
+RUN apt-get update && apt-get install -y \
+  build-essential \
+  gcc \
+  git \
+  make \
+  sudo \
+  python \
+  procps \
+  && rm -rf /var/lib/apt/lists/*
+
+# Install autodriver
+WORKDIR /home
+RUN useradd autolab
+RUN useradd autograde
+RUN mkdir autolab autograde output
+RUN chown autolab:autolab autolab
+RUN chown autolab:autolab output
+RUN chown autograde:autograde autograde
+RUN git clone --depth 1 https://github.com/autolab/Tango.git
+WORKDIR Tango/autodriver
+RUN make clean && make
+RUN cp autodriver /usr/bin/autodriver
+RUN chmod +s /usr/bin/autodriver
+
+# Do the python stuff.
+COPY requirements.txt requirements.txt
+RUN pip3 install -r requirements.txt
+
+# Clean up
+WORKDIR /home
+RUN apt-get remove -y git && apt-get -y autoremove && rm -rf Tango/
+
+# Check installation
+RUN ls -l /home
+RUN which autodriver
diff --git a/examples/example_autolab_deploy/docker/docker_tango_python/requirements.txt b/examples/example_autolab_deploy/docker/docker_tango_python/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..084c2114d646cf6216e9083aae4b72784c4206e5
--- /dev/null
+++ b/examples/example_autolab_deploy/docker/docker_tango_python/requirements.txt
@@ -0,0 +1,8 @@
+numpy
+tqdm
+jinja2
+tabulate
+pyfiglet
+colorama
+unitgrade>=0.1.23
+unitgrade-devel>=0.1.37 # Required to run automatic evaluation (load tokens etc.)
diff --git a/examples/example_autolab_deploy/docker/unitgrade-docker/Dockerfile b/examples/example_autolab_deploy/docker/unitgrade-docker/Dockerfile
new file mode 100644
index 0000000000000000000000000000000000000000..0ba4b77a284c02a8d364d63d1da9505e09ffc63c
--- /dev/null
+++ b/examples/example_autolab_deploy/docker/unitgrade-docker/Dockerfile
@@ -0,0 +1,19 @@
+# syntax=docker/dockerfile:1
+
+FROM python:3.8-slim-buster
+
+RUN apt-get -y update
+RUN apt-get -y install git
+
+WORKDIR /home
+
+# Remember to include requirements.
+COPY requirements.txt requirements.txt
+RUN pip3 install -r requirements.txt
+
+# Not required.
+# RUN pip install git+https://git@gitlab.compute.dtu.dk/tuhe/unitgrade.git
+
+COPY . .
+
+ADD . /home
diff --git a/examples/example_autolab_deploy/docker/unitgrade-docker/requirements.txt b/examples/example_autolab_deploy/docker/unitgrade-docker/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f415be306d4ff87b4943add66860a81f1cca01a3
--- /dev/null
+++ b/examples/example_autolab_deploy/docker/unitgrade-docker/requirements.txt
@@ -0,0 +1,9 @@
+numpy
+tqdm
+jinja2
+tabulate
+pyfiglet
+colorama
+importnb
+unitgrade # Perhaps just this and not the other.
+
diff --git a/src/unitgrade_private/autolab/autolab.py b/src/unitgrade_private/autolab/autolab.py
index 25230471d8d59489fc139be1c73198e09fb820f9..dee673b593ac0aed3234fe77d3079104c3ab564c 100644
--- a/src/unitgrade_private/autolab/autolab.py
+++ b/src/unitgrade_private/autolab/autolab.py
@@ -79,16 +79,17 @@ def run_relative(file, base):
             return code
     raise Exception("Could not run the file", file, "in dir", dir)
 
-
-
-
-
-def new_deploy_assignment(base_name, INSTRUCTOR_REPORT_CLASS, INSTRUCTOR_BASE, INSTRUCTOR_GRADE_FILE, STUDENT_BASE, STUDENT_GRADE_FILE,
+def new_deploy_assignment(base_name, INSTRUCTOR_BASE, INSTRUCTOR_GRADE_FILE, STUDENT_BASE,
+                          STUDENT_GRADE_FILE = None, # Defaults to instructor grade file.
                       output_tar=None,
                       COURSES_BASE=None,
                       autograde_image_tag='tango_python_tue',
                       student_should_upload_token=True,
-                    homework_file=None):
+                    homework_file=None,
+                          description=None):
+    if STUDENT_GRADE_FILE is None:
+        STUDENT_GRADE_FILE = INSTRUCTOR_GRADE_FILE
+
     """ Check we got correct paths. """
     assert os.path.isfile(INSTRUCTOR_GRADE_FILE)
     assert os.path.isfile(STUDENT_GRADE_FILE)
@@ -104,7 +105,8 @@ def new_deploy_assignment(base_name, INSTRUCTOR_REPORT_CLASS, INSTRUCTOR_BASE, I
             os.mkdir(COURSES_BASE)
 
     LAB_DEST = os.path.join(COURSES_BASE, base_name)
-
+    if homework_file is not None:
+        student_should_upload_token = False
 
     # STUDENT_HANDOUT_DIR = os.path.dirname(STUDENT_GRADE_FILE) #"/home/tuhe/Documents/unitgrade_private_v1/examples/example_simplest/students/programs"
     # INSTRUCTOR_GRADE_FILE = "/home/tuhe/Documents/unitgrade_private_v1/examples/example_simplest/instructor/programs/report5.py"
@@ -118,7 +120,7 @@ def new_deploy_assignment(base_name, INSTRUCTOR_REPORT_CLASS, INSTRUCTOR_BASE, I
     # Now we have the instructor token file. Let's get the student token file.
     total_ = res['total'][1]
     problems = []
-<<<<<<< HEAD
+    # <<<<<<< HEAD
     problems.append(dict(name='Unitgrade score', description='Score obtained by automatic grading', max_score=total_, optional='false'))
     problems.append(dict(name='Written feedback', description='Written (TA) feedback', max_score=0, optional='true'))
     # print(problems)
@@ -128,20 +130,20 @@ def new_deploy_assignment(base_name, INSTRUCTOR_REPORT_CLASS, INSTRUCTOR_BASE, I
 
     run_relative(STUDENT_GRADE_FILE, STUDENT_BASE)
 
-=======
-    problems.append(dict(name='Unitgrade score', description='Automatic score as computed using the _grade.py script', max_score=total_, optional='false'))
-    print(problems)
-    sc = [('Total', res['total'][0])] + [(q['title'], q['obtained']) for k, q in res['details'].items()]
-    ss = ", ".join([f'"{t}": {s}' for t, s in sc])
-    scores = '{"scores": {' + ss + '}}'
-    print(scores)
-    # Quickly make student .token file to upload:
-    # os.system(f"cd {os.path.dirname(STUDENT_HANDOUT_DIR)} && python -m programs.{os.path.basename(INSTRUCTOR_GRADE_FILE)[:-3]}")
-    # os.system(f"cd {STUDENT_HANDOUT_DIR} && python {os.path.basename(INSTRUCTOR_GRADE_FILE)}")
-    # handin_filename = os.path.basename(STUDENT_TOKEN_FILE)
-    run_relative(os.path.join(STUDENT_BASE, STUDENT_GRADE_FILE), STUDENT_BASE)
-    # if student_should_upload_token:
->>>>>>> 0429c721315832077f7682929c6f3a40449d85fc
+    # =======
+    #     problems.append(dict(name='Unitgrade score', description='Automatic score as computed using the _grade.py script', max_score=total_, optional='false'))
+    #     print(problems)
+    #     sc = [('Total', res['total'][0])] + [(q['title'], q['obtained']) for k, q in res['details'].items()]
+    #     ss = ", ".join([f'"{t}": {s}' for t, s in sc])
+    #     scores = '{"scores": {' + ss + '}}'
+    #     print(scores)
+    #     # Quickly make student .token file to upload:
+    #     # os.system(f"cd {os.path.dirname(STUDENT_HANDOUT_DIR)} && python -m programs.{os.path.basename(INSTRUCTOR_GRADE_FILE)[:-3]}")
+    #     # os.system(f"cd {STUDENT_HANDOUT_DIR} && python {os.path.basename(INSTRUCTOR_GRADE_FILE)}")
+    #     # handin_filename = os.path.basename(STUDENT_TOKEN_FILE)
+    #     run_relative(os.path.join(STUDENT_BASE, STUDENT_GRADE_FILE), STUDENT_BASE)
+    #     # if student_should_upload_token:
+    # >>>>>>> 0429c721315832077f7682929c6f3a40449d85fc
     STUDENT_TOKEN_FILE = glob.glob(os.path.dirname(STUDENT_GRADE_FILE) + "/*.token")[0]
     handin_filename = os.path.basename(STUDENT_TOKEN_FILE)
     for _ in range(3):
@@ -160,7 +162,8 @@ def new_deploy_assignment(base_name, INSTRUCTOR_REPORT_CLASS, INSTRUCTOR_BASE, I
     # Start in the src directory. You should make the handout files first.
     os.mkdir(LAB_DEST + "/src")
     INSTRUCTOR_REPORT_FILE = INSTRUCTOR_GRADE_FILE[:-9] + ".py"
-
+    if description is None:
+        description = f'Upload the file {homework_file}' if homework_file is not None else handin_filename
     print("Making data...")
     # /home/tuhe/Documents/unitgrade_private_v1/examples/example_simplest/instructor/programs/report5.py"
     data = {
@@ -179,7 +182,7 @@ def new_deploy_assignment(base_name, INSTRUCTOR_REPORT_CLASS, INSTRUCTOR_BASE, I
         'student_should_upload_token': student_should_upload_token,
         'homework_file': homework_file,
         'student_token_src_filename': student_token_src_filename,
-        'description': f'Upload the file {homework_file}' if homework_file is not None else handin_filename
+        'description': description
     }
     print("> Running jinja2")
     # shutil.copyfile(TEMPLATE_BASE + "/hello.yml", f"{LAB_DEST}/{base_name}.yml")
@@ -266,7 +269,7 @@ clean:
 
     # Make the autograder and the handouts...
     shutil.make_archive(f"{LAB_DEST}/autograde", 'tar', root_dir=f"{LAB_DEST}", base_dir=f"{base_name}-autograde")
-    shutil.make_archive(f"{LAB_DEST}/cs105h-handout", 'tar', root_dir=f"{LAB_DEST}", base_dir=f"{base_name}-handout")
+    shutil.make_archive(f"{LAB_DEST}/{base_name}-handout", 'zip', root_dir=f"{LAB_DEST}", base_dir=f"{base_name}-handout")
     shutil.make_archive(output_tar[:-4], 'tar', root_dir=COURSES_BASE, base_dir=base_name)
     return output_tar
 
diff --git a/src/unitgrade_private/hidden_create_files.py b/src/unitgrade_private/hidden_create_files.py
index 2051f4f5dc9defe6317aa692d521f7ee45e50aa6..83e7790f5ea8ef8e3e696dcaf19c473f8bfe1030 100644
--- a/src/unitgrade_private/hidden_create_files.py
+++ b/src/unitgrade_private/hidden_create_files.py
@@ -41,7 +41,10 @@ def lload(flist, excl):
 
 def setup_grade_file_report(ReportClass, execute=False, obfuscate=False, minify=False, bzip=True, nonlatin=False, source_process_fun=None, with_coverage=True):
     print("Setting up answers...")
+    url = ReportClass.url
+    ReportClass.url = None
     report = ReportClass()
+    # report.url = None # We set the URL to none to skip the consistency checks with the remote source.
     payload = report._setup_answers(with_coverage=with_coverage)
     payload['config'] = {}
     from unitgrade_private.hidden_gather_upload import gather_report_source_include
@@ -133,4 +136,5 @@ def setup_grade_file_report(ReportClass, execute=False, obfuscate=False, minify=
         exec("import " + s)
 
     print("====== EXECUTION AND PACKING OF REPORT IS COMPLETE ======")
+    ReportClass.url = url
     return output
\ No newline at end of file
diff --git a/src/unitgrade_private/hidden_gather_upload.py b/src/unitgrade_private/hidden_gather_upload.py
index f83f278c0567db0e74a63c0d01727a9d53b5e4ee..0764caa1009c3a45e6dd1a035dfaa1b89593aab9 100644
--- a/src/unitgrade_private/hidden_gather_upload.py
+++ b/src/unitgrade_private/hidden_gather_upload.py
@@ -153,8 +153,7 @@ def gather_upload_to_campusnet(report, output_dir=None, token_include_plaintext_
     payload_out_base = report.__class__.__name__ + "_handin"
 
     obtain, possible = results['total']
-    vstring = "_v"+report.version if report.version is not None else ""
-
+    vstring = f"_v{report.version}" if report.version is not None else ""
     token = "%s_%i_of_%i%s.token"%(payload_out_base, obtain, possible,vstring)
     token = os.path.normpath(os.path.join(output_dir, token))