Merge pull request CSNG-MFF#96 from RCagnol/SortAds

Adding functionality to sort ADS in DatastoreView

.github/workflows/fast-model-mpi-explosion.yml

@@ -0,0 +1,44 @@
+
+name: Fast model MPI tests
+
+on: [push, pull_request]
+
+jobs:
+
+  pytest:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v2
+    - name: Pytest Unit Tests
+      uses: actions/setup-python@v2
+      with:
+        python-version: '3.9'
+    - name: Install prerequisites
+      run: |
+        sudo apt-get update
+        sudo apt-get install python3-setuptools subversion git libopenmpi-dev g++ libjpeg8 libjpeg8-dev libfreetype6 libfreetype6-dev zlib1g-dev libpng++-dev libncurses5 libncurses5-dev libreadline-dev liblapack-dev libblas-dev gfortran libgsl0-dev openmpi-bin python-tk cmake
+        pip3 install pytest pytest-cov pytest-randomly coverage black
+        # TODO: Remove fixed numpy and pynn versions after the PyNN pull request
+        # https://github.com/NeuralEnsemble/PyNN/pull/762 is accepted
+        pip3 install numpy==1.23.5 scipy mpi4py matplotlib quantities lazyarray interval Pillow param==1.5.1 parameters neo cython pynn==0.10.0 psutil future requests elephant pytest-xdist pytest-timeout junitparser numba
+    - name: Download and install imagen
+      run: |
+        git clone https://github.com/antolikjan/imagen.git
+        cd imagen
+        python setup.py install
+        cd ..
+    - name: Install Nest
+      run: |
+        wget https://github.com/nest/nest-simulator/archive/v3.1.tar.gz
+        tar xvfz v3.1.tar.gz
+        cd nest-simulator-3.1
+        cmake -Dwith-mpi=ON -Dwith-boost=ON -DCMAKE_INSTALL_PREFIX:PATH=$pythonLocation -Dwith-optimize='-O3' ./
+        make -j8
+        make -j8 install
+        cd ..
+        python -c 'import nest'
+    - name: Install mozaik
+      run: python setup.py install
+
+    - name: Test with pytest
+      run: pytest -m "mpi and not not_github and mpi_explosion" ./tests/full_model/test_models_mpi.py --cov=mozaik

.github/workflows/fast-model-mpi.yml

@@ -41,4 +41,4 @@ jobs:
       run: python setup.py install
 
     - name: Test with pytest
-      run: pytest -v -s -m "mpi and not not_github" ./tests/full_model/test_models_mpi.py --cov=mozaik
+      run: pytest -m "mpi and not not_github and not mpi_explosion" ./tests/full_model/test_models_mpi.py --cov=mozaik

.github/workflows/fast-model-stepcurrentmodule.yml

@@ -30,7 +30,7 @@ jobs:
 
     - name: Download and install PyNN 
       run: |
-        git clone https://github.com/RCagnol/PyNN.git
+        git clone https://github.com/CSNG-MFF/PyNN.git
         cd PyNN
         git checkout PyNNStepCurrentModule 
         pip install .
@@ -49,7 +49,7 @@ jobs:
 
     - name: Install stepcurrentmodule
       run: |
-        git clone https://github.com/RCagnol/nest-step-current-module.git
+        git clone https://github.com/CSNG-MFF/nest-step-current-module.git
         cd nest-step-current-module
         cmake -Dwith-mpi=ON -Dwith-boost=ON -Dwith-nest=$pythonLocation/bin/nest-config -Dwith-optimize='-O3' ./
         make -j8

README.rst

@@ -51,7 +51,6 @@ ____________
 
 Now you can install all other dependencies in this protected environment::
 
-  pip3 install numpy scipy mpi4py matplotlib quantities lazyarray interval Pillow param==1.5.1 parameters neo cython psutil future requests elephant pytest-xdist pytest-timeout junitparser numba
   pip3 install numpy==1.23.5 scipy mpi4py matplotlib quantities lazyarray interval Pillow param==1.5.1 parameters neo cython psutil future requests elephant pytest-xdist pytest-timeout junitparser numba
 
 Next we will manually install several packages. It is probably the best if you create a separate directory in an appropriate
@@ -72,7 +71,7 @@ Then install the *PyNN* package from the PyNNStepCurrentModule branch::
 
 Next install the *Nest* simulator (always in the virtual environment):
 
-    - download the latest version from their `website <http://www.nest-initiative.org/index.php/Software:Download>`_
+    - download the latest version from their `website <http://www.nest-initiative.org/index.php/Software:Download>`_::
         
         wget https://github.com/nest/nest-simulator/archive/refs/tags/v3.4.tar.gz
         
@@ -109,7 +108,7 @@ Then install the *stepcurrentmodule* Nest module:
         git clone https://github.com/CSNG-MFF/nest-step-current-module.git
         cd nest-step-current-module
 
-    - then configure it relatively to your nest-config installation path (should reside in $HOME/virt_env/mozaik/bin/nest-config)::
+    - then, in the following command, replace NEST_CONFIG_PATH by your nest-config installation path (should reside in $HOME/virt_env/mozaik/bin/nest-config) and run it::
         
         (mozaik)$ cmake -Dwith-mpi=ON -Dwith-boost=ON -Dwith-optimize='-O3' -Dwith-nest=NEST_CONFIG_PATH ./
 

mozaik/storage/datastore.py

@@ -185,6 +185,30 @@ def get_analysis_result(self, **kwargs):
         """
         return filter_query(self.analysis_results,**kwargs)
 
+    def sort_analysis_results(self, key, reverse=False):
+        if key[0:3] == 'st_':
+            ads_id = []
+            ads_id_nkey = []
+            ads_nid = []
+            for ads in self.analysis_results:
+                if ads.stimulus_id is None:
+                    ads_nid.append(ads)
+                else:
+                    if hasattr(MozaikParametrized.idd(ads.stimulus_id),key[3:]):
+                        ads_id.append(ads)
+                    else:
+                        ads_id_nkey.append(ads)
+            self.analysis_results =  ads_nid + ads_id_nkey + sorted(ads_id, key= lambda x:getattr(MozaikParametrized.idd(x.stimulus_id),key[3:]),reverse=reverse)
+        else:
+            ads_key = []
+            ads_nkey = []
+            for ads in self.analysis_results:
+                if hasattr(ads,key):
+                    ads_key.append(ads)
+                else:
+                    ads_nkey.append(ads)
+            self.analysis_results = ads_nkey + sorted(ads_key, key= lambda x:getattr(x,key),reverse=reverse)
+
     def get_sensory_stimulus(self, stimuli=None):
         """
         Return the raw sensory stimulus that has been presented to the model due to stimuli specified by the stimuli argument.

pyproject.toml

@@ -6,6 +6,7 @@ markers = [
     "LSV1M_tiny: tests that need to run a tiny version of the LSV1M model to execute. Should be ",
     "merge: tests for the datastore merging",
     "mpi: tests using mpi to run the model",
+    "mpi_explosion: tests using mpi to run the model and tests the explosion detection",
     "not_github: tests using mpi to run the model",
     "stepcurrentmodule: tests using the nest stepcurrent module to run the model",
 ]

tests/full_model/test_models.py

@@ -36,8 +36,6 @@ def setup_class(cls):
         if os.path.exists(cls.result_path):
             os.system("rm -r " + cls.result_path)
         os.system(cls.model_run_command)
-        # Hack - For some reason MPI tests sometimes can't find the result datastore without this
-        print(os.listdir("tests/full_model/models/LSV1M_tiny/"))
         # Load DataStore of recordings from the model that just ran
         cls.ds = cls.load_datastore(cls.result_path)
         # Load DataStore of reference recordings

tests/full_model/test_models_mpi.py

@@ -67,11 +67,13 @@ class TestModelExplosionMonitoringMPI(TestModel):
 
     @pytest.mark.model
     @pytest.mark.mpi
+    @pytest.mark.mpi_explosion
     def test_explosion(self):
         assert self.ds.block.annotations["simulation_log"]["explosion_detected"]
 
     @pytest.mark.model
     @pytest.mark.mpi
+    @pytest.mark.mpi_explosion
     def test_fr_above_threshold(self):
         sheet_monitored = eval(self.ds.get_model_parameters())["explosion_monitoring"][
             "sheet_name"

tests/storage/test_storage.py

@@ -0,0 +1,119 @@
+import matplotlib
+
+matplotlib.use("Agg")
+from mozaik.analysis.analysis import *
+from mozaik.storage.datastore import *
+from mozaik.storage.queries import param_filter_query
+import mozaik
+import logging
+import os
+
+import pytest
+
+
+class TestDatastore:
+    ref_path = "tests/full_model/reference_data/LSV1M_tiny"
+
+    @classmethod
+    def setup_class(cls):
+        """
+        Runs the model and loads its result and a saved reference result
+        """
+        cls.ds = cls.load_datastore(cls.ref_path)
+        TrialAveragedFiringRate(
+            param_filter_query(cls.ds, st_name="FullfieldDriftingSinusoidalGrating"),
+            ParameterSet({}),
+        ).analyse()
+        cls.ads = cls.ds.get_analysis_result()
+
+    @staticmethod
+    def load_datastore(base_dir):
+        """
+        Load PickledDataStore for reading.
+
+        Parameters
+        ----------
+        base_dir : base directory where DataStore files are saved
+
+        Returns
+        -------
+        PickledDataStore with the data from base_dir
+        """
+        return PickledDataStore(
+            load=True,
+            parameters=ParameterSet(
+                {"root_directory": base_dir, "store_stimuli": False}
+            ),
+            replace=False,
+        )
+
+    def test_ADS_sorting_homogeneity(self):
+        dsv_sorted = param_filter_query(
+            self.ds, st_name="FullfieldDriftingSinusoidalGrating"
+        )
+        dsv_sorted.sort_analysis_results("sheet_name")
+        adss_sorted = dsv_sorted.get_analysis_result()
+        count_incorrect = 0
+        for ads in adss_sorted:
+            if ads not in self.ads:
+                count_incorrect += 1
+        for ads in self.ads:
+            if ads not in adss_sorted:
+                count_incorrect += 1
+        assert count_incorrect == 0
+
+    pass
+
+    def test_ADS_sorting_st_homogeneity(self):
+        dsv_sorted = param_filter_query(self.ds)
+        dsv_sorted.sort_analysis_results("st_orientation")
+        adss_sorted = dsv_sorted.get_analysis_result()
+        count_incorrect = 0
+        for ads in adss_sorted:
+            if ads not in self.ads:
+                count_incorrect += 1
+        for ads in self.ads:
+            if ads not in adss_sorted:
+                count_incorrect += 1
+        assert count_incorrect == 0
+
+    pass
+
+    def test_ADS_sorting_order(self):
+        dsv_sorted = param_filter_query(self.ds)
+        dsv_sorted.sort_analysis_results("sheet_name")
+        adss_sorted = dsv_sorted.get_analysis_result()
+        count_correct = 0
+        for ads, ads_next in zip(adss_sorted[:-1], adss_sorted[1:]):
+            if not hasattr(ads, "sheet_name"):
+                count_correct += 1
+            elif hasattr(ads_next, "sheet_name") and getattr(
+                ads, "sheet_name"
+            ) <= getattr(ads_next, "sheet_name"):
+                count_correct += 1
+        assert count_correct + 1 == len(adss_sorted)
+
+    pass
+
+    def test_ADS_sorting_st_order(self):
+        dsv_sorted = param_filter_query(self.ds)
+        dsv_sorted.sort_analysis_results("st_orientation")
+        adss_sorted = dsv_sorted.get_analysis_result()
+        count_correct = 0
+        for ads, ads_next in zip(adss_sorted[:-1], adss_sorted[1:]):
+            if ads.stimulus_id == None:
+                count_correct += 1
+            elif ads_next.stimulus_id and not hasattr(
+                ads.stimulus_id, "st_orientation"
+            ):
+                count_correct += 1
+            elif ads_next.stimulus_id and hasattr(
+                ads_next.stimulus_id, "st_orientation"
+            ):
+                if getattr(
+                    MozaikParametrized.idd(ads.stimulus_id), "st_orientation"
+                ) <= getattr(MozaikParametrized.idd(ads_next), "st_orientation"):
+                    count_correct += 1
+        assert count_correct + 1 == len(adss_sorted)
+
+    pass