address review issues

Pyrado/pyrado/domain_randomization/domain_parameter.py

@@ -403,19 +403,11 @@ def from_domain_randomizer(domain_randomizer, *, target_cov_factor=1.0, init_cov
         :param init_cov_factor: scaling of the randomizer's variance to get the init variance; defaults to `1/100`
         :return: the self-paced domain parameter
         """
-        (
-            name,
-            target_mean,
-            target_cov_flat,
-            init_mean,
-            init_cov_flat,
-        ) = (
-            [],
-            [],
-            [],
-            [],
-            [],
-        )
+        name = []
+        target_mean = []
+        target_cov_flat = []
+        init_mean = []
+        init_cov_flat = []
         for domain_param in domain_randomizer.domain_params:
             if not isinstance(domain_param, NormalDomainParam):
                 raise pyrado.TypeErr(

Pyrado/pyrado/plotting/heatmap.py

@@ -190,6 +190,10 @@ def draw_heatmap(
     :return: handles to the heat map and the color bar figures (`None` if not existent)
     """
     if isinstance(data, pd.DataFrame):
+        if not data.index.is_numeric():
+            raise pyrado.TypeErr(given=data.index, msg="expected numeric index")
+        if not data.index.is_numeric():
+            raise pyrado.TypeErr(given=data.columns, msg="expected numeric index")
         # Extract the data
         x = data.columns
         y = data.index

Pyrado/tests/test_domain_randomization.py

@@ -33,16 +33,31 @@
 import torch as to
 from tests.conftest import m_needs_bullet, m_needs_mujoco
 
+import pyrado
 from pyrado.domain_randomization.domain_parameter import (
     BernoulliDomainParam,
     MultivariateNormalDomainParam,
     NormalDomainParam,
+    SelfPacedDomainParam,
     UniformDomainParam,
 )
+from pyrado.domain_randomization.domain_randomizer import DomainRandomizer
 from pyrado.domain_randomization.utils import param_grid
 from pyrado.environments.sim_base import SimEnv
 
 
+def assert_is_close(param, info_expected):
+    info_actual = param.info()
+    assert list(sorted(info_actual.keys())) == list(sorted(info_expected.keys()))
+    for key, expected in info_expected.items():
+        if key in ["name", "clip_lo", "clip_up"]:
+            assert info_actual[key] == expected
+        else:
+            assert to.allclose(
+                info_actual[key], expected
+            ), f"key: {key}, actual: {info_actual[key]}, expected: {expected}"
+
+
 @pytest.mark.parametrize(
     "dp",
     [
@@ -66,6 +81,42 @@ def test_domain_param(dp, num_samples):
     assert len(s) == num_samples
 
 
+def test_self_paced_domain_param_make_broadening():
+    param = SelfPacedDomainParam.make_broadening(["a"], [1.0], 0.0004, 0.04)
+    assert_is_close(
+        param,
+        {
+            "name": ["a"],
+            "target_mean": to.tensor([1.0]).double(),
+            "target_cov_chol": to.tensor([0.2]).double(),
+            "init_mean": to.tensor([1.0]).double(),
+            "init_cov_chol": to.tensor([0.02]).double(),
+            "clip_lo": -pyrado.inf,
+            "clip_up": pyrado.inf,
+        },
+    )
+
+
+def test_self_paced_domain_param_from_domain_randomizer():
+    param = SelfPacedDomainParam.from_domain_randomizer(
+        DomainRandomizer(NormalDomainParam(name="a", mean=1.0, std=1.0)),
+        init_cov_factor=0.0004,
+        target_cov_factor=0.04,
+    )
+    assert_is_close(
+        param,
+        {
+            "name": ["a"],
+            "target_mean": to.tensor([1.0]).double(),
+            "target_cov_chol": to.tensor([0.2]).double(),
+            "init_mean": to.tensor([1.0]).double(),
+            "init_cov_chol": to.tensor([0.02]).double(),
+            "clip_lo": -pyrado.inf,
+            "clip_up": pyrado.inf,
+        },
+    )
+
+
 def test_randomizer(default_randomizer):
     print(default_randomizer)
     # Generate 7 samples