Some renaming and new tests

neurots/generate/algorithms/tmdgrower.py

@@ -27,7 +27,7 @@
 from neurots.generate.algorithms.common import section_data
 from neurots.morphmath import sample
 from neurots.morphmath.utils import norm
-from neurots.preprocess.relevancy_checkers import check_min_bar_length
+from neurots.preprocess.relevance_checkers import check_min_bar_length
 
 L = logging.getLogger(__name__)
 

neurots/preprocess/__init__.py

@@ -15,8 +15,8 @@
 # You should have received a copy of the GNU General Public License
 # along with this program.  If not, see <https://www.gnu.org/licenses/>.
 
-from neurots.preprocess import relevancy_checkers  # noqa
+from neurots.preprocess import relevance_checkers  # noqa
 from neurots.preprocess import validity_checkers  # noqa
 from neurots.preprocess.utils import preprocess_inputs  # noqa
-from neurots.preprocess.utils import register_preprocess  # noqa
+from neurots.preprocess.utils import register_preprocessor  # noqa
 from neurots.preprocess.utils import register_validator  # noqa

neurots/preprocess/exceptions.py

@@ -0,0 +1,20 @@
+"""Exceptions raised during validation process."""
+
+# Copyright (C) 2022  Blue Brain Project, EPFL
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+
+class NeuroTSValidationError(Exception):
+    """Exception raised when a configuration set is not valid."""

neurots/preprocess/preprocessors.py

@@ -23,6 +23,5 @@
 # You should have received a copy of the GNU General Public License
 # along with this program.  If not, see <https://www.gnu.org/licenses/>.
 
-# TODO: remove the next line and the 'noqa' when a preprocess is added
-# pylint: disable=unused-import
-from neurots.preprocess.utils import register_preprocess  # noqa
+# TODO: uncomment the next line when a preprocess is added
+# from neurots.preprocess.utils import register_preprocessor

neurots/preprocess/relevancy_checkers.py → neurots/preprocess/relevance_checkers.py

@@ -1,9 +1,11 @@
-"""Functions to check that the given parameters and distributions will given relevant results.
+"""Functions to check that the given parameters and distributions will give relevant results.
 
-The functions used as relevancy checkers should have a name like 'check_*' and have the following
+The functions used as relevance checkers should have a name like 'check_*' and have the following
 signature: `check_something(params, distrs, start_point=None, context=None)`. The `start_point` and
 `context` parameters should always be optional, as they will not be known during the preprocessing
 step.
+
+These functions can be called either in validity checkers or inside the grower codes.
 """
 
 # Copyright (C) 2022  Blue Brain Project, EPFL

neurots/preprocess/utils.py

@@ -22,18 +22,18 @@
 from neurots.validator import validate_neuron_distribs
 from neurots.validator import validate_neuron_params
 
-_PREPROCESS_FUNCTIONS = {
-    "preprocess": defaultdict(set),
-    "validator": defaultdict(set),
+_REGISTERED_FUNCTIONS = {
+    "preprocessors": defaultdict(set),
+    "validators": defaultdict(set),
 }
 
 
-def register_preprocess(*growth_methods):
+def register_preprocessor(*growth_methods):
     """Register a preprocess function."""
 
     def inner(func):
         for i in growth_methods:
-            _PREPROCESS_FUNCTIONS["preprocess"][i].add(func)
+            _REGISTERED_FUNCTIONS["preprocessors"][i].add(func)
         return func
 
     return inner
@@ -44,7 +44,7 @@ def register_validator(*growth_methods):
 
     def inner(func):
         for i in growth_methods:
-            _PREPROCESS_FUNCTIONS["validator"][i].add(func)
+            _REGISTERED_FUNCTIONS["validators"][i].add(func)
         return func
 
     return inner
@@ -60,8 +60,8 @@ def preprocess_inputs(params, distrs):
     for grow_type in params["grow_types"]:
         growth_method = params[grow_type]["growth_method"]
         for preprocess_func in chain(
-            _PREPROCESS_FUNCTIONS["validator"][growth_method],
-            _PREPROCESS_FUNCTIONS["preprocess"][growth_method],
+            _REGISTERED_FUNCTIONS["validators"][growth_method],
+            _REGISTERED_FUNCTIONS["preprocessors"][growth_method],
         ):
             preprocess_func(params[grow_type], distrs[grow_type])
 

neurots/preprocess/validity_checkers.py

@@ -23,7 +23,8 @@
 # You should have received a copy of the GNU General Public License
 # along with this program.  If not, see <https://www.gnu.org/licenses/>.
 
-from neurots.preprocess.relevancy_checkers import check_min_bar_length
+from neurots.preprocess.exceptions import NeuroTSValidationError
+from neurots.preprocess.relevance_checkers import check_min_bar_length
 from neurots.preprocess.utils import register_validator
 
 
@@ -32,7 +33,7 @@ def check_num_seg(params, distrs):
     """Check that params contains a 'num_seg' entry."""
     # pylint: disable=unused-argument
     if "num_seg" not in params:
-        raise KeyError(
+        raise NeuroTSValidationError(
             "The parameters must contain a 'num_seg' entry when the "
             "'growth_method' entry in parameters is 'trunk'."
         )
@@ -42,12 +43,12 @@ def check_num_seg(params, distrs):
 def check_bar_length(params, distrs):
     """Check consistency between parameters and persistence diagram."""
     if "min_bar_length" not in distrs:
-        raise KeyError(
+        raise NeuroTSValidationError(
             "The distributions must contain a 'min_bar_length' entry when the "
             "'growth_method' entry in parameters is in ['tmd', 'tmd_apical', 'tmd_gradient']."
         )
     if "mean" not in params.get("step_size", {}).get("norm", {}):
-        raise KeyError(
+        raise NeuroTSValidationError(
             "The parameters must contain a 'step_size' entry when the "
             "'growth_method' entry in parameters is in ['tmd', 'tmd_apical', 'tmd_gradient']."
         )

tests/test_preprocess.py

@@ -1,11 +1,17 @@
 """Test the neurots.preprocess functions."""
+# pylint: disable=redefined-outer-name
+# pylint: disable=unused-argument
 import json
 import logging
+from collections import defaultdict
 from pathlib import Path
 
 import pytest
 
 from neurots import preprocess
+from neurots.preprocess.exceptions import NeuroTSValidationError
+from neurots.preprocess.utils import register_preprocessor
+from neurots.preprocess.utils import register_validator
 from neurots.utils import convert_from_legacy_neurite_type
 
 DATA_PATH = Path(__file__).parent / "data"
@@ -88,7 +94,7 @@ def test_check_num_seg():
     params = {}
 
     with pytest.raises(
-        KeyError,
+        NeuroTSValidationError,
         match=(
             "The parameters must contain a 'num_seg' entry when the "
             "'growth_method' entry in parameters is 'trunk'."
@@ -107,7 +113,7 @@ def test_check_min_bar_length(caplog):
     """
 
     with pytest.raises(
-        KeyError,
+        NeuroTSValidationError,
         match=(
             r"The distributions must contain a 'min_bar_length' entry when the "
             r"'growth_method' entry in parameters is in \['tmd', 'tmd_apical', 'tmd_gradient'\]\."
@@ -120,7 +126,7 @@ def test_check_min_bar_length(caplog):
     }
 
     with pytest.raises(
-        KeyError,
+        NeuroTSValidationError,
         match=(
             r"The parameters must contain a 'step_size' entry when the "
             r"'growth_method' entry in parameters is in \['tmd', 'tmd_apical', 'tmd_gradient'\]\."
@@ -140,7 +146,7 @@ def test_check_min_bar_length(caplog):
         preprocess.validity_checkers.check_bar_length(params, distrs)
     assert caplog.record_tuples == [
         (
-            "neurots.preprocess.relevancy_checkers",
+            "neurots.preprocess.relevance_checkers",
             30,
             "Selected step size 999.000000 is too big for bars of size 1.000000",
         )
@@ -157,3 +163,58 @@ def test_check_min_bar_length(caplog):
     with caplog.at_level(logging.DEBUG):
         preprocess.validity_checkers.check_bar_length(params, distrs)
     assert caplog.record_tuples == []
+
+
+@pytest.fixture
+def dummy_register(monkeypatch):
+    """Monkeypatch the registered functions and reset it at the end of the test."""
+    monkeypatch.setattr(
+        preprocess.utils,
+        "_REGISTERED_FUNCTIONS",
+        {
+            "preprocessors": defaultdict(set),
+            "validators": defaultdict(set),
+        },
+    )
+
+
+def test_register_validator(dummy_register):
+    """Test validator registering."""
+    with (DATA_PATH / "axon_trunk_parameters.json").open(encoding="utf-8") as f:
+        params = convert_from_legacy_neurite_type(json.load(f))
+    with (DATA_PATH / "axon_trunk_distribution.json").open(encoding="utf-8") as f:
+        distrs = convert_from_legacy_neurite_type(json.load(f))
+
+    @register_validator("axon_trunk")
+    def dummy_validator(params, distrs):
+        assert params["randomness"] == 0
+        assert distrs["num_trees"]["data"]["bins"] == [1]
+
+    assert params["axon"]["randomness"] == 0
+    assert distrs["axon"]["num_trees"]["data"]["bins"] == [1]
+
+    preprocessed_params, preprocessed_distrs = preprocess.preprocess_inputs(params, distrs)
+
+    assert preprocessed_params == params
+    assert preprocessed_distrs == distrs
+
+
+def test_register_preprocessor(dummy_register):
+    """Test preprocessor registering."""
+    with (DATA_PATH / "axon_trunk_parameters.json").open(encoding="utf-8") as f:
+        params = convert_from_legacy_neurite_type(json.load(f))
+    with (DATA_PATH / "axon_trunk_distribution.json").open(encoding="utf-8") as f:
+        distrs = convert_from_legacy_neurite_type(json.load(f))
+
+    @register_preprocessor("axon_trunk")
+    def dummy_preprocessor(params, distrs):
+        params["randomness"] = 999
+        distrs["num_trees"]["data"]["bins"] = [999]
+
+    assert params["axon"]["randomness"] == 0
+    assert distrs["axon"]["num_trees"]["data"]["bins"] == [1]
+
+    preprocessed_params, preprocessed_distrs = preprocess.preprocess_inputs(params, distrs)
+
+    assert preprocessed_params["axon"]["randomness"] == 999
+    assert preprocessed_distrs["axon"]["num_trees"]["data"]["bins"] == [999]