1 files changed, 41 insertions, 3 deletions
diff --git a/lib/utils.py b/lib/utils.py
index 4850a53..fb76367 100644
--- a/lib/utils.py
+++ b/lib/utils.py
@@ -48,6 +48,8 @@ def running_mean(x: np.ndarray, N: int) -> np.ndarray:
 
 
 def human_readable(value, unit):
+    if value is None:
+        return value
     for prefix, factor in (
         ("p", 1e-12),
         ("n", 1e-9),
@@ -55,6 +57,8 @@ def human_readable(value, unit):
         ("m", 1e-3),
         ("", 1),
         ("k", 1e3),
+        ("M", 1e6),
+        ("G", 1e9),
     ):
         if value < 1e3 * factor:
             return "{:.2f} {}{}".format(value * (1 / factor), prefix, unit)
@@ -150,7 +154,7 @@ def parse_conf_str(conf_str):
     """
     conf_dict = dict()
     for option in conf_str.split(","):
-        key, value = option.split("=")
+        key, value = option.strip().split("=")
         conf_dict[key] = soft_cast_float(value)
     return conf_dict
 
@@ -205,6 +209,18 @@ def param_slice_eq(a, b, index):
     return False
 
 
+def param_eq_or_none(a, b):
+    """
+    Check if by_param keys a and b are identical, allowing a None in a to match any key in b.
+    """
+    set_keys = tuple(filter(lambda i: a[i] is not None, range(len(a))))
+    a_not_none = tuple(map(lambda i: a[i], set_keys))
+    b_not_none = tuple(map(lambda i: b[i], set_keys))
+    if a_not_none == b_not_none:
+        return True
+    return False
+
+
 def match_parameter_values(input_param: dict, match_param: dict):
     """
     Check whether one of the paramaters in `input_param` has the same value in `match_param`.
@@ -302,6 +318,21 @@ def param_dict_to_list(param_dict, parameter_names, default=None):
     return ret
 
 
+def param_dict_to_str(param_dict):
+    ret = list()
+    for parameter_name in sorted(param_dict.keys()):
+        ret.append(f"{parameter_name}={param_dict[parameter_name]}")
+    return " ".join(ret)
+
+
+def param_str_to_dict(param_str):
+    ret = dict()
+    for param_pair in param_str.split():
+        key, value = param_pair.split("=")
+        ret[key] = soft_cast_int_or_float(value)
+    return ret
+
+
 def observations_enum_to_bool(observations: list, kconfig=False):
     """
     Convert enum / categorical observations to boolean-only ones.
@@ -697,11 +728,18 @@ def regression_measures(predicted: np.ndarray, ground_truth: np.ndarray):
     rsq -- R^2 measure, see sklearn.metrics.r2_score
     count -- Number of values
     """
-    if type(predicted) != np.ndarray:
+
+    if type(predicted) is list:
+        predicted = np.array(predicted)
+
+    if type(ground_truth) is list:
+        ground_truth = np.array(ground_truth)
+
+    if type(predicted) is not np.ndarray:
         raise ValueError(
             "first arg ('predicted') must be ndarray, is {}".format(type(predicted))
         )
-    if type(ground_truth) != np.ndarray:
+    if type(ground_truth) is not np.ndarray:
         raise ValueError(
             "second arg ('ground_truth') must be ndarray, is {}".format(
                 type(ground_truth)