use 1D Histogram functions written in Cython

Support for Particle Shape Orders 0 and 1.

.travis.yml

@@ -19,7 +19,7 @@ virtualenv:
 # command to install dependencies
 install:
   - sudo apt-get update -qq
-  - if [[ $TRAVIS_PYTHON_VERSION == "2.7" ]]; then sudo apt-get install python-matplotlib python-scipy; fi
+  - if [[ $TRAVIS_PYTHON_VERSION == "2.7" ]]; then sudo apt-get install python-matplotlib python-scipy python-cython; fi
   - pip install -r pip-requirements.txt
 
 # run tests

examples/simpleexample.py

@@ -83,4 +83,8 @@ def r(pa):
         # use the plotter with the particle scalars defined above.
         plotter.plotField(pa.createField(r, p_r, optargsh={'bins':[400,400]}))  # plot 12
 
+        # show what particle shapes can do
+        plotter.plotField(pa.createField(PA.X, optargsh={'bins': 100, 'order': 0}), ylim=(3e0, 2e5))
+        plotter.plotField(pa.createField(PA.X, optargsh={'bins': 100, 'order': 1}), ylim=(3e0, 2e5)) 
+
 

postpic/analyzer/particles.py

@@ -598,9 +598,12 @@ def median(self, func, weights=1.0):
 
     # ---- Functions to create a Histogram. ---
 
-    def createHistgram1d(self, scalarfx, optargsh={'bins': 300},
+    def createHistgram1d(self, scalarfx, optargsh={},
                          simextent=False, simgrid=False, rangex=None,
                          weights=lambda x: 1):
+        optargshdefs = {'bins': 300, 'order': 1}
+        optargshdefs.update(optargsh)
+        optargsh = optargshdefs
         if simgrid:
             simextent = True
         xdata = scalarfx(self)
@@ -616,8 +619,16 @@ def createHistgram1d(self, scalarfx, optargsh={'bins': 300},
             if hasattr(scalarfx, 'gridpoints'):
                 optargsh['bins'] = scalarfx.gridpoints
         w = self.weight() * weights(self)
-        h, edges = np.histogram(xdata, weights=w,
-                                range=rangex, **optargsh)
+        try:
+            from .. import cythonfunctions as cyf
+            h, edges = cyf.histogram(xdata, weights=w,
+                                     range=rangex, **optargsh)
+        except ImportError:
+            import warnings
+            warnings.warn('cython libs could not be imported. Falling back to "numpy.histogram".')
+            optargsh.pop('order')
+            h, edges = np.histogram(xdata, weights=w,
+                                    range=rangex, **optargsh)
         h = h / np.diff(edges)  # to calculate particles per xunit.
         return h, edges
 

test/test_cythonfunctions.py

@@ -31,30 +31,30 @@ def setUp(self):
     def test_histogram(self):
         # in this special case, cf.histogram and np.histogram must yield equal results
         # check hist and edges
-        cfh, cfe = cf.histogram(self.data,bins=20, range=(0,1))
+        cfh, cfe = cf.histogram(self.data, bins=20, range=(0,1), order=0)
         nph, npe = np.histogram(self.data, bins=20, range=(0,1))
         self.assertListEqual(list(nph), list(cfh))
         self.assertListEqual(list(npe), list(cfe))
 
     def test_histogramw(self):
         # in this special case, cf.histogram and np.histogram must yield equal results
         # check hist and edges
-        cfh, cfe = cf.histogram(self.data,bins=100, range=(0,1), weights=self.weights)
+        cfh, cfe = cf.histogram(self.data, bins=100, range=(0,1), order=0, weights=self.weights)
         nph, npe = np.histogram(self.data, bins=100, range=(0,1), weights=self.weights)
         diff = np.abs(cfh - nph)
         self.assertAlmostEqual(np.sum(diff), 0)
         self.assertListEqual(list(npe), list(cfe))
 
     def test_histogramo1(self):
-        cfh, cfe = cf.histogram(self.data,bins=100, range=(-0.1,1.1), order=1)
+        cfh, cfe = cf.histogram(self.data, bins=100, range=(-0.1,1.1), order=1)
         nph, npe = np.histogram(self.data, bins=100, range=(-0.1,1.1))
         # just check that no mass is lost
         diff = np.sum(cfh) - np.sum(nph)
         self.assertAlmostEqual(diff, 0)
         self.assertListEqual(list(npe), list(cfe))
 
     def test_histogramo1w(self):
-        cfh, cfe = cf.histogram(self.data,bins=100, range=(-0.1,1.1), order=1, weights=self.weights)
+        cfh, cfe = cf.histogram(self.data, bins=100, range=(-0.1,1.1), order=1, weights=self.weights)
         nph, npe = np.histogram(self.data, bins=100, range=(-0.1,1.1), weights=self.weights)
         # just check that no mass is lost
         diff = np.sum(cfh) - np.sum(nph)