create directory for source code

SAFEpython/__init__.py → src/__init__.py

@@ -1,8 +1,8 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Thu Oct 25 14:48:18 2018
-
-@author: fs14746
-"""
-
-__version__ = "1.0.0"
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Oct 25 14:48:18 2018
+
+@author: fs14746
+"""
+
+__version__ = "1.0.0"

SAFEpython/ishigami_homma.py → src/ishigami_homma.py

@@ -1,86 +1,86 @@
-"""
-    Module to compute the Ishigami-Homma function
-
-    This module is part of the SAFE Toolbox by F. Pianosi, F. Sarrazin and
-    T. Wagener at Bristol University (2015).
-    SAFE is provided without any warranty and for non-commercial use only.
-    For more details, see the Licence file included in the root directory
-    of this distribution.
-    For any comment and feedback, or to discuss a Licence agreement for
-    commercial use, please contact: [email protected]
-    For details on how to cite SAFE in your publication, please see:
-    https://safetoolbox.github.io
-
-    Package version: SAFEpython_v0.1.0
-
-    References:
-
-     Saltelli et al. (2008) Global Sensitivity Analysis, The Primer, Wiley.
-"""
-
-from __future__ import division, absolute_import, print_function
-
-import numpy as np
-from numba import jit
-
-@jit
-def ishigami_homma_function(x):
-    """Implements the Ishigami-Homma function, a standard benchmark function in
-    the Sensitivity Analysis literature (see for instance Eq. (4.34) in
-    Saltelli et al. (2008)).
-
-    Usage:
-    y, V, Si_ex, STi_ex= ishigami_homma_function(x)
-
-    Input:
-         x = vector of inputs x[0],x[1],x[2]               - numpy.ndarray(3, )
-        x(i) ~ Unif(-pi,+pi) for all i
-    Output:
-         y = output                                        - numpy.ndarray(1,)
-         V = output variance (*)                           - numpy.ndarray(1,)
-     Si_ex = first-order sensitivity indices (*)           - numpy.ndarray(3, )
-    STi_ex = total-order sensitivity indices (*)           - numpy.ndarray(3, )
-
-    (*) = exact value computed analytically
-
-    REFERENCES
-
-    Saltelli et al. (2008) Global Sensitivity Analysis, The Primer, Wiley.
-
-    This function is part of the SAFE Toolbox by F. Pianosi, F. Sarrazin
-    and T. Wagener at Bristol University (2015).
-    SAFE is provided without any warranty and for non-commercial use only.
-    For more details, see the Licence file included in the root directory
-    of this distribution.
-    For any comment and feedback, or to discuss a Licence agreement for
-    commercial use, please contact: [email protected]
-    For details on how to cite SAFE in your publication, please see:
-    https://www.safetoolbox.info"""
-
-    a=2
-    b=1
-    y = np.array(np.sin(x[0]) + a*(np.sin(x[1]))**2 + b*(x[2])**4*np.sin(x[0]))
-
-    # By model definition, we should get:
-    # VARy = VAR(Y) = 1/2 + a^2/8 + b*pi^4/5 + b^2*pi^8/18
-    # V1 = VAR(E(Y|X1)) = 1/2 + b*pi^4/5 + b^2*pi^8/50
-    # V2 = VAR(E(Y|X2)) = a^2/8
-    # V3 = VAR(E(Y|X3)) = 0
-    # V13 = VAR(E(Y|X1,X3)) = b^2*pi^4/18 - b^2*pi^8/50
-    # V12 = V23 = V123 = 0
-    # and thus:
-    # ST1 = S1 + S13
-    # ST2 = S2
-    # ST3 = S13
-
-    Si_ex = np.nan * np.ones((3,))
-    STi_ex = np.nan * np.ones((3,))
-    V  = np.array(1/2 + a**2/8 + b*np.pi**4/5 + b**2*np.pi**8/18)
-    Si_ex[0] = (1/2 + b*np.pi**4/5 + b**2*np.pi**8/50)/V
-    Si_ex[1] = a**2/8 / V
-    Si_ex[2] = 0
-    STi_ex[0] = Si_ex[0] + (b**2*np.pi**8/18 - b**2*np.pi**8/50)/V
-    STi_ex[1] = Si_ex[1]
-    STi_ex[2] = (b**2*np.pi**8/18 - b**2*np.pi**8/50)/V
-
+"""
+    Module to compute the Ishigami-Homma function
+
+    This module is part of the SAFE Toolbox by F. Pianosi, F. Sarrazin and
+    T. Wagener at Bristol University (2015).
+    SAFE is provided without any warranty and for non-commercial use only.
+    For more details, see the Licence file included in the root directory
+    of this distribution.
+    For any comment and feedback, or to discuss a Licence agreement for
+    commercial use, please contact: [email protected]
+    For details on how to cite SAFE in your publication, please see:
+    https://safetoolbox.github.io
+
+    Package version: SAFEpython_v0.1.0
+
+    References:
+
+     Saltelli et al. (2008) Global Sensitivity Analysis, The Primer, Wiley.
+"""
+
+from __future__ import division, absolute_import, print_function
+
+import numpy as np
+from numba import jit
+
+@jit
+def ishigami_homma_function(x):
+    """Implements the Ishigami-Homma function, a standard benchmark function in
+    the Sensitivity Analysis literature (see for instance Eq. (4.34) in
+    Saltelli et al. (2008)).
+
+    Usage:
+    y, V, Si_ex, STi_ex= ishigami_homma_function(x)
+
+    Input:
+         x = vector of inputs x[0],x[1],x[2]               - numpy.ndarray(3, )
+        x(i) ~ Unif(-pi,+pi) for all i
+    Output:
+         y = output                                        - numpy.ndarray(1,)
+         V = output variance (*)                           - numpy.ndarray(1,)
+     Si_ex = first-order sensitivity indices (*)           - numpy.ndarray(3, )
+    STi_ex = total-order sensitivity indices (*)           - numpy.ndarray(3, )
+
+    (*) = exact value computed analytically
+
+    REFERENCES
+
+    Saltelli et al. (2008) Global Sensitivity Analysis, The Primer, Wiley.
+
+    This function is part of the SAFE Toolbox by F. Pianosi, F. Sarrazin
+    and T. Wagener at Bristol University (2015).
+    SAFE is provided without any warranty and for non-commercial use only.
+    For more details, see the Licence file included in the root directory
+    of this distribution.
+    For any comment and feedback, or to discuss a Licence agreement for
+    commercial use, please contact: [email protected]
+    For details on how to cite SAFE in your publication, please see:
+    https://www.safetoolbox.info"""
+
+    a=2
+    b=1
+    y = np.array(np.sin(x[0]) + a*(np.sin(x[1]))**2 + b*(x[2])**4*np.sin(x[0]))
+
+    # By model definition, we should get:
+    # VARy = VAR(Y) = 1/2 + a^2/8 + b*pi^4/5 + b^2*pi^8/18
+    # V1 = VAR(E(Y|X1)) = 1/2 + b*pi^4/5 + b^2*pi^8/50
+    # V2 = VAR(E(Y|X2)) = a^2/8
+    # V3 = VAR(E(Y|X3)) = 0
+    # V13 = VAR(E(Y|X1,X3)) = b^2*pi^4/18 - b^2*pi^8/50
+    # V12 = V23 = V123 = 0
+    # and thus:
+    # ST1 = S1 + S13
+    # ST2 = S2
+    # ST3 = S13
+
+    Si_ex = np.nan * np.ones((3,))
+    STi_ex = np.nan * np.ones((3,))
+    V  = np.array(1/2 + a**2/8 + b*np.pi**4/5 + b**2*np.pi**8/18)
+    Si_ex[0] = (1/2 + b*np.pi**4/5 + b**2*np.pi**8/50)/V
+    Si_ex[1] = a**2/8 / V
+    Si_ex[2] = 0
+    STi_ex[0] = Si_ex[0] + (b**2*np.pi**8/18 - b**2*np.pi**8/50)/V
+    STi_ex[1] = Si_ex[1]
+    STi_ex[2] = (b**2*np.pi**8/18 - b**2*np.pi**8/50)/V
+
     return y, V, Si_ex, STi_ex