first pass on github actions

Author: JesseLivezey

.github/workflows/dca_tests.yml

@@ -0,0 +1,38 @@
+name: DCA tests
+on:
+  push:
+    branches:
+      - main
+  pull_request:
+    branches:
+      - main
+jobs:
+  run-tests:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ["3.8", "3.8", "3.9"]
+    steps:
+      - name: Test DCA
+      - uses actions/checkout@v3
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v3
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
+          python -m pip install flake8 codecov pytest-cov sphinx_rtd_theme
+      - name: Lint with flake8
+        run: |
+          flake8 dca tests
+      - name: Test with pytest
+        run: |
+          pytest -sv --cov=./ tests
+      - name: Build docs
+        run: |
+          sphinx-build -W -b html docs/source docs/build
+      - name: Codecov
+        run: |
+          codecov

.travis.yml

@@ -79,7 +79,8 @@ def load_weather_data(filename):
 """
 
 
-def load_sabes_data(filename, bin_width_s=.05, preprocess=True):
+def load_sabes_data(filename, bin_width_s=.05, high_pass=True, sqrt=True, thresh=5000,
+                    zscore_pos=True):
     # Load MATLAB file
     with h5py.File(filename, "r") as f:
         # Get channel names (e.g. M1 001 or S1 001)
@@ -119,10 +120,10 @@ def load_sabes_data(filename, bin_width_s=.05, preprocess=True):
                     unique_idxs, counts = np.unique(bin_idx, return_counts=True)
                     # make sure to ignore the hash here...
                     binned_spikes[unique_idxs, chan_idx * n_sorted_units + unit_idx - 1] += counts
-            binned_spikes = binned_spikes[:, binned_spikes.sum(axis=0) > 0]
-            if preprocess:
-                binned_spikes = binned_spikes[:, binned_spikes.sum(axis=0) > 5000]
+            binned_spikes = binned_spikes[:, binned_spikes.sum(axis=0) > thresh]
+            if sqrt:
                 binned_spikes = np.sqrt(binned_spikes)
+            if high_pass:
                 binned_spikes = moving_center(binned_spikes, n=600)
             result[region] = binned_spikes
         # Get cursor position
@@ -131,7 +132,7 @@ def load_sabes_data(filename, bin_width_s=.05, preprocess=True):
         t_mid_bin = np.arange(len(binned_spikes)) * bin_width_s + bin_width_s / 2
         cursor_pos_interp = interp1d(t - t[0], cursor_pos, axis=0)
         cursor_interp = cursor_pos_interp(t_mid_bin)
-        if preprocess:
+        if zscore_pos:
             cursor_interp -= cursor_interp.mean(axis=0, keepdims=True)
             cursor_interp /= cursor_interp.std(axis=0, keepdims=True)
         result["cursor"] = cursor_interp

dca/data_util.py

@@ -284,6 +284,8 @@ def score(self, X=None):
             If X is given, calcuate the PI of X for the learned projections.
         """
         T = self.T_fit
+        if T is None:
+            T = self.T
         if X is None:
             cross_covs = self.cross_covs.cpu().numpy()
         else:

dca/dca.py

@@ -4,5 +4,5 @@ h5py
 pandas
 scikit-learn
 matplotlib
--f https://download.pytorch.org/whl/torch_stable.html
-torch>=1.7.0+cpu
+--extra-index-url https://download.pytorch.org/whl/cpu
+torch