NUMBA testing

.travis.yml

@@ -13,11 +13,11 @@ env:
     # This environment tests the newest supported anaconda env
     - DISTRIB="conda" PYTHON_VERSION="2.7"
       NUMPY_VERSION="1.10.4" SCIPY_VERSION="0.16.1"
-      SIX_VERSION="1.10.0" COVERAGE="true"
+      SIX_VERSION="1.10.0" NUMBA_VERSION="0.38.0" COVERAGE="true"
     - DISTRIB="conda" PYTHON_VERSION="3.5"
       NUMPY_VERSION="1.10.4" SCIPY_VERSION="0.16.1"
-      SIX_VERSION="1.10.0" COVERAGE="true"
-
+      SIX_VERSION="1.10.0" NUMBA_VERSION="0.38.0" COVERAGE="true"
+      
     # # This environment tests conda extras  matplotlib
     # - DISTRIB="conda_extra" PYTHON_VERSION="3.5"
     #   NUMPY_VERSION="1.10.4" SCIPY_VERSION="0.16.1"
@@ -26,10 +26,10 @@ env:
     # This environment tests minimal dependency versions
     - DISTRIB="conda_min" PYTHON_VERSION="2.7"
       SIX_VERSION="1.10.0" NUMPY_VERSION="1.10.4" SCIPY_VERSION="0.16.1"
-      COVERAGE="true"
-    - DISTRIB="conda_min" PYTHON_VERSION="3.4"
+      NUMBA_VERSION="0.38.0" COVERAGE="true"
+    - DISTRIB="conda_min" PYTHON_VERSION="3.5"
       SIX_VERSION="1.10.0" NUMPY_VERSION="1.10.4" SCIPY_VERSION="0.16.1"
-      COVERAGE="true"
+      NUMBA_VERSION="0.38.0" COVERAGE="true"
 
     # basic Ubuntu build environment
     # - DISTRIB="ubuntu" PYTHON_VERSION="2.7"

continuous_integration/install.sh

@@ -28,7 +28,7 @@ if [[ "$DISTRIB" == "conda_min" ]]; then
     # Configure the conda environment and put it in the path using the
     # provided versions
     conda create -n testenv --yes python=$PYTHON_VERSION pip nose coverage \
-        six=$SIX_VERSION numpy=$NUMPY_VERSION scipy=$SCIPY_VERSION
+        six=$SIX_VERSION numpy=$NUMPY_VERSION scipy=$SCIPY_VERSION numba=$NUMBA_VERSION
     source activate testenv
     pip install matplotlib
 
@@ -49,7 +49,7 @@ elif [[ "$DISTRIB" == "conda" ]]; then
     # Configure the conda environment and put it in the path using the
     # provided versions
     conda create -n testenv --yes python=$PYTHON_VERSION pip nose coverage six=$SIX_VERSION \
-        numpy=$NUMPY_VERSION scipy=$SCIPY_VERSION
+        numpy=$NUMPY_VERSION scipy=$SCIPY_VERSION numba=$NUMBA_VERSION
     source activate testenv
 
     if [[ "$COVERAGE" == "true" ]]; then
@@ -73,7 +73,7 @@ elif [[ "$DISTRIB" == "conda_extra" ]]; then
     # Configure the conda environment and put it in the path using the
     # provided versions
     conda create -n testenv --yes python=$PYTHON_VERSION pip nose coverage six=$SIX_VERSION \
-        numpy=$NUMPY_VERSION scipy=$SCIPY_VERSION
+        numpy=$NUMPY_VERSION scipy=$SCIPY_VERSION  numba=$NUMBA_VERSION
     source activate testenv
 
     pip install scikit-monaco
@@ -94,6 +94,7 @@ elif [[ "$DISTRIB" == "ubuntu" ]]; then
     pip install numpy==$NUMPY_VERSION
     pip install scipy==$SCIPY_VERSION
     pip install six==$SIX_VERSION
+    pip install numba==$NUMBA_VERSION
     pip install matplotlib
 
 elif [[ "$DISTRIB" == "ubuntu_extra" ]]; then
@@ -106,7 +107,7 @@ elif [[ "$DISTRIB" == "ubuntu_extra" ]]; then
     pip install numpy==$NUMPY_VERSION
     pip install scipy==$SCIPY_VERSION
     pip install six==$SIX_VERSION
-    pip install scikit-monaco==$SKMONACO_VERSION
+    pip install numba===$NUMBA_VERSION
     pip install matplotlib==$MATPLOTLIB_VERSION
 
 fi

kcsd/KCSD.py

@@ -12,6 +12,7 @@
 from __future__ import division, print_function, absolute_import
 
 import numpy as np
+from numba import jit 
 from numpy.linalg import LinAlgError, svd
 from scipy import special, integrate, interpolate
 from scipy.spatial import distance
@@ -570,6 +571,7 @@ def forward_model(self, x, R, h, sigma, src_type):
         pot *= 1./(2.0*sigma)
         return pot
 
+    @jit
     def int_pot_1D(self, xp, x, R, h, basis_func):
         """FWD model function.
         Returns contribution of a point xp,yp, belonging to a basis source
@@ -747,6 +749,7 @@ def forward_model(self, x, R, h, sigma, src_type):
         pot *= 1./(2.0*np.pi*sigma)  # Potential basis functions bi_x_y
         return pot
 
+    @jit
     def int_pot_2D(self, xp, yp, x, R, h, basis_func):
         """FWD model function.
         Returns contribution of a point xp,yp, belonging to a basis source
@@ -869,6 +872,7 @@ def forward_model(self, x, R, h, sigma, src_type):
         pot *= 1./(2.0*np.pi*sigma)
         return pot
 
+    @jit
     def int_pot_2D_moi(self, xp, yp, x, R, h, basis_func):
         """FWD model function. Incorporates the Method of Images.
         Returns contribution of a point xp,yp, belonging to a basis source
@@ -1092,6 +1096,7 @@ def forward_model(self, x, R, h, sigma, src_type):
         pot *= 1./(4.0*np.pi*sigma)
         return pot
 
+    @jit
     def int_pot_3D(self, xp, yp, zp, x, R, h, basis_func):
         """FWD model function.
         Returns contribution of a point xp,yp, belonging to a basis source

kcsd/basis_functions.py

@@ -13,8 +13,9 @@
 """
 from __future__ import division, print_function, absolute_import
 import numpy as np
+from numba import jit
 
-
+@jit(nopython=True)
 def gauss(d, stdev, dim):
     """Gaussian function
     Parameters
@@ -33,7 +34,7 @@ def gauss(d, stdev, dim):
     Z = np.exp(-(d**2) / (2*stdev**2)) / (np.sqrt(2*np.pi)*stdev)**dim
     return Z
 
-
+@jit(nopython=True)
 def step_1D(d, R):
     """Returns normalized 1D step function.
     Parameters
@@ -50,6 +51,7 @@ def step_1D(d, R):
     s = s / R #normalize with width
     return s
 
+@jit(nopython=True)
 def gauss_1D(d, three_stdev):
     """Returns normalized gaussian 2D scale function
     Parameters
@@ -66,7 +68,7 @@ def gauss_1D(d, three_stdev):
     Z = gauss(d, stdev, 1)
     return Z
 
-
+@jit(nopython=True)
 def gauss_lim_1D(d, three_stdev):
     """Returns gausian 2D function cut off after 3 standard deviations.
     Parameters
@@ -84,7 +86,7 @@ def gauss_lim_1D(d, three_stdev):
     Z *= (d < three_stdev)
     return Z
 
-
+@jit(nopython=True)
 def step_2D(d, R):
     """Returns normalized 2D step function.
     Parameters
@@ -101,6 +103,7 @@ def step_2D(d, R):
     s = (d <= R) / (np.pi*(R**2))
     return s
 
+@jit(nopython=True)
 def gauss_2D(d, three_stdev):
     """Returns normalized gaussian 2D scale function
     Parameters
@@ -118,7 +121,7 @@ def gauss_2D(d, three_stdev):
     Z = gauss(d, stdev, 2)
     return Z
 
-
+@jit(nopython=True)
 def gauss_lim_2D(d, three_stdev):
     """Returns gausian 2D function cut off after 3 standard deviations.
     Parameters
@@ -135,7 +138,7 @@ def gauss_lim_2D(d, three_stdev):
     Z = (d <= three_stdev)*gauss_2D(d, three_stdev)
     return Z
 
-
+@jit(nopython=True)
 def gauss_3D(d, three_stdev):
     """Returns normalized gaussian 3D scale function
     Parameters
@@ -153,7 +156,7 @@ def gauss_3D(d, three_stdev):
     Z = gauss(d, stdev, 3)
     return Z
 
-
+@jit(nopython=True)
 def gauss_lim_3D(d, three_stdev):
     """Returns normalized gaussian 3D scale function cut off after 3stdev
     Parameters
@@ -171,7 +174,7 @@ def gauss_lim_3D(d, three_stdev):
     Z = Z * (d < (three_stdev))
     return Z
 
-
+@jit(nopython=True)
 def step_3D(d, R):
     """Returns normalized 3D step function.
     Parameters

setup.py

@@ -38,8 +38,9 @@ def readme():
       # ]},
       include_package_data=True,
       install_requires=['future>=0.16.0',
-                        'numpy>=1.8.0',
-                        'scipy>=0.14.0',
+                        'numpy>=1.10.4',
+                        'scipy>=0.16.1',
+                        'numba>=0.38.0',
                         'matplotlib>=2.0'],
       extras_require={'docs': ['numpydoc>=0.5',
                                'sphinx>=1.2.2']},