move gplearn_to_function to functions module

5 files changed, 44 insertions, 50 deletions

diff --git a/bin/analyze-archive.py b/bin/analyze-archive.py
index a18c1e1..7cac27a 100755
--- a/bin/analyze-archive.py
+++ b/bin/analyze-archive.py
@@ -114,7 +114,7 @@ import re
 import sys
 from dfatool import plotter
 from dfatool.dfatool import RawData, pta_trace_to_aggregate
-from dfatool.dfatool import gplearn_to_function
+from dfatool.functions import gplearn_to_function
 from dfatool.model import PTAModel
 from dfatool.validation import CrossValidator
 from dfatool.utils import filter_aggregate_by_param
diff --git a/bin/analyze-timing.py b/bin/analyze-timing.py
index e27acbf..3503279 100755
--- a/bin/analyze-timing.py
+++ b/bin/analyze-timing.py
@@ -80,7 +80,7 @@ import re
 import sys
 from dfatool import plotter
 from dfatool.dfatool import TimingData, pta_trace_to_aggregate
-from dfatool.dfatool import gplearn_to_function
+from dfatool.functions import gplearn_to_function
 from dfatool.model import AnalyticModel
 from dfatool.validation import CrossValidator
 from dfatool.utils import filter_aggregate_by_param
diff --git a/bin/test_corrcoef.py b/bin/test_corrcoef.py
index 75b5d0d..fef0b24 100755
--- a/bin/test_corrcoef.py
+++ b/bin/test_corrcoef.py
@@ -5,7 +5,7 @@ import re
 import sys
 from dfatool import plotter
 from dfatool.dfatool import RawData, pta_trace_to_aggregate
-from dfatool.dfatool import gplearn_to_function
+from dfatool.functions import gplearn_to_function
 from dfatool.model import PTAModel
 
 opt = dict()
diff --git a/lib/dfatool.py b/lib/dfatool.py
index 07aa7b3..47ce24e 100644
--- a/lib/dfatool.py
+++ b/lib/dfatool.py
@@ -27,53 +27,6 @@ except ImportError:
 arg_support_enabled = True
 
 
-def gplearn_to_function(function_str: str):
-    """
-    Convert gplearn-style function string to Python function.
-
-    Takes a function string like "mul(add(X0, X1), X2)" and returns
-    a Python function implementing the specified behaviour,
-    e.g. "lambda x, y, z: (x + y) * z".
-
-    Supported functions:
-    add  --  x + y
-    sub  --  x - y
-    mul  --  x * y
-    div  --  x / y if |y| > 0.001, otherwise 1
-    sqrt --  sqrt(|x|)
-    log  --  log(|x|) if |x| > 0.001, otherwise 0
-    inv  --  1 / x if |x| > 0.001, otherwise 0
-    """
-    eval_globals = {
-        "add": lambda x, y: x + y,
-        "sub": lambda x, y: x - y,
-        "mul": lambda x, y: x * y,
-        "div": lambda x, y: np.divide(x, y) if np.abs(y) > 0.001 else 1.0,
-        "sqrt": lambda x: np.sqrt(np.abs(x)),
-        "log": lambda x: np.log(np.abs(x)) if np.abs(x) > 0.001 else 0.0,
-        "inv": lambda x: 1.0 / x if np.abs(x) > 0.001 else 0.0,
-    }
-
-    last_arg_index = 0
-    for i in range(0, 100):
-        if function_str.find("X{:d}".format(i)) >= 0:
-            last_arg_index = i
-
-    arg_list = []
-    for i in range(0, last_arg_index + 1):
-        arg_list.append("X{:d}".format(i))
-
-    eval_str = "lambda {}, *whatever: {}".format(",".join(arg_list), function_str)
-    logger.debug(eval_str)
-    return eval(eval_str, eval_globals)
-
-
-def append_if_set(aggregate: dict, data: dict, key: str):
-    """Append data[key] to aggregate if key in data."""
-    if key in data:
-        aggregate.append(data[key])
-
-
 def mean_or_none(arr):
     """
     Compute mean of NumPy array `arr`, return -1 if empty.
diff --git a/lib/functions.py b/lib/functions.py
index 99ba17d..94b1aaf 100644
--- a/lib/functions.py
+++ b/lib/functions.py
@@ -25,6 +25,47 @@ def powerset(iterable):
     return chain.from_iterable(combinations(s, r) for r in range(len(s) + 1))
 
 
+def gplearn_to_function(function_str: str):
+    """
+    Convert gplearn-style function string to Python function.
+
+    Takes a function string like "mul(add(X0, X1), X2)" and returns
+    a Python function implementing the specified behaviour,
+    e.g. "lambda x, y, z: (x + y) * z".
+
+    Supported functions:
+    add  --  x + y
+    sub  --  x - y
+    mul  --  x * y
+    div  --  x / y if |y| > 0.001, otherwise 1
+    sqrt --  sqrt(|x|)
+    log  --  log(|x|) if |x| > 0.001, otherwise 0
+    inv  --  1 / x if |x| > 0.001, otherwise 0
+    """
+    eval_globals = {
+        "add": lambda x, y: x + y,
+        "sub": lambda x, y: x - y,
+        "mul": lambda x, y: x * y,
+        "div": lambda x, y: np.divide(x, y) if np.abs(y) > 0.001 else 1.0,
+        "sqrt": lambda x: np.sqrt(np.abs(x)),
+        "log": lambda x: np.log(np.abs(x)) if np.abs(x) > 0.001 else 0.0,
+        "inv": lambda x: 1.0 / x if np.abs(x) > 0.001 else 0.0,
+    }
+
+    last_arg_index = 0
+    for i in range(0, 100):
+        if function_str.find("X{:d}".format(i)) >= 0:
+            last_arg_index = i
+
+    arg_list = []
+    for i in range(0, last_arg_index + 1):
+        arg_list.append("X{:d}".format(i))
+
+    eval_str = "lambda {}, *whatever: {}".format(",".join(arg_list), function_str)
+    logger.debug(eval_str)
+    return eval(eval_str, eval_globals)
+
+
 class ParamFunction:
     """
     A one-dimensional model function, ready for least squares optimization and similar.