Check numpy version to determine rcond value

Prior to numpy 1.14, numpy.linag.lstsq does not accept rcond=None
and will raise a TypeError.

In numpy 1.14 and after, None requests the new default behavior.

rmgpy/data/kinetics/groups.py

@@ -46,6 +46,9 @@
 from rmgpy.quantity import constants
 from rmgpy.exceptions import KineticsError, UndeterminableKineticsError, DatabaseError
 
+# Prior to numpy 1.14, `numpy.linalg.lstsq` does not accept None as a value
+RCOND = -1 if int(numpy.__version__.split('.')[1]) < 14 else None
+
 ################################################################################
 
 class KineticsGroups(Database):
@@ -409,7 +412,7 @@ def generateGroupAdditivityValues(self, trainingSet, kunits, method='Arrhenius')
             b = numpy.array(b)
             kdata = numpy.array(kdata)
 
-            x, residues, rank, s = numpy.linalg.lstsq(A, b, rcond=None)
+            x, residues, rank, s = numpy.linalg.lstsq(A, b, rcond=RCOND)
 
             for t, T in enumerate(Tdata):
 
@@ -513,7 +516,7 @@ def generateGroupAdditivityValues(self, trainingSet, kunits, method='Arrhenius')
             b = numpy.array(b)
             kdata = numpy.array(kdata)
 
-            x, residues, rank, s = numpy.linalg.lstsq(A, b, rcond=None)
+            x, residues, rank, s = numpy.linalg.lstsq(A, b, rcond=RCOND)
 
             # Store the results
             self.top[0].data = Arrhenius(
@@ -564,7 +567,7 @@ def generateGroupAdditivityValues(self, trainingSet, kunits, method='Arrhenius')
             A = numpy.array(A)
             b = numpy.array(b)
 
-            x, residues, rank, s = numpy.linalg.lstsq(A, b, rcond=None)
+            x, residues, rank, s = numpy.linalg.lstsq(A, b, rcond=RCOND)
 
             # Store the results
             self.top[0].data = Arrhenius(

rmgpy/kinetics/arrhenius.pyx

@@ -36,6 +36,10 @@ cimport rmgpy.constants as constants
 import rmgpy.quantity as quantity
 from rmgpy.exceptions import KineticsError
 from rmgpy.kinetics.uncertainies import rank_accuracy_map
+
+# Prior to numpy 1.14, `numpy.linalg.lstsq` does not accept None as a value
+RCOND = -1 if int(numpy.__version__.split('.')[1]) < 14 else None
+
 ################################################################################
 
 cdef class Arrhenius(KineticsModel):
@@ -162,7 +166,7 @@ cdef class Arrhenius(KineticsModel):
             for n in range(b.size):
                 A[n,:] *= weights[n]
                 b[n] *= weights[n]
-        x, residues, rank, s = numpy.linalg.lstsq(A, b, rcond=None)
+        x, residues, rank, s = numpy.linalg.lstsq(A, b, rcond=RCOND)
 
         # Determine covarianace matrix to obtain parameter uncertainties
         count = klist.size

rmgpy/kinetics/chebyshev.pyx

@@ -34,6 +34,10 @@ cimport rmgpy.constants as constants
 import rmgpy.quantity as quantity
 import logging
 from rmgpy.exceptions import KineticsError
+
+# Prior to numpy 1.14, `numpy.linalg.lstsq` does not accept None as a value
+RCOND = -1 if int(numpy.__version__.split('.')[1]) < 14 else None
+
 ################################################################################
 
 cdef class Chebyshev(PDepKineticsModel):
@@ -217,7 +221,7 @@ cdef class Chebyshev(PDepKineticsModel):
                 b[p1*nT+t1] = log10(K[t1,p1])
 
         # Do linear least-squares fit to get coefficients
-        x, residues, rank, s = numpy.linalg.lstsq(A, b, rcond=None)
+        x, residues, rank, s = numpy.linalg.lstsq(A, b, rcond=RCOND)
 
         # Extract coefficients
         coeffs = numpy.zeros((degreeT,degreeP), numpy.float64)

rmgpy/kinetics/surface.pyx

@@ -34,6 +34,9 @@ cimport rmgpy.constants as constants
 import rmgpy.quantity as quantity
 from rmgpy.exceptions import KineticsError
 
+# Prior to numpy 1.14, `numpy.linalg.lstsq` does not accept None as a value
+RCOND = -1 if int(numpy.__version__.split('.')[1]) < 14 else None
+
 ################################################################################
 
 cdef class StickingCoefficient(KineticsModel):
@@ -157,7 +160,7 @@ cdef class StickingCoefficient(KineticsModel):
             for n in range(b.size):
                 A[n, :] *= weights[n]
                 b[n] *= weights[n]
-        x, residues, rank, s = numpy.linalg.lstsq(A, b, rcond=None)
+        x, residues, rank, s = numpy.linalg.lstsq(A, b, rcond=RCOND)
 
         # Determine covarianace matrix to obtain parameter uncertainties
         count = klist.size

rmgpy/statmech/torsion.pyx

@@ -50,6 +50,9 @@ import rmgpy.statmech.schrodinger as schrodinger
 from rmgpy.exceptions import NegativeBarrierException
 import logging
 
+# Prior to numpy 1.14, `numpy.linalg.lstsq` does not accept None as a value
+RCOND = -1 if int(numpy.__version__.split('.')[1]) < 14 else None
+
 ################################################################################
 
 cdef class Torsion(Mode):
@@ -539,7 +542,7 @@ cdef class HinderedRotor(Torsion):
             # This row forces dV/dangle = 0 at angle = 0
             for m in range(numterms):
                 A[N,m+numterms] = 1
-            x, residues, rank, s = numpy.linalg.lstsq(A, b, rcond=None)
+            x, residues, rank, s = numpy.linalg.lstsq(A, b, rcond=RCOND)
             fit = numpy.dot(A,x)
             x *= 0.001
             # This checks if there are any negative values in the forier fit.

rmgpy/thermo/wilhoit.pyx

@@ -36,6 +36,9 @@ cimport rmgpy.constants as constants
 import rmgpy.quantity as quantity
 import scipy.linalg
 
+# Prior to numpy 1.14, `numpy.linalg.lstsq` does not accept None as a value
+RCOND = -1 if int(numpy.__version__.split('.')[1]) < 14 else None
+
 ################################################################################
 
 cdef class Wilhoit(HeatCapacityModel):
@@ -277,7 +280,7 @@ cdef class Wilhoit(HeatCapacityModel):
                 for j in range(4):
                     A[i,j] = (y*y*y - y*y) * y**j
                 b[i] = ((Cpdata[i] - Cp0) / (CpInf - Cp0) - y*y)
-            x, residues, rank, s = numpy.linalg.lstsq(A, b, rcond=None)
+            x, residues, rank, s = numpy.linalg.lstsq(A, b, rcond=RCOND)
 
             self.B = (float(B),"K")
             self.a0 = float(x[0])