GitHub Actions (DarkEnergySurvey#89)

Closes DarkEnergySurvey#83. * setup github action * convert imf steps to int * yaml safe_load * testing actions
willcerny · Jul 14, 2021 · 311e1ca · 311e1ca
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commit 311e1ca
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diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml
@@ -0,0 +1,67 @@
+# This workflow will install Python dependencies, run tests and lint with a variety of Python versions
+# For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
+# For more information on conda actions see: https://autobencoder.com/2020-08-24-conda-actions/
+# Starter workflow for conda: https://github.com/actions/starter-workflows/blob/main/ci/python-package-conda.yml
+
+name: build
+
+on:
+  push:
+    branches: [ master, actions ]
+  pull_request:
+    branches: [ master ]
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    strategy:
+      max-parallel: 3
+      matrix:
+        python-version: [2.7, 3.8]
+    steps:
+    - uses: actions/checkout@v2
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v2
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Setup conda
+      run: |
+        # Add $CONDA/bin to built-in path
+        echo $CONDA/bin >> $GITHUB_PATH
+        conda update -q conda
+        conda info
+    - name: Create conda environment
+      run: |
+        conda create -q -n env python=${{ matrix.python-version }} numpy scipy matplotlib astropy healpy pyyaml emcee fitsio corner -c conda-forge -c kadrlica
+    - name: Install package
+      run: |
+        source activate env
+        export UGALIDIR="$HOME/.ugali"
+        python setup.py -q install --isochrones --catalogs
+    - name: Install test data
+      run: |
+        wget https://github.com/DarkEnergySurvey/ugali/releases/download/v1.7.0/ugali-test-data.tar.gz -O ugali-test-data.tar.gz
+        tar -xzf ugali-test-data.tar.gz
+    - name: Lint with flake8
+      if: ${{ false }}
+      run: |
+        source activate env
+        conda install -q flake8 -c conda-forge
+        # stop the build if there are Python syntax errors or undefined names
+        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
+        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+    - name: Test with nose
+      run: |
+        source activate env
+        conda install -q nose -c conda-forge
+        export MPLBACKEND=Agg
+        # Exclude notebook tests
+        nosetests -v -I test_notebook.py;
+    - name: Test notebooks
+      if: ${{ matrix.python-version == '2.7' }}
+      run: |
+        source activate env
+        conda install -q jupyter nbconvert -c conda-forge
+        # Run notebook tests
+        nosetests -v tests/test_notebook.py
diff --git a/README.md b/README.md
@@ -1,4 +1,4 @@
-[![Build](https://img.shields.io/travis/DarkEnergySurvey/ugali.svg)](https://travis-ci.org/DarkEnergySurvey/ugali)
+[![Build](https://github.com/DarkEnergySurvey/ugali/actions/workflows/python-package.yml/badge.svg)](https://github.com/DarkEnergySurvey/ugali/actions/workflows/python-package.yml)
 [![PyPI](https://img.shields.io/pypi/v/ugali.svg)](https://pypi.python.org/pypi/ugali)
 [![Release](https://img.shields.io/github/release/DarkEnergySurvey/ugali.svg)](../../releases)
 [![License](https://img.shields.io/badge/license-MIT-blue.svg)](../../)

diff --git a/ugali/analysis/imf.py b/ugali/analysis/imf.py
@@ -20,21 +20,22 @@ def __call__(self, mass, **kwargs):
         """ Call the pdf of the mass function """
         return self.pdf(mass,**kwargs)
 
-    def integrate(self, mass_min, mass_max, log_mode=True, weight=False, steps=1e4):
+    def integrate(self, mass_min, mass_max, log_mode=True, weight=False, steps=10000):
         """ Numerical Riemannn integral of the IMF (stupid simple).
 
         Parameters:
         -----------
         mass_min: minimum mass bound for integration (solar masses)
         mass_max: maximum mass bound for integration (solar masses)
         log_mode[True]: use logarithmic steps in stellar mass as oppose to linear
-            weight[False]: weight the integral by stellar mass
-            steps: number of numerical integration steps
+        weight[False]: weight the integral by stellar mass
+        steps: number of numerical integration steps
 
         Returns:
         --------
         result of integral
         """
+        steps = int(steps)
         if log_mode:
             d_log_mass = (np.log10(mass_max) - np.log10(mass_min)) / float(steps)
             log_mass = np.linspace(np.log10(mass_min), np.log10(mass_max), steps)
@@ -73,6 +74,7 @@ def sample(self, n, mass_min=0.1, mass_max=10., steps=10000, seed=None):
         mass     : array of randomly sampled mass values
         """
         if seed is not None: np.random.seed(seed)
+        steps = int(steps)
         d_mass = (mass_max - mass_min) / float(steps)
         mass = np.linspace(mass_min, mass_max, steps)
         cdf = np.insert(np.cumsum(d_mass * self.pdf(mass[1:], log_mode=False)), 0, 0.)

diff --git a/ugali/utils/config.py b/ugali/utils/config.py
@@ -74,7 +74,7 @@ def _load(self, config):
         """
         if isstring(config):
             self.filename = config
-            params = yaml.load(open(config))
+            params = yaml.safe_load(open(config))
         elif isinstance(config, Config):
             # This is the copy constructor...
             self.filename = config.filename

diff --git a/ugali/utils/parabola.py b/ugali/utils/parabola.py
@@ -122,15 +122,15 @@ def profileUpperLimit(self, delta = 2.71):
 
         return max((np.sqrt(b**2 - 4. * a * c) - b) / (2. * a), (-1. * np.sqrt(b**2 - 4. * a * c) - b) / (2. * a)) 
 
-    def bayesianUpperLimit(self, alpha, steps=1.e5, plot=False):
+    def bayesianUpperLimit(self, alpha, steps=1e5, plot=False):
         """
         Compute one-sided upper limit using Bayesian Method of Helene.
         Several methods of increasing numerical stability have been implemented.
         """
         x_dense, y_dense = self.densify()
         y_dense -= np.max(y_dense) # Numeric stability
         f = scipy.interpolate.interp1d(x_dense, y_dense, kind='linear')
-        x = np.linspace(0., np.max(x_dense), steps)
+        x = np.linspace(0., np.max(x_dense), int(steps))
         pdf = np.exp(f(x) / 2.)
         cut = (pdf / np.max(pdf)) > 1.e-10
         x = x[cut]
@@ -147,7 +147,7 @@ def bayesianUpperLimit(self, alpha, steps=1.e5, plot=False):
 
         return cdf_reflect(alpha)
 
-    def bayesianUpperLimit2(self, alpha, steps=1.e5, plot=False):
+    def bayesianUpperLimit2(self, alpha, steps=1e5, plot=False):
         """
         Compute one-sided upper limit using Bayesian Method of Helene.
         """
@@ -156,7 +156,7 @@ def bayesianUpperLimit2(self, alpha, steps=1.e5, plot=False):
             f = scipy.interpolate.interp1d(self.x[cut], self.y[cut], kind='cubic')
         except:
             f = scipy.interpolate.interp1d(self.x[cut], self.y[cut], kind='linear')
-        x = np.linspace(0., np.max(self.x[cut]), steps)
+        x = np.linspace(0., np.max(self.x[cut]), int(steps))
         y = np.exp(f(x) / 2.)
         #forbidden = np.nonzero((y / np.exp(self.vertex_y / 2.)) < 1.e-10)[0]
         forbidden = np.nonzero((y / self.vertex_y) < 1.e-10)[0]
@@ -171,17 +171,17 @@ def bayesianUpperLimit2(self, alpha, steps=1.e5, plot=False):
         return cdf_reflect(alpha)
 
 
-    def confidenceInterval(self, alpha=0.6827, steps=1.e5, plot=False):
+    def confidenceInterval(self, alpha=0.6827, steps=1e5, plot=False):
         """
         Compute two-sided confidence interval by taking x-values corresponding to the largest PDF-values first.
         """
         x_dense, y_dense = self.densify()
         y_dense -= np.max(y_dense) # Numeric stability
         f = scipy.interpolate.interp1d(x_dense, y_dense, kind='linear')
-        x = np.linspace(0., np.max(x_dense), steps)
+        x = np.linspace(0., np.max(x_dense), int(steps))
         # ADW: Why does this start at 0, which often outside the input range?
         # Wouldn't starting at xmin be better:
-        #x = np.linspace(np.min(x_dense), np.max(x_dense), steps)
+        #x = np.linspace(np.min(x_dense), np.max(x_dense), int(steps))
         pdf = np.exp(f(x) / 2.)
         cut = (pdf / np.max(pdf)) > 1.e-10
         x = x[cut]
@@ -206,7 +206,7 @@ def upperLimitsDeltaTS(confidence_level, one_sided=True, degrees_of_freedom=1):
     ts_min = 0 # TS = Test Statistic
     ts_max = 5
     ts_steps = 1000
-    x = np.linspace(ts_min, ts_max, ts_steps)
+    x = np.linspace(ts_min, ts_max, int(ts_steps))
     y = (0.5 * scipy.stats.chi2.sf(x, degrees_of_freedom) - (1. - confidence_level))**2
     return x[np.argmin(y)]