Patch plugin cache

strax/context.py

@@ -636,7 +636,19 @@ def _get_plugins(self,
         instance, which is referenced under multiple keys in the output dict.
         """
         if self._plugins_are_cached(targets):
-            return self.__get_plugins_from_cache(run_id)
+            cached_plugins = self.__get_plugins_from_cache(run_id)
+            plugins = {}
+            targets = list(targets)
+            for target in targets:
+                if target in plugins:
+                    continue
+
+                target_plugin = cached_plugins[target]
+                for provides in target_plugin.provides:
+                    plugins[provides] = target_plugin
+
+                targets += list(target_plugin.depends_on)
+            return plugins
 
         # Check all config options are taken by some registered plugin class
         # (helps spot typos)

strax/testutils.py

@@ -208,6 +208,30 @@ def compute(self, records):
         return p
 
 
+@strax.takes_config(
+    strax.Option('base_area', type=int, default=0),
+    strax.Option('give_wrong_dtype', type=bool, default=False),
+    strax.Option('bonus_area', type=int, default=0))
+class PeaksWoPerRunDefault(strax.Plugin):
+    """Same as peak plugin but without per run default option
+    to allow for plugin caching.
+    """
+    provides = 'peaks'
+    data_kind = 'peaks'
+    depends_on = ('records',)
+    dtype = strax.peak_dtype()
+    parallel = True
+
+    def compute(self, records):
+        if self.config['give_wrong_dtype']:
+            return np.zeros(5, [('a', np.int64), ('b', np.float64)])
+        p = np.zeros(len(records), self.dtype)
+        p['time'] = records['time']
+        p['length'] = p['dt'] = 1
+        p['area'] = self.config['base_area'] + self.config['bonus_area']
+        return p
+
+
 # Another peak-kind plugin, to test time_range selection
 # with unaligned chunks
 class PeakClassification(strax.Plugin):

tests/test_context.py

@@ -1,5 +1,5 @@
 import strax
-from strax.testutils import Records, Peaks, PeakClassification, run_id
+from strax.testutils import Records, Peaks, PeaksWoPerRunDefault, PeakClassification, run_id
 import tempfile
 import numpy as np
 from hypothesis import given, settings
@@ -215,6 +215,17 @@ def tearDown(self):
         if os.path.exists(self.tempdir):
             shutil.rmtree(self.tempdir)
 
+    def test_get_plugins_with_cache(self):
+        st = self.get_context(False)
+        st.register(Records)
+        st.register(PeaksWoPerRunDefault)
+        st.register(PeakClassification)
+
+        not_cached_plugins = st._get_plugins(('peaks',), run_id)
+        st._get_plugins(('peak_classification',), run_id)
+        cached_plugins = st._get_plugins(('peaks',), run_id)
+        assert not_cached_plugins.keys() == cached_plugins.keys(), f'_get_plugins returns different plugins if cached!'
+
     def test_register_no_defaults(self, runs_default_allowed=False):
         """Test if we only register a plugin with no run-defaults"""
         st = self.get_context(runs_default_allowed)