Datasets:

---

gangiswag

/

python_ablation

like 0

[ "def putvarboundlistconst(self,sub_,bkx_,blx_,bux_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n res = __library__.MSK_XX_putvarboundlistconst(self.__nativep,num_,_sub_tmp,bkx_,blx_,bux_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putvarboundlist(self,sub,bkx,blx,bux): # 3\n num_ = None\n if num_ is None:\n num_ = len(sub)\n elif num_ != len(sub):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None:\n num_ = len(bkx)\n elif num_ != len(bkx):\n raise IndexError(\"Inconsistent length of array bkx\")\n if num_ is None:\n num_ = len(blx)\n elif num_ != len(blx):\n raise IndexError(\"Inconsistent length of array blx\")\n if num_ is None:\n num_ = len(bux)\n elif num_ != len(bux):\n raise IndexError(\"Inconsistent length of array bux\")\n if num_ is None: num_ = 0\n if sub is None: raise TypeError(\"Invalid type for argument sub\")\n if sub is None:\n sub_ = None\n else:\n try:\n sub_ = memoryview(sub)\n except TypeError:\n try:\n _tmparr_sub = array.array(\"i\",sub)\n except TypeError:\n raise TypeError(\"Argument sub has wrong type\")\n else:\n sub_ = memoryview(_tmparr_sub)\n \n else:\n if sub_.format != \"i\":\n sub_ = memoryview(array.array(\"i\",sub))\n \n if bkx is None: raise TypeError(\"Invalid type for argument bkx\")\n if bkx is None:\n bkx_ = None\n else:\n try:\n bkx_ = memoryview(bkx)\n except TypeError:\n try:\n _tmparr_bkx = array.array(\"i\",bkx)\n except TypeError:\n raise TypeError(\"Argument bkx has wrong type\")\n else:\n bkx_ = memoryview(_tmparr_bkx)\n \n else:\n if bkx_.format != \"i\":\n bkx_ = memoryview(array.array(\"i\",bkx))\n \n if blx is None: raise TypeError(\"Invalid type for argument blx\")\n if blx is None:\n blx_ = None\n else:\n try:\n blx_ = memoryview(blx)\n except TypeError:\n try:\n _tmparr_blx = array.array(\"d\",blx)\n except TypeError:\n raise TypeError(\"Argument blx has wrong type\")\n else:\n blx_ = memoryview(_tmparr_blx)\n \n else:\n if blx_.format != \"d\":\n blx_ = memoryview(array.array(\"d\",blx))\n \n if bux is None: raise TypeError(\"Invalid type for argument bux\")\n if bux is None:\n bux_ = None\n else:\n try:\n bux_ = memoryview(bux)\n except TypeError:\n try:\n _tmparr_bux = array.array(\"d\",bux)\n except TypeError:\n raise TypeError(\"Argument bux has wrong type\")\n else:\n bux_ = memoryview(_tmparr_bux)\n \n else:\n if bux_.format != \"d\":\n bux_ = memoryview(array.array(\"d\",bux))\n \n res = self.__obj.putvarboundlist(num_,sub_,bkx_,blx_,bux_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putvarbound(self,j_,bkx_,blx_,bux_):\n res = __library__.MSK_XX_putvarbound(self.__nativep,j_,bkx_,blx_,bux_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putvarbound(self,j_,bk_,bl_,bu_): # 3\n if not isinstance(bk_,boundkey): raise TypeError(\"Argument bk has wrong type\")\n res = self.__obj.putvarbound(j_,bk_,bl_,bu_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putvarboundsliceconst(self,first_,last_,bkx_,blx_,bux_):\n res = __library__.MSK_XX_putvarboundsliceconst(self.__nativep,first_,last_,bkx_,blx_,bux_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putvarboundslice(self,first_,last_,bkx_,blx_,bux_):\n _bkx_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and bkx_ is not None and len(bkx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bkx is not long enough: Is %d, expected %d\" % (len(bkx_),((last_) - (first_))))\n if bkx_ is None:\n raise ValueError(\"Argument bkx cannot be None\")\n if bkx_ is None:\n raise ValueError(\"Argument bkx may not be None\")\n if bkx_ is not None:\n _bkx_tmp = (ctypes.c_int32 * len(bkx_))(*bkx_)\n else:\n _bkx_tmp = None\n _blx_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and blx_ is not None and len(blx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument blx is not long enough: Is %d, expected %d\" % (len(blx_),((last_) - (first_))))\n if blx_ is None:\n raise ValueError(\"Argument blx cannot be None\")\n if blx_ is None:\n raise ValueError(\"Argument blx may not be None\")\n if isinstance(blx_, numpy.ndarray) and blx_.dtype is numpy.dtype(numpy.float64) and blx_.flags.contiguous:\n _blx_copyarray = False\n _blx_tmp = ctypes.cast(blx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif blx_ is not None:\n _blx_copyarray = True\n _blx_np_tmp = numpy.zeros(len(blx_),numpy.dtype(numpy.float64))\n _blx_np_tmp[:] = blx_\n assert _blx_np_tmp.flags.contiguous\n _blx_tmp = ctypes.cast(_blx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _blx_copyarray = False\n _blx_tmp = None\n \n _bux_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and bux_ is not None and len(bux_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bux is not long enough: Is %d, expected %d\" % (len(bux_),((last_) - (first_))))\n if bux_ is None:\n raise ValueError(\"Argument bux cannot be None\")\n if bux_ is None:\n raise ValueError(\"Argument bux may not be None\")\n if isinstance(bux_, numpy.ndarray) and bux_.dtype is numpy.dtype(numpy.float64) and bux_.flags.contiguous:\n _bux_copyarray = False\n _bux_tmp = ctypes.cast(bux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif bux_ is not None:\n _bux_copyarray = True\n _bux_np_tmp = numpy.zeros(len(bux_),numpy.dtype(numpy.float64))\n _bux_np_tmp[:] = bux_\n assert _bux_np_tmp.flags.contiguous\n _bux_tmp = ctypes.cast(_bux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _bux_copyarray = False\n _bux_tmp = None\n \n res = __library__.MSK_XX_putvarboundslice(self.__nativep,first_,last_,_bkx_tmp,_blx_tmp,_bux_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putconboundlist(self,sub,bk,bl,bu): # 3\n num_ = None\n if num_ is None:\n num_ = len(sub)\n elif num_ != len(sub):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None:\n num_ = len(bk)\n elif num_ != len(bk):\n raise IndexError(\"Inconsistent length of array bk\")\n if num_ is None:\n num_ = len(bl)\n elif num_ != len(bl):\n raise IndexError(\"Inconsistent length of array bl\")\n if num_ is None:\n num_ = len(bu)\n elif num_ != len(bu):\n raise IndexError(\"Inconsistent length of array bu\")\n if num_ is None: num_ = 0\n if sub is None: raise TypeError(\"Invalid type for argument sub\")\n if sub is None:\n sub_ = None\n else:\n try:\n sub_ = memoryview(sub)\n except TypeError:\n try:\n _tmparr_sub = array.array(\"i\",sub)\n except TypeError:\n raise TypeError(\"Argument sub has wrong type\")\n else:\n sub_ = memoryview(_tmparr_sub)\n \n else:\n if sub_.format != \"i\":\n sub_ = memoryview(array.array(\"i\",sub))\n \n if bk is None: raise TypeError(\"Invalid type for argument bk\")\n if bk is None:\n bk_ = None\n else:\n try:\n bk_ = memoryview(bk)\n except TypeError:\n try:\n _tmparr_bk = array.array(\"i\",bk)\n except TypeError:\n raise TypeError(\"Argument bk has wrong type\")\n else:\n bk_ = memoryview(_tmparr_bk)\n \n else:\n if bk_.format != \"i\":\n bk_ = memoryview(array.array(\"i\",bk))\n \n if bl is None: raise TypeError(\"Invalid type for argument bl\")\n if bl is None:\n bl_ = None\n else:\n try:\n bl_ = memoryview(bl)\n except TypeError:\n try:\n _tmparr_bl = array.array(\"d\",bl)\n except TypeError:\n raise TypeError(\"Argument bl has wrong type\")\n else:\n bl_ = memoryview(_tmparr_bl)\n \n else:\n if bl_.format != \"d\":\n bl_ = memoryview(array.array(\"d\",bl))\n \n if bu is None: raise TypeError(\"Invalid type for argument bu\")\n if bu is None:\n bu_ = None\n else:\n try:\n bu_ = memoryview(bu)\n except TypeError:\n try:\n _tmparr_bu = array.array(\"d\",bu)\n except TypeError:\n raise TypeError(\"Argument bu has wrong type\")\n else:\n bu_ = memoryview(_tmparr_bu)\n \n else:\n if bu_.format != \"d\":\n bu_ = memoryview(array.array(\"d\",bu))\n \n res = self.__obj.putconboundlist(num_,sub_,bk_,bl_,bu_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putboundlist(self,accmode_,sub,bk,bl,bu): # 3\n if not isinstance(accmode_,accmode): raise TypeError(\"Argument accmode has wrong type\")\n num_ = None\n if num_ is None:\n num_ = len(sub)\n elif num_ != len(sub):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None:\n num_ = len(bk)\n elif num_ != len(bk):\n raise IndexError(\"Inconsistent length of array bk\")\n if num_ is None:\n num_ = len(bl)\n elif num_ != len(bl):\n raise IndexError(\"Inconsistent length of array bl\")\n if num_ is None:\n num_ = len(bu)\n elif num_ != len(bu):\n raise IndexError(\"Inconsistent length of array bu\")\n if num_ is None: num_ = 0\n if sub is None: raise TypeError(\"Invalid type for argument sub\")\n if sub is None:\n sub_ = None\n else:\n try:\n sub_ = memoryview(sub)\n except TypeError:\n try:\n _tmparr_sub = array.array(\"i\",sub)\n except TypeError:\n raise TypeError(\"Argument sub has wrong type\")\n else:\n sub_ = memoryview(_tmparr_sub)\n \n else:\n if sub_.format != \"i\":\n sub_ = memoryview(array.array(\"i\",sub))\n \n if bk is None: raise TypeError(\"Invalid type for argument bk\")\n if bk is None:\n bk_ = None\n else:\n try:\n bk_ = memoryview(bk)\n except TypeError:\n try:\n _tmparr_bk = array.array(\"i\",bk)\n except TypeError:\n raise TypeError(\"Argument bk has wrong type\")\n else:\n bk_ = memoryview(_tmparr_bk)\n \n else:\n if bk_.format != \"i\":\n bk_ = memoryview(array.array(\"i\",bk))\n \n if bl is None: raise TypeError(\"Invalid type for argument bl\")\n if bl is None:\n bl_ = None\n else:\n try:\n bl_ = memoryview(bl)\n except TypeError:\n try:\n _tmparr_bl = array.array(\"d\",bl)\n except TypeError:\n raise TypeError(\"Argument bl has wrong type\")\n else:\n bl_ = memoryview(_tmparr_bl)\n \n else:\n if bl_.format != \"d\":\n bl_ = memoryview(array.array(\"d\",bl))\n \n if bu is None: raise TypeError(\"Invalid type for argument bu\")\n if bu is None:\n bu_ = None\n else:\n try:\n bu_ = memoryview(bu)\n except TypeError:\n try:\n _tmparr_bu = array.array(\"d\",bu)\n except TypeError:\n raise TypeError(\"Argument bu has wrong type\")\n else:\n bu_ = memoryview(_tmparr_bu)\n \n else:\n if bu_.format != \"d\":\n bu_ = memoryview(array.array(\"d\",bu))\n \n res = self.__obj.putboundlist(accmode_,num_,sub_,bk_,bl_,bu_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putconboundlistconst(self,sub_,bkc_,blc_,buc_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n res = __library__.MSK_XX_putconboundlistconst(self.__nativep,num_,_sub_tmp,bkc_,blc_,buc_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putconboundlist(self,sub_,bkc_,blc_,buc_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None:\n num_ = len(bkc_)\n elif num_ != len(bkc_):\n raise IndexError(\"Inconsistent length of array bkc\")\n if num_ is None:\n num_ = len(blc_)\n elif num_ != len(blc_):\n raise IndexError(\"Inconsistent length of array blc\")\n if num_ is None:\n num_ = len(buc_)\n elif num_ != len(buc_):\n raise IndexError(\"Inconsistent length of array buc\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n if bkc_ is None:\n raise ValueError(\"Argument bkc cannot be None\")\n if bkc_ is None:\n raise ValueError(\"Argument bkc may not be None\")\n if bkc_ is not None:\n _bkc_tmp = (ctypes.c_int32 * len(bkc_))(*bkc_)\n else:\n _bkc_tmp = None\n if blc_ is None:\n raise ValueError(\"Argument blc cannot be None\")\n if blc_ is None:\n raise ValueError(\"Argument blc may not be None\")\n if isinstance(blc_, numpy.ndarray) and blc_.dtype is numpy.dtype(numpy.float64) and blc_.flags.contiguous:\n _blc_copyarray = False\n _blc_tmp = ctypes.cast(blc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif blc_ is not None:\n _blc_copyarray = True\n _blc_np_tmp = numpy.zeros(len(blc_),numpy.dtype(numpy.float64))\n _blc_np_tmp[:] = blc_\n assert _blc_np_tmp.flags.contiguous\n _blc_tmp = ctypes.cast(_blc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _blc_copyarray = False\n _blc_tmp = None\n \n if buc_ is None:\n raise ValueError(\"Argument buc cannot be None\")\n if buc_ is None:\n raise ValueError(\"Argument buc may not be None\")\n if isinstance(buc_, numpy.ndarray) and buc_.dtype is numpy.dtype(numpy.float64) and buc_.flags.contiguous:\n _buc_copyarray = False\n _buc_tmp = ctypes.cast(buc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif buc_ is not None:\n _buc_copyarray = True\n _buc_np_tmp = numpy.zeros(len(buc_),numpy.dtype(numpy.float64))\n _buc_np_tmp[:] = buc_\n assert _buc_np_tmp.flags.contiguous\n _buc_tmp = ctypes.cast(_buc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _buc_copyarray = False\n _buc_tmp = None\n \n res = __library__.MSK_XX_putconboundlist(self.__nativep,num_,_sub_tmp,_bkc_tmp,_blc_tmp,_buc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putvarboundslice(self,first_,last_,bk,bl,bu): # 3\n if bk is None: raise TypeError(\"Invalid type for argument bk\")\n if bk is None:\n bk_ = None\n else:\n try:\n bk_ = memoryview(bk)\n except TypeError:\n try:\n _tmparr_bk = array.array(\"i\",bk)\n except TypeError:\n raise TypeError(\"Argument bk has wrong type\")\n else:\n bk_ = memoryview(_tmparr_bk)\n \n else:\n if bk_.format != \"i\":\n bk_ = memoryview(array.array(\"i\",bk))\n \n if bk_ is not None and len(bk_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bk has wrong length\")\n if bl is None: raise TypeError(\"Invalid type for argument bl\")\n if bl is None:\n bl_ = None\n else:\n try:\n bl_ = memoryview(bl)\n except TypeError:\n try:\n _tmparr_bl = array.array(\"d\",bl)\n except TypeError:\n raise TypeError(\"Argument bl has wrong type\")\n else:\n bl_ = memoryview(_tmparr_bl)\n \n else:\n if bl_.format != \"d\":\n bl_ = memoryview(array.array(\"d\",bl))\n \n if bl_ is not None and len(bl_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bl has wrong length\")\n if bu is None: raise TypeError(\"Invalid type for argument bu\")\n if bu is None:\n bu_ = None\n else:\n try:\n bu_ = memoryview(bu)\n except TypeError:\n try:\n _tmparr_bu = array.array(\"d\",bu)\n except TypeError:\n raise TypeError(\"Argument bu has wrong type\")\n else:\n bu_ = memoryview(_tmparr_bu)\n \n else:\n if bu_.format != \"d\":\n bu_ = memoryview(array.array(\"d\",bu))\n \n if bu_ is not None and len(bu_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bu has wrong length\")\n res = self.__obj.putvarboundslice(first_,last_,bk_,bl_,bu_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def chgvarbound(self,j_,lower_,finite_,value_): # 3\n res = self.__obj.chgvarbound(j_,lower_,finite_,value_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def chgvarbound(self,j_,lower_,finite_,value_):\n res = __library__.MSK_XX_chgvarbound(self.__nativep,j_,lower_,finite_,value_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def set_bounds_atom(self,bounds):\n assert bounds.shape == (2,self.Phi.d)\n self.bounds = bounds # data bounds\n self.bounds_atom = bounds.T.tolist()\n for i in range(self.Phi.d): # bounds for the variance in each dimension\n max_variance_this_dimension = (bounds[1][i]-bounds[0][i])**2\n self.bounds_atom.append([self.variance_relative_lowerbound*max_variance_this_dimension,\n self.variance_relative_upperbound*max_variance_this_dimension])", "def create_from_bounds(self, lbs, ubs):\n self.base_vertices = (np.array([lbs])+np.array([ubs])).T/2\n self.base_vectors = np.diag((np.array(ubs)-np.array(lbs))/2)", "def getvarboundslice(self,first_,last_,bk_,bl_,bu_):\n _bk_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and bk_ is not None and len(bk_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bk is not long enough: Is %d, expected %d\" % (len(bk_),((last_) - (first_))))\n if isinstance(bk_,numpy.ndarray) and not bk_.flags.writeable:\n raise ValueError(\"Argument bk must be writable\")\n if bk_ is not None:\n _bk_tmp = (ctypes.c_int32 * len(bk_))()\n else:\n _bk_tmp = None\n _bl_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and bl_ is not None and len(bl_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bl is not long enough: Is %d, expected %d\" % (len(bl_),((last_) - (first_))))\n if isinstance(bl_,numpy.ndarray) and not bl_.flags.writeable:\n raise ValueError(\"Argument bl must be writable\")\n if isinstance(bl_, numpy.ndarray) and bl_.dtype is numpy.dtype(numpy.float64) and bl_.flags.contiguous:\n _bl_copyarray = False\n _bl_tmp = ctypes.cast(bl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif bl_ is not None:\n _bl_copyarray = True\n _bl_np_tmp = numpy.zeros(len(bl_),numpy.dtype(numpy.float64))\n _bl_np_tmp[:] = bl_\n assert _bl_np_tmp.flags.contiguous\n _bl_tmp = ctypes.cast(_bl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _bl_copyarray = False\n _bl_tmp = None\n \n _bu_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and bu_ is not None and len(bu_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bu is not long enough: Is %d, expected %d\" % (len(bu_),((last_) - (first_))))\n if isinstance(bu_,numpy.ndarray) and not bu_.flags.writeable:\n raise ValueError(\"Argument bu must be writable\")\n if isinstance(bu_, numpy.ndarray) and bu_.dtype is numpy.dtype(numpy.float64) and bu_.flags.contiguous:\n _bu_copyarray = False\n _bu_tmp = ctypes.cast(bu_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif bu_ is not None:\n _bu_copyarray = True\n _bu_np_tmp = numpy.zeros(len(bu_),numpy.dtype(numpy.float64))\n _bu_np_tmp[:] = bu_\n assert _bu_np_tmp.flags.contiguous\n _bu_tmp = ctypes.cast(_bu_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _bu_copyarray = False\n _bu_tmp = None\n \n res = __library__.MSK_XX_getvarboundslice(self.__nativep,first_,last_,_bk_tmp,_bl_tmp,_bu_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if bk_ is not None: bk_[:] = [ boundkey(v) for v in _bk_tmp[0:len(bk_)] ]\n if _bl_copyarray:\n bl_[:] = _bl_np_tmp\n if _bu_copyarray:\n bu_[:] = _bu_np_tmp", "def apply_bound(x, var_min, var_max):\n x.position = np.maximum(x.position, var_min)\n x.position = np.minimum(x.position, var_max)", "def _initialize_bounds(problem, bounds, get_bound, set_bound):\n for constraint in problem.constraints:\n root_expr = constraint.root_expr\n expr_bounds = Interval(constraint.lower_bound, constraint.upper_bound)\n if root_expr not in bounds:\n set_bound(root_expr, expr_bounds)\n else:\n existing_bounds = get_bound(root_expr)\n new_bounds = existing_bounds.intersect(expr_bounds)\n set_bound(root_expr, new_bounds)", "def getvarboundslice(self,first_,last_,bk,bl,bu): # 3\n _copyback_bk = False\n if bk is None:\n bk_ = None\n else:\n try:\n bk_ = memoryview(bk)\n except TypeError:\n try:\n _tmparr_bk = array.array(\"i\",bk)\n except TypeError:\n raise TypeError(\"Argument bk has wrong type\")\n else:\n bk_ = memoryview(_tmparr_bk)\n _copyback_bk = True\n else:\n if bk_.format != \"i\":\n bk_ = memoryview(array.array(\"i\",bk))\n _copyback_bk = True\n if bk_ is not None and len(bk_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bk has wrong length\")\n _copyback_bl = False\n if bl is None:\n bl_ = None\n else:\n try:\n bl_ = memoryview(bl)\n except TypeError:\n try:\n _tmparr_bl = array.array(\"d\",bl)\n except TypeError:\n raise TypeError(\"Argument bl has wrong type\")\n else:\n bl_ = memoryview(_tmparr_bl)\n _copyback_bl = True\n else:\n if bl_.format != \"d\":\n bl_ = memoryview(array.array(\"d\",bl))\n _copyback_bl = True\n if bl_ is not None and len(bl_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bl has wrong length\")\n _copyback_bu = False\n if bu is None:\n bu_ = None\n else:\n try:\n bu_ = memoryview(bu)\n except TypeError:\n try:\n _tmparr_bu = array.array(\"d\",bu)\n except TypeError:\n raise TypeError(\"Argument bu has wrong type\")\n else:\n bu_ = memoryview(_tmparr_bu)\n _copyback_bu = True\n else:\n if bu_.format != \"d\":\n bu_ = memoryview(array.array(\"d\",bu))\n _copyback_bu = True\n if bu_ is not None and len(bu_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bu has wrong length\")\n res = self.__obj.getvarboundslice(first_,last_,bk_,bl_,bu_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_bu:\n bu[:] = _tmparr_bu\n if _copyback_bl:\n bl[:] = _tmparr_bl\n if _copyback_bk:\n for __tmp_var_0 in range(len(bk_)): bk[__tmp_var_0] = boundkey(_tmparr_bk[__tmp_var_0])", "def simple_bounds(child, lb, ub):\n assert len(lb) == len(ub), 'Lower and upper bounds have different #s of design variables in simple_bounds function.'\n assert len(lb) == len(child), 'Bounds and child have different #s of design variables in simple_bounds function.'\n for i in range(0, len(child), 1):\n if child[i] < lb[i]:\n child[i] = lb[i]\n\n for i in range(0, len(child), 1):\n if child[i] > ub[i]:\n child[i] = ub[i]\n\n return child", "def removebarvars(self,subset_):\n num_ = None\n if num_ is None:\n num_ = len(subset_)\n elif num_ != len(subset_):\n raise IndexError(\"Inconsistent length of array subset\")\n if subset_ is None:\n raise ValueError(\"Argument subset cannot be None\")\n if subset_ is None:\n raise ValueError(\"Argument subset may not be None\")\n if isinstance(subset_, numpy.ndarray) and subset_.dtype is numpy.dtype(numpy.int32) and subset_.flags.contiguous:\n _subset_copyarray = False\n _subset_tmp = ctypes.cast(subset_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subset_ is not None:\n _subset_copyarray = True\n _subset_np_tmp = numpy.zeros(len(subset_),numpy.dtype(numpy.int32))\n _subset_np_tmp[:] = subset_\n assert _subset_np_tmp.flags.contiguous\n _subset_tmp = ctypes.cast(_subset_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subset_copyarray = False\n _subset_tmp = None\n \n res = __library__.MSK_XX_removebarvars(self.__nativep,num_,_subset_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def set_bounds(self, new_bounds):\n\n # Update the internal object stored dict\n self.pbounds.update(new_bounds)\n\n # Loop through the all bounds and reset the min-max bound matrix\n for row, key in enumerate(self.pbounds.keys()):\n\n # Reset all entries, even if the same.\n self.bounds[row] = self.pbounds[key]", "def bounds(self, pos):", "def putconbound(self,i_,bk_,bl_,bu_): # 3\n if not isinstance(bk_,boundkey): raise TypeError(\"Argument bk has wrong type\")\n res = self.__obj.putconbound(i_,bk_,bl_,bu_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def create_bound_for_scipy(lb, ub):\n lb = tuple(map(convert_inf_to_none, lb))\n ub = tuple(map(convert_inf_to_none, ub))\n return list((lb[i], ub[i]) for i in range(len(ub)))", "def set_bounds_atom(self,bounds):\n assert bounds.shape == (2,self.Phi.d)\n self.bounds = bounds # data bounds\n self.bounds_atom = bounds.T.tolist()", "def RestrictionRangeBound(self, compsIdList, lowerBound, upperBound):\n for i in range(len(compsIdList)): compsIdList[i] -= 1\n if self.solverTypeOptimize:\n self.solver.add(sum([self.a[compId * self.nrVM + j] for compId in compsIdList for j in range(self.nrVM)]) >= lowerBound)\n else:\n self.solver.assert_and_track(\n PbGe(sum([self.a[compId * self.nrVM + j] for compId in compsIdList for j in range(self.nrVM)]),\n lowerBound), \"LabelRangeBound: \" + str(self.labelIdx))\n self.labelIdx += 1\n if self.solverTypeOptimize:\n PbLe(self.solver.add(sum([self.a[compId * self.nrVM + j] for compId in compsIdList for j in range(self.nrVM)]),\n upperBound))\n else:\n self.solver.assert_and_track(\n sum([self.a[compId * self.nrVM + j] for compId in compsIdList for j in range(self.nrVM)]) <= upperBound, \"LabelRangeBound: \" + str(self.labelIdx))\n self.labelIdx += 1", "def set_variable_slices(self, variables):\n # Set up y_slices and bounds\n y_slices = defaultdict(list)\n y_slices_explicit = defaultdict(list)\n start = 0\n end = 0\n lower_bounds = []\n upper_bounds = []\n # Iterate through unpacked variables, adding appropriate slices to y_slices\n for variable in variables:\n # Add up the size of all the domains in variable.domain\n if isinstance(variable, pybamm.ConcatenationVariable):\n start_ = start\n spatial_method = self.spatial_methods[variable.domain[0]]\n children = variable.children\n meshes = OrderedDict()\n for child in children:\n meshes[child] = [spatial_method.mesh[dom] for dom in child.domain]\n sec_points = spatial_method._get_auxiliary_domain_repeats(\n variable.domains\n )\n for i in range(sec_points):\n for child, mesh in meshes.items():\n for domain_mesh in mesh:\n end += domain_mesh.npts_for_broadcast_to_nodes\n # Add to slices\n y_slices[child].append(slice(start_, end))\n y_slices_explicit[child].append(slice(start_, end))\n # Increment start_\n start_ = end\n else:\n end += self._get_variable_size(variable)\n\n # Add to slices\n y_slices[variable].append(slice(start, end))\n y_slices_explicit[variable].append(slice(start, end))\n\n # Add to bounds\n def evaluate_bound(bound, side):\n if bound.has_symbol_of_classes(pybamm.InputParameter):\n if side == \"lower\":\n return -np.inf\n elif side == \"upper\":\n return np.inf\n else:\n return bound.evaluate()\n\n lower_bounds.extend(\n [evaluate_bound(variable.bounds[0], \"lower\")] * (end - start)\n )\n upper_bounds.extend(\n [evaluate_bound(variable.bounds[1], \"upper\")] * (end - start)\n )\n # Increment start\n start = end\n\n # Convert y_slices back to normal dictionary\n self.y_slices = dict(y_slices)\n # Also keep a record of what the y_slices are, to be stored in the model\n self.y_slices_explicit = dict(y_slices_explicit)\n\n # Also keep a record of bounds\n self.bounds = (np.array(lower_bounds), np.array(upper_bounds))\n\n # reset discretised_symbols\n self._discretised_symbols = {}", "def get_variables_binds(self, predicate, bound_variables=None, variables_binds=None, recursion_level=1):\n\n # print(\"EXPLORING\", recursion_level, predicate, variables_binds)\n\n # Set of bound variables in predicate body\n if bound_variables is None:\n bound_variables = set()\n\n # Possible binds\n if variables_binds is None:\n variables_binds = [{}]\n\n recursion_level -= 1\n\n new_possible_binds = []\n\n for body_clause in predicate.body:\n adornments = self.compute_adornments(body_clause.parameters, bound_variables)\n\n # For each fact search if we can match every bound variable and assign free ones\n if body_clause.name in self._facts:\n for fact in self._facts[body_clause.name]:\n possible_binds = self.check_fact_with_adornment(fact, body_clause, adornments, variables_binds)\n if len(possible_binds):\n # A fact matched, we add variables binds to sup\n new_possible_binds.extend(possible_binds)\n\n # if len(new_possible_binds):\n # variables_binds = new_possible_binds\n\n if recursion_level > 0:\n # For each rule\n if body_clause.name in self._rules:\n for applicable_rule in self._rules[body_clause.name]:\n\n n_bound_variables = set()\n n_variables_binds = [{}]\n\n for index, argument in enumerate(body_clause.parameters):\n rule_corresponding_parameter = applicable_rule.head.parameters[index]\n\n if rule_corresponding_parameter.is_constant():\n if argument.is_constant():\n if rule_corresponding_parameter.value != argument.value:\n break\n else:\n if adornments[index]:\n if argument.is_constant():\n n_bound_variables.add(rule_corresponding_parameter.name)\n n_variables_binds[0][rule_corresponding_parameter.name] = argument.value\n elif argument.name in bound_variables and argument.name in variables_binds[0]:\n n_bound_variables.add(rule_corresponding_parameter.name)\n n_variables_binds[0][rule_corresponding_parameter.name] = variables_binds[0][argument.name]\n\n applicable_predicate_binds = self.get_variables_binds(applicable_rule, n_bound_variables, n_variables_binds, recursion_level)\n for n_bind in applicable_predicate_binds:\n adapted_bind = self.substitute_variable_names(n_bind, applicable_rule.head, body_clause)\n new_possible_binds.extend(adapted_bind)\n\n if len(new_possible_binds):\n variables_binds = new_possible_binds.copy()\n new_possible_binds.clear()\n else:\n variables_binds = [{}]\n\n new_possible_binds_no_duplicates = self.remove_duplicate_binds(variables_binds)\n\n if len(new_possible_binds_no_duplicates):\n yield new_possible_binds_no_duplicates", "def bounds(self, new_bounds: devices.PrimaryBounds) -> None:\n self._assert_bounds_are_valid(new_bounds)\n self._bounds = list(new_bounds)" ]

[ "0.8234259", "0.7890434", "0.70631224", "0.6849752", "0.6744828", "0.6431723", "0.6421552", "0.64171445", "0.641259", "0.6356358", "0.6287844", "0.606733", "0.60325545", "0.5835947", "0.5711706", "0.56917834", "0.5684577", "0.5666441", "0.56318825", "0.5488267", "0.54620206", "0.5442061", "0.54355013", "0.5359389", "0.53495306", "0.5347671", "0.5328592", "0.53052133", "0.5293114", "0.52929676" ]

[ "def putvarboundlist(self,sub_,bkx_,blx_,bux_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None:\n num_ = len(bkx_)\n elif num_ != len(bkx_):\n raise IndexError(\"Inconsistent length of array bkx\")\n if num_ is None:\n num_ = len(blx_)\n elif num_ != len(blx_):\n raise IndexError(\"Inconsistent length of array blx\")\n if num_ is None:\n num_ = len(bux_)\n elif num_ != len(bux_):\n raise IndexError(\"Inconsistent length of array bux\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n if bkx_ is None:\n raise ValueError(\"Argument bkx cannot be None\")\n if bkx_ is None:\n raise ValueError(\"Argument bkx may not be None\")\n if bkx_ is not None:\n _bkx_tmp = (ctypes.c_int32 * len(bkx_))(*bkx_)\n else:\n _bkx_tmp = None\n if blx_ is None:\n raise ValueError(\"Argument blx cannot be None\")\n if blx_ is None:\n raise ValueError(\"Argument blx may not be None\")\n if isinstance(blx_, numpy.ndarray) and blx_.dtype is numpy.dtype(numpy.float64) and blx_.flags.contiguous:\n _blx_copyarray = False\n _blx_tmp = ctypes.cast(blx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif blx_ is not None:\n _blx_copyarray = True\n _blx_np_tmp = numpy.zeros(len(blx_),numpy.dtype(numpy.float64))\n _blx_np_tmp[:] = blx_\n assert _blx_np_tmp.flags.contiguous\n _blx_tmp = ctypes.cast(_blx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _blx_copyarray = False\n _blx_tmp = None\n \n if bux_ is None:\n raise ValueError(\"Argument bux cannot be None\")\n if bux_ is None:\n raise ValueError(\"Argument bux may not be None\")\n if isinstance(bux_, numpy.ndarray) and bux_.dtype is numpy.dtype(numpy.float64) and bux_.flags.contiguous:\n _bux_copyarray = False\n _bux_tmp = ctypes.cast(bux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif bux_ is not None:\n _bux_copyarray = True\n _bux_np_tmp = numpy.zeros(len(bux_),numpy.dtype(numpy.float64))\n _bux_np_tmp[:] = bux_\n assert _bux_np_tmp.flags.contiguous\n _bux_tmp = ctypes.cast(_bux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _bux_copyarray = False\n _bux_tmp = None\n \n res = __library__.MSK_XX_putvarboundlist(self.__nativep,num_,_sub_tmp,_bkx_tmp,_blx_tmp,_bux_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putvarboundlist(self,sub,bkx,blx,bux): # 3\n num_ = None\n if num_ is None:\n num_ = len(sub)\n elif num_ != len(sub):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None:\n num_ = len(bkx)\n elif num_ != len(bkx):\n raise IndexError(\"Inconsistent length of array bkx\")\n if num_ is None:\n num_ = len(blx)\n elif num_ != len(blx):\n raise IndexError(\"Inconsistent length of array blx\")\n if num_ is None:\n num_ = len(bux)\n elif num_ != len(bux):\n raise IndexError(\"Inconsistent length of array bux\")\n if num_ is None: num_ = 0\n if sub is None: raise TypeError(\"Invalid type for argument sub\")\n if sub is None:\n sub_ = None\n else:\n try:\n sub_ = memoryview(sub)\n except TypeError:\n try:\n _tmparr_sub = array.array(\"i\",sub)\n except TypeError:\n raise TypeError(\"Argument sub has wrong type\")\n else:\n sub_ = memoryview(_tmparr_sub)\n \n else:\n if sub_.format != \"i\":\n sub_ = memoryview(array.array(\"i\",sub))\n \n if bkx is None: raise TypeError(\"Invalid type for argument bkx\")\n if bkx is None:\n bkx_ = None\n else:\n try:\n bkx_ = memoryview(bkx)\n except TypeError:\n try:\n _tmparr_bkx = array.array(\"i\",bkx)\n except TypeError:\n raise TypeError(\"Argument bkx has wrong type\")\n else:\n bkx_ = memoryview(_tmparr_bkx)\n \n else:\n if bkx_.format != \"i\":\n bkx_ = memoryview(array.array(\"i\",bkx))\n \n if blx is None: raise TypeError(\"Invalid type for argument blx\")\n if blx is None:\n blx_ = None\n else:\n try:\n blx_ = memoryview(blx)\n except TypeError:\n try:\n _tmparr_blx = array.array(\"d\",blx)\n except TypeError:\n raise TypeError(\"Argument blx has wrong type\")\n else:\n blx_ = memoryview(_tmparr_blx)\n \n else:\n if blx_.format != \"d\":\n blx_ = memoryview(array.array(\"d\",blx))\n \n if bux is None: raise TypeError(\"Invalid type for argument bux\")\n if bux is None:\n bux_ = None\n else:\n try:\n bux_ = memoryview(bux)\n except TypeError:\n try:\n _tmparr_bux = array.array(\"d\",bux)\n except TypeError:\n raise TypeError(\"Argument bux has wrong type\")\n else:\n bux_ = memoryview(_tmparr_bux)\n \n else:\n if bux_.format != \"d\":\n bux_ = memoryview(array.array(\"d\",bux))\n \n res = self.__obj.putvarboundlist(num_,sub_,bkx_,blx_,bux_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putvarboundsliceconst(self,first_,last_,bkx_,blx_,bux_):\n res = __library__.MSK_XX_putvarboundsliceconst(self.__nativep,first_,last_,bkx_,blx_,bux_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putconboundlistconst(self,sub_,bkc_,blc_,buc_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n res = __library__.MSK_XX_putconboundlistconst(self.__nativep,num_,_sub_tmp,bkc_,blc_,buc_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putvarbound(self,j_,bkx_,blx_,bux_):\n res = __library__.MSK_XX_putvarbound(self.__nativep,j_,bkx_,blx_,bux_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putvarbound(self,j_,bk_,bl_,bu_): # 3\n if not isinstance(bk_,boundkey): raise TypeError(\"Argument bk has wrong type\")\n res = self.__obj.putvarbound(j_,bk_,bl_,bu_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putconboundlist(self,sub_,bkc_,blc_,buc_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None:\n num_ = len(bkc_)\n elif num_ != len(bkc_):\n raise IndexError(\"Inconsistent length of array bkc\")\n if num_ is None:\n num_ = len(blc_)\n elif num_ != len(blc_):\n raise IndexError(\"Inconsistent length of array blc\")\n if num_ is None:\n num_ = len(buc_)\n elif num_ != len(buc_):\n raise IndexError(\"Inconsistent length of array buc\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n if bkc_ is None:\n raise ValueError(\"Argument bkc cannot be None\")\n if bkc_ is None:\n raise ValueError(\"Argument bkc may not be None\")\n if bkc_ is not None:\n _bkc_tmp = (ctypes.c_int32 * len(bkc_))(*bkc_)\n else:\n _bkc_tmp = None\n if blc_ is None:\n raise ValueError(\"Argument blc cannot be None\")\n if blc_ is None:\n raise ValueError(\"Argument blc may not be None\")\n if isinstance(blc_, numpy.ndarray) and blc_.dtype is numpy.dtype(numpy.float64) and blc_.flags.contiguous:\n _blc_copyarray = False\n _blc_tmp = ctypes.cast(blc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif blc_ is not None:\n _blc_copyarray = True\n _blc_np_tmp = numpy.zeros(len(blc_),numpy.dtype(numpy.float64))\n _blc_np_tmp[:] = blc_\n assert _blc_np_tmp.flags.contiguous\n _blc_tmp = ctypes.cast(_blc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _blc_copyarray = False\n _blc_tmp = None\n \n if buc_ is None:\n raise ValueError(\"Argument buc cannot be None\")\n if buc_ is None:\n raise ValueError(\"Argument buc may not be None\")\n if isinstance(buc_, numpy.ndarray) and buc_.dtype is numpy.dtype(numpy.float64) and buc_.flags.contiguous:\n _buc_copyarray = False\n _buc_tmp = ctypes.cast(buc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif buc_ is not None:\n _buc_copyarray = True\n _buc_np_tmp = numpy.zeros(len(buc_),numpy.dtype(numpy.float64))\n _buc_np_tmp[:] = buc_\n assert _buc_np_tmp.flags.contiguous\n _buc_tmp = ctypes.cast(_buc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _buc_copyarray = False\n _buc_tmp = None\n \n res = __library__.MSK_XX_putconboundlist(self.__nativep,num_,_sub_tmp,_bkc_tmp,_blc_tmp,_buc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putvarboundslice(self,first_,last_,bkx_,blx_,bux_):\n _bkx_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and bkx_ is not None and len(bkx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bkx is not long enough: Is %d, expected %d\" % (len(bkx_),((last_) - (first_))))\n if bkx_ is None:\n raise ValueError(\"Argument bkx cannot be None\")\n if bkx_ is None:\n raise ValueError(\"Argument bkx may not be None\")\n if bkx_ is not None:\n _bkx_tmp = (ctypes.c_int32 * len(bkx_))(*bkx_)\n else:\n _bkx_tmp = None\n _blx_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and blx_ is not None and len(blx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument blx is not long enough: Is %d, expected %d\" % (len(blx_),((last_) - (first_))))\n if blx_ is None:\n raise ValueError(\"Argument blx cannot be None\")\n if blx_ is None:\n raise ValueError(\"Argument blx may not be None\")\n if isinstance(blx_, numpy.ndarray) and blx_.dtype is numpy.dtype(numpy.float64) and blx_.flags.contiguous:\n _blx_copyarray = False\n _blx_tmp = ctypes.cast(blx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif blx_ is not None:\n _blx_copyarray = True\n _blx_np_tmp = numpy.zeros(len(blx_),numpy.dtype(numpy.float64))\n _blx_np_tmp[:] = blx_\n assert _blx_np_tmp.flags.contiguous\n _blx_tmp = ctypes.cast(_blx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _blx_copyarray = False\n _blx_tmp = None\n \n _bux_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and bux_ is not None and len(bux_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bux is not long enough: Is %d, expected %d\" % (len(bux_),((last_) - (first_))))\n if bux_ is None:\n raise ValueError(\"Argument bux cannot be None\")\n if bux_ is None:\n raise ValueError(\"Argument bux may not be None\")\n if isinstance(bux_, numpy.ndarray) and bux_.dtype is numpy.dtype(numpy.float64) and bux_.flags.contiguous:\n _bux_copyarray = False\n _bux_tmp = ctypes.cast(bux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif bux_ is not None:\n _bux_copyarray = True\n _bux_np_tmp = numpy.zeros(len(bux_),numpy.dtype(numpy.float64))\n _bux_np_tmp[:] = bux_\n assert _bux_np_tmp.flags.contiguous\n _bux_tmp = ctypes.cast(_bux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _bux_copyarray = False\n _bux_tmp = None\n \n res = __library__.MSK_XX_putvarboundslice(self.__nativep,first_,last_,_bkx_tmp,_blx_tmp,_bux_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putconboundlist(self,sub,bk,bl,bu): # 3\n num_ = None\n if num_ is None:\n num_ = len(sub)\n elif num_ != len(sub):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None:\n num_ = len(bk)\n elif num_ != len(bk):\n raise IndexError(\"Inconsistent length of array bk\")\n if num_ is None:\n num_ = len(bl)\n elif num_ != len(bl):\n raise IndexError(\"Inconsistent length of array bl\")\n if num_ is None:\n num_ = len(bu)\n elif num_ != len(bu):\n raise IndexError(\"Inconsistent length of array bu\")\n if num_ is None: num_ = 0\n if sub is None: raise TypeError(\"Invalid type for argument sub\")\n if sub is None:\n sub_ = None\n else:\n try:\n sub_ = memoryview(sub)\n except TypeError:\n try:\n _tmparr_sub = array.array(\"i\",sub)\n except TypeError:\n raise TypeError(\"Argument sub has wrong type\")\n else:\n sub_ = memoryview(_tmparr_sub)\n \n else:\n if sub_.format != \"i\":\n sub_ = memoryview(array.array(\"i\",sub))\n \n if bk is None: raise TypeError(\"Invalid type for argument bk\")\n if bk is None:\n bk_ = None\n else:\n try:\n bk_ = memoryview(bk)\n except TypeError:\n try:\n _tmparr_bk = array.array(\"i\",bk)\n except TypeError:\n raise TypeError(\"Argument bk has wrong type\")\n else:\n bk_ = memoryview(_tmparr_bk)\n \n else:\n if bk_.format != \"i\":\n bk_ = memoryview(array.array(\"i\",bk))\n \n if bl is None: raise TypeError(\"Invalid type for argument bl\")\n if bl is None:\n bl_ = None\n else:\n try:\n bl_ = memoryview(bl)\n except TypeError:\n try:\n _tmparr_bl = array.array(\"d\",bl)\n except TypeError:\n raise TypeError(\"Argument bl has wrong type\")\n else:\n bl_ = memoryview(_tmparr_bl)\n \n else:\n if bl_.format != \"d\":\n bl_ = memoryview(array.array(\"d\",bl))\n \n if bu is None: raise TypeError(\"Invalid type for argument bu\")\n if bu is None:\n bu_ = None\n else:\n try:\n bu_ = memoryview(bu)\n except TypeError:\n try:\n _tmparr_bu = array.array(\"d\",bu)\n except TypeError:\n raise TypeError(\"Argument bu has wrong type\")\n else:\n bu_ = memoryview(_tmparr_bu)\n \n else:\n if bu_.format != \"d\":\n bu_ = memoryview(array.array(\"d\",bu))\n \n res = self.__obj.putconboundlist(num_,sub_,bk_,bl_,bu_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def chgvarbound(self,j_,lower_,finite_,value_): # 3\n res = self.__obj.chgvarbound(j_,lower_,finite_,value_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def chgvarbound(self,j_,lower_,finite_,value_):\n res = __library__.MSK_XX_chgvarbound(self.__nativep,j_,lower_,finite_,value_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putboundlist(self,accmode_,sub,bk,bl,bu): # 3\n if not isinstance(accmode_,accmode): raise TypeError(\"Argument accmode has wrong type\")\n num_ = None\n if num_ is None:\n num_ = len(sub)\n elif num_ != len(sub):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None:\n num_ = len(bk)\n elif num_ != len(bk):\n raise IndexError(\"Inconsistent length of array bk\")\n if num_ is None:\n num_ = len(bl)\n elif num_ != len(bl):\n raise IndexError(\"Inconsistent length of array bl\")\n if num_ is None:\n num_ = len(bu)\n elif num_ != len(bu):\n raise IndexError(\"Inconsistent length of array bu\")\n if num_ is None: num_ = 0\n if sub is None: raise TypeError(\"Invalid type for argument sub\")\n if sub is None:\n sub_ = None\n else:\n try:\n sub_ = memoryview(sub)\n except TypeError:\n try:\n _tmparr_sub = array.array(\"i\",sub)\n except TypeError:\n raise TypeError(\"Argument sub has wrong type\")\n else:\n sub_ = memoryview(_tmparr_sub)\n \n else:\n if sub_.format != \"i\":\n sub_ = memoryview(array.array(\"i\",sub))\n \n if bk is None: raise TypeError(\"Invalid type for argument bk\")\n if bk is None:\n bk_ = None\n else:\n try:\n bk_ = memoryview(bk)\n except TypeError:\n try:\n _tmparr_bk = array.array(\"i\",bk)\n except TypeError:\n raise TypeError(\"Argument bk has wrong type\")\n else:\n bk_ = memoryview(_tmparr_bk)\n \n else:\n if bk_.format != \"i\":\n bk_ = memoryview(array.array(\"i\",bk))\n \n if bl is None: raise TypeError(\"Invalid type for argument bl\")\n if bl is None:\n bl_ = None\n else:\n try:\n bl_ = memoryview(bl)\n except TypeError:\n try:\n _tmparr_bl = array.array(\"d\",bl)\n except TypeError:\n raise TypeError(\"Argument bl has wrong type\")\n else:\n bl_ = memoryview(_tmparr_bl)\n \n else:\n if bl_.format != \"d\":\n bl_ = memoryview(array.array(\"d\",bl))\n \n if bu is None: raise TypeError(\"Invalid type for argument bu\")\n if bu is None:\n bu_ = None\n else:\n try:\n bu_ = memoryview(bu)\n except TypeError:\n try:\n _tmparr_bu = array.array(\"d\",bu)\n except TypeError:\n raise TypeError(\"Argument bu has wrong type\")\n else:\n bu_ = memoryview(_tmparr_bu)\n \n else:\n if bu_.format != \"d\":\n bu_ = memoryview(array.array(\"d\",bu))\n \n res = self.__obj.putboundlist(accmode_,num_,sub_,bk_,bl_,bu_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putvarboundslice(self,first_,last_,bk,bl,bu): # 3\n if bk is None: raise TypeError(\"Invalid type for argument bk\")\n if bk is None:\n bk_ = None\n else:\n try:\n bk_ = memoryview(bk)\n except TypeError:\n try:\n _tmparr_bk = array.array(\"i\",bk)\n except TypeError:\n raise TypeError(\"Argument bk has wrong type\")\n else:\n bk_ = memoryview(_tmparr_bk)\n \n else:\n if bk_.format != \"i\":\n bk_ = memoryview(array.array(\"i\",bk))\n \n if bk_ is not None and len(bk_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bk has wrong length\")\n if bl is None: raise TypeError(\"Invalid type for argument bl\")\n if bl is None:\n bl_ = None\n else:\n try:\n bl_ = memoryview(bl)\n except TypeError:\n try:\n _tmparr_bl = array.array(\"d\",bl)\n except TypeError:\n raise TypeError(\"Argument bl has wrong type\")\n else:\n bl_ = memoryview(_tmparr_bl)\n \n else:\n if bl_.format != \"d\":\n bl_ = memoryview(array.array(\"d\",bl))\n \n if bl_ is not None and len(bl_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bl has wrong length\")\n if bu is None: raise TypeError(\"Invalid type for argument bu\")\n if bu is None:\n bu_ = None\n else:\n try:\n bu_ = memoryview(bu)\n except TypeError:\n try:\n _tmparr_bu = array.array(\"d\",bu)\n except TypeError:\n raise TypeError(\"Argument bu has wrong type\")\n else:\n bu_ = memoryview(_tmparr_bu)\n \n else:\n if bu_.format != \"d\":\n bu_ = memoryview(array.array(\"d\",bu))\n \n if bu_ is not None and len(bu_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bu has wrong length\")\n res = self.__obj.putvarboundslice(first_,last_,bk_,bl_,bu_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putconboundsliceconst(self,first_,last_,bkc_,blc_,buc_):\n res = __library__.MSK_XX_putconboundsliceconst(self.__nativep,first_,last_,bkc_,blc_,buc_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def _initialize_bounds(problem, bounds, get_bound, set_bound):\n for constraint in problem.constraints:\n root_expr = constraint.root_expr\n expr_bounds = Interval(constraint.lower_bound, constraint.upper_bound)\n if root_expr not in bounds:\n set_bound(root_expr, expr_bounds)\n else:\n existing_bounds = get_bound(root_expr)\n new_bounds = existing_bounds.intersect(expr_bounds)\n set_bound(root_expr, new_bounds)", "def simple_bounds(child, lb, ub):\n assert len(lb) == len(ub), 'Lower and upper bounds have different #s of design variables in simple_bounds function.'\n assert len(lb) == len(child), 'Bounds and child have different #s of design variables in simple_bounds function.'\n for i in range(0, len(child), 1):\n if child[i] < lb[i]:\n child[i] = lb[i]\n\n for i in range(0, len(child), 1):\n if child[i] > ub[i]:\n child[i] = ub[i]\n\n return child", "def apply_bound(x, var_min, var_max):\n x.position = np.maximum(x.position, var_min)\n x.position = np.minimum(x.position, var_max)", "def getvarboundslice(self,first_,last_,bk_,bl_,bu_):\n _bk_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and bk_ is not None and len(bk_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bk is not long enough: Is %d, expected %d\" % (len(bk_),((last_) - (first_))))\n if isinstance(bk_,numpy.ndarray) and not bk_.flags.writeable:\n raise ValueError(\"Argument bk must be writable\")\n if bk_ is not None:\n _bk_tmp = (ctypes.c_int32 * len(bk_))()\n else:\n _bk_tmp = None\n _bl_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and bl_ is not None and len(bl_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bl is not long enough: Is %d, expected %d\" % (len(bl_),((last_) - (first_))))\n if isinstance(bl_,numpy.ndarray) and not bl_.flags.writeable:\n raise ValueError(\"Argument bl must be writable\")\n if isinstance(bl_, numpy.ndarray) and bl_.dtype is numpy.dtype(numpy.float64) and bl_.flags.contiguous:\n _bl_copyarray = False\n _bl_tmp = ctypes.cast(bl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif bl_ is not None:\n _bl_copyarray = True\n _bl_np_tmp = numpy.zeros(len(bl_),numpy.dtype(numpy.float64))\n _bl_np_tmp[:] = bl_\n assert _bl_np_tmp.flags.contiguous\n _bl_tmp = ctypes.cast(_bl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _bl_copyarray = False\n _bl_tmp = None\n \n _bu_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and bu_ is not None and len(bu_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bu is not long enough: Is %d, expected %d\" % (len(bu_),((last_) - (first_))))\n if isinstance(bu_,numpy.ndarray) and not bu_.flags.writeable:\n raise ValueError(\"Argument bu must be writable\")\n if isinstance(bu_, numpy.ndarray) and bu_.dtype is numpy.dtype(numpy.float64) and bu_.flags.contiguous:\n _bu_copyarray = False\n _bu_tmp = ctypes.cast(bu_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif bu_ is not None:\n _bu_copyarray = True\n _bu_np_tmp = numpy.zeros(len(bu_),numpy.dtype(numpy.float64))\n _bu_np_tmp[:] = bu_\n assert _bu_np_tmp.flags.contiguous\n _bu_tmp = ctypes.cast(_bu_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _bu_copyarray = False\n _bu_tmp = None\n \n res = __library__.MSK_XX_getvarboundslice(self.__nativep,first_,last_,_bk_tmp,_bl_tmp,_bu_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if bk_ is not None: bk_[:] = [ boundkey(v) for v in _bk_tmp[0:len(bk_)] ]\n if _bl_copyarray:\n bl_[:] = _bl_np_tmp\n if _bu_copyarray:\n bu_[:] = _bu_np_tmp", "def RestrictionRangeBound(self, compsIdList, lowerBound, upperBound):\n for i in range(len(compsIdList)): compsIdList[i] -= 1\n if self.solverTypeOptimize:\n self.solver.add(sum([self.a[compId * self.nrVM + j] for compId in compsIdList for j in range(self.nrVM)]) >= lowerBound)\n else:\n self.solver.assert_and_track(\n PbGe(sum([self.a[compId * self.nrVM + j] for compId in compsIdList for j in range(self.nrVM)]),\n lowerBound), \"LabelRangeBound: \" + str(self.labelIdx))\n self.labelIdx += 1\n if self.solverTypeOptimize:\n PbLe(self.solver.add(sum([self.a[compId * self.nrVM + j] for compId in compsIdList for j in range(self.nrVM)]),\n upperBound))\n else:\n self.solver.assert_and_track(\n sum([self.a[compId * self.nrVM + j] for compId in compsIdList for j in range(self.nrVM)]) <= upperBound, \"LabelRangeBound: \" + str(self.labelIdx))\n self.labelIdx += 1", "def set_bounds_atom(self,bounds):\n assert bounds.shape == (2,self.Phi.d)\n self.bounds = bounds # data bounds\n self.bounds_atom = bounds.T.tolist()\n for i in range(self.Phi.d): # bounds for the variance in each dimension\n max_variance_this_dimension = (bounds[1][i]-bounds[0][i])**2\n self.bounds_atom.append([self.variance_relative_lowerbound*max_variance_this_dimension,\n self.variance_relative_upperbound*max_variance_this_dimension])", "def create_from_bounds(self, lbs, ubs):\n self.base_vertices = (np.array([lbs])+np.array([ubs])).T/2\n self.base_vectors = np.diag((np.array(ubs)-np.array(lbs))/2)", "def getvarboundslice(self,first_,last_,bk,bl,bu): # 3\n _copyback_bk = False\n if bk is None:\n bk_ = None\n else:\n try:\n bk_ = memoryview(bk)\n except TypeError:\n try:\n _tmparr_bk = array.array(\"i\",bk)\n except TypeError:\n raise TypeError(\"Argument bk has wrong type\")\n else:\n bk_ = memoryview(_tmparr_bk)\n _copyback_bk = True\n else:\n if bk_.format != \"i\":\n bk_ = memoryview(array.array(\"i\",bk))\n _copyback_bk = True\n if bk_ is not None and len(bk_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bk has wrong length\")\n _copyback_bl = False\n if bl is None:\n bl_ = None\n else:\n try:\n bl_ = memoryview(bl)\n except TypeError:\n try:\n _tmparr_bl = array.array(\"d\",bl)\n except TypeError:\n raise TypeError(\"Argument bl has wrong type\")\n else:\n bl_ = memoryview(_tmparr_bl)\n _copyback_bl = True\n else:\n if bl_.format != \"d\":\n bl_ = memoryview(array.array(\"d\",bl))\n _copyback_bl = True\n if bl_ is not None and len(bl_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bl has wrong length\")\n _copyback_bu = False\n if bu is None:\n bu_ = None\n else:\n try:\n bu_ = memoryview(bu)\n except TypeError:\n try:\n _tmparr_bu = array.array(\"d\",bu)\n except TypeError:\n raise TypeError(\"Argument bu has wrong type\")\n else:\n bu_ = memoryview(_tmparr_bu)\n _copyback_bu = True\n else:\n if bu_.format != \"d\":\n bu_ = memoryview(array.array(\"d\",bu))\n _copyback_bu = True\n if bu_ is not None and len(bu_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bu has wrong length\")\n res = self.__obj.getvarboundslice(first_,last_,bk_,bl_,bu_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_bu:\n bu[:] = _tmparr_bu\n if _copyback_bl:\n bl[:] = _tmparr_bl\n if _copyback_bk:\n for __tmp_var_0 in range(len(bk_)): bk[__tmp_var_0] = boundkey(_tmparr_bk[__tmp_var_0])", "def putconbound(self,i_,bk_,bl_,bu_): # 3\n if not isinstance(bk_,boundkey): raise TypeError(\"Argument bk has wrong type\")\n res = self.__obj.putconbound(i_,bk_,bl_,bu_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def compute_bounds(self, weights, add_coeff, dual_vars, lower_bounds, upper_bounds, store_primal=False):\n x0_coeff = -weights[0].backward(dual_vars.mus[0])\n x0 = torch.where(x0_coeff >= 0, lower_bounds[0].unsqueeze(1), upper_bounds[0].unsqueeze(1))\n bound = utils.bdot(x0, x0_coeff)\n if store_primal:\n self.bounds_primal = x0\n else:\n del x0\n del x0_coeff\n\n for lay_idx in range(1, len(weights)):\n lbs = lower_bounds[lay_idx].unsqueeze(1).clamp(None, 0)\n ubs = upper_bounds[lay_idx].unsqueeze(1).clamp(0, None)\n neg_bias = ((lbs * ubs) / (ubs - lbs))\n neg_bias.masked_fill_(ubs == lbs, 0) # cover case in which ubs & lbs coincide\n bound += utils.bdot(dual_vars.lambdas[lay_idx - 1].clamp(0, None), neg_bias)\n bound -= utils.bdot(dual_vars.mus[lay_idx - 1], weights[lay_idx - 1].get_bias())\n\n bound += utils.bdot(add_coeff, weights[-1].get_bias())\n return bound", "def create_bound_for_scipy(lb, ub):\n lb = tuple(map(convert_inf_to_none, lb))\n ub = tuple(map(convert_inf_to_none, ub))\n return list((lb[i], ub[i]) for i in range(len(ub)))", "def bounds(self, new_bounds: devices.PrimaryBounds) -> None:\n self._assert_bounds_are_valid(new_bounds)\n self._bounds = list(new_bounds)", "def set_bounds(self, new_bounds):\n\n # Update the internal object stored dict\n self.pbounds.update(new_bounds)\n\n # Loop through the all bounds and reset the min-max bound matrix\n for row, key in enumerate(self.pbounds.keys()):\n\n # Reset all entries, even if the same.\n self.bounds[row] = self.pbounds[key]", "def bounds(self, pos):", "def removebarvars(self,subset_):\n num_ = None\n if num_ is None:\n num_ = len(subset_)\n elif num_ != len(subset_):\n raise IndexError(\"Inconsistent length of array subset\")\n if subset_ is None:\n raise ValueError(\"Argument subset cannot be None\")\n if subset_ is None:\n raise ValueError(\"Argument subset may not be None\")\n if isinstance(subset_, numpy.ndarray) and subset_.dtype is numpy.dtype(numpy.int32) and subset_.flags.contiguous:\n _subset_copyarray = False\n _subset_tmp = ctypes.cast(subset_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subset_ is not None:\n _subset_copyarray = True\n _subset_np_tmp = numpy.zeros(len(subset_),numpy.dtype(numpy.int32))\n _subset_np_tmp[:] = subset_\n assert _subset_np_tmp.flags.contiguous\n _subset_tmp = ctypes.cast(_subset_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subset_copyarray = False\n _subset_tmp = None\n \n res = __library__.MSK_XX_removebarvars(self.__nativep,num_,_subset_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def cb_bounds(self, variable, results_dict, keys, fixed_bounds):\n tas_bound, pr_bound = fixed_bounds\n if variable == \"tas\":\n if tas_bound:\n bound_limit = tas_bound\n else:\n bound_limit = self.find_abs_bound_range(results_dict, keys)\n cmap = plt.cm.RdBu_r\n else:\n if pr_bound:\n bound_limit = pr_bound\n else:\n bound_limit = self.find_abs_bound_range(results_dict,\n keys,\n avg_over=25)\n cmap = plt.cm.BrBG\n bounds = np.linspace(-1 * bound_limit, bound_limit, 11)\n return [bounds, cmap]" ]

[ "0.7802839", "0.74736947", "0.7351789", "0.71760994", "0.68241155", "0.65913326", "0.65883243", "0.6351352", "0.63493115", "0.62824434", "0.62661535", "0.6100925", "0.60384923", "0.59712017", "0.58503205", "0.5785788", "0.57782364", "0.5726529", "0.5715237", "0.5681339", "0.5545659", "0.5491208", "0.54890114", "0.5483636", "0.546886", "0.539461", "0.5387674", "0.5384747", "0.5364006", "0.5359506" ]

[ "def putvarboundslice(self,first_,last_,bk,bl,bu): # 3\n if bk is None: raise TypeError(\"Invalid type for argument bk\")\n if bk is None:\n bk_ = None\n else:\n try:\n bk_ = memoryview(bk)\n except TypeError:\n try:\n _tmparr_bk = array.array(\"i\",bk)\n except TypeError:\n raise TypeError(\"Argument bk has wrong type\")\n else:\n bk_ = memoryview(_tmparr_bk)\n \n else:\n if bk_.format != \"i\":\n bk_ = memoryview(array.array(\"i\",bk))\n \n if bk_ is not None and len(bk_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bk has wrong length\")\n if bl is None: raise TypeError(\"Invalid type for argument bl\")\n if bl is None:\n bl_ = None\n else:\n try:\n bl_ = memoryview(bl)\n except TypeError:\n try:\n _tmparr_bl = array.array(\"d\",bl)\n except TypeError:\n raise TypeError(\"Argument bl has wrong type\")\n else:\n bl_ = memoryview(_tmparr_bl)\n \n else:\n if bl_.format != \"d\":\n bl_ = memoryview(array.array(\"d\",bl))\n \n if bl_ is not None and len(bl_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bl has wrong length\")\n if bu is None: raise TypeError(\"Invalid type for argument bu\")\n if bu is None:\n bu_ = None\n else:\n try:\n bu_ = memoryview(bu)\n except TypeError:\n try:\n _tmparr_bu = array.array(\"d\",bu)\n except TypeError:\n raise TypeError(\"Argument bu has wrong type\")\n else:\n bu_ = memoryview(_tmparr_bu)\n \n else:\n if bu_.format != \"d\":\n bu_ = memoryview(array.array(\"d\",bu))\n \n if bu_ is not None and len(bu_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bu has wrong length\")\n res = self.__obj.putvarboundslice(first_,last_,bk_,bl_,bu_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putvarboundslice(self,first_,last_,bkx_,blx_,bux_):\n _bkx_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and bkx_ is not None and len(bkx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bkx is not long enough: Is %d, expected %d\" % (len(bkx_),((last_) - (first_))))\n if bkx_ is None:\n raise ValueError(\"Argument bkx cannot be None\")\n if bkx_ is None:\n raise ValueError(\"Argument bkx may not be None\")\n if bkx_ is not None:\n _bkx_tmp = (ctypes.c_int32 * len(bkx_))(*bkx_)\n else:\n _bkx_tmp = None\n _blx_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and blx_ is not None and len(blx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument blx is not long enough: Is %d, expected %d\" % (len(blx_),((last_) - (first_))))\n if blx_ is None:\n raise ValueError(\"Argument blx cannot be None\")\n if blx_ is None:\n raise ValueError(\"Argument blx may not be None\")\n if isinstance(blx_, numpy.ndarray) and blx_.dtype is numpy.dtype(numpy.float64) and blx_.flags.contiguous:\n _blx_copyarray = False\n _blx_tmp = ctypes.cast(blx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif blx_ is not None:\n _blx_copyarray = True\n _blx_np_tmp = numpy.zeros(len(blx_),numpy.dtype(numpy.float64))\n _blx_np_tmp[:] = blx_\n assert _blx_np_tmp.flags.contiguous\n _blx_tmp = ctypes.cast(_blx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _blx_copyarray = False\n _blx_tmp = None\n \n _bux_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and bux_ is not None and len(bux_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bux is not long enough: Is %d, expected %d\" % (len(bux_),((last_) - (first_))))\n if bux_ is None:\n raise ValueError(\"Argument bux cannot be None\")\n if bux_ is None:\n raise ValueError(\"Argument bux may not be None\")\n if isinstance(bux_, numpy.ndarray) and bux_.dtype is numpy.dtype(numpy.float64) and bux_.flags.contiguous:\n _bux_copyarray = False\n _bux_tmp = ctypes.cast(bux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif bux_ is not None:\n _bux_copyarray = True\n _bux_np_tmp = numpy.zeros(len(bux_),numpy.dtype(numpy.float64))\n _bux_np_tmp[:] = bux_\n assert _bux_np_tmp.flags.contiguous\n _bux_tmp = ctypes.cast(_bux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _bux_copyarray = False\n _bux_tmp = None\n \n res = __library__.MSK_XX_putvarboundslice(self.__nativep,first_,last_,_bkx_tmp,_blx_tmp,_bux_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def getvarboundslice(self,first_,last_,bk,bl,bu): # 3\n _copyback_bk = False\n if bk is None:\n bk_ = None\n else:\n try:\n bk_ = memoryview(bk)\n except TypeError:\n try:\n _tmparr_bk = array.array(\"i\",bk)\n except TypeError:\n raise TypeError(\"Argument bk has wrong type\")\n else:\n bk_ = memoryview(_tmparr_bk)\n _copyback_bk = True\n else:\n if bk_.format != \"i\":\n bk_ = memoryview(array.array(\"i\",bk))\n _copyback_bk = True\n if bk_ is not None and len(bk_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bk has wrong length\")\n _copyback_bl = False\n if bl is None:\n bl_ = None\n else:\n try:\n bl_ = memoryview(bl)\n except TypeError:\n try:\n _tmparr_bl = array.array(\"d\",bl)\n except TypeError:\n raise TypeError(\"Argument bl has wrong type\")\n else:\n bl_ = memoryview(_tmparr_bl)\n _copyback_bl = True\n else:\n if bl_.format != \"d\":\n bl_ = memoryview(array.array(\"d\",bl))\n _copyback_bl = True\n if bl_ is not None and len(bl_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bl has wrong length\")\n _copyback_bu = False\n if bu is None:\n bu_ = None\n else:\n try:\n bu_ = memoryview(bu)\n except TypeError:\n try:\n _tmparr_bu = array.array(\"d\",bu)\n except TypeError:\n raise TypeError(\"Argument bu has wrong type\")\n else:\n bu_ = memoryview(_tmparr_bu)\n _copyback_bu = True\n else:\n if bu_.format != \"d\":\n bu_ = memoryview(array.array(\"d\",bu))\n _copyback_bu = True\n if bu_ is not None and len(bu_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bu has wrong length\")\n res = self.__obj.getvarboundslice(first_,last_,bk_,bl_,bu_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_bu:\n bu[:] = _tmparr_bu\n if _copyback_bl:\n bl[:] = _tmparr_bl\n if _copyback_bk:\n for __tmp_var_0 in range(len(bk_)): bk[__tmp_var_0] = boundkey(_tmparr_bk[__tmp_var_0])", "def putconboundsliceconst(self,first_,last_,bkc_,blc_,buc_):\n res = __library__.MSK_XX_putconboundsliceconst(self.__nativep,first_,last_,bkc_,blc_,buc_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def getvarboundslice(self,first_,last_,bk_,bl_,bu_):\n _bk_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and bk_ is not None and len(bk_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bk is not long enough: Is %d, expected %d\" % (len(bk_),((last_) - (first_))))\n if isinstance(bk_,numpy.ndarray) and not bk_.flags.writeable:\n raise ValueError(\"Argument bk must be writable\")\n if bk_ is not None:\n _bk_tmp = (ctypes.c_int32 * len(bk_))()\n else:\n _bk_tmp = None\n _bl_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and bl_ is not None and len(bl_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bl is not long enough: Is %d, expected %d\" % (len(bl_),((last_) - (first_))))\n if isinstance(bl_,numpy.ndarray) and not bl_.flags.writeable:\n raise ValueError(\"Argument bl must be writable\")\n if isinstance(bl_, numpy.ndarray) and bl_.dtype is numpy.dtype(numpy.float64) and bl_.flags.contiguous:\n _bl_copyarray = False\n _bl_tmp = ctypes.cast(bl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif bl_ is not None:\n _bl_copyarray = True\n _bl_np_tmp = numpy.zeros(len(bl_),numpy.dtype(numpy.float64))\n _bl_np_tmp[:] = bl_\n assert _bl_np_tmp.flags.contiguous\n _bl_tmp = ctypes.cast(_bl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _bl_copyarray = False\n _bl_tmp = None\n \n _bu_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and bu_ is not None and len(bu_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bu is not long enough: Is %d, expected %d\" % (len(bu_),((last_) - (first_))))\n if isinstance(bu_,numpy.ndarray) and not bu_.flags.writeable:\n raise ValueError(\"Argument bu must be writable\")\n if isinstance(bu_, numpy.ndarray) and bu_.dtype is numpy.dtype(numpy.float64) and bu_.flags.contiguous:\n _bu_copyarray = False\n _bu_tmp = ctypes.cast(bu_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif bu_ is not None:\n _bu_copyarray = True\n _bu_np_tmp = numpy.zeros(len(bu_),numpy.dtype(numpy.float64))\n _bu_np_tmp[:] = bu_\n assert _bu_np_tmp.flags.contiguous\n _bu_tmp = ctypes.cast(_bu_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _bu_copyarray = False\n _bu_tmp = None\n \n res = __library__.MSK_XX_getvarboundslice(self.__nativep,first_,last_,_bk_tmp,_bl_tmp,_bu_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if bk_ is not None: bk_[:] = [ boundkey(v) for v in _bk_tmp[0:len(bk_)] ]\n if _bl_copyarray:\n bl_[:] = _bl_np_tmp\n if _bu_copyarray:\n bu_[:] = _bu_np_tmp", "def putvarboundlistconst(self,sub_,bkx_,blx_,bux_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n res = __library__.MSK_XX_putvarboundlistconst(self.__nativep,num_,_sub_tmp,bkx_,blx_,bux_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putconboundslice(self,first_,last_,bk,bl,bu): # 3\n if bk is None: raise TypeError(\"Invalid type for argument bk\")\n if bk is None:\n bk_ = None\n else:\n try:\n bk_ = memoryview(bk)\n except TypeError:\n try:\n _tmparr_bk = array.array(\"i\",bk)\n except TypeError:\n raise TypeError(\"Argument bk has wrong type\")\n else:\n bk_ = memoryview(_tmparr_bk)\n \n else:\n if bk_.format != \"i\":\n bk_ = memoryview(array.array(\"i\",bk))\n \n if bk_ is not None and len(bk_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bk has wrong length\")\n if bl is None: raise TypeError(\"Invalid type for argument bl\")\n if bl is None:\n bl_ = None\n else:\n try:\n bl_ = memoryview(bl)\n except TypeError:\n try:\n _tmparr_bl = array.array(\"d\",bl)\n except TypeError:\n raise TypeError(\"Argument bl has wrong type\")\n else:\n bl_ = memoryview(_tmparr_bl)\n \n else:\n if bl_.format != \"d\":\n bl_ = memoryview(array.array(\"d\",bl))\n \n if bl_ is not None and len(bl_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bl has wrong length\")\n if bu is None: raise TypeError(\"Invalid type for argument bu\")\n if bu is None:\n bu_ = None\n else:\n try:\n bu_ = memoryview(bu)\n except TypeError:\n try:\n _tmparr_bu = array.array(\"d\",bu)\n except TypeError:\n raise TypeError(\"Argument bu has wrong type\")\n else:\n bu_ = memoryview(_tmparr_bu)\n \n else:\n if bu_.format != \"d\":\n bu_ = memoryview(array.array(\"d\",bu))\n \n if bu_ is not None and len(bu_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bu has wrong length\")\n res = self.__obj.putconboundslice(first_,last_,bk_,bl_,bu_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putboundslice(self,con_,first_,last_,bk,bl,bu): # 3\n if not isinstance(con_,accmode): raise TypeError(\"Argument con has wrong type\")\n if bk is None: raise TypeError(\"Invalid type for argument bk\")\n if bk is None:\n bk_ = None\n else:\n try:\n bk_ = memoryview(bk)\n except TypeError:\n try:\n _tmparr_bk = array.array(\"i\",bk)\n except TypeError:\n raise TypeError(\"Argument bk has wrong type\")\n else:\n bk_ = memoryview(_tmparr_bk)\n \n else:\n if bk_.format != \"i\":\n bk_ = memoryview(array.array(\"i\",bk))\n \n if bk_ is not None and len(bk_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bk has wrong length\")\n if bl is None: raise TypeError(\"Invalid type for argument bl\")\n if bl is None:\n bl_ = None\n else:\n try:\n bl_ = memoryview(bl)\n except TypeError:\n try:\n _tmparr_bl = array.array(\"d\",bl)\n except TypeError:\n raise TypeError(\"Argument bl has wrong type\")\n else:\n bl_ = memoryview(_tmparr_bl)\n \n else:\n if bl_.format != \"d\":\n bl_ = memoryview(array.array(\"d\",bl))\n \n if bl_ is not None and len(bl_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bl has wrong length\")\n if bu is None: raise TypeError(\"Invalid type for argument bu\")\n if bu is None:\n bu_ = None\n else:\n try:\n bu_ = memoryview(bu)\n except TypeError:\n try:\n _tmparr_bu = array.array(\"d\",bu)\n except TypeError:\n raise TypeError(\"Argument bu has wrong type\")\n else:\n bu_ = memoryview(_tmparr_bu)\n \n else:\n if bu_.format != \"d\":\n bu_ = memoryview(array.array(\"d\",bu))\n \n if bu_ is not None and len(bu_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bu has wrong length\")\n res = self.__obj.putboundslice(con_,first_,last_,bk_,bl_,bu_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putconboundslice(self,first_,last_,bkc_,blc_,buc_):\n _bkc_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and bkc_ is not None and len(bkc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bkc is not long enough: Is %d, expected %d\" % (len(bkc_),((last_) - (first_))))\n if bkc_ is None:\n raise ValueError(\"Argument bkc cannot be None\")\n if bkc_ is None:\n raise ValueError(\"Argument bkc may not be None\")\n if bkc_ is not None:\n _bkc_tmp = (ctypes.c_int32 * len(bkc_))(*bkc_)\n else:\n _bkc_tmp = None\n _blc_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and blc_ is not None and len(blc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument blc is not long enough: Is %d, expected %d\" % (len(blc_),((last_) - (first_))))\n if blc_ is None:\n raise ValueError(\"Argument blc cannot be None\")\n if blc_ is None:\n raise ValueError(\"Argument blc may not be None\")\n if isinstance(blc_, numpy.ndarray) and blc_.dtype is numpy.dtype(numpy.float64) and blc_.flags.contiguous:\n _blc_copyarray = False\n _blc_tmp = ctypes.cast(blc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif blc_ is not None:\n _blc_copyarray = True\n _blc_np_tmp = numpy.zeros(len(blc_),numpy.dtype(numpy.float64))\n _blc_np_tmp[:] = blc_\n assert _blc_np_tmp.flags.contiguous\n _blc_tmp = ctypes.cast(_blc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _blc_copyarray = False\n _blc_tmp = None\n \n _buc_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and buc_ is not None and len(buc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument buc is not long enough: Is %d, expected %d\" % (len(buc_),((last_) - (first_))))\n if buc_ is None:\n raise ValueError(\"Argument buc cannot be None\")\n if buc_ is None:\n raise ValueError(\"Argument buc may not be None\")\n if isinstance(buc_, numpy.ndarray) and buc_.dtype is numpy.dtype(numpy.float64) and buc_.flags.contiguous:\n _buc_copyarray = False\n _buc_tmp = ctypes.cast(buc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif buc_ is not None:\n _buc_copyarray = True\n _buc_np_tmp = numpy.zeros(len(buc_),numpy.dtype(numpy.float64))\n _buc_np_tmp[:] = buc_\n assert _buc_np_tmp.flags.contiguous\n _buc_tmp = ctypes.cast(_buc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _buc_copyarray = False\n _buc_tmp = None\n \n res = __library__.MSK_XX_putconboundslice(self.__nativep,first_,last_,_bkc_tmp,_blc_tmp,_buc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putvarboundlist(self,sub_,bkx_,blx_,bux_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None:\n num_ = len(bkx_)\n elif num_ != len(bkx_):\n raise IndexError(\"Inconsistent length of array bkx\")\n if num_ is None:\n num_ = len(blx_)\n elif num_ != len(blx_):\n raise IndexError(\"Inconsistent length of array blx\")\n if num_ is None:\n num_ = len(bux_)\n elif num_ != len(bux_):\n raise IndexError(\"Inconsistent length of array bux\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n if bkx_ is None:\n raise ValueError(\"Argument bkx cannot be None\")\n if bkx_ is None:\n raise ValueError(\"Argument bkx may not be None\")\n if bkx_ is not None:\n _bkx_tmp = (ctypes.c_int32 * len(bkx_))(*bkx_)\n else:\n _bkx_tmp = None\n if blx_ is None:\n raise ValueError(\"Argument blx cannot be None\")\n if blx_ is None:\n raise ValueError(\"Argument blx may not be None\")\n if isinstance(blx_, numpy.ndarray) and blx_.dtype is numpy.dtype(numpy.float64) and blx_.flags.contiguous:\n _blx_copyarray = False\n _blx_tmp = ctypes.cast(blx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif blx_ is not None:\n _blx_copyarray = True\n _blx_np_tmp = numpy.zeros(len(blx_),numpy.dtype(numpy.float64))\n _blx_np_tmp[:] = blx_\n assert _blx_np_tmp.flags.contiguous\n _blx_tmp = ctypes.cast(_blx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _blx_copyarray = False\n _blx_tmp = None\n \n if bux_ is None:\n raise ValueError(\"Argument bux cannot be None\")\n if bux_ is None:\n raise ValueError(\"Argument bux may not be None\")\n if isinstance(bux_, numpy.ndarray) and bux_.dtype is numpy.dtype(numpy.float64) and bux_.flags.contiguous:\n _bux_copyarray = False\n _bux_tmp = ctypes.cast(bux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif bux_ is not None:\n _bux_copyarray = True\n _bux_np_tmp = numpy.zeros(len(bux_),numpy.dtype(numpy.float64))\n _bux_np_tmp[:] = bux_\n assert _bux_np_tmp.flags.contiguous\n _bux_tmp = ctypes.cast(_bux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _bux_copyarray = False\n _bux_tmp = None\n \n res = __library__.MSK_XX_putvarboundlist(self.__nativep,num_,_sub_tmp,_bkx_tmp,_blx_tmp,_bux_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def getconboundslice(self,first_,last_,bk_,bl_,bu_):\n _bk_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and bk_ is not None and len(bk_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bk is not long enough: Is %d, expected %d\" % (len(bk_),((last_) - (first_))))\n if isinstance(bk_,numpy.ndarray) and not bk_.flags.writeable:\n raise ValueError(\"Argument bk must be writable\")\n if bk_ is not None:\n _bk_tmp = (ctypes.c_int32 * len(bk_))()\n else:\n _bk_tmp = None\n _bl_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and bl_ is not None and len(bl_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bl is not long enough: Is %d, expected %d\" % (len(bl_),((last_) - (first_))))\n if isinstance(bl_,numpy.ndarray) and not bl_.flags.writeable:\n raise ValueError(\"Argument bl must be writable\")\n if isinstance(bl_, numpy.ndarray) and bl_.dtype is numpy.dtype(numpy.float64) and bl_.flags.contiguous:\n _bl_copyarray = False\n _bl_tmp = ctypes.cast(bl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif bl_ is not None:\n _bl_copyarray = True\n _bl_np_tmp = numpy.zeros(len(bl_),numpy.dtype(numpy.float64))\n _bl_np_tmp[:] = bl_\n assert _bl_np_tmp.flags.contiguous\n _bl_tmp = ctypes.cast(_bl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _bl_copyarray = False\n _bl_tmp = None\n \n _bu_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and bu_ is not None and len(bu_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bu is not long enough: Is %d, expected %d\" % (len(bu_),((last_) - (first_))))\n if isinstance(bu_,numpy.ndarray) and not bu_.flags.writeable:\n raise ValueError(\"Argument bu must be writable\")\n if isinstance(bu_, numpy.ndarray) and bu_.dtype is numpy.dtype(numpy.float64) and bu_.flags.contiguous:\n _bu_copyarray = False\n _bu_tmp = ctypes.cast(bu_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif bu_ is not None:\n _bu_copyarray = True\n _bu_np_tmp = numpy.zeros(len(bu_),numpy.dtype(numpy.float64))\n _bu_np_tmp[:] = bu_\n assert _bu_np_tmp.flags.contiguous\n _bu_tmp = ctypes.cast(_bu_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _bu_copyarray = False\n _bu_tmp = None\n \n res = __library__.MSK_XX_getconboundslice(self.__nativep,first_,last_,_bk_tmp,_bl_tmp,_bu_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if bk_ is not None: bk_[:] = [ boundkey(v) for v in _bk_tmp[0:len(bk_)] ]\n if _bl_copyarray:\n bl_[:] = _bl_np_tmp\n if _bu_copyarray:\n bu_[:] = _bu_np_tmp", "def putxxslice(self,whichsol_,first_,last_,xx_):\n _xx_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and xx_ is not None and len(xx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument xx is not long enough: Is %d, expected %d\" % (len(xx_),((last_) - (first_))))\n if xx_ is None:\n raise ValueError(\"Argument xx cannot be None\")\n if xx_ is None:\n raise ValueError(\"Argument xx may not be None\")\n if isinstance(xx_, numpy.ndarray) and xx_.dtype is numpy.dtype(numpy.float64) and xx_.flags.contiguous:\n _xx_copyarray = False\n _xx_tmp = ctypes.cast(xx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif xx_ is not None:\n _xx_copyarray = True\n _xx_np_tmp = numpy.zeros(len(xx_),numpy.dtype(numpy.float64))\n _xx_np_tmp[:] = xx_\n assert _xx_np_tmp.flags.contiguous\n _xx_tmp = ctypes.cast(_xx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _xx_copyarray = False\n _xx_tmp = None\n \n res = __library__.MSK_XX_putxxslice(self.__nativep,whichsol_,first_,last_,_xx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def getconboundslice(self,first_,last_,bk,bl,bu): # 3\n _copyback_bk = False\n if bk is None:\n bk_ = None\n else:\n try:\n bk_ = memoryview(bk)\n except TypeError:\n try:\n _tmparr_bk = array.array(\"i\",bk)\n except TypeError:\n raise TypeError(\"Argument bk has wrong type\")\n else:\n bk_ = memoryview(_tmparr_bk)\n _copyback_bk = True\n else:\n if bk_.format != \"i\":\n bk_ = memoryview(array.array(\"i\",bk))\n _copyback_bk = True\n if bk_ is not None and len(bk_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bk has wrong length\")\n _copyback_bl = False\n if bl is None:\n bl_ = None\n else:\n try:\n bl_ = memoryview(bl)\n except TypeError:\n try:\n _tmparr_bl = array.array(\"d\",bl)\n except TypeError:\n raise TypeError(\"Argument bl has wrong type\")\n else:\n bl_ = memoryview(_tmparr_bl)\n _copyback_bl = True\n else:\n if bl_.format != \"d\":\n bl_ = memoryview(array.array(\"d\",bl))\n _copyback_bl = True\n if bl_ is not None and len(bl_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bl has wrong length\")\n _copyback_bu = False\n if bu is None:\n bu_ = None\n else:\n try:\n bu_ = memoryview(bu)\n except TypeError:\n try:\n _tmparr_bu = array.array(\"d\",bu)\n except TypeError:\n raise TypeError(\"Argument bu has wrong type\")\n else:\n bu_ = memoryview(_tmparr_bu)\n _copyback_bu = True\n else:\n if bu_.format != \"d\":\n bu_ = memoryview(array.array(\"d\",bu))\n _copyback_bu = True\n if bu_ is not None and len(bu_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bu has wrong length\")\n res = self.__obj.getconboundslice(first_,last_,bk_,bl_,bu_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_bu:\n bu[:] = _tmparr_bu\n if _copyback_bl:\n bl[:] = _tmparr_bl\n if _copyback_bk:\n for __tmp_var_0 in range(len(bk_)): bk[__tmp_var_0] = boundkey(_tmparr_bk[__tmp_var_0])", "def putxxslice(self,whichsol_,first_,last_,xx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if xx is None: raise TypeError(\"Invalid type for argument xx\")\n if xx is None:\n xx_ = None\n else:\n try:\n xx_ = memoryview(xx)\n except TypeError:\n try:\n _tmparr_xx = array.array(\"d\",xx)\n except TypeError:\n raise TypeError(\"Argument xx has wrong type\")\n else:\n xx_ = memoryview(_tmparr_xx)\n \n else:\n if xx_.format != \"d\":\n xx_ = memoryview(array.array(\"d\",xx))\n \n if xx_ is not None and len(xx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument xx has wrong length\")\n res = self.__obj.putxxslice(whichsol_,first_,last_,xx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putcslice(self,first_,last_,slice_):\n _slice_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and slice_ is not None and len(slice_) != ((last_) - (first_)):\n raise ValueError(\"Array argument slice is not long enough: Is %d, expected %d\" % (len(slice_),((last_) - (first_))))\n if slice_ is None:\n raise ValueError(\"Argument slice cannot be None\")\n if slice_ is None:\n raise ValueError(\"Argument slice may not be None\")\n if isinstance(slice_, numpy.ndarray) and slice_.dtype is numpy.dtype(numpy.float64) and slice_.flags.contiguous:\n _slice_copyarray = False\n _slice_tmp = ctypes.cast(slice_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slice_ is not None:\n _slice_copyarray = True\n _slice_np_tmp = numpy.zeros(len(slice_),numpy.dtype(numpy.float64))\n _slice_np_tmp[:] = slice_\n assert _slice_np_tmp.flags.contiguous\n _slice_tmp = ctypes.cast(_slice_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slice_copyarray = False\n _slice_tmp = None\n \n res = __library__.MSK_XX_putcslice(self.__nativep,first_,last_,_slice_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putvarbound(self,j_,bkx_,blx_,bux_):\n res = __library__.MSK_XX_putvarbound(self.__nativep,j_,bkx_,blx_,bux_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def set_variable_slices(self, variables):\n # Set up y_slices and bounds\n y_slices = defaultdict(list)\n y_slices_explicit = defaultdict(list)\n start = 0\n end = 0\n lower_bounds = []\n upper_bounds = []\n # Iterate through unpacked variables, adding appropriate slices to y_slices\n for variable in variables:\n # Add up the size of all the domains in variable.domain\n if isinstance(variable, pybamm.ConcatenationVariable):\n start_ = start\n spatial_method = self.spatial_methods[variable.domain[0]]\n children = variable.children\n meshes = OrderedDict()\n for child in children:\n meshes[child] = [spatial_method.mesh[dom] for dom in child.domain]\n sec_points = spatial_method._get_auxiliary_domain_repeats(\n variable.domains\n )\n for i in range(sec_points):\n for child, mesh in meshes.items():\n for domain_mesh in mesh:\n end += domain_mesh.npts_for_broadcast_to_nodes\n # Add to slices\n y_slices[child].append(slice(start_, end))\n y_slices_explicit[child].append(slice(start_, end))\n # Increment start_\n start_ = end\n else:\n end += self._get_variable_size(variable)\n\n # Add to slices\n y_slices[variable].append(slice(start, end))\n y_slices_explicit[variable].append(slice(start, end))\n\n # Add to bounds\n def evaluate_bound(bound, side):\n if bound.has_symbol_of_classes(pybamm.InputParameter):\n if side == \"lower\":\n return -np.inf\n elif side == \"upper\":\n return np.inf\n else:\n return bound.evaluate()\n\n lower_bounds.extend(\n [evaluate_bound(variable.bounds[0], \"lower\")] * (end - start)\n )\n upper_bounds.extend(\n [evaluate_bound(variable.bounds[1], \"upper\")] * (end - start)\n )\n # Increment start\n start = end\n\n # Convert y_slices back to normal dictionary\n self.y_slices = dict(y_slices)\n # Also keep a record of what the y_slices are, to be stored in the model\n self.y_slices_explicit = dict(y_slices_explicit)\n\n # Also keep a record of bounds\n self.bounds = (np.array(lower_bounds), np.array(upper_bounds))\n\n # reset discretised_symbols\n self._discretised_symbols = {}", "def apply_bound(x, var_min, var_max):\n x.position = np.maximum(x.position, var_min)\n x.position = np.minimum(x.position, var_max)", "def chgvarbound(self,j_,lower_,finite_,value_): # 3\n res = self.__obj.chgvarbound(j_,lower_,finite_,value_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putvarboundlist(self,sub,bkx,blx,bux): # 3\n num_ = None\n if num_ is None:\n num_ = len(sub)\n elif num_ != len(sub):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None:\n num_ = len(bkx)\n elif num_ != len(bkx):\n raise IndexError(\"Inconsistent length of array bkx\")\n if num_ is None:\n num_ = len(blx)\n elif num_ != len(blx):\n raise IndexError(\"Inconsistent length of array blx\")\n if num_ is None:\n num_ = len(bux)\n elif num_ != len(bux):\n raise IndexError(\"Inconsistent length of array bux\")\n if num_ is None: num_ = 0\n if sub is None: raise TypeError(\"Invalid type for argument sub\")\n if sub is None:\n sub_ = None\n else:\n try:\n sub_ = memoryview(sub)\n except TypeError:\n try:\n _tmparr_sub = array.array(\"i\",sub)\n except TypeError:\n raise TypeError(\"Argument sub has wrong type\")\n else:\n sub_ = memoryview(_tmparr_sub)\n \n else:\n if sub_.format != \"i\":\n sub_ = memoryview(array.array(\"i\",sub))\n \n if bkx is None: raise TypeError(\"Invalid type for argument bkx\")\n if bkx is None:\n bkx_ = None\n else:\n try:\n bkx_ = memoryview(bkx)\n except TypeError:\n try:\n _tmparr_bkx = array.array(\"i\",bkx)\n except TypeError:\n raise TypeError(\"Argument bkx has wrong type\")\n else:\n bkx_ = memoryview(_tmparr_bkx)\n \n else:\n if bkx_.format != \"i\":\n bkx_ = memoryview(array.array(\"i\",bkx))\n \n if blx is None: raise TypeError(\"Invalid type for argument blx\")\n if blx is None:\n blx_ = None\n else:\n try:\n blx_ = memoryview(blx)\n except TypeError:\n try:\n _tmparr_blx = array.array(\"d\",blx)\n except TypeError:\n raise TypeError(\"Argument blx has wrong type\")\n else:\n blx_ = memoryview(_tmparr_blx)\n \n else:\n if blx_.format != \"d\":\n blx_ = memoryview(array.array(\"d\",blx))\n \n if bux is None: raise TypeError(\"Invalid type for argument bux\")\n if bux is None:\n bux_ = None\n else:\n try:\n bux_ = memoryview(bux)\n except TypeError:\n try:\n _tmparr_bux = array.array(\"d\",bux)\n except TypeError:\n raise TypeError(\"Argument bux has wrong type\")\n else:\n bux_ = memoryview(_tmparr_bux)\n \n else:\n if bux_.format != \"d\":\n bux_ = memoryview(array.array(\"d\",bux))\n \n res = self.__obj.putvarboundlist(num_,sub_,bkx_,blx_,bux_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def chgvarbound(self,j_,lower_,finite_,value_):\n res = __library__.MSK_XX_chgvarbound(self.__nativep,j_,lower_,finite_,value_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putcslice(self,first_,last_,slice): # 3\n if slice is None: raise TypeError(\"Invalid type for argument slice\")\n if slice is None:\n slice_ = None\n else:\n try:\n slice_ = memoryview(slice)\n except TypeError:\n try:\n _tmparr_slice = array.array(\"d\",slice)\n except TypeError:\n raise TypeError(\"Argument slice has wrong type\")\n else:\n slice_ = memoryview(_tmparr_slice)\n \n else:\n if slice_.format != \"d\":\n slice_ = memoryview(array.array(\"d\",slice))\n \n if slice_ is not None and len(slice_) != ((last_) - (first_)):\n raise ValueError(\"Array argument slice has wrong length\")\n res = self.__obj.putcslice(first_,last_,slice_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def set_bounds_atom(self,bounds):\n assert bounds.shape == (2,self.Phi.d)\n self.bounds = bounds # data bounds\n self.bounds_atom = bounds.T.tolist()\n for i in range(self.Phi.d): # bounds for the variance in each dimension\n max_variance_this_dimension = (bounds[1][i]-bounds[0][i])**2\n self.bounds_atom.append([self.variance_relative_lowerbound*max_variance_this_dimension,\n self.variance_relative_upperbound*max_variance_this_dimension])", "def getbarxslice(self,whichsol_,first_,last_,slicesize_,barxslice_):\n _barxslice_minlength = (slicesize_)\n if (slicesize_) > 0 and barxslice_ is not None and len(barxslice_) != (slicesize_):\n raise ValueError(\"Array argument barxslice is not long enough: Is %d, expected %d\" % (len(barxslice_),(slicesize_)))\n if isinstance(barxslice_,numpy.ndarray) and not barxslice_.flags.writeable:\n raise ValueError(\"Argument barxslice must be writable\")\n if barxslice_ is None:\n raise ValueError(\"Argument barxslice may not be None\")\n if isinstance(barxslice_, numpy.ndarray) and barxslice_.dtype is numpy.dtype(numpy.float64) and barxslice_.flags.contiguous:\n _barxslice_copyarray = False\n _barxslice_tmp = ctypes.cast(barxslice_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif barxslice_ is not None:\n _barxslice_copyarray = True\n _barxslice_np_tmp = numpy.zeros(len(barxslice_),numpy.dtype(numpy.float64))\n _barxslice_np_tmp[:] = barxslice_\n assert _barxslice_np_tmp.flags.contiguous\n _barxslice_tmp = ctypes.cast(_barxslice_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _barxslice_copyarray = False\n _barxslice_tmp = None\n \n res = __library__.MSK_XX_getbarxslice(self.__nativep,whichsol_,first_,last_,slicesize_,_barxslice_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _barxslice_copyarray:\n barxslice_[:] = _barxslice_np_tmp", "def getbarsslice(self,whichsol_,first_,last_,slicesize_,barsslice_):\n _barsslice_minlength = (slicesize_)\n if (slicesize_) > 0 and barsslice_ is not None and len(barsslice_) != (slicesize_):\n raise ValueError(\"Array argument barsslice is not long enough: Is %d, expected %d\" % (len(barsslice_),(slicesize_)))\n if isinstance(barsslice_,numpy.ndarray) and not barsslice_.flags.writeable:\n raise ValueError(\"Argument barsslice must be writable\")\n if barsslice_ is None:\n raise ValueError(\"Argument barsslice may not be None\")\n if isinstance(barsslice_, numpy.ndarray) and barsslice_.dtype is numpy.dtype(numpy.float64) and barsslice_.flags.contiguous:\n _barsslice_copyarray = False\n _barsslice_tmp = ctypes.cast(barsslice_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif barsslice_ is not None:\n _barsslice_copyarray = True\n _barsslice_np_tmp = numpy.zeros(len(barsslice_),numpy.dtype(numpy.float64))\n _barsslice_np_tmp[:] = barsslice_\n assert _barsslice_np_tmp.flags.contiguous\n _barsslice_tmp = ctypes.cast(_barsslice_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _barsslice_copyarray = False\n _barsslice_tmp = None\n \n res = __library__.MSK_XX_getbarsslice(self.__nativep,whichsol_,first_,last_,slicesize_,_barsslice_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _barsslice_copyarray:\n barsslice_[:] = _barsslice_np_tmp", "def getboundslice(self,accmode_,first_,last_,bk,bl,bu): # 3\n if not isinstance(accmode_,accmode): raise TypeError(\"Argument accmode has wrong type\")\n _copyback_bk = False\n if bk is None:\n bk_ = None\n else:\n try:\n bk_ = memoryview(bk)\n except TypeError:\n try:\n _tmparr_bk = array.array(\"i\",bk)\n except TypeError:\n raise TypeError(\"Argument bk has wrong type\")\n else:\n bk_ = memoryview(_tmparr_bk)\n _copyback_bk = True\n else:\n if bk_.format != \"i\":\n bk_ = memoryview(array.array(\"i\",bk))\n _copyback_bk = True\n if bk_ is not None and len(bk_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bk has wrong length\")\n _copyback_bl = False\n if bl is None:\n bl_ = None\n else:\n try:\n bl_ = memoryview(bl)\n except TypeError:\n try:\n _tmparr_bl = array.array(\"d\",bl)\n except TypeError:\n raise TypeError(\"Argument bl has wrong type\")\n else:\n bl_ = memoryview(_tmparr_bl)\n _copyback_bl = True\n else:\n if bl_.format != \"d\":\n bl_ = memoryview(array.array(\"d\",bl))\n _copyback_bl = True\n if bl_ is not None and len(bl_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bl has wrong length\")\n _copyback_bu = False\n if bu is None:\n bu_ = None\n else:\n try:\n bu_ = memoryview(bu)\n except TypeError:\n try:\n _tmparr_bu = array.array(\"d\",bu)\n except TypeError:\n raise TypeError(\"Argument bu has wrong type\")\n else:\n bu_ = memoryview(_tmparr_bu)\n _copyback_bu = True\n else:\n if bu_.format != \"d\":\n bu_ = memoryview(array.array(\"d\",bu))\n _copyback_bu = True\n if bu_ is not None and len(bu_) != ((last_) - (first_)):\n raise ValueError(\"Array argument bu has wrong length\")\n res = self.__obj.getboundslice(accmode_,first_,last_,bk_,bl_,bu_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_bu:\n bu[:] = _tmparr_bu\n if _copyback_bl:\n bl[:] = _tmparr_bl\n if _copyback_bk:\n for __tmp_var_0 in range(len(bk_)): bk[__tmp_var_0] = boundkey(_tmparr_bk[__tmp_var_0])", "def putvarbound(self,j_,bk_,bl_,bu_): # 3\n if not isinstance(bk_,boundkey): raise TypeError(\"Argument bk has wrong type\")\n res = self.__obj.putvarbound(j_,bk_,bl_,bu_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def getxxslice(self,whichsol_,first_,last_,xx_):\n _xx_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and xx_ is not None and len(xx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument xx is not long enough: Is %d, expected %d\" % (len(xx_),((last_) - (first_))))\n if isinstance(xx_,numpy.ndarray) and not xx_.flags.writeable:\n raise ValueError(\"Argument xx must be writable\")\n if isinstance(xx_, numpy.ndarray) and xx_.dtype is numpy.dtype(numpy.float64) and xx_.flags.contiguous:\n _xx_copyarray = False\n _xx_tmp = ctypes.cast(xx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif xx_ is not None:\n _xx_copyarray = True\n _xx_np_tmp = numpy.zeros(len(xx_),numpy.dtype(numpy.float64))\n _xx_np_tmp[:] = xx_\n assert _xx_np_tmp.flags.contiguous\n _xx_tmp = ctypes.cast(_xx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _xx_copyarray = False\n _xx_tmp = None\n \n res = __library__.MSK_XX_getxxslice(self.__nativep,whichsol_,first_,last_,_xx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _xx_copyarray:\n xx_[:] = _xx_np_tmp", "def set_bounds(self, new_bounds):\n\n # Update the internal object stored dict\n self.pbounds.update(new_bounds)\n\n # Loop through the all bounds and reset the min-max bound matrix\n for row, key in enumerate(self.pbounds.keys()):\n\n # Reset all entries, even if the same.\n self.bounds[row] = self.pbounds[key]", "def _initialize_bounds(problem, bounds, get_bound, set_bound):\n for constraint in problem.constraints:\n root_expr = constraint.root_expr\n expr_bounds = Interval(constraint.lower_bound, constraint.upper_bound)\n if root_expr not in bounds:\n set_bound(root_expr, expr_bounds)\n else:\n existing_bounds = get_bound(root_expr)\n new_bounds = existing_bounds.intersect(expr_bounds)\n set_bound(root_expr, new_bounds)" ]

[ "0.78326833", "0.7734105", "0.7384246", "0.73374903", "0.7264578", "0.676517", "0.6706333", "0.65771973", "0.6273341", "0.6261887", "0.62256247", "0.6213124", "0.61776054", "0.6171745", "0.6162715", "0.61598974", "0.61103725", "0.6071546", "0.601926", "0.6003408", "0.58926064", "0.5874434", "0.5857309", "0.582052", "0.5790033", "0.57805425", "0.57727635", "0.5750098", "0.5706496", "0.5672962" ]

[ "def putcfix(self,cfix_): # 3\n res = self.__obj.putcfix(cfix_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def fixC(self,i,value):\n if self.coeffPattern[2] == None:\n m,n=self.m,self.n\n self.coeffPattern[2] = [None]*m\n self.coeffPattern[2][i]=value\n self._updateEstimatorSize(i)", "def f_fixed(self):\n self.fx_free = self.fy_free = self.fz_free = False\n return self", "def x(self, new_x):\n if new_x is None:\n logging.warning(\"Cochain features were set to None. \")\n else:\n assert self.num_cells == len(new_x)\n self.__x = new_x", "def removeFixedEffect(self, index=None):\n if self._n_terms==0:\n pass\n if index is None or index==(self._n_terms-1):\n\n self._n_terms-=1\n F = self._F.pop() #= self.F[:-1]\n A = self._A.pop() #= self.A[:-1]\n self._A_identity.pop() #= self.A_identity[:-1]\n REML_term = self._REML_term.pop()# = self.REML_term[:-1]\n self._B.pop()# = self.B[:-1]\n self._n_fixed_effs-=F.shape[1]*A.shape[0]\n if REML_term:\n self._n_fixed_effs_REML-=F.shape[1]*A.shape[0]\n\n pass\n elif index >= self.n_terms:\n raise Exception(\"index exceeds max index of terms\")\n else:\n raise NotImplementedError(\"currently only last term can be removed\")\n pass\n self._rebuild_indicator()\n self.clear_cache('Fstar','Astar','Xstar','Xhat',\n 'Areml','Areml_eigh','Areml_chol','Areml_inv','beta_hat','B_hat',\n 'LRLdiag_Xhat_tens','Areml_grad',\n 'beta_grad','Xstar_beta_grad','Zstar','DLZ')", "def m_fixed(self):\n self.mx_free = self.my_free = self.mz_free = False\n return self", "def cpf(self, cpf):\n self._cpf = cpf", "def __init__(self, f, index_value_pairs):\n # super(FixVariables, self).__init__(\n ComposedFunction.__init__(self, [f, self.insert_variables])\n self.index_value_pairs = dict(index_value_pairs)", "def addFixedEffect(self,F=None,A=None, REML=True, index=None):\n if F is None: F = np.ones((self.N,1))\n if A is None:\n A = np.eye(self.P)\n A_identity = True\n elif (A.shape == (self.P,self.P)) & (A==np.eye(self.P)).all():\n A_identity = True\n else:\n A_identity = False\n\n assert F.shape[0]==self.N, \"F dimension mismatch\"\n assert A.shape[1]==self.P, \"A dimension mismatch\"\n if index is None or index==self.n_terms:\n self.F.append(F)\n self.A.append(A)\n self.A_identity.append(A_identity)\n self.REML_term.append(REML)\n # build B matrix and indicator\n self.B.append(np.zeros((F.shape[1],A.shape[0])))\n self._n_terms+=1\n self._update_indicator(F.shape[1],A.shape[0])\n elif index >self.n_terms:\n raise Exception(\"index exceeds max index of terms\")\n else:\n self._n_fixed_effs-=self.F[index].shape[1]*self.A[index].shape[0]\n if self.REML_term[index]:\n self._n_fixed_effs_REML-=self.F[index].shape[1]*self.A[index].shape[0]\n self.F[index] = F\n self.A[index] = A\n self.A_identity[index] = A_identity\n self.REML_term[index]=REML\n self.B[index] = np.zeros((F.shape[1],A.shape[0]))\n self._rebuild_indicator()\n\n self._n_fixed_effs+=F.shape[1]*A.shape[0]\n if REML:\n self._n_fixed_effs_REML+=F.shape[1]*A.shape[0]\n self.clear_cache('Fstar','Astar','Xstar','Xhat',\n 'Areml','Areml_eigh','Areml_chol','Areml_inv','beta_hat','B_hat',\n 'LRLdiag_Xhat_tens','Areml_grad',\n 'beta_grad','Xstar_beta_grad','Zstar','DLZ')", "def fixB(self,i,j,value):\n if self.coeffPattern[1] == None:\n m,n=self.m,self.n\n self.coeffPattern = [[None]*n for i in range(m)]\n self.coeffPattern[1][i][j]=value\n self._updateEstimatorSize(i)", "def _set_fixed(o, d):\n if d:\n o.fix()\n else:\n o.unfix()", "def problem_reduction_single(self, i, val):\n y_update = - val * self.A.getcol(i).toarray().flatten()\n self.y += y_update\n self.A = sparse.hstack([self.A[:, :i], self.A[:, i + 1:]], format='csr')\n z_index = self.mask.searchsorted(i)\n self.mask = np.insert(self.mask, z_index, i)\n self.z = np.insert(self.z, z_index, val)", "def fixA(self,i,j,value):\n if self.coeffPattern[0] == None:\n m,n=self.m,self.n\n self.coeffPattern[0] = [[None]*m for i in range(m)]\n self.coeffPattern[0][i][j]=value\n self._updateEstimatorSize(i)", "def putxc(self,whichsol_,xc_):\n _xc_minlength = self.getnumcon()\n if self.getnumcon() > 0 and xc_ is not None and len(xc_) != self.getnumcon():\n raise ValueError(\"Array argument xc is not long enough: Is %d, expected %d\" % (len(xc_),self.getnumcon()))\n if isinstance(xc_,numpy.ndarray) and not xc_.flags.writeable:\n raise ValueError(\"Argument xc must be writable\")\n if xc_ is None:\n raise ValueError(\"Argument xc may not be None\")\n if isinstance(xc_, numpy.ndarray) and xc_.dtype is numpy.dtype(numpy.float64) and xc_.flags.contiguous:\n _xc_copyarray = False\n _xc_tmp = ctypes.cast(xc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif xc_ is not None:\n _xc_copyarray = True\n _xc_np_tmp = numpy.zeros(len(xc_),numpy.dtype(numpy.float64))\n _xc_np_tmp[:] = xc_\n assert _xc_np_tmp.flags.contiguous\n _xc_tmp = ctypes.cast(_xc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _xc_copyarray = False\n _xc_tmp = None\n \n res = __library__.MSK_XX_putxc(self.__nativep,whichsol_,_xc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _xc_copyarray:\n xc_[:] = _xc_np_tmp", "def clearFixedEffect(self):\n self._A = []\n self._F = []\n self._B = []\n self._A_identity = []\n self._REML_term = []\n self._n_terms = 0\n self._n_fixed_effs = 0\n self._n_fixed_effs_REML = 0\n self.indicator = {'term':np.array([]),\n 'row':np.array([]),\n 'col':np.array([])}\n self.clear_cache('Fstar','Astar','Xstar','Xhat',\n 'Areml','Areml_eigh','Areml_chol','Areml_inv','beta_hat','B_hat',\n 'LRLdiag_Xhat_tens','Areml_grad',\n 'beta_grad','Xstar_beta_grad','Zstar','DLZ')", "def getcfix(self):\n cfix_ = ctypes.c_double()\n res = __library__.MSK_XX_getcfix(self.__nativep,ctypes.byref(cfix_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n cfix_ = cfix_.value\n _cfix_return_value = cfix_\n return (_cfix_return_value)", "def tctfdfc(x):\n if isinstance(x,Fdf) :\n pass\n else : \n x = Fdf.constant(x)\n return x", "def apply_qc(self, qf=1):\n logger = logging.getLogger(\"timeseries_logger\")\n logger.info('Applying mask to the data')\n self.temperature = np.ma.masked_where(self.temperatureQF != qf, self.temperature)\n self.salinity = np.ma.masked_where(self.salinityQF !=qf, self.salinity)", "def MakeFixed(self,content):\n return self.register(Fixed(content,reg=self))", "def affect_cnf(dimacs_cnf, literal_index, literal_value):\n remove_indexes = []\n for clause_index in range(dimacs_cnf.shape[0]):\n if dimacs_cnf[clause_index][literal_index] * literal_value == 1:\n dimacs_cnf[clause_index] = 1\n remove_indexes.append(clause_index)\n elif dimacs_cnf[clause_index][literal_index] * literal_value == -1:\n dimacs_cnf[clause_index][literal_index] = 0\n return np.delete(dimacs_cnf, remove_indexes, axis=0)", "def fixed_cost(self):\n return np.einsum('i->', self.c[self.f])", "def Coaxial(movableAxis: str, fixedAxis: str, flip: Boolean) -> \"Feature\":\n return Feature()", "def set_ctf(ima, p):\n\tfrom utilities import generate_ctf\n\tctf = generate_ctf( p )\n\tima.set_attr( \"ctf\", ctf )", "def putxc(self,whichsol_,xc): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if xc is None: raise TypeError(\"Invalid type for argument xc\")\n _copyback_xc = False\n if xc is None:\n xc_ = None\n else:\n try:\n xc_ = memoryview(xc)\n except TypeError:\n try:\n _tmparr_xc = array.array(\"d\",xc)\n except TypeError:\n raise TypeError(\"Argument xc has wrong type\")\n else:\n xc_ = memoryview(_tmparr_xc)\n _copyback_xc = True\n else:\n if xc_.format != \"d\":\n xc_ = memoryview(array.array(\"d\",xc))\n _copyback_xc = True\n if xc_ is not None and len(xc_) != self.getnumcon():\n raise ValueError(\"Array argument xc has wrong length\")\n res = self.__obj.putxc(whichsol_,xc_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_xc:\n xc[:] = _tmparr_xc", "def __init__(self, word_fd, quadgram_fd, ii, iii, ixi, ixxi, iixi, ixii):\n AbstractCollocationFinder.__init__(self, word_fd, quadgram_fd)\n self.iii = iii\n self.ii = ii\n self.ixi = ixi\n self.ixxi = ixxi\n self.iixi = iixi\n self.ixii = ixii", "def subst_i(term):\n return Term(term.sums, term.amp.xreplace({i: k}), term.vecs)", "def set_idx(self, i, other, tensor_value):\n for k, v in self.variables.items():\n if k not in other.variables:\n self.variables[k][i] *= 0\n\n for k, v in other.variables.items():\n if k not in self.variables:\n self.variables[k] = np.zeros(tensor_value.shape)\n self.variables[k][i] = other.variables[k]", "def fusion(self, x):\n new_x = x.copy()\n for i in range(self.z.size):\n new_x[self.mask[i]] = self.z[i]\n return new_x", "def modify(self, feature, newValues):\n self.data[:,self._getFIdx(feature)] = newValues\n return 0", "def setCoefficient(self, *args):\n return _libsbml.FluxObjective_setCoefficient(self, *args)" ]

[ "0.708022", "0.6352814", "0.5975144", "0.58999455", "0.5676646", "0.5464873", "0.53494346", "0.5349058", "0.5343659", "0.53432745", "0.53168523", "0.52869856", "0.5279766", "0.5264671", "0.51587206", "0.5142443", "0.5114193", "0.51119894", "0.5060143", "0.5042062", "0.5015368", "0.50101244", "0.49665415", "0.4955151", "0.493829", "0.49057582", "0.48905075", "0.48514515", "0.4827352", "0.48138055" ]

[ "def putcj(self,j_,cj_): # 3\n res = self.__obj.putcj(j_,cj_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def jacobian(self, c):\n\n raise NotImplementedError", "def getcj(self,j_): # 3\n res,resargs = self.__obj.getcj(j_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _cj_return_value = resargs\n return _cj_return_value", "def substitute_cost(self, i, j):\n raise NotImplementedError", "def getcj(self,j_):\n cj_ = ctypes.c_double()\n res = __library__.MSK_XX_getcj(self.__nativep,j_,ctypes.byref(cj_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n cj_ = cj_.value\n _cj_return_value = cj_\n return (_cj_return_value)", "def jacobian(self, x):\n pass", "def setCoefficient(self, *args):\n return _libsbml.FluxObjective_setCoefficient(self, *args)", "def putaij(self,i_,j_,aij_): # 3\n res = self.__obj.putaij(i_,j_,aij_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putaij(self,i_,j_,aij_):\n res = __library__.MSK_XX_putaij(self.__nativep,i_,j_,aij_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def jac(self):\n return self.unit_jac if self._jac is None else self._jac", "def JCoeff(l, m, s, lp, mp, sp):\n coeff = int((l == lp) & (m == -mp) & (s == sp))*1j*(-1)**(int(m-1/2))*s\n return coeff", "def fixA(self,i,j,value):\n if self.coeffPattern[0] == None:\n m,n=self.m,self.n\n self.coeffPattern[0] = [[None]*m for i in range(m)]\n self.coeffPattern[0][i][j]=value\n self._updateEstimatorSize(i)", "def _update_objective(self):\n # rewrap the cost if the solver has been run\n self.Finalize()\n return", "def objective(self, x):\n pass", "def objective(self, x):\n pass", "def insert_cost(self, i, j):\n raise NotImplementedError", "def jaccard_coeff(self):\n a, c, _, b = self.to_ccw()\n return _div(a, a + b + c)", "def problem_reduction_single(self, i, val):\n y_update = - val * self.A.getcol(i).toarray().flatten()\n self.y += y_update\n self.A = sparse.hstack([self.A[:, :i], self.A[:, i + 1:]], format='csr')\n z_index = self.mask.searchsorted(i)\n self.mask = np.insert(self.mask, z_index, i)\n self.z = np.insert(self.z, z_index, val)", "def compute_j(self, trajectory):\r\n J = 0\r\n for i, (_,_,r,_) in enumerate(trajectory):\r\n J += (self.domain.discount**i) * r\r\n return J", "def jacobian(self,x,y,l,a):\n J = np.zeros([*x.shape,2,2])\n\n J = _jacobian(x,y,l,a,J)\n\n return J", "def J_plus_component(j_prime: int, m_prime: int, j: int, m: int) -> float:\n if (j_prime != j) or (m_prime != m + 1):\n return 0\n return J_plus_coefficient(j, m)", "def add(self, i, j):\n \n # use running average to update CoM coordinates.\n self._x = (self._x * self._P + i) / (self._P + 1)\n self._y = (self._y * self._P + j) / (self._P + 1)\n # increment mass\n self._P += 1", "def jacobian(self, dt):\n raise NotImplementedError", "def putvarsolutionj(self,j_,whichsol_,sk_,x_,sl_,su_,sn_):\n res = __library__.MSK_XX_putvarsolutionj(self.__nativep,j_,whichsol_,sk_,x_,sl_,su_,sn_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def jacobian_c(self, x, out=None, **kwargs):\n return empty_matrix(0, self.nx)", "def _partial_min_solution(self, j):\n beta_without_j = np.delete(self.betas, j, axis=0)\n X_without_j = np.delete(self.X, j, axis=0)\n X_j = self.X[j] # these are the X values for the jth feature in the model\n # Make predictions and obtain residuals on the full set of Ys, without the effect of the jth predictor included\n R_without_j = (self.Y - (beta_without_j.T @ X_without_j))\n c_j = 2/self.n * (X_j @ R_without_j) # This quantity is described in the notes\n # The following if statements are due to the subgradient of the L1 penality\n if abs(c_j) <= self.lam: # this step is what causes the lasso to shrink coefficients to 0 based on lambda\n return 0\n a_j = 2 * sum(X_j**2) # also described in notes\n if c_j < -self.lam:\n return (c_j + self.lam) / (a_j / self.n)\n elif c_j > self.lam:\n return (c_j - self.lam) / (a_j / self.n)", "def add_com_jac(ui):\n global com_jac_list\n\n content = content_fk_jac_loops(ui, \"com_jac\")\n if content in com_jac_list:\n return\n com_jac_list.append(content)\n ui.listWidget_com_jac.addItem(f\"Center of Mass Jacobian \"\n f\"{parse_content(content)}\")", "def optimize_cjp_displacements(self, method='lm', init_coeffs=None):\n if init_coeffs is None:\n init_coeffs = np.random.rand(5)\n else:\n init_coeffs += np.random.rand(5)\n # optimize least squares\n return optimize.least_squares(fun=self.residuals_cjp_displacements,\n x0=init_coeffs,\n method=method)", "def lasso_cd_weight_update(\n x: FloatTensor,\n r: FloatTensor,\n j: int,\n w_j: Union[float, FloatTensor],\n col_l2: float,\n lmb: float,\n) -> float:\n if col_l2 == 0.0:\n return 0.0\n n, d = x.size()\n # quick hack for multiclass (tensor instead of float) check\n multiclass = hasattr(w_j, \"__len__\")\n z = (n * lmb) / 2\n if multiclass:\n # w_j is tensor; multiclass regression\n rho = x[:, j].matmul(\n r + x[:, j].unsqueeze(1).matmul(w_j.unsqueeze(0))\n ) # type: ignore\n return torch.nn.functional.softshrink(rho, z).data / col_l2\n else:\n # w_j is scalar; scalar regression\n rho = x[:, j].matmul(r + x[:, j] * w_j)\n return soft_thresh(rho, z) / col_l2", "def _append_cx(self, i, j):\n\n if not 0 <= i < self.num_qubits or not 0 <= j < self.num_qubits:\n raise QiskitError(\"CX qubits are out of bounds.\")\n self.linear[j] = (self.linear[i] + self.linear[j]) % 2\n self.shift[j] = (self.shift[i] + self.shift[j]) % 2" ]

[ "0.71400785", "0.6213591", "0.61696875", "0.6164791", "0.60605234", "0.59805757", "0.58748615", "0.57106733", "0.5628812", "0.56003463", "0.55685896", "0.5553388", "0.5516792", "0.5434863", "0.5434863", "0.5425381", "0.540043", "0.5388786", "0.5384671", "0.53840244", "0.5376267", "0.5371235", "0.5354306", "0.5333264", "0.53274924", "0.53267163", "0.5322429", "0.5313385", "0.52935594", "0.5290578" ]

[ "def putobjsense(self,sense_): # 3\n if not isinstance(sense_,objsense): raise TypeError(\"Argument sense has wrong type\")\n res = self.__obj.putobjsense(sense_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def objective(self, objective):\n\n self._objective = objective", "def optimize(self, objective_sense=None, **kwargs):\n\n if objective_sense:\n self.objective.direction = objective_sense\n\n try:\n # self._hidden_optimize_call(kwargs)\n Model.optimize(self, **kwargs)\n solution = self.get_solution()\n self.solution = solution\n return solution\n except SolverError as SE:\n status = self.solver.status\n self.logger.error(SE)\n self.logger.warning('Solver status: {}'.format(status))\n raise (SE)", "def getobjsense(self): # 3\n res,resargs = self.__obj.getobjsense()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _sense_return_value = resargs\n _sense_return_value = objsense(_sense_return_value)\n return _sense_return_value", "def put(cls, obj):\n pass", "def set_occupant(self, obj):\n\t\tpass", "def setMode(self):\n if self.currentTarget != None and self.finishedAssault == 0:\n if self.isAssault == 1:\n if self.currentTarget != None:\n self.mode = 'assault'\n else:\n self.mode = 'escape'\n else:\n self.log.debug('COUNT: %s: %s TARGET-> %s' % (self.myGalaxy.count, self.name, self.currentTarget.name))\n ##self.myGalaxy.resultList.append('COUNT: %s: %s TARGET-> %s' % (self.myGalaxy.count, self.name, self.currentTarget.name))\n if ((len(self.activeWeapons) == 0 or (self.currentISP/self.myShipHull.maxISP) < 0.7)) and self.__module__ == 'anw.war.ship':\n self.mode = 'escape'\n else:\n range = funcs.getTargetRange(self.posX, self.posY, self.currentTarget.posX, self.currentTarget.posY)\n if range <= self.range:\n self.mode = 'engage'\n else:\n self.mode = 'close'\n else:\n self.mode == 'escape'\n if globals.serverMode == 0:\n self.shipsim.updateShipMode()", "def add_objective(self, objective):\n self.objectives.append(objective)", "def set(self, obj, value):\n pass", "def set(self, obj, value):\n raise NotImplementedError", "def set_sensible_obstacles(self, obstacles):\n self.sensible_obstacles = obstacles", "def SetObject(self, obj):\n return _gmat_py.EphemManager_SetObject(self, obj)", "def sense_and_act(self):\n pass", "def sense(self):\n self.robot.update_state_position()\n self.robot.update_state_proximity(self.world_props)\n self.robot.update_state_compass()\n self.robot.update_state_odometry()", "def set_solo(self, track, xclip, ident, value = None):\n if track in self.song().tracks + self.song().return_tracks:\n if value in KEYWORDS:\n track.solo = KEYWORDS[value]\n else:\n track.solo = not(track.solo)", "def on_sense_sonar(self, dist):\n raise NotImplementedError()", "def __set__(self, obj, value):\n # We need to take a copy in case these are the class topics\n topics = obj._help_topics.copy()\n topics.update(value)\n obj._help_topics = topics", "def save(self):\r\n for obs_name in self.__dict__.keys():\r\n if obs_name is not \"_ObjetSimu__obs\":\r\n if not obs_name in self.__sous_objets:\r\n if obs_name in self.__obs.keys():\r\n if \"copy\" in dir(self.__dict__[obs_name]):\r\n self.__obs[obs_name].append(self.__dict__[obs_name].copy())\r\n else:\r\n self.__obs[obs_name].append(self.__dict__[obs_name])\r\n else:\r\n self.__dict__[obs_name].save()", "def set_sm_userdata(self, obj_desc):\n rospy.loginfo('Getting parameters from server')\n\n #obj_desc = rospy.get_param('obj_desc','{\"type\" : \"Bar\"}')\n\n self.sm.userdata.sm_obj_desc = json.loads(obj_desc)\n\n self.sm.userdata.state = 'pose_selection' \n \n self.sm.userdata.sm_min_objs = rospy.get_param('min_objs',1)\n self.sm.userdata.sm_max_objs = rospy.get_param('max_objs',1)\n self.sm.userdata.sm_max_time = rospy.get_param('max_time', 120)\n self.sm.userdata.sm_max_poses = rospy.get_param('max_poses', 10)\n\n rospy.loginfo(\"Search for %s\", self.sm.userdata.sm_obj_desc)\n rospy.loginfo(\"min_objs: %s\", self.sm.userdata.sm_min_objs)\n rospy.loginfo(\"max_objs: %s\", self.sm.userdata.sm_max_objs)\n rospy.loginfo(\"max_time: %s\", self.sm.userdata.sm_max_time)\n rospy.loginfo(\"max_poses: %s\", self.sm.userdata.sm_max_poses)\n \n # initialize empty obj list\n self.sm.userdata.sm_obj_list = []", "def slo(self, objective=99.99):\n self.objective = objective\n return objective", "def getobjsense(self):\n sense_ = ctypes.c_int32()\n res = __library__.MSK_XX_getobjsense(self.__nativep,ctypes.byref(sense_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _sense_return_value = objsense(sense_.value)\n return (_sense_return_value)", "def _sense_and_act(self):\n pass", "def getObjSense(self, problemname : str, x : pd.Series):\n if problemname in self.objsensedict:\n return self.objsensedict[problemname]\n elif not pd.isnull(x.get(Key.ObjectiveSense, None)):\n return x.get(Key.ObjectiveSense)\n else:\n logger.warning(\"No objective sense for {}, assuming minimization\".format(problemname))\n return ObjectiveSenseCode.MINIMIZE", "def observe(self, obs):\n self.observation = obs\n self.selected = obs.selected\n \n #############################\n # Update of turn statistics #\n #############################\n if self.id == (obs.step % 6):\n # Store base locations\n if self.__class__.home_base is None:\n self.__class__.home_base = (obs.loc[0]+16, obs.loc[1]+8)\n self.__class__.enemy_base = \\\n self.getSymmetricOpposite(self.__class__.home_base)\n \n # Reset trendingSpot\n self.__class__.trendingSpot = {}\n \n # Update friendly CPs\n self.__class__.friendlyCPs = map(lambda x: x[0:2], \n filter(lambda x: x[2] == self.team, obs.cps))\n \n # Update enemy CPs\n self.__class__.enemyCPs = map(lambda x:x[0:2], \n filter(lambda x: x[2] != self.team, obs.cps))\n \n # Update ammo packs \n ammopacks = filter(lambda x: x[2] == \"Ammo\", obs.objects)\n if ammopacks:\n self.updateAllAmmoSpots(ammopacks)\n\n # Update inFriendlyHands stat\n if SETTINGS_DOMINATION_ADDS_UP:\n inFriendlyHands = self.__class__.inFriendlyHands\n else:\n inFriendlyHands = {}\n for cp in self.__class__.friendlyCPs:\n if cp in self.__class__.inFriendlyHands:\n inFriendlyHands[cp] = self.__class__.inFriendlyHands[cp] + 1\n else:\n inFriendlyHands[cp] = 1\n self.__class__.inFriendlyHands = inFriendlyHands", "def define_objective(self, objective, capability_param, capability_value,\n assignment_path):\n RATIO = 20\n # pdcli must receive a long number - cant receive float.\n if capability_value > RATIO:\n if objective == Obj.DONT:\n ctx.cluster.cli.basic_objective_create(**{\n 'name': capability_param, capability_param:\n long(capability_value) * RATIO})\n\n self._logger.info('Objective {0} was set to {1}'.format(\n capability_param, str(capability_value * RATIO)))\n\n else:\n ctx.cluster.cli.basic_objective_create(**{\n 'name': capability_param, capability_param:\n long(capability_value)})\n\n self._logger.info('Objective {0} was set to {1}'.format(\n capability_param, str(capability_value)))\n\n ctx.cluster.cli.share_objective_add(name=self.s_name,\n objective=capability_param,\n path=assignment_path)", "def set_arm(self, track, xclip, ident, value = None):\n if track in self.song().tracks and track.can_be_armed:\n if value in KEYWORDS:\n track.arm = KEYWORDS[value]\n else:\n track.arm = not(track.arm)", "def _update_optimizer(self, hyperparameters, score, fit=True):\n if self.do_maximize:\n score = -score\n self.optimizer_result = self.optimizer.tell(hyperparameters, score, fit=fit)", "def __set__(self, obj, value):\r\n pass", "def set_object_description(self, agent, Description):\n\n self.send_ObjectDescription(agent, agent.agent_id, agent.session_id, {1:[self.LocalID, Description]})", "def putOn(self,obj):\n if obj not in self.on:\n self.on.append(obj)\n if self not in obj.on:\n obj.putOn(self)\n if obj not in self.road.on:\n self.road.putOn(obj)" ]

[ "0.79471225", "0.52841735", "0.5171148", "0.5096876", "0.50680315", "0.48795915", "0.48363435", "0.48355487", "0.48335677", "0.48176384", "0.47935885", "0.47814864", "0.4768401", "0.47334376", "0.4723602", "0.469889", "0.46933207", "0.46930823", "0.4692844", "0.46748367", "0.46448252", "0.4631909", "0.46222875", "0.46169978", "0.46156844", "0.45625475", "0.45423335", "0.45310906", "0.45248115", "0.45099097" ]

[ "def getobjsense(self): # 3\n res,resargs = self.__obj.getobjsense()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _sense_return_value = resargs\n _sense_return_value = objsense(_sense_return_value)\n return _sense_return_value", "def objective(self):\n return self._objective", "def get_objective_terms(self):\n return # osid.learning.ObjectiveQueryInspector", "def get_objective_terms(self):\n return # osid.learning.ObjectiveQueryInspector", "def get_objective_terms(self):\n return # osid.learning.ObjectiveQueryInspector", "def offense(self):\n #return self.stats.strength + self.stats.level\n return self.stats.offense", "def getObjSense(self, problemname : str, x : pd.Series):\n if problemname in self.objsensedict:\n return self.objsensedict[problemname]\n elif not pd.isnull(x.get(Key.ObjectiveSense, None)):\n return x.get(Key.ObjectiveSense)\n else:\n logger.warning(\"No objective sense for {}, assuming minimization\".format(problemname))\n return ObjectiveSenseCode.MINIMIZE", "def getActiveObjective(self):\n return _libsbml.ListOfObjectives_getActiveObjective(self)", "def objective_val(self):\n return self.m.ObjVal", "def getObjectiveType(self):\n return _libsbml.Objective_getObjectiveType(self)", "def get_objectives(self, scaled=True, use_indices=True):\n return self._get_variables_of_type('objective', scaled, use_indices)", "def get_requisite_objective_terms(self):\n return # osid.learning.ObjectiveQueryInspector", "def getObjective(self, *args):\n return _libsbml.FbcModelPlugin_getObjective(self, *args)", "def objective(self):\n pass", "def get_observed_objective(self) -> float:\n # pylint: disable=invalid-name\n obj = 0.\n for gr in self.grounded.values():\n dist = gr.get_observed_dist_to_satisfaction()\n obj += 1 - self.weight * max(0, dist) ** 2\n return obj", "def get_objectives(self):\n return copy.deepcopy(self.objectives), self.gates_names", "def get_equivalent_objective_terms(self):\n return # osid.learning.ObjectiveQueryInspector", "def stats(self):\n return self._solution", "def stats(self):\n return self._solution", "def stats(self):\n return self._solution", "def getType(self):\n return _libsbml.Objective_getType(self)", "def getActiveObjective(self, *args):\n return _libsbml.FbcModelPlugin_getActiveObjective(self, *args)", "def get_contenu(self):\n return self.objets", "def expense(self):\n return self._expense", "def get_dependent_objective_terms(self):\n return # osid.learning.ObjectiveQueryInspector", "def defense(self):\n #return self.stats.dexterity + (self.stats.reiatsu * self.stats.density)\n return self.stats.defense", "def getObservationCount(self):\r\n return self._s_obs", "def get_ancestor_objective_terms(self):\n return # osid.learning.ObjectiveQueryInspector", "def getObservation(self):\n sensors = self.env.getSensors()\n if self.sensor_limits:\n sensors = self.normalize(sensors)\n return sensors", "def getNumObjectives(self):\n return _libsbml.FbcModelPlugin_getNumObjectives(self)" ]

[ "0.8077817", "0.6475919", "0.62005496", "0.62005496", "0.62005496", "0.61305094", "0.6124314", "0.6089001", "0.5931077", "0.58792186", "0.5827234", "0.58187944", "0.57927275", "0.5724244", "0.57183784", "0.5676843", "0.56714916", "0.5586467", "0.5586467", "0.5586467", "0.5568052", "0.5497707", "0.54583293", "0.54484195", "0.5361648", "0.53509575", "0.533727", "0.5314603", "0.5296677", "0.52641076" ]

[ "def putclist(self,subj,val): # 3\n num_ = None\n if num_ is None:\n num_ = len(subj)\n elif num_ != len(subj):\n raise IndexError(\"Inconsistent length of array subj\")\n if num_ is None:\n num_ = len(val)\n elif num_ != len(val):\n raise IndexError(\"Inconsistent length of array val\")\n if num_ is None: num_ = 0\n if subj is None: raise TypeError(\"Invalid type for argument subj\")\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n \n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n \n if val is None: raise TypeError(\"Invalid type for argument val\")\n if val is None:\n val_ = None\n else:\n try:\n val_ = memoryview(val)\n except TypeError:\n try:\n _tmparr_val = array.array(\"d\",val)\n except TypeError:\n raise TypeError(\"Argument val has wrong type\")\n else:\n val_ = memoryview(_tmparr_val)\n \n else:\n if val_.format != \"d\":\n val_ = memoryview(array.array(\"d\",val))\n \n res = self.__obj.putclist(num_,subj_,val_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def __setitem__( self, l, c_l ) :\n\n if( l == len( self ) ) :\n self.coefficients.append( float( c_l ) )\n else :\n self.coefficients[l] = float( c_l )", "def putaijlist(self,subi_,subj_,valij_):\n num_ = None\n if num_ is None:\n num_ = len(subi_)\n elif num_ != len(subi_):\n raise IndexError(\"Inconsistent length of array subi\")\n if num_ is None:\n num_ = len(subj_)\n elif num_ != len(subj_):\n raise IndexError(\"Inconsistent length of array subj\")\n if num_ is None:\n num_ = len(valij_)\n elif num_ != len(valij_):\n raise IndexError(\"Inconsistent length of array valij\")\n if subi_ is None:\n raise ValueError(\"Argument subi cannot be None\")\n if subi_ is None:\n raise ValueError(\"Argument subi may not be None\")\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\n _subi_copyarray = False\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subi_ is not None:\n _subi_copyarray = True\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\n _subi_np_tmp[:] = subi_\n assert _subi_np_tmp.flags.contiguous\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subi_copyarray = False\n _subi_tmp = None\n \n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n if valij_ is None:\n raise ValueError(\"Argument valij cannot be None\")\n if valij_ is None:\n raise ValueError(\"Argument valij may not be None\")\n if isinstance(valij_, numpy.ndarray) and valij_.dtype is numpy.dtype(numpy.float64) and valij_.flags.contiguous:\n _valij_copyarray = False\n _valij_tmp = ctypes.cast(valij_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif valij_ is not None:\n _valij_copyarray = True\n _valij_np_tmp = numpy.zeros(len(valij_),numpy.dtype(numpy.float64))\n _valij_np_tmp[:] = valij_\n assert _valij_np_tmp.flags.contiguous\n _valij_tmp = ctypes.cast(_valij_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _valij_copyarray = False\n _valij_tmp = None\n \n res = __library__.MSK_XX_putaijlist64(self.__nativep,num_,_subi_tmp,_subj_tmp,_valij_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def getclist(self,subj_,c_):\n num_ = None\n if num_ is None:\n num_ = len(subj_)\n elif num_ != len(subj_):\n raise IndexError(\"Inconsistent length of array subj\")\n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n _c_minlength = (num_)\n if (num_) > 0 and c_ is not None and len(c_) != (num_):\n raise ValueError(\"Array argument c is not long enough: Is %d, expected %d\" % (len(c_),(num_)))\n if isinstance(c_,numpy.ndarray) and not c_.flags.writeable:\n raise ValueError(\"Argument c must be writable\")\n if c_ is None:\n raise ValueError(\"Argument c may not be None\")\n if isinstance(c_, numpy.ndarray) and c_.dtype is numpy.dtype(numpy.float64) and c_.flags.contiguous:\n _c_copyarray = False\n _c_tmp = ctypes.cast(c_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif c_ is not None:\n _c_copyarray = True\n _c_np_tmp = numpy.zeros(len(c_),numpy.dtype(numpy.float64))\n _c_np_tmp[:] = c_\n assert _c_np_tmp.flags.contiguous\n _c_tmp = ctypes.cast(_c_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _c_copyarray = False\n _c_tmp = None\n \n res = __library__.MSK_XX_getclist(self.__nativep,num_,_subj_tmp,_c_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _c_copyarray:\n c_[:] = _c_np_tmp", "def putaijlist(self,subi,subj,valij): # 3\n num_ = None\n if num_ is None:\n num_ = len(subi)\n elif num_ != len(subi):\n raise IndexError(\"Inconsistent length of array subi\")\n if num_ is None:\n num_ = len(subj)\n elif num_ != len(subj):\n raise IndexError(\"Inconsistent length of array subj\")\n if num_ is None:\n num_ = len(valij)\n elif num_ != len(valij):\n raise IndexError(\"Inconsistent length of array valij\")\n if num_ is None: num_ = 0\n if subi is None: raise TypeError(\"Invalid type for argument subi\")\n if subi is None:\n subi_ = None\n else:\n try:\n subi_ = memoryview(subi)\n except TypeError:\n try:\n _tmparr_subi = array.array(\"i\",subi)\n except TypeError:\n raise TypeError(\"Argument subi has wrong type\")\n else:\n subi_ = memoryview(_tmparr_subi)\n \n else:\n if subi_.format != \"i\":\n subi_ = memoryview(array.array(\"i\",subi))\n \n if subj is None: raise TypeError(\"Invalid type for argument subj\")\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n \n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n \n if valij is None: raise TypeError(\"Invalid type for argument valij\")\n if valij is None:\n valij_ = None\n else:\n try:\n valij_ = memoryview(valij)\n except TypeError:\n try:\n _tmparr_valij = array.array(\"d\",valij)\n except TypeError:\n raise TypeError(\"Argument valij has wrong type\")\n else:\n valij_ = memoryview(_tmparr_valij)\n \n else:\n if valij_.format != \"d\":\n valij_ = memoryview(array.array(\"d\",valij))\n \n res = self.__obj.putaijlist64(num_,subi_,subj_,valij_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def _set_coef(self, coef_):\n self.coef_ = np.asfortranarray(array2d(coef_))", "def putvartypelist(self,subj_,vartype_):\n num_ = None\n if num_ is None:\n num_ = len(subj_)\n elif num_ != len(subj_):\n raise IndexError(\"Inconsistent length of array subj\")\n if num_ is None:\n num_ = len(vartype_)\n elif num_ != len(vartype_):\n raise IndexError(\"Inconsistent length of array vartype\")\n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n if vartype_ is None:\n raise ValueError(\"Argument vartype cannot be None\")\n if vartype_ is None:\n raise ValueError(\"Argument vartype may not be None\")\n if vartype_ is not None:\n _vartype_tmp = (ctypes.c_int32 * len(vartype_))(*vartype_)\n else:\n _vartype_tmp = None\n res = __library__.MSK_XX_putvartypelist(self.__nativep,num_,_subj_tmp,_vartype_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def __setitem__(self, key: Tuple[int, int], value: complex) -> None:\n self.coeff[self._core.index_alpha(key[0]),\n self._core.index_beta(key[1])] = value", "def appendsparsesymmatlist(self,dims_,nz_,subi_,subj_,valij_,idx_):\n num_ = None\n if num_ is None:\n num_ = len(dims_)\n elif num_ != len(dims_):\n raise IndexError(\"Inconsistent length of array dims\")\n if num_ is None:\n num_ = len(nz_)\n elif num_ != len(nz_):\n raise IndexError(\"Inconsistent length of array nz\")\n if dims_ is None:\n raise ValueError(\"Argument dims cannot be None\")\n if dims_ is None:\n raise ValueError(\"Argument dims may not be None\")\n if isinstance(dims_, numpy.ndarray) and dims_.dtype is numpy.dtype(numpy.int32) and dims_.flags.contiguous:\n _dims_copyarray = False\n _dims_tmp = ctypes.cast(dims_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif dims_ is not None:\n _dims_copyarray = True\n _dims_np_tmp = numpy.zeros(len(dims_),numpy.dtype(numpy.int32))\n _dims_np_tmp[:] = dims_\n assert _dims_np_tmp.flags.contiguous\n _dims_tmp = ctypes.cast(_dims_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _dims_copyarray = False\n _dims_tmp = None\n \n if nz_ is None:\n raise ValueError(\"Argument nz cannot be None\")\n if nz_ is None:\n raise ValueError(\"Argument nz may not be None\")\n if isinstance(nz_, numpy.ndarray) and nz_.dtype is numpy.dtype(numpy.int64) and nz_.flags.contiguous:\n _nz_copyarray = False\n _nz_tmp = ctypes.cast(nz_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif nz_ is not None:\n _nz_copyarray = True\n _nz_np_tmp = numpy.zeros(len(nz_),numpy.dtype(numpy.int64))\n _nz_np_tmp[:] = nz_\n assert _nz_np_tmp.flags.contiguous\n _nz_tmp = ctypes.cast(_nz_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _nz_copyarray = False\n _nz_tmp = None\n \n _subi_minlength = sum((nz_))\n if sum((nz_)) > 0 and subi_ is not None and len(subi_) != sum((nz_)):\n raise ValueError(\"Array argument subi is not long enough: Is %d, expected %d\" % (len(subi_),sum((nz_))))\n if subi_ is None:\n raise ValueError(\"Argument subi cannot be None\")\n if subi_ is None:\n raise ValueError(\"Argument subi may not be None\")\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\n _subi_copyarray = False\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subi_ is not None:\n _subi_copyarray = True\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\n _subi_np_tmp[:] = subi_\n assert _subi_np_tmp.flags.contiguous\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subi_copyarray = False\n _subi_tmp = None\n \n _subj_minlength = sum((nz_))\n if sum((nz_)) > 0 and subj_ is not None and len(subj_) != sum((nz_)):\n raise ValueError(\"Array argument subj is not long enough: Is %d, expected %d\" % (len(subj_),sum((nz_))))\n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n _valij_minlength = sum((nz_))\n if sum((nz_)) > 0 and valij_ is not None and len(valij_) != sum((nz_)):\n raise ValueError(\"Array argument valij is not long enough: Is %d, expected %d\" % (len(valij_),sum((nz_))))\n if valij_ is None:\n raise ValueError(\"Argument valij cannot be None\")\n if valij_ is None:\n raise ValueError(\"Argument valij may not be None\")\n if isinstance(valij_, numpy.ndarray) and valij_.dtype is numpy.dtype(numpy.float64) and valij_.flags.contiguous:\n _valij_copyarray = False\n _valij_tmp = ctypes.cast(valij_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif valij_ is not None:\n _valij_copyarray = True\n _valij_np_tmp = numpy.zeros(len(valij_),numpy.dtype(numpy.float64))\n _valij_np_tmp[:] = valij_\n assert _valij_np_tmp.flags.contiguous\n _valij_tmp = ctypes.cast(_valij_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _valij_copyarray = False\n _valij_tmp = None\n \n _idx_minlength = (num_)\n if (num_) > 0 and idx_ is not None and len(idx_) != (num_):\n raise ValueError(\"Array argument idx is not long enough: Is %d, expected %d\" % (len(idx_),(num_)))\n if isinstance(idx_,numpy.ndarray) and not idx_.flags.writeable:\n raise ValueError(\"Argument idx must be writable\")\n if idx_ is None:\n raise ValueError(\"Argument idx may not be None\")\n if isinstance(idx_, numpy.ndarray) and idx_.dtype is numpy.dtype(numpy.int64) and idx_.flags.contiguous:\n _idx_copyarray = False\n _idx_tmp = ctypes.cast(idx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif idx_ is not None:\n _idx_copyarray = True\n _idx_np_tmp = numpy.zeros(len(idx_),numpy.dtype(numpy.int64))\n _idx_np_tmp[:] = idx_\n assert _idx_np_tmp.flags.contiguous\n _idx_tmp = ctypes.cast(_idx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _idx_copyarray = False\n _idx_tmp = None\n \n res = __library__.MSK_XX_appendsparsesymmatlist(self.__nativep,num_,_dims_tmp,_nz_tmp,_subi_tmp,_subj_tmp,_valij_tmp,_idx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _idx_copyarray:\n idx_[:] = _idx_np_tmp", "def putconboundlistconst(self,sub_,bkc_,blc_,buc_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n res = __library__.MSK_XX_putconboundlistconst(self.__nativep,num_,_sub_tmp,bkc_,blc_,buc_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def store(self, subj):\n if subj in self.__lst:\n raise ValueError('Disciplina exista deja')\n self.__lst.append(subj)", "def _store_2D_coefficients(hdr, poly_model, coeff_prefix, keeplinear=False):\n mindeg = int(not keeplinear)\n degree = poly_model.degree\n for i in range(0, degree + 1):\n for j in range(0, degree + 1):\n if (i + j) > mindeg and (i + j < degree + 1):\n hdr[f'{coeff_prefix}_{i}_{j}'] = getattr(poly_model, f'c{i}_{j}').value", "def appendsparsesymmat(self,dim_,subi_,subj_,valij_):\n nz_ = None\n if nz_ is None:\n nz_ = len(subi_)\n elif nz_ != len(subi_):\n raise IndexError(\"Inconsistent length of array subi\")\n if nz_ is None:\n nz_ = len(subj_)\n elif nz_ != len(subj_):\n raise IndexError(\"Inconsistent length of array subj\")\n if nz_ is None:\n nz_ = len(valij_)\n elif nz_ != len(valij_):\n raise IndexError(\"Inconsistent length of array valij\")\n if subi_ is None:\n raise ValueError(\"Argument subi cannot be None\")\n if subi_ is None:\n raise ValueError(\"Argument subi may not be None\")\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\n _subi_copyarray = False\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subi_ is not None:\n _subi_copyarray = True\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\n _subi_np_tmp[:] = subi_\n assert _subi_np_tmp.flags.contiguous\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subi_copyarray = False\n _subi_tmp = None\n \n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n if valij_ is None:\n raise ValueError(\"Argument valij cannot be None\")\n if valij_ is None:\n raise ValueError(\"Argument valij may not be None\")\n if isinstance(valij_, numpy.ndarray) and valij_.dtype is numpy.dtype(numpy.float64) and valij_.flags.contiguous:\n _valij_copyarray = False\n _valij_tmp = ctypes.cast(valij_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif valij_ is not None:\n _valij_copyarray = True\n _valij_np_tmp = numpy.zeros(len(valij_),numpy.dtype(numpy.float64))\n _valij_np_tmp[:] = valij_\n assert _valij_np_tmp.flags.contiguous\n _valij_tmp = ctypes.cast(_valij_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _valij_copyarray = False\n _valij_tmp = None\n \n idx_ = ctypes.c_int64()\n res = __library__.MSK_XX_appendsparsesymmat(self.__nativep,dim_,nz_,_subi_tmp,_subj_tmp,_valij_tmp,ctypes.byref(idx_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n idx_ = idx_.value\n _idx_return_value = idx_\n return (_idx_return_value)", "def parameters_polynomial(cobj, prop, prop_units, alist, blist):\n for i, aval in enumerate(alist):\n if i == 0:\n param_units = prop_units\n else:\n param_units = prop_units / pyunits.K**i\n\n coeff = Var(doc=\"A parameter for CoolProp polynomial form\", units=param_units)\n cobj.add_component(prop + \"_coeff_A\" + str(i), coeff)\n coeff.fix(aval)\n\n for i, bval in enumerate(blist):\n if i == 0:\n param_units = pyunits.dimensionless\n else:\n param_units = pyunits.K**-i\n\n coeff = Var(doc=\"B parameter for CoolProp exponential form\", units=param_units)\n cobj.add_component(prop + \"_coeff_B\" + str(i), coeff)\n coeff.fix(bval)", "def add_coefficients(self, L, overwrite=False):\n if not isinstance(L, dict):\n raise ValueError(\"Call with dictionary as argument!\")\n\n for p in L.keys():\n c = mpmath.mpmathify(L[p])\n # print \"c=\",c\n cd = ceil(mpmath.log10(abs(c)))\n if(cd > self.maxdigs):\n self.maxdigs = cd\n # print \"p=\",p\n if(is_int(p)):\n (r, n) = rn_from_D(self._space.WR, p)\n elif(isinstance(p, tuple)):\n (r, n) = p\n if r in self._coeffs:\n if n in self._coeffs[r]:\n c_old = self._coeffs[r][n]\n # Try to determine (heuristically) if the new coefficient is really better\n d1 = dist_from_int(c)[0]\n d2 = dist_from_int(c_old)[0]\n if(overwrite):\n self._coeffs[r][n] = c\n else:\n self._coeffs[r][n] = c\n else:\n # see if it is a possible index at all\n if not r < 0 or r > self._space.multiplier().ambient_rank():\n raise ValueError(\"Key {0} corr to (r,n)=({1},{2}) is invalid for the current space!\".format(p, r, n))\n elif r not in self._space.multiplier().D():\n if self._space._sym_type == -1 and (r == 0 or r == self._space.multiplier().N):\n # Should be 0 by symmetry\n if abs(c) > 10**(1 - self.prec):\n raise ValueError(\"Coefficient should be zero by symmetry. Got c({0},{1})={2}!\".format(r, n, c))\n else:\n self._coeffs[r][n] = 0\n else:\n # is equal to +- c(-r,n)\n mr = 2 * self.multiplier().N - r\n if mr in self._coeffs:\n if n in self._coeffs[mr]:\n c_old = self._coeffs[mr][n]\n if abs(c - self._space.multiplier()._sym_type * c_old) > 10**(1 - self.prec):\n st = \"Might add an erroneous coefficient! Got c({0},{1})={2}. \".format(r, n, c)\n st += \"From previous coefficients should have {0}\".format(self._space._sym_type * c_old)\n raise ValueError(st)\n if overwrite:\n self._coeffs[mr][n] = c\n else:\n raise ValueError(\"Coefficient should be zero by symmetry. Got c({0},{1})={2}!\" .format(r, n, c))", "def perturbe_clist(cl_array,bins,amount):\n cltt_list=[]\n for i in range(len(bins)):\n cl=cl_array.copy()\n cl[int(bins[i])]=amount*cl_array[int(bins[i])]\n cltt_list.append(cl)\n return cltt_list", "def putslc(self,whichsol_,slc_):\n _slc_minlength = self.getnumcon()\n if self.getnumcon() > 0 and slc_ is not None and len(slc_) != self.getnumcon():\n raise ValueError(\"Array argument slc is not long enough: Is %d, expected %d\" % (len(slc_),self.getnumcon()))\n if slc_ is None:\n raise ValueError(\"Argument slc cannot be None\")\n if slc_ is None:\n raise ValueError(\"Argument slc may not be None\")\n if isinstance(slc_, numpy.ndarray) and slc_.dtype is numpy.dtype(numpy.float64) and slc_.flags.contiguous:\n _slc_copyarray = False\n _slc_tmp = ctypes.cast(slc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slc_ is not None:\n _slc_copyarray = True\n _slc_np_tmp = numpy.zeros(len(slc_),numpy.dtype(numpy.float64))\n _slc_np_tmp[:] = slc_\n assert _slc_np_tmp.flags.contiguous\n _slc_tmp = ctypes.cast(_slc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slc_copyarray = False\n _slc_tmp = None\n \n res = __library__.MSK_XX_putslc(self.__nativep,whichsol_,_slc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def __getitem__( self, l ) :\n\n return( self.coefficients[l] )", "def update_coeff(self, **kwargs: float) -> None:\n for rule_name, coeff in kwargs.items():\n if rule_name not in self.rules:\n raise ValueError(f\"Behavioral rule {rule_name} does not exist\")\n else:\n self.rules[getattr(self, rule_name)] = coeff", "def add_com_jac(ui):\n global com_jac_list\n\n content = content_fk_jac_loops(ui, \"com_jac\")\n if content in com_jac_list:\n return\n com_jac_list.append(content)\n ui.listWidget_com_jac.addItem(f\"Center of Mass Jacobian \"\n f\"{parse_content(content)}\")", "def putvarboundlistconst(self,sub_,bkx_,blx_,bux_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n res = __library__.MSK_XX_putvarboundlistconst(self.__nativep,num_,_sub_tmp,bkx_,blx_,bux_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def UpdateCostMatrix( self, extraXs ):\n for x in extraXs:\n newRow = [ self.EuclideanDistanceSq(x,y) for y in self.Y ]\n self.C.append(newRow)", "def setListPunctCmplx(self, list):\n\t\tself.list_punct_cmplx = list", "def __init__(self, coef_list):\n assert type(coef_list) is list, 'error message indicating that coef is not a list'\n self.degree = len(coef_list) - 1\n self.coefs = []\n for coef in coef_list:\n self.coefs.append(coef)", "def appendsparsesymmat(self,dim_,subi,subj,valij): # 3\n nz_ = None\n if nz_ is None:\n nz_ = len(subi)\n elif nz_ != len(subi):\n raise IndexError(\"Inconsistent length of array subi\")\n if nz_ is None:\n nz_ = len(subj)\n elif nz_ != len(subj):\n raise IndexError(\"Inconsistent length of array subj\")\n if nz_ is None:\n nz_ = len(valij)\n elif nz_ != len(valij):\n raise IndexError(\"Inconsistent length of array valij\")\n if nz_ is None: nz_ = 0\n if subi is None: raise TypeError(\"Invalid type for argument subi\")\n if subi is None:\n subi_ = None\n else:\n try:\n subi_ = memoryview(subi)\n except TypeError:\n try:\n _tmparr_subi = array.array(\"i\",subi)\n except TypeError:\n raise TypeError(\"Argument subi has wrong type\")\n else:\n subi_ = memoryview(_tmparr_subi)\n \n else:\n if subi_.format != \"i\":\n subi_ = memoryview(array.array(\"i\",subi))\n \n if subj is None: raise TypeError(\"Invalid type for argument subj\")\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n \n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n \n if valij is None: raise TypeError(\"Invalid type for argument valij\")\n if valij is None:\n valij_ = None\n else:\n try:\n valij_ = memoryview(valij)\n except TypeError:\n try:\n _tmparr_valij = array.array(\"d\",valij)\n except TypeError:\n raise TypeError(\"Argument valij has wrong type\")\n else:\n valij_ = memoryview(_tmparr_valij)\n \n else:\n if valij_.format != \"d\":\n valij_ = memoryview(array.array(\"d\",valij))\n \n res,resargs = self.__obj.appendsparsesymmat(dim_,nz_,subi_,subj_,valij_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _idx_return_value = resargs\n return _idx_return_value", "def __init__(self, sect_list, coef=1):\n self.subsectors = copy.deepcopy(sect_list)\n self.ds = {}\n self.coef = coef\n self._UpdateDS()\n self.domains = []\n self.excluded_vars = list()", "def __init__(self, coefficients):\n self.coefficients = coefficients", "def putbaraijlist(self,subi_,subj_,alphaptrb_,alphaptre_,matidx_,weights_):\n num_ = None\n if num_ is None:\n num_ = len(subi_)\n elif num_ != len(subi_):\n raise IndexError(\"Inconsistent length of array subi\")\n if num_ is None:\n num_ = len(subj_)\n elif num_ != len(subj_):\n raise IndexError(\"Inconsistent length of array subj\")\n if num_ is None:\n num_ = len(alphaptrb_)\n elif num_ != len(alphaptrb_):\n raise IndexError(\"Inconsistent length of array alphaptrb\")\n if num_ is None:\n num_ = len(alphaptre_)\n elif num_ != len(alphaptre_):\n raise IndexError(\"Inconsistent length of array alphaptre\")\n if subi_ is None:\n raise ValueError(\"Argument subi cannot be None\")\n if subi_ is None:\n raise ValueError(\"Argument subi may not be None\")\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\n _subi_copyarray = False\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subi_ is not None:\n _subi_copyarray = True\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\n _subi_np_tmp[:] = subi_\n assert _subi_np_tmp.flags.contiguous\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subi_copyarray = False\n _subi_tmp = None\n \n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n if alphaptrb_ is None:\n raise ValueError(\"Argument alphaptrb cannot be None\")\n if alphaptrb_ is None:\n raise ValueError(\"Argument alphaptrb may not be None\")\n if isinstance(alphaptrb_, numpy.ndarray) and alphaptrb_.dtype is numpy.dtype(numpy.int64) and alphaptrb_.flags.contiguous:\n _alphaptrb_copyarray = False\n _alphaptrb_tmp = ctypes.cast(alphaptrb_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif alphaptrb_ is not None:\n _alphaptrb_copyarray = True\n _alphaptrb_np_tmp = numpy.zeros(len(alphaptrb_),numpy.dtype(numpy.int64))\n _alphaptrb_np_tmp[:] = alphaptrb_\n assert _alphaptrb_np_tmp.flags.contiguous\n _alphaptrb_tmp = ctypes.cast(_alphaptrb_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _alphaptrb_copyarray = False\n _alphaptrb_tmp = None\n \n if alphaptre_ is None:\n raise ValueError(\"Argument alphaptre cannot be None\")\n if alphaptre_ is None:\n raise ValueError(\"Argument alphaptre may not be None\")\n if isinstance(alphaptre_, numpy.ndarray) and alphaptre_.dtype is numpy.dtype(numpy.int64) and alphaptre_.flags.contiguous:\n _alphaptre_copyarray = False\n _alphaptre_tmp = ctypes.cast(alphaptre_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif alphaptre_ is not None:\n _alphaptre_copyarray = True\n _alphaptre_np_tmp = numpy.zeros(len(alphaptre_),numpy.dtype(numpy.int64))\n _alphaptre_np_tmp[:] = alphaptre_\n assert _alphaptre_np_tmp.flags.contiguous\n _alphaptre_tmp = ctypes.cast(_alphaptre_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _alphaptre_copyarray = False\n _alphaptre_tmp = None\n \n if matidx_ is None:\n raise ValueError(\"Argument matidx cannot be None\")\n if matidx_ is None:\n raise ValueError(\"Argument matidx may not be None\")\n if isinstance(matidx_, numpy.ndarray) and matidx_.dtype is numpy.dtype(numpy.int64) and matidx_.flags.contiguous:\n _matidx_copyarray = False\n _matidx_tmp = ctypes.cast(matidx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif matidx_ is not None:\n _matidx_copyarray = True\n _matidx_np_tmp = numpy.zeros(len(matidx_),numpy.dtype(numpy.int64))\n _matidx_np_tmp[:] = matidx_\n assert _matidx_np_tmp.flags.contiguous\n _matidx_tmp = ctypes.cast(_matidx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _matidx_copyarray = False\n _matidx_tmp = None\n \n if weights_ is None:\n raise ValueError(\"Argument weights cannot be None\")\n if weights_ is None:\n raise ValueError(\"Argument weights may not be None\")\n if isinstance(weights_, numpy.ndarray) and weights_.dtype is numpy.dtype(numpy.float64) and weights_.flags.contiguous:\n _weights_copyarray = False\n _weights_tmp = ctypes.cast(weights_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif weights_ is not None:\n _weights_copyarray = True\n _weights_np_tmp = numpy.zeros(len(weights_),numpy.dtype(numpy.float64))\n _weights_np_tmp[:] = weights_\n assert _weights_np_tmp.flags.contiguous\n _weights_tmp = ctypes.cast(_weights_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _weights_copyarray = False\n _weights_tmp = None\n \n res = __library__.MSK_XX_putbaraijlist(self.__nativep,num_,_subi_tmp,_subj_tmp,_alphaptrb_tmp,_alphaptre_tmp,_matidx_tmp,_weights_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putvartypelist(self,subj,vartype): # 3\n num_ = None\n if num_ is None:\n num_ = len(subj)\n elif num_ != len(subj):\n raise IndexError(\"Inconsistent length of array subj\")\n if num_ is None:\n num_ = len(vartype)\n elif num_ != len(vartype):\n raise IndexError(\"Inconsistent length of array vartype\")\n if num_ is None: num_ = 0\n if subj is None: raise TypeError(\"Invalid type for argument subj\")\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n \n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n \n if vartype is None: raise TypeError(\"Invalid type for argument vartype\")\n if vartype is None:\n vartype_ = None\n else:\n try:\n vartype_ = memoryview(vartype)\n except TypeError:\n try:\n _tmparr_vartype = array.array(\"i\",vartype)\n except TypeError:\n raise TypeError(\"Argument vartype has wrong type\")\n else:\n vartype_ = memoryview(_tmparr_vartype)\n \n else:\n if vartype_.format != \"i\":\n vartype_ = memoryview(array.array(\"i\",vartype))\n \n res = self.__obj.putvartypelist(num_,subj_,vartype_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putqcon(self,qcsubk_,qcsubi_,qcsubj_,qcval_):\n numqcnz_ = None\n if numqcnz_ is None:\n numqcnz_ = len(qcsubi_)\n elif numqcnz_ != len(qcsubi_):\n raise IndexError(\"Inconsistent length of array qcsubi\")\n if numqcnz_ is None:\n numqcnz_ = len(qcsubj_)\n elif numqcnz_ != len(qcsubj_):\n raise IndexError(\"Inconsistent length of array qcsubj\")\n if numqcnz_ is None:\n numqcnz_ = len(qcval_)\n elif numqcnz_ != len(qcval_):\n raise IndexError(\"Inconsistent length of array qcval\")\n if qcsubk_ is None:\n raise ValueError(\"Argument qcsubk cannot be None\")\n if qcsubk_ is None:\n raise ValueError(\"Argument qcsubk may not be None\")\n if isinstance(qcsubk_, numpy.ndarray) and qcsubk_.dtype is numpy.dtype(numpy.int32) and qcsubk_.flags.contiguous:\n _qcsubk_copyarray = False\n _qcsubk_tmp = ctypes.cast(qcsubk_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubk_ is not None:\n _qcsubk_copyarray = True\n _qcsubk_np_tmp = numpy.zeros(len(qcsubk_),numpy.dtype(numpy.int32))\n _qcsubk_np_tmp[:] = qcsubk_\n assert _qcsubk_np_tmp.flags.contiguous\n _qcsubk_tmp = ctypes.cast(_qcsubk_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubk_copyarray = False\n _qcsubk_tmp = None\n \n if qcsubi_ is None:\n raise ValueError(\"Argument qcsubi cannot be None\")\n if qcsubi_ is None:\n raise ValueError(\"Argument qcsubi may not be None\")\n if isinstance(qcsubi_, numpy.ndarray) and qcsubi_.dtype is numpy.dtype(numpy.int32) and qcsubi_.flags.contiguous:\n _qcsubi_copyarray = False\n _qcsubi_tmp = ctypes.cast(qcsubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubi_ is not None:\n _qcsubi_copyarray = True\n _qcsubi_np_tmp = numpy.zeros(len(qcsubi_),numpy.dtype(numpy.int32))\n _qcsubi_np_tmp[:] = qcsubi_\n assert _qcsubi_np_tmp.flags.contiguous\n _qcsubi_tmp = ctypes.cast(_qcsubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubi_copyarray = False\n _qcsubi_tmp = None\n \n if qcsubj_ is None:\n raise ValueError(\"Argument qcsubj cannot be None\")\n if qcsubj_ is None:\n raise ValueError(\"Argument qcsubj may not be None\")\n if isinstance(qcsubj_, numpy.ndarray) and qcsubj_.dtype is numpy.dtype(numpy.int32) and qcsubj_.flags.contiguous:\n _qcsubj_copyarray = False\n _qcsubj_tmp = ctypes.cast(qcsubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubj_ is not None:\n _qcsubj_copyarray = True\n _qcsubj_np_tmp = numpy.zeros(len(qcsubj_),numpy.dtype(numpy.int32))\n _qcsubj_np_tmp[:] = qcsubj_\n assert _qcsubj_np_tmp.flags.contiguous\n _qcsubj_tmp = ctypes.cast(_qcsubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubj_copyarray = False\n _qcsubj_tmp = None\n \n if qcval_ is None:\n raise ValueError(\"Argument qcval cannot be None\")\n if qcval_ is None:\n raise ValueError(\"Argument qcval may not be None\")\n if isinstance(qcval_, numpy.ndarray) and qcval_.dtype is numpy.dtype(numpy.float64) and qcval_.flags.contiguous:\n _qcval_copyarray = False\n _qcval_tmp = ctypes.cast(qcval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif qcval_ is not None:\n _qcval_copyarray = True\n _qcval_np_tmp = numpy.zeros(len(qcval_),numpy.dtype(numpy.float64))\n _qcval_np_tmp[:] = qcval_\n assert _qcval_np_tmp.flags.contiguous\n _qcval_tmp = ctypes.cast(_qcval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _qcval_copyarray = False\n _qcval_tmp = None\n \n res = __library__.MSK_XX_putqcon(self.__nativep,numqcnz_,_qcsubk_tmp,_qcsubi_tmp,_qcsubj_tmp,_qcval_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)" ]

[ "0.7019918", "0.6498057", "0.565561", "0.55103093", "0.5359275", "0.53481793", "0.5285191", "0.5283986", "0.5262921", "0.5168434", "0.5103253", "0.50901055", "0.50725174", "0.5070623", "0.50672764", "0.5047127", "0.49618518", "0.49521777", "0.49174896", "0.4880588", "0.48753652", "0.48740962", "0.48617876", "0.48550335", "0.48400462", "0.4826834", "0.48260972", "0.48197344", "0.4815076", "0.4808084" ]

[ "def putcslice(self,first_,last_,slice): # 3\n if slice is None: raise TypeError(\"Invalid type for argument slice\")\n if slice is None:\n slice_ = None\n else:\n try:\n slice_ = memoryview(slice)\n except TypeError:\n try:\n _tmparr_slice = array.array(\"d\",slice)\n except TypeError:\n raise TypeError(\"Argument slice has wrong type\")\n else:\n slice_ = memoryview(_tmparr_slice)\n \n else:\n if slice_.format != \"d\":\n slice_ = memoryview(array.array(\"d\",slice))\n \n if slice_ is not None and len(slice_) != ((last_) - (first_)):\n raise ValueError(\"Array argument slice has wrong length\")\n res = self.__obj.putcslice(first_,last_,slice_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putskcslice(self,whichsol_,first_,last_,skc): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if skc is None:\n skc_ = None\n else:\n try:\n skc_ = memoryview(skc)\n except TypeError:\n try:\n _tmparr_skc = array.array(\"i\",skc)\n except TypeError:\n raise TypeError(\"Argument skc has wrong type\")\n else:\n skc_ = memoryview(_tmparr_skc)\n \n else:\n if skc_.format != \"i\":\n skc_ = memoryview(array.array(\"i\",skc))\n \n if skc_ is not None and len(skc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument skc has wrong length\")\n res = self.__obj.putskcslice(whichsol_,first_,last_,skc_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putslcslice(self,whichsol_,first_,last_,slc_):\n _slc_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and slc_ is not None and len(slc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument slc is not long enough: Is %d, expected %d\" % (len(slc_),((last_) - (first_))))\n if slc_ is None:\n raise ValueError(\"Argument slc cannot be None\")\n if slc_ is None:\n raise ValueError(\"Argument slc may not be None\")\n if isinstance(slc_, numpy.ndarray) and slc_.dtype is numpy.dtype(numpy.float64) and slc_.flags.contiguous:\n _slc_copyarray = False\n _slc_tmp = ctypes.cast(slc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slc_ is not None:\n _slc_copyarray = True\n _slc_np_tmp = numpy.zeros(len(slc_),numpy.dtype(numpy.float64))\n _slc_np_tmp[:] = slc_\n assert _slc_np_tmp.flags.contiguous\n _slc_tmp = ctypes.cast(_slc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slc_copyarray = False\n _slc_tmp = None\n \n res = __library__.MSK_XX_putslcslice(self.__nativep,whichsol_,first_,last_,_slc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putslcslice(self,whichsol_,first_,last_,slc): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if slc is None: raise TypeError(\"Invalid type for argument slc\")\n if slc is None:\n slc_ = None\n else:\n try:\n slc_ = memoryview(slc)\n except TypeError:\n try:\n _tmparr_slc = array.array(\"d\",slc)\n except TypeError:\n raise TypeError(\"Argument slc has wrong type\")\n else:\n slc_ = memoryview(_tmparr_slc)\n \n else:\n if slc_.format != \"d\":\n slc_ = memoryview(array.array(\"d\",slc))\n \n if slc_ is not None and len(slc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument slc has wrong length\")\n res = self.__obj.putslcslice(whichsol_,first_,last_,slc_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putxcslice(self,whichsol_,first_,last_,xc): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if xc is None: raise TypeError(\"Invalid type for argument xc\")\n if xc is None:\n xc_ = None\n else:\n try:\n xc_ = memoryview(xc)\n except TypeError:\n try:\n _tmparr_xc = array.array(\"d\",xc)\n except TypeError:\n raise TypeError(\"Argument xc has wrong type\")\n else:\n xc_ = memoryview(_tmparr_xc)\n \n else:\n if xc_.format != \"d\":\n xc_ = memoryview(array.array(\"d\",xc))\n \n if xc_ is not None and len(xc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument xc has wrong length\")\n res = self.__obj.putxcslice(whichsol_,first_,last_,xc_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putxxslice(self,whichsol_,first_,last_,xx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if xx is None: raise TypeError(\"Invalid type for argument xx\")\n if xx is None:\n xx_ = None\n else:\n try:\n xx_ = memoryview(xx)\n except TypeError:\n try:\n _tmparr_xx = array.array(\"d\",xx)\n except TypeError:\n raise TypeError(\"Argument xx has wrong type\")\n else:\n xx_ = memoryview(_tmparr_xx)\n \n else:\n if xx_.format != \"d\":\n xx_ = memoryview(array.array(\"d\",xx))\n \n if xx_ is not None and len(xx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument xx has wrong length\")\n res = self.__obj.putxxslice(whichsol_,first_,last_,xx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putskcslice(self,whichsol_,first_,last_,skc_):\n _skc_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and skc_ is not None and len(skc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument skc is not long enough: Is %d, expected %d\" % (len(skc_),((last_) - (first_))))\n if skc_ is not None:\n _skc_tmp = (ctypes.c_int32 * len(skc_))(*skc_)\n else:\n _skc_tmp = None\n res = __library__.MSK_XX_putskcslice(self.__nativep,whichsol_,first_,last_,_skc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putxcslice(self,whichsol_,first_,last_,xc_):\n _xc_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and xc_ is not None and len(xc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument xc is not long enough: Is %d, expected %d\" % (len(xc_),((last_) - (first_))))\n if xc_ is None:\n raise ValueError(\"Argument xc cannot be None\")\n if xc_ is None:\n raise ValueError(\"Argument xc may not be None\")\n if isinstance(xc_, numpy.ndarray) and xc_.dtype is numpy.dtype(numpy.float64) and xc_.flags.contiguous:\n _xc_copyarray = False\n _xc_tmp = ctypes.cast(xc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif xc_ is not None:\n _xc_copyarray = True\n _xc_np_tmp = numpy.zeros(len(xc_),numpy.dtype(numpy.float64))\n _xc_np_tmp[:] = xc_\n assert _xc_np_tmp.flags.contiguous\n _xc_tmp = ctypes.cast(_xc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _xc_copyarray = False\n _xc_tmp = None\n \n res = __library__.MSK_XX_putxcslice(self.__nativep,whichsol_,first_,last_,_xc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def __setslice__(self, *args):\n return _itkLineSpatialObjectPointPython.vectoritkLineSpatialObjectPoint3___setslice__(self, *args)", "def putxxslice(self,whichsol_,first_,last_,xx_):\n _xx_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and xx_ is not None and len(xx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument xx is not long enough: Is %d, expected %d\" % (len(xx_),((last_) - (first_))))\n if xx_ is None:\n raise ValueError(\"Argument xx cannot be None\")\n if xx_ is None:\n raise ValueError(\"Argument xx may not be None\")\n if isinstance(xx_, numpy.ndarray) and xx_.dtype is numpy.dtype(numpy.float64) and xx_.flags.contiguous:\n _xx_copyarray = False\n _xx_tmp = ctypes.cast(xx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif xx_ is not None:\n _xx_copyarray = True\n _xx_np_tmp = numpy.zeros(len(xx_),numpy.dtype(numpy.float64))\n _xx_np_tmp[:] = xx_\n assert _xx_np_tmp.flags.contiguous\n _xx_tmp = ctypes.cast(_xx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _xx_copyarray = False\n _xx_tmp = None\n \n res = __library__.MSK_XX_putxxslice(self.__nativep,whichsol_,first_,last_,_xx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putconboundsliceconst(self,first_,last_,bkc_,blc_,buc_):\n res = __library__.MSK_XX_putconboundsliceconst(self.__nativep,first_,last_,bkc_,blc_,buc_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putskxslice(self,whichsol_,first_,last_,skx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if skx is None: raise TypeError(\"Invalid type for argument skx\")\n if skx is None:\n skx_ = None\n else:\n try:\n skx_ = memoryview(skx)\n except TypeError:\n try:\n _tmparr_skx = array.array(\"i\",skx)\n except TypeError:\n raise TypeError(\"Argument skx has wrong type\")\n else:\n skx_ = memoryview(_tmparr_skx)\n \n else:\n if skx_.format != \"i\":\n skx_ = memoryview(array.array(\"i\",skx))\n \n if skx_ is not None and len(skx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument skx has wrong length\")\n res = self.__obj.putskxslice(whichsol_,first_,last_,skx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def getslcslice(self,whichsol_,first_,last_,slc_):\n _slc_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and slc_ is not None and len(slc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument slc is not long enough: Is %d, expected %d\" % (len(slc_),((last_) - (first_))))\n if isinstance(slc_,numpy.ndarray) and not slc_.flags.writeable:\n raise ValueError(\"Argument slc must be writable\")\n if isinstance(slc_, numpy.ndarray) and slc_.dtype is numpy.dtype(numpy.float64) and slc_.flags.contiguous:\n _slc_copyarray = False\n _slc_tmp = ctypes.cast(slc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slc_ is not None:\n _slc_copyarray = True\n _slc_np_tmp = numpy.zeros(len(slc_),numpy.dtype(numpy.float64))\n _slc_np_tmp[:] = slc_\n assert _slc_np_tmp.flags.contiguous\n _slc_tmp = ctypes.cast(_slc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slc_copyarray = False\n _slc_tmp = None\n \n res = __library__.MSK_XX_getslcslice(self.__nativep,whichsol_,first_,last_,_slc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _slc_copyarray:\n slc_[:] = _slc_np_tmp", "def getcslice(self,first_,last_,c): # 3\n _copyback_c = False\n if c is None:\n c_ = None\n else:\n try:\n c_ = memoryview(c)\n except TypeError:\n try:\n _tmparr_c = array.array(\"d\",c)\n except TypeError:\n raise TypeError(\"Argument c has wrong type\")\n else:\n c_ = memoryview(_tmparr_c)\n _copyback_c = True\n else:\n if c_.format != \"d\":\n c_ = memoryview(array.array(\"d\",c))\n _copyback_c = True\n if c_ is not None and len(c_) != ((last_) - (first_)):\n raise ValueError(\"Array argument c has wrong length\")\n res = self.__obj.getcslice(first_,last_,c_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_c:\n c[:] = _tmparr_c", "def __setslice__(self, *args):\n return _itkLineSpatialObjectPointPython.vectoritkLineSpatialObjectPoint2___setslice__(self, *args)", "def putvarboundsliceconst(self,first_,last_,bkx_,blx_,bux_):\n res = __library__.MSK_XX_putvarboundsliceconst(self.__nativep,first_,last_,bkx_,blx_,bux_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def test_getslice_setslice2(self):\n class C(object):\n def __getitem__(self, index):\n return 'Ok'\n def __setitem__(self, index, value):\n self.lastCall = 'set'\n def __delitem__(self, index):\n self.lastCall = 'delete'\n\n a = C()\n self.assertEqual(a[5:10], 'Ok')\n\n a.lastCall = ''\n a[5:10] = 'abc'\n self.assertEqual(a.lastCall, 'set')\n\n a.lastCall = ''\n del(a[5:10])\n self.assertEqual(a.lastCall, 'delete')", "def slice(self, start=None, end=None, inplace=False):\n if inplace:\n self.data = self.data[start:end]\n else:\n cpy = self.copy()\n\n cpy.data = cpy.data[start:end]\n\n return cpy\n return", "def putyslice(self,whichsol_,first_,last_,y): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if y is None: raise TypeError(\"Invalid type for argument y\")\n if y is None:\n y_ = None\n else:\n try:\n y_ = memoryview(y)\n except TypeError:\n try:\n _tmparr_y = array.array(\"d\",y)\n except TypeError:\n raise TypeError(\"Argument y has wrong type\")\n else:\n y_ = memoryview(_tmparr_y)\n \n else:\n if y_.format != \"d\":\n y_ = memoryview(array.array(\"d\",y))\n \n if y_ is not None and len(y_) != ((last_) - (first_)):\n raise ValueError(\"Array argument y has wrong length\")\n res = self.__obj.putyslice(whichsol_,first_,last_,y_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putyslice(self,whichsol_,first_,last_,y_):\n _y_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and y_ is not None and len(y_) != ((last_) - (first_)):\n raise ValueError(\"Array argument y is not long enough: Is %d, expected %d\" % (len(y_),((last_) - (first_))))\n if y_ is None:\n raise ValueError(\"Argument y cannot be None\")\n if y_ is None:\n raise ValueError(\"Argument y may not be None\")\n if isinstance(y_, numpy.ndarray) and y_.dtype is numpy.dtype(numpy.float64) and y_.flags.contiguous:\n _y_copyarray = False\n _y_tmp = ctypes.cast(y_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif y_ is not None:\n _y_copyarray = True\n _y_np_tmp = numpy.zeros(len(y_),numpy.dtype(numpy.float64))\n _y_np_tmp[:] = y_\n assert _y_np_tmp.flags.contiguous\n _y_tmp = ctypes.cast(_y_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _y_copyarray = False\n _y_tmp = None\n \n res = __library__.MSK_XX_putyslice(self.__nativep,whichsol_,first_,last_,_y_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def getslcslice(self,whichsol_,first_,last_,slc): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n _copyback_slc = False\n if slc is None:\n slc_ = None\n else:\n try:\n slc_ = memoryview(slc)\n except TypeError:\n try:\n _tmparr_slc = array.array(\"d\",slc)\n except TypeError:\n raise TypeError(\"Argument slc has wrong type\")\n else:\n slc_ = memoryview(_tmparr_slc)\n _copyback_slc = True\n else:\n if slc_.format != \"d\":\n slc_ = memoryview(array.array(\"d\",slc))\n _copyback_slc = True\n if slc_ is not None and len(slc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument slc has wrong length\")\n res = self.__obj.getslcslice(whichsol_,first_,last_,slc_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_slc:\n slc[:] = _tmparr_slc", "def putsucslice(self,whichsol_,first_,last_,suc_):\n _suc_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and suc_ is not None and len(suc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument suc is not long enough: Is %d, expected %d\" % (len(suc_),((last_) - (first_))))\n if suc_ is None:\n raise ValueError(\"Argument suc cannot be None\")\n if suc_ is None:\n raise ValueError(\"Argument suc may not be None\")\n if isinstance(suc_, numpy.ndarray) and suc_.dtype is numpy.dtype(numpy.float64) and suc_.flags.contiguous:\n _suc_copyarray = False\n _suc_tmp = ctypes.cast(suc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif suc_ is not None:\n _suc_copyarray = True\n _suc_np_tmp = numpy.zeros(len(suc_),numpy.dtype(numpy.float64))\n _suc_np_tmp[:] = suc_\n assert _suc_np_tmp.flags.contiguous\n _suc_tmp = ctypes.cast(_suc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _suc_copyarray = False\n _suc_tmp = None\n \n res = __library__.MSK_XX_putsucslice(self.__nativep,whichsol_,first_,last_,_suc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putskxslice(self,whichsol_,first_,last_,skx_):\n _skx_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and skx_ is not None and len(skx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument skx is not long enough: Is %d, expected %d\" % (len(skx_),((last_) - (first_))))\n if skx_ is None:\n raise ValueError(\"Argument skx cannot be None\")\n if skx_ is None:\n raise ValueError(\"Argument skx may not be None\")\n if skx_ is not None:\n _skx_tmp = (ctypes.c_int32 * len(skx_))(*skx_)\n else:\n _skx_tmp = None\n res = __library__.MSK_XX_putskxslice(self.__nativep,whichsol_,first_,last_,_skx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def __setslice__(self, *args):\n return _itkSurfaceSpatialObjectPointPython.vectoritkSurfaceSpatialObjectPoint3___setslice__(self, *args)", "def getcslice(self,first_,last_,c_):\n _c_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and c_ is not None and len(c_) != ((last_) - (first_)):\n raise ValueError(\"Array argument c is not long enough: Is %d, expected %d\" % (len(c_),((last_) - (first_))))\n if isinstance(c_,numpy.ndarray) and not c_.flags.writeable:\n raise ValueError(\"Argument c must be writable\")\n if isinstance(c_, numpy.ndarray) and c_.dtype is numpy.dtype(numpy.float64) and c_.flags.contiguous:\n _c_copyarray = False\n _c_tmp = ctypes.cast(c_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif c_ is not None:\n _c_copyarray = True\n _c_np_tmp = numpy.zeros(len(c_),numpy.dtype(numpy.float64))\n _c_np_tmp[:] = c_\n assert _c_np_tmp.flags.contiguous\n _c_tmp = ctypes.cast(_c_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _c_copyarray = False\n _c_tmp = None\n \n res = __library__.MSK_XX_getcslice(self.__nativep,first_,last_,_c_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _c_copyarray:\n c_[:] = _c_np_tmp", "def _fix_slice(self, inputs, new_attr):\n begin = new_attr.get('begin')\n end = new_attr.get('end')\n axes = new_attr.get('axis', tuple(range(len(begin))))\n slice_op = mx.sym.slice_axis(inputs[0], axis=axes[0], begin=begin[0], end=end[0])\n if len(axes) > 1:\n for i, axis in enumerate(axes):\n slice_op = mx.sym.slice_axis(slice_op, axis=axis, begin=begin[i], end=end[i])\n return slice_op", "def putsucslice(self,whichsol_,first_,last_,suc): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if suc is None: raise TypeError(\"Invalid type for argument suc\")\n if suc is None:\n suc_ = None\n else:\n try:\n suc_ = memoryview(suc)\n except TypeError:\n try:\n _tmparr_suc = array.array(\"d\",suc)\n except TypeError:\n raise TypeError(\"Argument suc has wrong type\")\n else:\n suc_ = memoryview(_tmparr_suc)\n \n else:\n if suc_.format != \"d\":\n suc_ = memoryview(array.array(\"d\",suc))\n \n if suc_ is not None and len(suc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument suc has wrong length\")\n res = self.__obj.putsucslice(whichsol_,first_,last_,suc_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def __getslice__(self, *args):\n return _itkLineSpatialObjectPointPython.vectoritkLineSpatialObjectPoint3___getslice__(self, *args)", "def getxxslice(self,whichsol_,first_,last_,xx_):\n _xx_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and xx_ is not None and len(xx_) != ((last_) - (first_)):\n raise ValueError(\"Array argument xx is not long enough: Is %d, expected %d\" % (len(xx_),((last_) - (first_))))\n if isinstance(xx_,numpy.ndarray) and not xx_.flags.writeable:\n raise ValueError(\"Argument xx must be writable\")\n if isinstance(xx_, numpy.ndarray) and xx_.dtype is numpy.dtype(numpy.float64) and xx_.flags.contiguous:\n _xx_copyarray = False\n _xx_tmp = ctypes.cast(xx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif xx_ is not None:\n _xx_copyarray = True\n _xx_np_tmp = numpy.zeros(len(xx_),numpy.dtype(numpy.float64))\n _xx_np_tmp[:] = xx_\n assert _xx_np_tmp.flags.contiguous\n _xx_tmp = ctypes.cast(_xx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _xx_copyarray = False\n _xx_tmp = None\n \n res = __library__.MSK_XX_getxxslice(self.__nativep,whichsol_,first_,last_,_xx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _xx_copyarray:\n xx_[:] = _xx_np_tmp", "def getskcslice(self,whichsol_,first_,last_,skc_):\n _skc_minlength = ((last_) - (first_))\n if ((last_) - (first_)) > 0 and skc_ is not None and len(skc_) != ((last_) - (first_)):\n raise ValueError(\"Array argument skc is not long enough: Is %d, expected %d\" % (len(skc_),((last_) - (first_))))\n if isinstance(skc_,numpy.ndarray) and not skc_.flags.writeable:\n raise ValueError(\"Argument skc must be writable\")\n if skc_ is not None:\n _skc_tmp = (ctypes.c_int32 * len(skc_))()\n else:\n _skc_tmp = None\n res = __library__.MSK_XX_getskcslice(self.__nativep,whichsol_,first_,last_,_skc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if skc_ is not None: skc_[:] = [ stakey(v) for v in _skc_tmp[0:len(skc_)] ]" ]

[ "0.7177398", "0.64559644", "0.64415175", "0.6327401", "0.63102835", "0.62641084", "0.6245864", "0.6206491", "0.61138916", "0.60814995", "0.597995", "0.593315", "0.5893596", "0.58712506", "0.5860539", "0.5855395", "0.58295006", "0.58105403", "0.5787265", "0.5746839", "0.57019055", "0.57017094", "0.56873286", "0.568603", "0.568457", "0.56585735", "0.5654836", "0.56272733", "0.5614747", "0.56144816" ]

[ "def putbarcj(self,j_,sub,weights): # 3\n num_ = None\n if num_ is None:\n num_ = len(sub)\n elif num_ != len(sub):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None:\n num_ = len(weights)\n elif num_ != len(weights):\n raise IndexError(\"Inconsistent length of array weights\")\n if num_ is None: num_ = 0\n if sub is None: raise TypeError(\"Invalid type for argument sub\")\n if sub is None:\n sub_ = None\n else:\n try:\n sub_ = memoryview(sub)\n except TypeError:\n try:\n _tmparr_sub = array.array(\"q\",sub)\n except TypeError:\n raise TypeError(\"Argument sub has wrong type\")\n else:\n sub_ = memoryview(_tmparr_sub)\n \n else:\n if sub_.format != \"q\":\n sub_ = memoryview(array.array(\"q\",sub))\n \n if weights is None: raise TypeError(\"Invalid type for argument weights\")\n if weights is None:\n weights_ = None\n else:\n try:\n weights_ = memoryview(weights)\n except TypeError:\n try:\n _tmparr_weights = array.array(\"d\",weights)\n except TypeError:\n raise TypeError(\"Argument weights has wrong type\")\n else:\n weights_ = memoryview(_tmparr_weights)\n \n else:\n if weights_.format != \"d\":\n weights_ = memoryview(array.array(\"d\",weights))\n \n res = self.__obj.putbarcj(j_,num_,sub_,weights_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putbaraij(self,i_,j_,sub,weights): # 3\n num_ = None\n if num_ is None:\n num_ = len(sub)\n elif num_ != len(sub):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None:\n num_ = len(weights)\n elif num_ != len(weights):\n raise IndexError(\"Inconsistent length of array weights\")\n if num_ is None: num_ = 0\n if sub is None: raise TypeError(\"Invalid type for argument sub\")\n if sub is None:\n sub_ = None\n else:\n try:\n sub_ = memoryview(sub)\n except TypeError:\n try:\n _tmparr_sub = array.array(\"q\",sub)\n except TypeError:\n raise TypeError(\"Argument sub has wrong type\")\n else:\n sub_ = memoryview(_tmparr_sub)\n \n else:\n if sub_.format != \"q\":\n sub_ = memoryview(array.array(\"q\",sub))\n \n if weights is None: raise TypeError(\"Invalid type for argument weights\")\n if weights is None:\n weights_ = None\n else:\n try:\n weights_ = memoryview(weights)\n except TypeError:\n try:\n _tmparr_weights = array.array(\"d\",weights)\n except TypeError:\n raise TypeError(\"Argument weights has wrong type\")\n else:\n weights_ = memoryview(_tmparr_weights)\n \n else:\n if weights_.format != \"d\":\n weights_ = memoryview(array.array(\"d\",weights))\n \n res = self.__obj.putbaraij(i_,j_,num_,sub_,weights_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putbaraij(self,i_,j_,sub_,weights_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None:\n num_ = len(weights_)\n elif num_ != len(weights_):\n raise IndexError(\"Inconsistent length of array weights\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int64) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int64))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n if weights_ is None:\n raise ValueError(\"Argument weights cannot be None\")\n if weights_ is None:\n raise ValueError(\"Argument weights may not be None\")\n if isinstance(weights_, numpy.ndarray) and weights_.dtype is numpy.dtype(numpy.float64) and weights_.flags.contiguous:\n _weights_copyarray = False\n _weights_tmp = ctypes.cast(weights_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif weights_ is not None:\n _weights_copyarray = True\n _weights_np_tmp = numpy.zeros(len(weights_),numpy.dtype(numpy.float64))\n _weights_np_tmp[:] = weights_\n assert _weights_np_tmp.flags.contiguous\n _weights_tmp = ctypes.cast(_weights_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _weights_copyarray = False\n _weights_tmp = None\n \n res = __library__.MSK_XX_putbaraij(self.__nativep,i_,j_,num_,_sub_tmp,_weights_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putbarxj(self,whichsol_,j_,barxj_):\n _barxj_minlength = self.getlenbarvarj((j_))\n if self.getlenbarvarj((j_)) > 0 and barxj_ is not None and len(barxj_) != self.getlenbarvarj((j_)):\n raise ValueError(\"Array argument barxj is not long enough: Is %d, expected %d\" % (len(barxj_),self.getlenbarvarj((j_))))\n if barxj_ is None:\n raise ValueError(\"Argument barxj cannot be None\")\n if barxj_ is None:\n raise ValueError(\"Argument barxj may not be None\")\n if isinstance(barxj_, numpy.ndarray) and barxj_.dtype is numpy.dtype(numpy.float64) and barxj_.flags.contiguous:\n _barxj_copyarray = False\n _barxj_tmp = ctypes.cast(barxj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif barxj_ is not None:\n _barxj_copyarray = True\n _barxj_np_tmp = numpy.zeros(len(barxj_),numpy.dtype(numpy.float64))\n _barxj_np_tmp[:] = barxj_\n assert _barxj_np_tmp.flags.contiguous\n _barxj_tmp = ctypes.cast(_barxj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _barxj_copyarray = False\n _barxj_tmp = None\n \n res = __library__.MSK_XX_putbarxj(self.__nativep,whichsol_,j_,_barxj_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putbaraijlist(self,subi_,subj_,alphaptrb_,alphaptre_,matidx_,weights_):\n num_ = None\n if num_ is None:\n num_ = len(subi_)\n elif num_ != len(subi_):\n raise IndexError(\"Inconsistent length of array subi\")\n if num_ is None:\n num_ = len(subj_)\n elif num_ != len(subj_):\n raise IndexError(\"Inconsistent length of array subj\")\n if num_ is None:\n num_ = len(alphaptrb_)\n elif num_ != len(alphaptrb_):\n raise IndexError(\"Inconsistent length of array alphaptrb\")\n if num_ is None:\n num_ = len(alphaptre_)\n elif num_ != len(alphaptre_):\n raise IndexError(\"Inconsistent length of array alphaptre\")\n if subi_ is None:\n raise ValueError(\"Argument subi cannot be None\")\n if subi_ is None:\n raise ValueError(\"Argument subi may not be None\")\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\n _subi_copyarray = False\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subi_ is not None:\n _subi_copyarray = True\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\n _subi_np_tmp[:] = subi_\n assert _subi_np_tmp.flags.contiguous\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subi_copyarray = False\n _subi_tmp = None\n \n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n if alphaptrb_ is None:\n raise ValueError(\"Argument alphaptrb cannot be None\")\n if alphaptrb_ is None:\n raise ValueError(\"Argument alphaptrb may not be None\")\n if isinstance(alphaptrb_, numpy.ndarray) and alphaptrb_.dtype is numpy.dtype(numpy.int64) and alphaptrb_.flags.contiguous:\n _alphaptrb_copyarray = False\n _alphaptrb_tmp = ctypes.cast(alphaptrb_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif alphaptrb_ is not None:\n _alphaptrb_copyarray = True\n _alphaptrb_np_tmp = numpy.zeros(len(alphaptrb_),numpy.dtype(numpy.int64))\n _alphaptrb_np_tmp[:] = alphaptrb_\n assert _alphaptrb_np_tmp.flags.contiguous\n _alphaptrb_tmp = ctypes.cast(_alphaptrb_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _alphaptrb_copyarray = False\n _alphaptrb_tmp = None\n \n if alphaptre_ is None:\n raise ValueError(\"Argument alphaptre cannot be None\")\n if alphaptre_ is None:\n raise ValueError(\"Argument alphaptre may not be None\")\n if isinstance(alphaptre_, numpy.ndarray) and alphaptre_.dtype is numpy.dtype(numpy.int64) and alphaptre_.flags.contiguous:\n _alphaptre_copyarray = False\n _alphaptre_tmp = ctypes.cast(alphaptre_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif alphaptre_ is not None:\n _alphaptre_copyarray = True\n _alphaptre_np_tmp = numpy.zeros(len(alphaptre_),numpy.dtype(numpy.int64))\n _alphaptre_np_tmp[:] = alphaptre_\n assert _alphaptre_np_tmp.flags.contiguous\n _alphaptre_tmp = ctypes.cast(_alphaptre_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _alphaptre_copyarray = False\n _alphaptre_tmp = None\n \n if matidx_ is None:\n raise ValueError(\"Argument matidx cannot be None\")\n if matidx_ is None:\n raise ValueError(\"Argument matidx may not be None\")\n if isinstance(matidx_, numpy.ndarray) and matidx_.dtype is numpy.dtype(numpy.int64) and matidx_.flags.contiguous:\n _matidx_copyarray = False\n _matidx_tmp = ctypes.cast(matidx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif matidx_ is not None:\n _matidx_copyarray = True\n _matidx_np_tmp = numpy.zeros(len(matidx_),numpy.dtype(numpy.int64))\n _matidx_np_tmp[:] = matidx_\n assert _matidx_np_tmp.flags.contiguous\n _matidx_tmp = ctypes.cast(_matidx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _matidx_copyarray = False\n _matidx_tmp = None\n \n if weights_ is None:\n raise ValueError(\"Argument weights cannot be None\")\n if weights_ is None:\n raise ValueError(\"Argument weights may not be None\")\n if isinstance(weights_, numpy.ndarray) and weights_.dtype is numpy.dtype(numpy.float64) and weights_.flags.contiguous:\n _weights_copyarray = False\n _weights_tmp = ctypes.cast(weights_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif weights_ is not None:\n _weights_copyarray = True\n _weights_np_tmp = numpy.zeros(len(weights_),numpy.dtype(numpy.float64))\n _weights_np_tmp[:] = weights_\n assert _weights_np_tmp.flags.contiguous\n _weights_tmp = ctypes.cast(_weights_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _weights_copyarray = False\n _weights_tmp = None\n \n res = __library__.MSK_XX_putbaraijlist(self.__nativep,num_,_subi_tmp,_subj_tmp,_alphaptrb_tmp,_alphaptre_tmp,_matidx_tmp,_weights_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putbarsj(self,whichsol_,j_,barsj_):\n _barsj_minlength = self.getlenbarvarj((j_))\n if self.getlenbarvarj((j_)) > 0 and barsj_ is not None and len(barsj_) != self.getlenbarvarj((j_)):\n raise ValueError(\"Array argument barsj is not long enough: Is %d, expected %d\" % (len(barsj_),self.getlenbarvarj((j_))))\n if barsj_ is None:\n raise ValueError(\"Argument barsj cannot be None\")\n if barsj_ is None:\n raise ValueError(\"Argument barsj may not be None\")\n if isinstance(barsj_, numpy.ndarray) and barsj_.dtype is numpy.dtype(numpy.float64) and barsj_.flags.contiguous:\n _barsj_copyarray = False\n _barsj_tmp = ctypes.cast(barsj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif barsj_ is not None:\n _barsj_copyarray = True\n _barsj_np_tmp = numpy.zeros(len(barsj_),numpy.dtype(numpy.float64))\n _barsj_np_tmp[:] = barsj_\n assert _barsj_np_tmp.flags.contiguous\n _barsj_tmp = ctypes.cast(_barsj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _barsj_copyarray = False\n _barsj_tmp = None\n \n res = __library__.MSK_XX_putbarsj(self.__nativep,whichsol_,j_,_barsj_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putbarxj(self,whichsol_,j_,barxj): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if barxj is None: raise TypeError(\"Invalid type for argument barxj\")\n if barxj is None:\n barxj_ = None\n else:\n try:\n barxj_ = memoryview(barxj)\n except TypeError:\n try:\n _tmparr_barxj = array.array(\"d\",barxj)\n except TypeError:\n raise TypeError(\"Argument barxj has wrong type\")\n else:\n barxj_ = memoryview(_tmparr_barxj)\n \n else:\n if barxj_.format != \"d\":\n barxj_ = memoryview(array.array(\"d\",barxj))\n \n if barxj_ is not None and len(barxj_) != self.getlenbarvarj((j_)):\n raise ValueError(\"Array argument barxj has wrong length\")\n res = self.__obj.putbarxj(whichsol_,j_,barxj_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def getbarcidx(self,idx_,sub_,weights_):\n maxnum_ = self.getbarcidxinfo((idx_))\n j_ = ctypes.c_int32()\n num_ = ctypes.c_int64()\n _sub_minlength = (maxnum_)\n if (maxnum_) > 0 and sub_ is not None and len(sub_) != (maxnum_):\n raise ValueError(\"Array argument sub is not long enough: Is %d, expected %d\" % (len(sub_),(maxnum_)))\n if isinstance(sub_,numpy.ndarray) and not sub_.flags.writeable:\n raise ValueError(\"Argument sub must be writable\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int64) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int64))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n _weights_minlength = (maxnum_)\n if (maxnum_) > 0 and weights_ is not None and len(weights_) != (maxnum_):\n raise ValueError(\"Array argument weights is not long enough: Is %d, expected %d\" % (len(weights_),(maxnum_)))\n if isinstance(weights_,numpy.ndarray) and not weights_.flags.writeable:\n raise ValueError(\"Argument weights must be writable\")\n if weights_ is None:\n raise ValueError(\"Argument weights may not be None\")\n if isinstance(weights_, numpy.ndarray) and weights_.dtype is numpy.dtype(numpy.float64) and weights_.flags.contiguous:\n _weights_copyarray = False\n _weights_tmp = ctypes.cast(weights_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif weights_ is not None:\n _weights_copyarray = True\n _weights_np_tmp = numpy.zeros(len(weights_),numpy.dtype(numpy.float64))\n _weights_np_tmp[:] = weights_\n assert _weights_np_tmp.flags.contiguous\n _weights_tmp = ctypes.cast(_weights_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _weights_copyarray = False\n _weights_tmp = None\n \n res = __library__.MSK_XX_getbarcidx(self.__nativep,idx_,maxnum_,ctypes.byref(j_),ctypes.byref(num_),_sub_tmp,_weights_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n j_ = j_.value\n _j_return_value = j_\n num_ = num_.value\n _num_return_value = num_\n if _sub_copyarray:\n sub_[:] = _sub_np_tmp\n if _weights_copyarray:\n weights_[:] = _weights_np_tmp\n return (_j_return_value,_num_return_value)", "def getbarcidx(self,idx_,sub,weights): # 3\n maxnum_ = self.getbarcidxinfo((idx_))\n if sub is None: raise TypeError(\"Invalid type for argument sub\")\n _copyback_sub = False\n if sub is None:\n sub_ = None\n else:\n try:\n sub_ = memoryview(sub)\n except TypeError:\n try:\n _tmparr_sub = array.array(\"q\",sub)\n except TypeError:\n raise TypeError(\"Argument sub has wrong type\")\n else:\n sub_ = memoryview(_tmparr_sub)\n _copyback_sub = True\n else:\n if sub_.format != \"q\":\n sub_ = memoryview(array.array(\"q\",sub))\n _copyback_sub = True\n if sub_ is not None and len(sub_) != (maxnum_):\n raise ValueError(\"Array argument sub has wrong length\")\n if weights is None: raise TypeError(\"Invalid type for argument weights\")\n _copyback_weights = False\n if weights is None:\n weights_ = None\n else:\n try:\n weights_ = memoryview(weights)\n except TypeError:\n try:\n _tmparr_weights = array.array(\"d\",weights)\n except TypeError:\n raise TypeError(\"Argument weights has wrong type\")\n else:\n weights_ = memoryview(_tmparr_weights)\n _copyback_weights = True\n else:\n if weights_.format != \"d\":\n weights_ = memoryview(array.array(\"d\",weights))\n _copyback_weights = True\n if weights_ is not None and len(weights_) != (maxnum_):\n raise ValueError(\"Array argument weights has wrong length\")\n res,resargs = self.__obj.getbarcidx(idx_,maxnum_,sub_,weights_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _j_return_value,_num_return_value = resargs\n if _copyback_weights:\n weights[:] = _tmparr_weights\n if _copyback_sub:\n sub[:] = _tmparr_sub\n return _j_return_value,_num_return_value", "def fixB(self,i,j,value):\n if self.coeffPattern[1] == None:\n m,n=self.m,self.n\n self.coeffPattern = [[None]*n for i in range(m)]\n self.coeffPattern[1][i][j]=value\n self._updateEstimatorSize(i)", "def _bucket_setitem(self, j, k, v):\n pass", "def putbarsj(self,whichsol_,j_,barsj): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if barsj is None: raise TypeError(\"Invalid type for argument barsj\")\n if barsj is None:\n barsj_ = None\n else:\n try:\n barsj_ = memoryview(barsj)\n except TypeError:\n try:\n _tmparr_barsj = array.array(\"d\",barsj)\n except TypeError:\n raise TypeError(\"Argument barsj has wrong type\")\n else:\n barsj_ = memoryview(_tmparr_barsj)\n \n else:\n if barsj_.format != \"d\":\n barsj_ = memoryview(array.array(\"d\",barsj))\n \n if barsj_ is not None and len(barsj_) != self.getlenbarvarj((j_)):\n raise ValueError(\"Array argument barsj has wrong length\")\n res = self.__obj.putbarsj(whichsol_,j_,barsj_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def getbarxj(self,whichsol_,j_,barxj_):\n _barxj_minlength = self.getlenbarvarj((j_))\n if self.getlenbarvarj((j_)) > 0 and barxj_ is not None and len(barxj_) != self.getlenbarvarj((j_)):\n raise ValueError(\"Array argument barxj is not long enough: Is %d, expected %d\" % (len(barxj_),self.getlenbarvarj((j_))))\n if isinstance(barxj_,numpy.ndarray) and not barxj_.flags.writeable:\n raise ValueError(\"Argument barxj must be writable\")\n if barxj_ is None:\n raise ValueError(\"Argument barxj may not be None\")\n if isinstance(barxj_, numpy.ndarray) and barxj_.dtype is numpy.dtype(numpy.float64) and barxj_.flags.contiguous:\n _barxj_copyarray = False\n _barxj_tmp = ctypes.cast(barxj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif barxj_ is not None:\n _barxj_copyarray = True\n _barxj_np_tmp = numpy.zeros(len(barxj_),numpy.dtype(numpy.float64))\n _barxj_np_tmp[:] = barxj_\n assert _barxj_np_tmp.flags.contiguous\n _barxj_tmp = ctypes.cast(_barxj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _barxj_copyarray = False\n _barxj_tmp = None\n \n res = __library__.MSK_XX_getbarxj(self.__nativep,whichsol_,j_,_barxj_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _barxj_copyarray:\n barxj_[:] = _barxj_np_tmp", "def putbararowlist(self,subi_,ptrb_,ptre_,subj_,nummat_,matidx_,weights_):\n num_ = None\n if num_ is None:\n num_ = len(subi_)\n elif num_ != len(subi_):\n raise IndexError(\"Inconsistent length of array subi\")\n if num_ is None:\n num_ = len(ptrb_)\n elif num_ != len(ptrb_):\n raise IndexError(\"Inconsistent length of array ptrb\")\n if num_ is None:\n num_ = len(ptre_)\n elif num_ != len(ptre_):\n raise IndexError(\"Inconsistent length of array ptre\")\n if subi_ is None:\n raise ValueError(\"Argument subi cannot be None\")\n if subi_ is None:\n raise ValueError(\"Argument subi may not be None\")\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\n _subi_copyarray = False\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subi_ is not None:\n _subi_copyarray = True\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\n _subi_np_tmp[:] = subi_\n assert _subi_np_tmp.flags.contiguous\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subi_copyarray = False\n _subi_tmp = None\n \n if ptrb_ is None:\n raise ValueError(\"Argument ptrb cannot be None\")\n if ptrb_ is None:\n raise ValueError(\"Argument ptrb may not be None\")\n if isinstance(ptrb_, numpy.ndarray) and ptrb_.dtype is numpy.dtype(numpy.int64) and ptrb_.flags.contiguous:\n _ptrb_copyarray = False\n _ptrb_tmp = ctypes.cast(ptrb_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif ptrb_ is not None:\n _ptrb_copyarray = True\n _ptrb_np_tmp = numpy.zeros(len(ptrb_),numpy.dtype(numpy.int64))\n _ptrb_np_tmp[:] = ptrb_\n assert _ptrb_np_tmp.flags.contiguous\n _ptrb_tmp = ctypes.cast(_ptrb_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _ptrb_copyarray = False\n _ptrb_tmp = None\n \n if ptre_ is None:\n raise ValueError(\"Argument ptre cannot be None\")\n if ptre_ is None:\n raise ValueError(\"Argument ptre may not be None\")\n if isinstance(ptre_, numpy.ndarray) and ptre_.dtype is numpy.dtype(numpy.int64) and ptre_.flags.contiguous:\n _ptre_copyarray = False\n _ptre_tmp = ctypes.cast(ptre_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif ptre_ is not None:\n _ptre_copyarray = True\n _ptre_np_tmp = numpy.zeros(len(ptre_),numpy.dtype(numpy.int64))\n _ptre_np_tmp[:] = ptre_\n assert _ptre_np_tmp.flags.contiguous\n _ptre_tmp = ctypes.cast(_ptre_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _ptre_copyarray = False\n _ptre_tmp = None\n \n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n _nummat_minlength = len((subj_))\n if len((subj_)) > 0 and nummat_ is not None and len(nummat_) != len((subj_)):\n raise ValueError(\"Array argument nummat is not long enough: Is %d, expected %d\" % (len(nummat_),len((subj_))))\n if nummat_ is None:\n raise ValueError(\"Argument nummat cannot be None\")\n if nummat_ is None:\n raise ValueError(\"Argument nummat may not be None\")\n if isinstance(nummat_, numpy.ndarray) and nummat_.dtype is numpy.dtype(numpy.int64) and nummat_.flags.contiguous:\n _nummat_copyarray = False\n _nummat_tmp = ctypes.cast(nummat_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif nummat_ is not None:\n _nummat_copyarray = True\n _nummat_np_tmp = numpy.zeros(len(nummat_),numpy.dtype(numpy.int64))\n _nummat_np_tmp[:] = nummat_\n assert _nummat_np_tmp.flags.contiguous\n _nummat_tmp = ctypes.cast(_nummat_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _nummat_copyarray = False\n _nummat_tmp = None\n \n _matidx_minlength = sum((nummat_))\n if sum((nummat_)) > 0 and matidx_ is not None and len(matidx_) != sum((nummat_)):\n raise ValueError(\"Array argument matidx is not long enough: Is %d, expected %d\" % (len(matidx_),sum((nummat_))))\n if matidx_ is None:\n raise ValueError(\"Argument matidx cannot be None\")\n if matidx_ is None:\n raise ValueError(\"Argument matidx may not be None\")\n if isinstance(matidx_, numpy.ndarray) and matidx_.dtype is numpy.dtype(numpy.int64) and matidx_.flags.contiguous:\n _matidx_copyarray = False\n _matidx_tmp = ctypes.cast(matidx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif matidx_ is not None:\n _matidx_copyarray = True\n _matidx_np_tmp = numpy.zeros(len(matidx_),numpy.dtype(numpy.int64))\n _matidx_np_tmp[:] = matidx_\n assert _matidx_np_tmp.flags.contiguous\n _matidx_tmp = ctypes.cast(_matidx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _matidx_copyarray = False\n _matidx_tmp = None\n \n _weights_minlength = sum((nummat_))\n if sum((nummat_)) > 0 and weights_ is not None and len(weights_) != sum((nummat_)):\n raise ValueError(\"Array argument weights is not long enough: Is %d, expected %d\" % (len(weights_),sum((nummat_))))\n if weights_ is None:\n raise ValueError(\"Argument weights cannot be None\")\n if weights_ is None:\n raise ValueError(\"Argument weights may not be None\")\n if isinstance(weights_, numpy.ndarray) and weights_.dtype is numpy.dtype(numpy.float64) and weights_.flags.contiguous:\n _weights_copyarray = False\n _weights_tmp = ctypes.cast(weights_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif weights_ is not None:\n _weights_copyarray = True\n _weights_np_tmp = numpy.zeros(len(weights_),numpy.dtype(numpy.float64))\n _weights_np_tmp[:] = weights_\n assert _weights_np_tmp.flags.contiguous\n _weights_tmp = ctypes.cast(_weights_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _weights_copyarray = False\n _weights_tmp = None\n \n res = __library__.MSK_XX_putbararowlist(self.__nativep,num_,_subi_tmp,_ptrb_tmp,_ptre_tmp,_subj_tmp,_nummat_tmp,_matidx_tmp,_weights_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def getbarsj(self,whichsol_,j_,barsj_):\n _barsj_minlength = self.getlenbarvarj((j_))\n if self.getlenbarvarj((j_)) > 0 and barsj_ is not None and len(barsj_) != self.getlenbarvarj((j_)):\n raise ValueError(\"Array argument barsj is not long enough: Is %d, expected %d\" % (len(barsj_),self.getlenbarvarj((j_))))\n if isinstance(barsj_,numpy.ndarray) and not barsj_.flags.writeable:\n raise ValueError(\"Argument barsj must be writable\")\n if barsj_ is None:\n raise ValueError(\"Argument barsj may not be None\")\n if isinstance(barsj_, numpy.ndarray) and barsj_.dtype is numpy.dtype(numpy.float64) and barsj_.flags.contiguous:\n _barsj_copyarray = False\n _barsj_tmp = ctypes.cast(barsj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif barsj_ is not None:\n _barsj_copyarray = True\n _barsj_np_tmp = numpy.zeros(len(barsj_),numpy.dtype(numpy.float64))\n _barsj_np_tmp[:] = barsj_\n assert _barsj_np_tmp.flags.contiguous\n _barsj_tmp = ctypes.cast(_barsj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _barsj_copyarray = False\n _barsj_tmp = None\n \n res = __library__.MSK_XX_getbarsj(self.__nativep,whichsol_,j_,_barsj_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _barsj_copyarray:\n barsj_[:] = _barsj_np_tmp", "def getbarcidxj(self,idx_): # 3\n res,resargs = self.__obj.getbarcidxj(idx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _j_return_value = resargs\n return _j_return_value", "def UBM_update_double_prime(self, j_ind, k_ind, alpha_1, alpha_2, beta_1, beta_2, Gamma, Lambda, Omega):\n #set nodeType to \"-1,1\"\n self.nodeType = \"-11\"\n\n\n lambda_entry = Lambda[0, 1]\n gamma_entry = Gamma[0, 1]\n #two new hidden nodes\n self.n_h += 2\n\n #store old parameters\n old_b_v = np.copy(self.b_v)\n old_b_h = np.copy(self.b_h)\n\n old_w_hv = np.copy(self.w_hv)\n\n\n #update visible biases\n self.b_v[j_ind] = alpha_1\n self.b_v[k_ind] = alpha_2\n\n #update hidden biases\n self.b_h = np.zeros(self.n_h, dtype=complex)\n self.b_h[:-2] = old_b_h\n self.b_h[-2] = beta_1 + old_b_v[j_ind]\n self.b_h[-1] = beta_2 + old_b_v[k_ind]\n\n #update weight_matrix\n \"here could be the reason why successiive 2-qubit gates don't work! \"\n if hasattr(self, \"updated\"):\n self.w_hv = np.zeros((self.n_h, self.n_v), dtype=complex)\n\n self.w_hv[:-2, :j_ind] = old_w_hv[:, :j_ind]\n self.w_hv[:-2, j_ind+1:k_ind] = old_w_hv[:, j_ind+1:k_ind]\n self.w_hv[:-2, k_ind+1:] = old_w_hv[:, k_ind+1:]\n\n self.w_hv[-2, :] = self.w_Z[j_ind, :]\n self.w_hv[-1, :] = self.w_Z[k_ind, :]\n #replace the Z-entries by unitary parameters\n self.w_hv[-2, j_ind] = Omega[0, 0]\n self.w_hv[-1, j_ind] = Omega[1, 0]\n self.w_hv[-2, k_ind] = Omega[0, 1]\n self.w_hv[-1, k_ind] = Omega[1, 1]\n else:\n self.w_hv = np.zeros((self.n_h, self.n_v), dtype=complex)\n self.w_hv[:-2, :j_ind] = old_w_hv[:, :j_ind]\n self.w_hv[:-2, j_ind+1:k_ind] = old_w_hv[:, j_ind+1:k_ind]\n self.w_hv[:-2, k_ind+1:] = old_w_hv[:, k_ind+1:]\n self.w_hv[-2, j_ind] = Omega[0, 0]\n self.w_hv[-1, j_ind] = Omega[1, 0]\n self.w_hv[-2, k_ind] = Omega[0, 1]\n self.w_hv[-1, k_ind] = Omega[1, 1]\n\n\n\n\n\n if hasattr(self, \"updated\"):\n print(\"already updated\")\n old_w_X = np.copy(self.w_X)\n self.w_X = np.zeros((self.n_h, self.n_h), dtype=complex)\n self.w_X[-2, :-2] = old_w_hv[:, j_ind].T\n self.w_X[-1, :-2] = old_w_hv[:, k_ind].T\n self.w_X[:-2, -2] = old_w_hv[:, j_ind]\n self.w_X[:-2, -1] = old_w_hv[:, k_ind]\n self.w_X[-1, -2] = gamma_entry + self.w_Z[j_ind, k_ind]\n self.w_X[-2, -1] = gamma_entry + self.w_Z[j_ind, k_ind]\n self.w_X[:-2, :-2] = old_w_X\n\n\n self.w_Z[j_ind, k_ind] = lambda_entry\n self.w_Z[k_ind, j_ind] = lambda_entry\n\n\n else:\n print(\"First RBM update \")\n self.w_X = np.zeros((self.n_h, self.n_h), dtype=complex)\n self.w_X[-2, :-2] = old_w_hv[:, j_ind].T\n self.w_X[-1, :-2] = old_w_hv[:, k_ind].T\n self.w_X[:-2, -2] = old_w_hv[:, j_ind]\n self.w_X[:-2, -1] = old_w_hv[:, k_ind]\n self.w_X[-1, -2] = gamma_entry\n self.w_X[-2, -1] = gamma_entry\n\n\n self.w_Z = np.zeros((self.n_v, self.n_v), dtype=complex)\n self.w_Z[j_ind, k_ind] = lambda_entry\n self.w_Z[k_ind, j_ind] = lambda_entry\n\n\n\n\n self.updated = True", "def updateWeights(self,weightUpdate):\n\t\n\t\tbranches = self.collectAllBranches()\n\n\t\tfor i in range(self.nBranches):\n\n\t\t\tbranches[i].weight -= weightUpdate[i]", "def _bucket_setitem(self, j, k, v):\n if self._table[j] is None:\n self._table[j] = UnsortedTableMap() # create new bucket at index j\n oldSize = len(self._table[j])\n self._table[j][k] = v\n if len(self._table[j]) > oldSize: # key is new to the table\n self._n += 1", "def reassignWeights(self,weights):\n\t\n\t\tbranches = self.collectAllBranches()\n\n\t\tfor i in range(self.nBranches):\n\n\t\t\tbranches[i].weight = weights[i]", "def UBM_update_double(self, j_ind, k_ind, alpha_1, alpha_2, beta_1, beta_2, Gamma, Lambda, Omega):\n #set nodeType to \"-1,1\"\n self.nodeType = \"-11\"\n\n lambda_entry = Lambda[0, 1]\n gamma_entry = Gamma[0, 1]\n #two new hidden nodes\n self.n_h += 2\n\n #store old parameters\n old_biases_v = np.copy(self.b_v)\n old_b_h = np.copy(self.b_h)\n\n old_weights_h = np.copy(self.w_hv)\n\n #update visible biases\n self.b_v[j_ind] = alpha_1\n self.b_v[k_ind] = alpha_2\n\n #update hidden biases\n self.b_h = np.zeros(self.n_h, dtype=complex)\n self.b_h[:-2] = old_b_h\n self.b_h[-2] = beta_1 + old_biases_v[j_ind]\n self.b_h[-1] = beta_2 + old_biases_v[k_ind]\n\n #update weight_matrix\n self.w_hv = np.zeros((self.n_h, self.n_v), dtype=complex)\n self.w_hv[:-2, :j_ind] = old_weights_h[:, :j_ind]\n self.w_hv[:-2, j_ind+1:k_ind] = old_weights_h[:, j_ind+1:k_ind]\n self.w_hv[:-2, k_ind+1:] = old_weights_h[:, k_ind+1:]\n self.w_hv[-2, j_ind] = Omega[0, 0]\n self.w_hv[-1, j_ind] = Omega[1, 0]\n self.w_hv[-2, k_ind] = Omega[0, 1]\n self.w_hv[-1, k_ind] = Omega[1, 1]\n\n #introduce X (=h-h matrix)\n self.w_X = np.zeros((self.n_h, self.n_h), dtype=complex)\n self.w_X[-2, :-2] = old_weights_h[:, j_ind].T\n self.w_X[-1, :-2] = old_weights_h[:, k_ind].T\n self.w_X[:-2, -2] = old_weights_h[:, j_ind]\n self.w_X[:-2, -1] = old_weights_h[:, k_ind]\n self.w_X[-1, -2] = gamma_entry\n self.w_X[-2, -1] = gamma_entry\n\n #introduce Y (=v-v matrix)\n self.w_Z = np.zeros((self.n_v, self.n_v), dtype=complex)\n self.w_Z[j_ind, k_ind] = lambda_entry\n self.w_Z[k_ind, j_ind] = lambda_entry", "def getbarcsparsity(self,idxj_):\n maxnumnz_ = self.getnumbarcnz()\n numnz_ = ctypes.c_int64()\n _idxj_minlength = (maxnumnz_)\n if (maxnumnz_) > 0 and idxj_ is not None and len(idxj_) != (maxnumnz_):\n raise ValueError(\"Array argument idxj is not long enough: Is %d, expected %d\" % (len(idxj_),(maxnumnz_)))\n if isinstance(idxj_,numpy.ndarray) and not idxj_.flags.writeable:\n raise ValueError(\"Argument idxj must be writable\")\n if idxj_ is None:\n raise ValueError(\"Argument idxj may not be None\")\n if isinstance(idxj_, numpy.ndarray) and idxj_.dtype is numpy.dtype(numpy.int64) and idxj_.flags.contiguous:\n _idxj_copyarray = False\n _idxj_tmp = ctypes.cast(idxj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif idxj_ is not None:\n _idxj_copyarray = True\n _idxj_np_tmp = numpy.zeros(len(idxj_),numpy.dtype(numpy.int64))\n _idxj_np_tmp[:] = idxj_\n assert _idxj_np_tmp.flags.contiguous\n _idxj_tmp = ctypes.cast(_idxj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _idxj_copyarray = False\n _idxj_tmp = None\n \n res = __library__.MSK_XX_getbarcsparsity(self.__nativep,maxnumnz_,ctypes.byref(numnz_),_idxj_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n numnz_ = numnz_.value\n _numnz_return_value = numnz_\n if _idxj_copyarray:\n idxj_[:] = _idxj_np_tmp\n return (_numnz_return_value)", "def updateBar(self):\n pass", "def getbarxj(self,whichsol_,j_,barxj): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if barxj is None: raise TypeError(\"Invalid type for argument barxj\")\n _copyback_barxj = False\n if barxj is None:\n barxj_ = None\n else:\n try:\n barxj_ = memoryview(barxj)\n except TypeError:\n try:\n _tmparr_barxj = array.array(\"d\",barxj)\n except TypeError:\n raise TypeError(\"Argument barxj has wrong type\")\n else:\n barxj_ = memoryview(_tmparr_barxj)\n _copyback_barxj = True\n else:\n if barxj_.format != \"d\":\n barxj_ = memoryview(array.array(\"d\",barxj))\n _copyback_barxj = True\n if barxj_ is not None and len(barxj_) != self.getlenbarvarj((j_)):\n raise ValueError(\"Array argument barxj has wrong length\")\n res = self.__obj.getbarxj(whichsol_,j_,barxj_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_barxj:\n barxj[:] = _tmparr_barxj", "def getbarsj(self,whichsol_,j_,barsj): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if barsj is None: raise TypeError(\"Invalid type for argument barsj\")\n _copyback_barsj = False\n if barsj is None:\n barsj_ = None\n else:\n try:\n barsj_ = memoryview(barsj)\n except TypeError:\n try:\n _tmparr_barsj = array.array(\"d\",barsj)\n except TypeError:\n raise TypeError(\"Argument barsj has wrong type\")\n else:\n barsj_ = memoryview(_tmparr_barsj)\n _copyback_barsj = True\n else:\n if barsj_.format != \"d\":\n barsj_ = memoryview(array.array(\"d\",barsj))\n _copyback_barsj = True\n if barsj_ is not None and len(barsj_) != self.getlenbarvarj((j_)):\n raise ValueError(\"Array argument barsj has wrong length\")\n res = self.__obj.getbarsj(whichsol_,j_,barsj_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_barsj:\n barsj[:] = _tmparr_barsj", "def putbarcblocktriplet(self,num_,subj_,subk_,subl_,valjkl_):\n _subj_minlength = (num_)\n if (num_) > 0 and subj_ is not None and len(subj_) != (num_):\n raise ValueError(\"Array argument subj is not long enough: Is %d, expected %d\" % (len(subj_),(num_)))\n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n _subk_minlength = (num_)\n if (num_) > 0 and subk_ is not None and len(subk_) != (num_):\n raise ValueError(\"Array argument subk is not long enough: Is %d, expected %d\" % (len(subk_),(num_)))\n if subk_ is None:\n raise ValueError(\"Argument subk cannot be None\")\n if subk_ is None:\n raise ValueError(\"Argument subk may not be None\")\n if isinstance(subk_, numpy.ndarray) and subk_.dtype is numpy.dtype(numpy.int32) and subk_.flags.contiguous:\n _subk_copyarray = False\n _subk_tmp = ctypes.cast(subk_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subk_ is not None:\n _subk_copyarray = True\n _subk_np_tmp = numpy.zeros(len(subk_),numpy.dtype(numpy.int32))\n _subk_np_tmp[:] = subk_\n assert _subk_np_tmp.flags.contiguous\n _subk_tmp = ctypes.cast(_subk_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subk_copyarray = False\n _subk_tmp = None\n \n _subl_minlength = (num_)\n if (num_) > 0 and subl_ is not None and len(subl_) != (num_):\n raise ValueError(\"Array argument subl is not long enough: Is %d, expected %d\" % (len(subl_),(num_)))\n if subl_ is None:\n raise ValueError(\"Argument subl cannot be None\")\n if subl_ is None:\n raise ValueError(\"Argument subl may not be None\")\n if isinstance(subl_, numpy.ndarray) and subl_.dtype is numpy.dtype(numpy.int32) and subl_.flags.contiguous:\n _subl_copyarray = False\n _subl_tmp = ctypes.cast(subl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subl_ is not None:\n _subl_copyarray = True\n _subl_np_tmp = numpy.zeros(len(subl_),numpy.dtype(numpy.int32))\n _subl_np_tmp[:] = subl_\n assert _subl_np_tmp.flags.contiguous\n _subl_tmp = ctypes.cast(_subl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subl_copyarray = False\n _subl_tmp = None\n \n _valjkl_minlength = (num_)\n if (num_) > 0 and valjkl_ is not None and len(valjkl_) != (num_):\n raise ValueError(\"Array argument valjkl is not long enough: Is %d, expected %d\" % (len(valjkl_),(num_)))\n if valjkl_ is None:\n raise ValueError(\"Argument valjkl cannot be None\")\n if valjkl_ is None:\n raise ValueError(\"Argument valjkl may not be None\")\n if isinstance(valjkl_, numpy.ndarray) and valjkl_.dtype is numpy.dtype(numpy.float64) and valjkl_.flags.contiguous:\n _valjkl_copyarray = False\n _valjkl_tmp = ctypes.cast(valjkl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif valjkl_ is not None:\n _valjkl_copyarray = True\n _valjkl_np_tmp = numpy.zeros(len(valjkl_),numpy.dtype(numpy.float64))\n _valjkl_np_tmp[:] = valjkl_\n assert _valjkl_np_tmp.flags.contiguous\n _valjkl_tmp = ctypes.cast(_valjkl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _valjkl_copyarray = False\n _valjkl_tmp = None\n \n res = __library__.MSK_XX_putbarcblocktriplet(self.__nativep,num_,_subj_tmp,_subk_tmp,_subl_tmp,_valjkl_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def update_bar(self, bar):\n self.count += 1\n if not self.inited and self.count >= self.size:\n self.inited = True\n\n self.open_array[:-1] = self.open_array[1:]\n self.high_array[:-1] = self.high_array[1:]\n self.low_array[:-1] = self.low_array[1:]\n self.close_array[:-1] = self.close_array[1:]\n self.volume_array[:-1] = self.volume_array[1:]\n self.time_array[:-1] = self.time_array[1:]\n self.extra_array[:-1] = self.extra_array[1:]\n self.range_array[:-1] = self.range_array[1:]\n\n self.open_array[-1] = bar.open_price\n self.high_array[-1] = bar.high_price\n self.low_array[-1] = bar.low_price\n self.close_array[-1] = bar.close_price\n self.volume_array[-1] = bar.volume\n self.time_array[-1] = bar.datetime\n self.extra_array[-1] = {\"pattern\":[]}\n if self.count > 1:\n self.range_array[:-1] = self.range_array[1:]\n self.range_array[-1] = round(self.close_array[-1] / self.close_array[-2] - 1, 6)\n else:\n self.range_array[-1] = 0", "def putbarcblocktriplet(self,num_,subj,subk,subl,valjkl): # 3\n if subj is None: raise TypeError(\"Invalid type for argument subj\")\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n \n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n \n if subj_ is not None and len(subj_) != (num_):\n raise ValueError(\"Array argument subj has wrong length\")\n if subk is None: raise TypeError(\"Invalid type for argument subk\")\n if subk is None:\n subk_ = None\n else:\n try:\n subk_ = memoryview(subk)\n except TypeError:\n try:\n _tmparr_subk = array.array(\"i\",subk)\n except TypeError:\n raise TypeError(\"Argument subk has wrong type\")\n else:\n subk_ = memoryview(_tmparr_subk)\n \n else:\n if subk_.format != \"i\":\n subk_ = memoryview(array.array(\"i\",subk))\n \n if subk_ is not None and len(subk_) != (num_):\n raise ValueError(\"Array argument subk has wrong length\")\n if subl is None: raise TypeError(\"Invalid type for argument subl\")\n if subl is None:\n subl_ = None\n else:\n try:\n subl_ = memoryview(subl)\n except TypeError:\n try:\n _tmparr_subl = array.array(\"i\",subl)\n except TypeError:\n raise TypeError(\"Argument subl has wrong type\")\n else:\n subl_ = memoryview(_tmparr_subl)\n \n else:\n if subl_.format != \"i\":\n subl_ = memoryview(array.array(\"i\",subl))\n \n if subl_ is not None and len(subl_) != (num_):\n raise ValueError(\"Array argument subl has wrong length\")\n if valjkl is None: raise TypeError(\"Invalid type for argument valjkl\")\n if valjkl is None:\n valjkl_ = None\n else:\n try:\n valjkl_ = memoryview(valjkl)\n except TypeError:\n try:\n _tmparr_valjkl = array.array(\"d\",valjkl)\n except TypeError:\n raise TypeError(\"Argument valjkl has wrong type\")\n else:\n valjkl_ = memoryview(_tmparr_valjkl)\n \n else:\n if valjkl_.format != \"d\":\n valjkl_ = memoryview(array.array(\"d\",valjkl))\n \n if valjkl_ is not None and len(valjkl_) != (num_):\n raise ValueError(\"Array argument valjkl has wrong length\")\n res = self.__obj.putbarcblocktriplet(num_,subj_,subk_,subl_,valjkl_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def getbarcidxj(self,idx_):\n j_ = ctypes.c_int32()\n res = __library__.MSK_XX_getbarcidxj(self.__nativep,idx_,ctypes.byref(j_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n j_ = j_.value\n _j_return_value = j_\n return (_j_return_value)", "def refine(self, ijk):\n if self.cbc is None or not self.sub_block_count:\n raise ValueError(\n \"Cannot refine sub block model without specifying number \"\n \"of parent and sub blocks\"\n )\n try:\n inds = self.ijk_array_to_indices(ijk)\n except ValueError:\n inds = self.ijk_to_index(ijk)\n self.cbc.array[inds] = np.prod(self.sub_block_count) # pylint: disable=E1137" ]

[ "0.8175987", "0.73748964", "0.7216971", "0.62823015", "0.62727207", "0.6010744", "0.58721524", "0.5819966", "0.57759297", "0.5744071", "0.56815547", "0.5676308", "0.55539197", "0.54821527", "0.53647524", "0.53490466", "0.52245176", "0.52084583", "0.5198786", "0.5183286", "0.5151148", "0.51038736", "0.5077571", "0.5038549", "0.5034123", "0.5000068", "0.4992607", "0.49911627", "0.49890035", "0.4976444" ]

[ "def putcone(self,k_,ct_,conepar_,submem): # 3\n if not isinstance(ct_,conetype): raise TypeError(\"Argument ct has wrong type\")\n nummem_ = None\n if nummem_ is None:\n nummem_ = len(submem)\n elif nummem_ != len(submem):\n raise IndexError(\"Inconsistent length of array submem\")\n if nummem_ is None: nummem_ = 0\n if submem is None: raise TypeError(\"Invalid type for argument submem\")\n if submem is None:\n submem_ = None\n else:\n try:\n submem_ = memoryview(submem)\n except TypeError:\n try:\n _tmparr_submem = array.array(\"i\",submem)\n except TypeError:\n raise TypeError(\"Argument submem has wrong type\")\n else:\n submem_ = memoryview(_tmparr_submem)\n \n else:\n if submem_.format != \"i\":\n submem_ = memoryview(array.array(\"i\",submem))\n \n res = self.__obj.putcone(k_,ct_,conepar_,nummem_,submem_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def appendcone(self,ct_,conepar_,submem_):\n nummem_ = None\n if nummem_ is None:\n nummem_ = len(submem_)\n elif nummem_ != len(submem_):\n raise IndexError(\"Inconsistent length of array submem\")\n if submem_ is None:\n raise ValueError(\"Argument submem cannot be None\")\n if submem_ is None:\n raise ValueError(\"Argument submem may not be None\")\n if isinstance(submem_, numpy.ndarray) and submem_.dtype is numpy.dtype(numpy.int32) and submem_.flags.contiguous:\n _submem_copyarray = False\n _submem_tmp = ctypes.cast(submem_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif submem_ is not None:\n _submem_copyarray = True\n _submem_np_tmp = numpy.zeros(len(submem_),numpy.dtype(numpy.int32))\n _submem_np_tmp[:] = submem_\n assert _submem_np_tmp.flags.contiguous\n _submem_tmp = ctypes.cast(_submem_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _submem_copyarray = False\n _submem_tmp = None\n \n res = __library__.MSK_XX_appendcone(self.__nativep,ct_,conepar_,nummem_,_submem_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def appendcone(self,ct_,conepar_,submem): # 3\n if not isinstance(ct_,conetype): raise TypeError(\"Argument ct has wrong type\")\n nummem_ = None\n if nummem_ is None:\n nummem_ = len(submem)\n elif nummem_ != len(submem):\n raise IndexError(\"Inconsistent length of array submem\")\n if nummem_ is None: nummem_ = 0\n if submem is None: raise TypeError(\"Invalid type for argument submem\")\n if submem is None:\n submem_ = None\n else:\n try:\n submem_ = memoryview(submem)\n except TypeError:\n try:\n _tmparr_submem = array.array(\"i\",submem)\n except TypeError:\n raise TypeError(\"Argument submem has wrong type\")\n else:\n submem_ = memoryview(_tmparr_submem)\n \n else:\n if submem_.format != \"i\":\n submem_ = memoryview(array.array(\"i\",submem))\n \n res = self.__obj.appendcone(ct_,conepar_,nummem_,submem_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def appendconeseq(self,ct_,conepar_,nummem_,j_):\n res = __library__.MSK_XX_appendconeseq(self.__nativep,ct_,conepar_,nummem_,j_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def appendconeseq(self,ct_,conepar_,nummem_,j_): # 3\n if not isinstance(ct_,conetype): raise TypeError(\"Argument ct has wrong type\")\n res = self.__obj.appendconeseq(ct_,conepar_,nummem_,j_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def getcone(self,k_,submem): # 3\n _copyback_submem = False\n if submem is None:\n submem_ = None\n else:\n try:\n submem_ = memoryview(submem)\n except TypeError:\n try:\n _tmparr_submem = array.array(\"i\",submem)\n except TypeError:\n raise TypeError(\"Argument submem has wrong type\")\n else:\n submem_ = memoryview(_tmparr_submem)\n _copyback_submem = True\n else:\n if submem_.format != \"i\":\n submem_ = memoryview(array.array(\"i\",submem))\n _copyback_submem = True\n if submem_ is not None and len(submem_) != self.getconeinfo((k_))[2]:\n raise ValueError(\"Array argument submem has wrong length\")\n res,resargs = self.__obj.getcone(k_,submem_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _ct_return_value,_conepar_return_value,_nummem_return_value = resargs\n if _copyback_submem:\n submem[:] = _tmparr_submem\n _ct_return_value = conetype(_ct_return_value)\n return _ct_return_value,_conepar_return_value,_nummem_return_value", "def getcone(self,k_,submem_):\n ct_ = ctypes.c_int32()\n conepar_ = ctypes.c_double()\n nummem_ = ctypes.c_int32()\n _submem_minlength = self.getconeinfo((k_))[2]\n if self.getconeinfo((k_))[2] > 0 and submem_ is not None and len(submem_) != self.getconeinfo((k_))[2]:\n raise ValueError(\"Array argument submem is not long enough: Is %d, expected %d\" % (len(submem_),self.getconeinfo((k_))[2]))\n if isinstance(submem_,numpy.ndarray) and not submem_.flags.writeable:\n raise ValueError(\"Argument submem must be writable\")\n if isinstance(submem_, numpy.ndarray) and submem_.dtype is numpy.dtype(numpy.int32) and submem_.flags.contiguous:\n _submem_copyarray = False\n _submem_tmp = ctypes.cast(submem_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif submem_ is not None:\n _submem_copyarray = True\n _submem_np_tmp = numpy.zeros(len(submem_),numpy.dtype(numpy.int32))\n _submem_np_tmp[:] = submem_\n assert _submem_np_tmp.flags.contiguous\n _submem_tmp = ctypes.cast(_submem_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _submem_copyarray = False\n _submem_tmp = None\n \n res = __library__.MSK_XX_getcone(self.__nativep,k_,ctypes.byref(ct_),ctypes.byref(conepar_),ctypes.byref(nummem_),_submem_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _ct_return_value = conetype(ct_.value)\n conepar_ = conepar_.value\n _conepar_return_value = conepar_\n nummem_ = nummem_.value\n _nummem_return_value = nummem_\n if _submem_copyarray:\n submem_[:] = _submem_np_tmp\n return (_ct_return_value,_conepar_return_value,_nummem_return_value)", "def putqconk(self,k_,qcsubi_,qcsubj_,qcval_):\n numqcnz_ = None\n if numqcnz_ is None:\n numqcnz_ = len(qcsubi_)\n elif numqcnz_ != len(qcsubi_):\n raise IndexError(\"Inconsistent length of array qcsubi\")\n if numqcnz_ is None:\n numqcnz_ = len(qcsubj_)\n elif numqcnz_ != len(qcsubj_):\n raise IndexError(\"Inconsistent length of array qcsubj\")\n if numqcnz_ is None:\n numqcnz_ = len(qcval_)\n elif numqcnz_ != len(qcval_):\n raise IndexError(\"Inconsistent length of array qcval\")\n if qcsubi_ is None:\n raise ValueError(\"Argument qcsubi cannot be None\")\n if qcsubi_ is None:\n raise ValueError(\"Argument qcsubi may not be None\")\n if isinstance(qcsubi_, numpy.ndarray) and qcsubi_.dtype is numpy.dtype(numpy.int32) and qcsubi_.flags.contiguous:\n _qcsubi_copyarray = False\n _qcsubi_tmp = ctypes.cast(qcsubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubi_ is not None:\n _qcsubi_copyarray = True\n _qcsubi_np_tmp = numpy.zeros(len(qcsubi_),numpy.dtype(numpy.int32))\n _qcsubi_np_tmp[:] = qcsubi_\n assert _qcsubi_np_tmp.flags.contiguous\n _qcsubi_tmp = ctypes.cast(_qcsubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubi_copyarray = False\n _qcsubi_tmp = None\n \n if qcsubj_ is None:\n raise ValueError(\"Argument qcsubj cannot be None\")\n if qcsubj_ is None:\n raise ValueError(\"Argument qcsubj may not be None\")\n if isinstance(qcsubj_, numpy.ndarray) and qcsubj_.dtype is numpy.dtype(numpy.int32) and qcsubj_.flags.contiguous:\n _qcsubj_copyarray = False\n _qcsubj_tmp = ctypes.cast(qcsubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubj_ is not None:\n _qcsubj_copyarray = True\n _qcsubj_np_tmp = numpy.zeros(len(qcsubj_),numpy.dtype(numpy.int32))\n _qcsubj_np_tmp[:] = qcsubj_\n assert _qcsubj_np_tmp.flags.contiguous\n _qcsubj_tmp = ctypes.cast(_qcsubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubj_copyarray = False\n _qcsubj_tmp = None\n \n if qcval_ is None:\n raise ValueError(\"Argument qcval cannot be None\")\n if qcval_ is None:\n raise ValueError(\"Argument qcval may not be None\")\n if isinstance(qcval_, numpy.ndarray) and qcval_.dtype is numpy.dtype(numpy.float64) and qcval_.flags.contiguous:\n _qcval_copyarray = False\n _qcval_tmp = ctypes.cast(qcval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif qcval_ is not None:\n _qcval_copyarray = True\n _qcval_np_tmp = numpy.zeros(len(qcval_),numpy.dtype(numpy.float64))\n _qcval_np_tmp[:] = qcval_\n assert _qcval_np_tmp.flags.contiguous\n _qcval_tmp = ctypes.cast(_qcval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _qcval_copyarray = False\n _qcval_tmp = None\n \n res = __library__.MSK_XX_putqconk(self.__nativep,k_,numqcnz_,_qcsubi_tmp,_qcsubj_tmp,_qcval_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def _k_coaffine_pair(pair, bound=math.inf):\n g = pair.graph\n sigma = pair.coaffination\n kg = clique_graph(g, bound)\n coaf_k = dict([])\n for q in kg:\n coaf_k[q] = Clique([sigma[x] for x in q])\n return CoaffinePair(kg, coaf_k)", "def set_ecuacion_constitutiva(self, param_con, ec_con_id):\n self.param_con = param_con\n self.ecucon_id = ec_con_id\n self.ecuacion_constitutiva = self.ecuaciones_constitutivas(ec_con_id)", "def putqcon(self,qcsubk_,qcsubi_,qcsubj_,qcval_):\n numqcnz_ = None\n if numqcnz_ is None:\n numqcnz_ = len(qcsubi_)\n elif numqcnz_ != len(qcsubi_):\n raise IndexError(\"Inconsistent length of array qcsubi\")\n if numqcnz_ is None:\n numqcnz_ = len(qcsubj_)\n elif numqcnz_ != len(qcsubj_):\n raise IndexError(\"Inconsistent length of array qcsubj\")\n if numqcnz_ is None:\n numqcnz_ = len(qcval_)\n elif numqcnz_ != len(qcval_):\n raise IndexError(\"Inconsistent length of array qcval\")\n if qcsubk_ is None:\n raise ValueError(\"Argument qcsubk cannot be None\")\n if qcsubk_ is None:\n raise ValueError(\"Argument qcsubk may not be None\")\n if isinstance(qcsubk_, numpy.ndarray) and qcsubk_.dtype is numpy.dtype(numpy.int32) and qcsubk_.flags.contiguous:\n _qcsubk_copyarray = False\n _qcsubk_tmp = ctypes.cast(qcsubk_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubk_ is not None:\n _qcsubk_copyarray = True\n _qcsubk_np_tmp = numpy.zeros(len(qcsubk_),numpy.dtype(numpy.int32))\n _qcsubk_np_tmp[:] = qcsubk_\n assert _qcsubk_np_tmp.flags.contiguous\n _qcsubk_tmp = ctypes.cast(_qcsubk_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubk_copyarray = False\n _qcsubk_tmp = None\n \n if qcsubi_ is None:\n raise ValueError(\"Argument qcsubi cannot be None\")\n if qcsubi_ is None:\n raise ValueError(\"Argument qcsubi may not be None\")\n if isinstance(qcsubi_, numpy.ndarray) and qcsubi_.dtype is numpy.dtype(numpy.int32) and qcsubi_.flags.contiguous:\n _qcsubi_copyarray = False\n _qcsubi_tmp = ctypes.cast(qcsubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubi_ is not None:\n _qcsubi_copyarray = True\n _qcsubi_np_tmp = numpy.zeros(len(qcsubi_),numpy.dtype(numpy.int32))\n _qcsubi_np_tmp[:] = qcsubi_\n assert _qcsubi_np_tmp.flags.contiguous\n _qcsubi_tmp = ctypes.cast(_qcsubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubi_copyarray = False\n _qcsubi_tmp = None\n \n if qcsubj_ is None:\n raise ValueError(\"Argument qcsubj cannot be None\")\n if qcsubj_ is None:\n raise ValueError(\"Argument qcsubj may not be None\")\n if isinstance(qcsubj_, numpy.ndarray) and qcsubj_.dtype is numpy.dtype(numpy.int32) and qcsubj_.flags.contiguous:\n _qcsubj_copyarray = False\n _qcsubj_tmp = ctypes.cast(qcsubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubj_ is not None:\n _qcsubj_copyarray = True\n _qcsubj_np_tmp = numpy.zeros(len(qcsubj_),numpy.dtype(numpy.int32))\n _qcsubj_np_tmp[:] = qcsubj_\n assert _qcsubj_np_tmp.flags.contiguous\n _qcsubj_tmp = ctypes.cast(_qcsubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubj_copyarray = False\n _qcsubj_tmp = None\n \n if qcval_ is None:\n raise ValueError(\"Argument qcval cannot be None\")\n if qcval_ is None:\n raise ValueError(\"Argument qcval may not be None\")\n if isinstance(qcval_, numpy.ndarray) and qcval_.dtype is numpy.dtype(numpy.float64) and qcval_.flags.contiguous:\n _qcval_copyarray = False\n _qcval_tmp = ctypes.cast(qcval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif qcval_ is not None:\n _qcval_copyarray = True\n _qcval_np_tmp = numpy.zeros(len(qcval_),numpy.dtype(numpy.float64))\n _qcval_np_tmp[:] = qcval_\n assert _qcval_np_tmp.flags.contiguous\n _qcval_tmp = ctypes.cast(_qcval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _qcval_copyarray = False\n _qcval_tmp = None\n \n res = __library__.MSK_XX_putqcon(self.__nativep,numqcnz_,_qcsubk_tmp,_qcsubi_tmp,_qcsubj_tmp,_qcval_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putconbound(self,i_,bkc_,blc_,buc_):\n res = __library__.MSK_XX_putconbound(self.__nativep,i_,bkc_,blc_,buc_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putconboundlistconst(self,sub_,bkc_,blc_,buc_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n res = __library__.MSK_XX_putconboundlistconst(self.__nativep,num_,_sub_tmp,bkc_,blc_,buc_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putskc(self,whichsol_,skc_):\n _skc_minlength = self.getnumcon()\n if self.getnumcon() > 0 and skc_ is not None and len(skc_) != self.getnumcon():\n raise ValueError(\"Array argument skc is not long enough: Is %d, expected %d\" % (len(skc_),self.getnumcon()))\n if skc_ is None:\n raise ValueError(\"Argument skc cannot be None\")\n if skc_ is None:\n raise ValueError(\"Argument skc may not be None\")\n if skc_ is not None:\n _skc_tmp = (ctypes.c_int32 * len(skc_))(*skc_)\n else:\n _skc_tmp = None\n res = __library__.MSK_XX_putskc(self.__nativep,whichsol_,_skc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def constraint(self, c):\n self.add_constraint(c)", "def cz(control: QubitInput, target: QubitInput) -> Instruction:\n return Instruction(CZ(), target=[control, target])", "def cone(*args, axis: Union[List[float, float, float], bool]=None, caching: bool=True, degree:\n Union[int, bool]=3, endSweep: Union[float, bool]=2, heightRatio: Union[float,\n bool]=2.0, nodeState: Union[int, bool]=0, pivot: Union[List[float, float, float],\n bool]=None, radius: Union[float, bool]=1.0, sections: Union[int, bool]=8, spans:\n Union[int, bool]=1, startSweep: Union[float, bool]=0, tolerance: Union[float,\n bool]=0.01, useOldInitBehaviour: bool=False, useTolerance: bool=False,\n constructionHistory: bool=True, name: AnyStr=\"\", object: bool=True, polygon: int=0,\n q=True, query=True, e=True, edit=True, **kwargs)->Union[List[AnyStr], Any]:\n pass", "def putqconk(self,k_,qcsubi,qcsubj,qcval): # 3\n numqcnz_ = None\n if numqcnz_ is None:\n numqcnz_ = len(qcsubi)\n elif numqcnz_ != len(qcsubi):\n raise IndexError(\"Inconsistent length of array qcsubi\")\n if numqcnz_ is None:\n numqcnz_ = len(qcsubj)\n elif numqcnz_ != len(qcsubj):\n raise IndexError(\"Inconsistent length of array qcsubj\")\n if numqcnz_ is None:\n numqcnz_ = len(qcval)\n elif numqcnz_ != len(qcval):\n raise IndexError(\"Inconsistent length of array qcval\")\n if numqcnz_ is None: numqcnz_ = 0\n if qcsubi is None: raise TypeError(\"Invalid type for argument qcsubi\")\n if qcsubi is None:\n qcsubi_ = None\n else:\n try:\n qcsubi_ = memoryview(qcsubi)\n except TypeError:\n try:\n _tmparr_qcsubi = array.array(\"i\",qcsubi)\n except TypeError:\n raise TypeError(\"Argument qcsubi has wrong type\")\n else:\n qcsubi_ = memoryview(_tmparr_qcsubi)\n \n else:\n if qcsubi_.format != \"i\":\n qcsubi_ = memoryview(array.array(\"i\",qcsubi))\n \n if qcsubj is None: raise TypeError(\"Invalid type for argument qcsubj\")\n if qcsubj is None:\n qcsubj_ = None\n else:\n try:\n qcsubj_ = memoryview(qcsubj)\n except TypeError:\n try:\n _tmparr_qcsubj = array.array(\"i\",qcsubj)\n except TypeError:\n raise TypeError(\"Argument qcsubj has wrong type\")\n else:\n qcsubj_ = memoryview(_tmparr_qcsubj)\n \n else:\n if qcsubj_.format != \"i\":\n qcsubj_ = memoryview(array.array(\"i\",qcsubj))\n \n if qcval is None: raise TypeError(\"Invalid type for argument qcval\")\n if qcval is None:\n qcval_ = None\n else:\n try:\n qcval_ = memoryview(qcval)\n except TypeError:\n try:\n _tmparr_qcval = array.array(\"d\",qcval)\n except TypeError:\n raise TypeError(\"Argument qcval has wrong type\")\n else:\n qcval_ = memoryview(_tmparr_qcval)\n \n else:\n if qcval_.format != \"d\":\n qcval_ = memoryview(array.array(\"d\",qcval))\n \n res = self.__obj.putqconk(k_,numqcnz_,qcsubi_,qcsubj_,qcval_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putmaxnumcone(self,maxnumcone_):\n res = __library__.MSK_XX_putmaxnumcone(self.__nativep,maxnumcone_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def set_conectividad(self, conec):\n self.add_conec_listoflists(conec) # calcula el ne y el je", "def con_ceq(x,project):\n \n cons = project.con_ceq(x)\n \n if cons: cons = array(cons)\n else: cons = zeros([0])\n \n return cons", "def putqcon(self,qcsubk,qcsubi,qcsubj,qcval): # 3\n numqcnz_ = None\n if numqcnz_ is None:\n numqcnz_ = len(qcsubi)\n elif numqcnz_ != len(qcsubi):\n raise IndexError(\"Inconsistent length of array qcsubi\")\n if numqcnz_ is None:\n numqcnz_ = len(qcsubj)\n elif numqcnz_ != len(qcsubj):\n raise IndexError(\"Inconsistent length of array qcsubj\")\n if numqcnz_ is None:\n numqcnz_ = len(qcval)\n elif numqcnz_ != len(qcval):\n raise IndexError(\"Inconsistent length of array qcval\")\n if numqcnz_ is None: numqcnz_ = 0\n if qcsubk is None: raise TypeError(\"Invalid type for argument qcsubk\")\n if qcsubk is None:\n qcsubk_ = None\n else:\n try:\n qcsubk_ = memoryview(qcsubk)\n except TypeError:\n try:\n _tmparr_qcsubk = array.array(\"i\",qcsubk)\n except TypeError:\n raise TypeError(\"Argument qcsubk has wrong type\")\n else:\n qcsubk_ = memoryview(_tmparr_qcsubk)\n \n else:\n if qcsubk_.format != \"i\":\n qcsubk_ = memoryview(array.array(\"i\",qcsubk))\n \n if qcsubi is None: raise TypeError(\"Invalid type for argument qcsubi\")\n if qcsubi is None:\n qcsubi_ = None\n else:\n try:\n qcsubi_ = memoryview(qcsubi)\n except TypeError:\n try:\n _tmparr_qcsubi = array.array(\"i\",qcsubi)\n except TypeError:\n raise TypeError(\"Argument qcsubi has wrong type\")\n else:\n qcsubi_ = memoryview(_tmparr_qcsubi)\n \n else:\n if qcsubi_.format != \"i\":\n qcsubi_ = memoryview(array.array(\"i\",qcsubi))\n \n if qcsubj is None: raise TypeError(\"Invalid type for argument qcsubj\")\n if qcsubj is None:\n qcsubj_ = None\n else:\n try:\n qcsubj_ = memoryview(qcsubj)\n except TypeError:\n try:\n _tmparr_qcsubj = array.array(\"i\",qcsubj)\n except TypeError:\n raise TypeError(\"Argument qcsubj has wrong type\")\n else:\n qcsubj_ = memoryview(_tmparr_qcsubj)\n \n else:\n if qcsubj_.format != \"i\":\n qcsubj_ = memoryview(array.array(\"i\",qcsubj))\n \n if qcval is None: raise TypeError(\"Invalid type for argument qcval\")\n if qcval is None:\n qcval_ = None\n else:\n try:\n qcval_ = memoryview(qcval)\n except TypeError:\n try:\n _tmparr_qcval = array.array(\"d\",qcval)\n except TypeError:\n raise TypeError(\"Argument qcval has wrong type\")\n else:\n qcval_ = memoryview(_tmparr_qcval)\n \n else:\n if qcval_.format != \"d\":\n qcval_ = memoryview(array.array(\"d\",qcval))\n \n res = self.__obj.putqcon(numqcnz_,qcsubk_,qcsubi_,qcsubj_,qcval_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def getconeinfo(self,k_): # 3\n res,resargs = self.__obj.getconeinfo(k_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _ct_return_value,_conepar_return_value,_nummem_return_value = resargs\n _ct_return_value = conetype(_ct_return_value)\n return _ct_return_value,_conepar_return_value,_nummem_return_value", "def transform_coors(self, mtx_t, ref_coors=None):\n if ref_coors is None:\n ref_coors = self.coors\n\n if mtx_t.shape[1] > self.coors.shape[1]:\n self.coors[:] = nm.dot(ref_coors, mtx_t[:,:-1].T) + mtx_t[:,-1]\n else:\n self.coors[:] = nm.dot(ref_coors, mtx_t.T)", "def SetPRCatConstraint(self, model ) :\n tot = np.multiply(self.wish, self.dispo)\n for line in tot :\n for val in line :\n if not val : continue\n if self.bound>0 : model += val <= self.valBound\n elif self.bound<0 : model += val >= self.valBound", "def c_code_contiguous(self, node, name, inp, out, sub):\r\n raise theano.gof.utils.MethodNotDefined()", "def remove_constraint(self, ckey):\n if ckey not in self.constraints:\n raise KeyError(\"Constraints not found on object key: {}\".format(ckey))\n del self.constraints[ckey]", "def censor_contig(contig_end, u_contigs, o_dict):\n for c_e in [contig_end, other_end(contig_end)]:\n if c_e in u_contigs:\n u_contigs.remove(c_e)\n if c_e in o_dict:\n o_dic = o_dict[c_e]\n if o_dic != {}:\n overlapped_contig = list(o_dic.keys())[0]\n if overlapped_contig in o_dict: del o_dict[overlapped_contig][c_e]\n del o_dict[c_e]\n return", "def putmaxnumcone(self,maxnumcone_): # 3\n res = self.__obj.putmaxnumcone(maxnumcone_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putxc(self,whichsol_,xc_):\n _xc_minlength = self.getnumcon()\n if self.getnumcon() > 0 and xc_ is not None and len(xc_) != self.getnumcon():\n raise ValueError(\"Array argument xc is not long enough: Is %d, expected %d\" % (len(xc_),self.getnumcon()))\n if isinstance(xc_,numpy.ndarray) and not xc_.flags.writeable:\n raise ValueError(\"Argument xc must be writable\")\n if xc_ is None:\n raise ValueError(\"Argument xc may not be None\")\n if isinstance(xc_, numpy.ndarray) and xc_.dtype is numpy.dtype(numpy.float64) and xc_.flags.contiguous:\n _xc_copyarray = False\n _xc_tmp = ctypes.cast(xc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif xc_ is not None:\n _xc_copyarray = True\n _xc_np_tmp = numpy.zeros(len(xc_),numpy.dtype(numpy.float64))\n _xc_np_tmp[:] = xc_\n assert _xc_np_tmp.flags.contiguous\n _xc_tmp = ctypes.cast(_xc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _xc_copyarray = False\n _xc_tmp = None\n \n res = __library__.MSK_XX_putxc(self.__nativep,whichsol_,_xc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _xc_copyarray:\n xc_[:] = _xc_np_tmp" ]

[ "0.79132587", "0.65889823", "0.6287175", "0.60342395", "0.58452046", "0.584365", "0.54893136", "0.53622764", "0.5344895", "0.52238137", "0.5210514", "0.50818294", "0.50687397", "0.5066322", "0.5015226", "0.50098133", "0.4991474", "0.49618068", "0.49253765", "0.4921023", "0.48985314", "0.48561004", "0.48323688", "0.48116612", "0.48082897", "0.48021096", "0.4792872", "0.477442", "0.47744092", "0.4772568" ]

[ "def appendsparsesymmat(self,dim_,subi,subj,valij): # 3\n nz_ = None\n if nz_ is None:\n nz_ = len(subi)\n elif nz_ != len(subi):\n raise IndexError(\"Inconsistent length of array subi\")\n if nz_ is None:\n nz_ = len(subj)\n elif nz_ != len(subj):\n raise IndexError(\"Inconsistent length of array subj\")\n if nz_ is None:\n nz_ = len(valij)\n elif nz_ != len(valij):\n raise IndexError(\"Inconsistent length of array valij\")\n if nz_ is None: nz_ = 0\n if subi is None: raise TypeError(\"Invalid type for argument subi\")\n if subi is None:\n subi_ = None\n else:\n try:\n subi_ = memoryview(subi)\n except TypeError:\n try:\n _tmparr_subi = array.array(\"i\",subi)\n except TypeError:\n raise TypeError(\"Argument subi has wrong type\")\n else:\n subi_ = memoryview(_tmparr_subi)\n \n else:\n if subi_.format != \"i\":\n subi_ = memoryview(array.array(\"i\",subi))\n \n if subj is None: raise TypeError(\"Invalid type for argument subj\")\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n \n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n \n if valij is None: raise TypeError(\"Invalid type for argument valij\")\n if valij is None:\n valij_ = None\n else:\n try:\n valij_ = memoryview(valij)\n except TypeError:\n try:\n _tmparr_valij = array.array(\"d\",valij)\n except TypeError:\n raise TypeError(\"Argument valij has wrong type\")\n else:\n valij_ = memoryview(_tmparr_valij)\n \n else:\n if valij_.format != \"d\":\n valij_ = memoryview(array.array(\"d\",valij))\n \n res,resargs = self.__obj.appendsparsesymmat(dim_,nz_,subi_,subj_,valij_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _idx_return_value = resargs\n return _idx_return_value", "def getsparsesymmat(self,idx_,subi_,subj_,valij_):\n maxlen_ = self.getsymmatinfo((idx_))[1]\n _subi_minlength = (maxlen_)\n if (maxlen_) > 0 and subi_ is not None and len(subi_) != (maxlen_):\n raise ValueError(\"Array argument subi is not long enough: Is %d, expected %d\" % (len(subi_),(maxlen_)))\n if isinstance(subi_,numpy.ndarray) and not subi_.flags.writeable:\n raise ValueError(\"Argument subi must be writable\")\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\n _subi_copyarray = False\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subi_ is not None:\n _subi_copyarray = True\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\n _subi_np_tmp[:] = subi_\n assert _subi_np_tmp.flags.contiguous\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subi_copyarray = False\n _subi_tmp = None\n \n _subj_minlength = (maxlen_)\n if (maxlen_) > 0 and subj_ is not None and len(subj_) != (maxlen_):\n raise ValueError(\"Array argument subj is not long enough: Is %d, expected %d\" % (len(subj_),(maxlen_)))\n if isinstance(subj_,numpy.ndarray) and not subj_.flags.writeable:\n raise ValueError(\"Argument subj must be writable\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n _valij_minlength = (maxlen_)\n if (maxlen_) > 0 and valij_ is not None and len(valij_) != (maxlen_):\n raise ValueError(\"Array argument valij is not long enough: Is %d, expected %d\" % (len(valij_),(maxlen_)))\n if isinstance(valij_,numpy.ndarray) and not valij_.flags.writeable:\n raise ValueError(\"Argument valij must be writable\")\n if isinstance(valij_, numpy.ndarray) and valij_.dtype is numpy.dtype(numpy.float64) and valij_.flags.contiguous:\n _valij_copyarray = False\n _valij_tmp = ctypes.cast(valij_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif valij_ is not None:\n _valij_copyarray = True\n _valij_np_tmp = numpy.zeros(len(valij_),numpy.dtype(numpy.float64))\n _valij_np_tmp[:] = valij_\n assert _valij_np_tmp.flags.contiguous\n _valij_tmp = ctypes.cast(_valij_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _valij_copyarray = False\n _valij_tmp = None\n \n res = __library__.MSK_XX_getsparsesymmat(self.__nativep,idx_,maxlen_,_subi_tmp,_subj_tmp,_valij_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _subi_copyarray:\n subi_[:] = _subi_np_tmp\n if _subj_copyarray:\n subj_[:] = _subj_np_tmp\n if _valij_copyarray:\n valij_[:] = _valij_np_tmp", "def getsparsesymmat(self,idx_,subi,subj,valij): # 3\n maxlen_ = self.getsymmatinfo((idx_))[1]\n _copyback_subi = False\n if subi is None:\n subi_ = None\n else:\n try:\n subi_ = memoryview(subi)\n except TypeError:\n try:\n _tmparr_subi = array.array(\"i\",subi)\n except TypeError:\n raise TypeError(\"Argument subi has wrong type\")\n else:\n subi_ = memoryview(_tmparr_subi)\n _copyback_subi = True\n else:\n if subi_.format != \"i\":\n subi_ = memoryview(array.array(\"i\",subi))\n _copyback_subi = True\n if subi_ is not None and len(subi_) != (maxlen_):\n raise ValueError(\"Array argument subi has wrong length\")\n _copyback_subj = False\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n _copyback_subj = True\n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n _copyback_subj = True\n if subj_ is not None and len(subj_) != (maxlen_):\n raise ValueError(\"Array argument subj has wrong length\")\n _copyback_valij = False\n if valij is None:\n valij_ = None\n else:\n try:\n valij_ = memoryview(valij)\n except TypeError:\n try:\n _tmparr_valij = array.array(\"d\",valij)\n except TypeError:\n raise TypeError(\"Argument valij has wrong type\")\n else:\n valij_ = memoryview(_tmparr_valij)\n _copyback_valij = True\n else:\n if valij_.format != \"d\":\n valij_ = memoryview(array.array(\"d\",valij))\n _copyback_valij = True\n if valij_ is not None and len(valij_) != (maxlen_):\n raise ValueError(\"Array argument valij has wrong length\")\n res = self.__obj.getsparsesymmat(idx_,maxlen_,subi_,subj_,valij_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_valij:\n valij[:] = _tmparr_valij\n if _copyback_subj:\n subj[:] = _tmparr_subj\n if _copyback_subi:\n subi[:] = _tmparr_subi", "def appendsparsesymmatlist(self,dims_,nz_,subi_,subj_,valij_,idx_):\n num_ = None\n if num_ is None:\n num_ = len(dims_)\n elif num_ != len(dims_):\n raise IndexError(\"Inconsistent length of array dims\")\n if num_ is None:\n num_ = len(nz_)\n elif num_ != len(nz_):\n raise IndexError(\"Inconsistent length of array nz\")\n if dims_ is None:\n raise ValueError(\"Argument dims cannot be None\")\n if dims_ is None:\n raise ValueError(\"Argument dims may not be None\")\n if isinstance(dims_, numpy.ndarray) and dims_.dtype is numpy.dtype(numpy.int32) and dims_.flags.contiguous:\n _dims_copyarray = False\n _dims_tmp = ctypes.cast(dims_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif dims_ is not None:\n _dims_copyarray = True\n _dims_np_tmp = numpy.zeros(len(dims_),numpy.dtype(numpy.int32))\n _dims_np_tmp[:] = dims_\n assert _dims_np_tmp.flags.contiguous\n _dims_tmp = ctypes.cast(_dims_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _dims_copyarray = False\n _dims_tmp = None\n \n if nz_ is None:\n raise ValueError(\"Argument nz cannot be None\")\n if nz_ is None:\n raise ValueError(\"Argument nz may not be None\")\n if isinstance(nz_, numpy.ndarray) and nz_.dtype is numpy.dtype(numpy.int64) and nz_.flags.contiguous:\n _nz_copyarray = False\n _nz_tmp = ctypes.cast(nz_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif nz_ is not None:\n _nz_copyarray = True\n _nz_np_tmp = numpy.zeros(len(nz_),numpy.dtype(numpy.int64))\n _nz_np_tmp[:] = nz_\n assert _nz_np_tmp.flags.contiguous\n _nz_tmp = ctypes.cast(_nz_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _nz_copyarray = False\n _nz_tmp = None\n \n _subi_minlength = sum((nz_))\n if sum((nz_)) > 0 and subi_ is not None and len(subi_) != sum((nz_)):\n raise ValueError(\"Array argument subi is not long enough: Is %d, expected %d\" % (len(subi_),sum((nz_))))\n if subi_ is None:\n raise ValueError(\"Argument subi cannot be None\")\n if subi_ is None:\n raise ValueError(\"Argument subi may not be None\")\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\n _subi_copyarray = False\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subi_ is not None:\n _subi_copyarray = True\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\n _subi_np_tmp[:] = subi_\n assert _subi_np_tmp.flags.contiguous\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subi_copyarray = False\n _subi_tmp = None\n \n _subj_minlength = sum((nz_))\n if sum((nz_)) > 0 and subj_ is not None and len(subj_) != sum((nz_)):\n raise ValueError(\"Array argument subj is not long enough: Is %d, expected %d\" % (len(subj_),sum((nz_))))\n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n _valij_minlength = sum((nz_))\n if sum((nz_)) > 0 and valij_ is not None and len(valij_) != sum((nz_)):\n raise ValueError(\"Array argument valij is not long enough: Is %d, expected %d\" % (len(valij_),sum((nz_))))\n if valij_ is None:\n raise ValueError(\"Argument valij cannot be None\")\n if valij_ is None:\n raise ValueError(\"Argument valij may not be None\")\n if isinstance(valij_, numpy.ndarray) and valij_.dtype is numpy.dtype(numpy.float64) and valij_.flags.contiguous:\n _valij_copyarray = False\n _valij_tmp = ctypes.cast(valij_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif valij_ is not None:\n _valij_copyarray = True\n _valij_np_tmp = numpy.zeros(len(valij_),numpy.dtype(numpy.float64))\n _valij_np_tmp[:] = valij_\n assert _valij_np_tmp.flags.contiguous\n _valij_tmp = ctypes.cast(_valij_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _valij_copyarray = False\n _valij_tmp = None\n \n _idx_minlength = (num_)\n if (num_) > 0 and idx_ is not None and len(idx_) != (num_):\n raise ValueError(\"Array argument idx is not long enough: Is %d, expected %d\" % (len(idx_),(num_)))\n if isinstance(idx_,numpy.ndarray) and not idx_.flags.writeable:\n raise ValueError(\"Argument idx must be writable\")\n if idx_ is None:\n raise ValueError(\"Argument idx may not be None\")\n if isinstance(idx_, numpy.ndarray) and idx_.dtype is numpy.dtype(numpy.int64) and idx_.flags.contiguous:\n _idx_copyarray = False\n _idx_tmp = ctypes.cast(idx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif idx_ is not None:\n _idx_copyarray = True\n _idx_np_tmp = numpy.zeros(len(idx_),numpy.dtype(numpy.int64))\n _idx_np_tmp[:] = idx_\n assert _idx_np_tmp.flags.contiguous\n _idx_tmp = ctypes.cast(_idx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _idx_copyarray = False\n _idx_tmp = None\n \n res = __library__.MSK_XX_appendsparsesymmatlist(self.__nativep,num_,_dims_tmp,_nz_tmp,_subi_tmp,_subj_tmp,_valij_tmp,_idx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _idx_copyarray:\n idx_[:] = _idx_np_tmp", "def add_sparse(self, key, element):\n self.add(self._sparse2seq(key), element)", "def matrix_add_symmetric(M, M_sym):\n M[0, 0] += M_sym[0]\n M[0, 1] += M_sym[1]\n M[1, 0] += M_sym[1]\n M[0, 2] += M_sym[2]\n M[2, 0] += M_sym[2]\n\n M[1, 1] += M_sym[3]\n M[1, 2] += M_sym[4]\n M[2, 1] += M_sym[4]\n\n M[2, 2] += M_sym[5]\n\n return M", "def _build_sparse(self, name, wrt, consize, param_vals, sub_param_conns,\n full_param_conns, rels):\n\n jac = None\n\n # Additional sparsity for index connections\n for param in wrt:\n\n sub_conns = sub_param_conns.get(param)\n if not sub_conns:\n continue\n\n # If we have a simultaneous full connection, then we move on\n full_conns = full_param_conns.get(param)\n if full_conns.intersection(rels):\n continue\n\n rel_idx = set()\n for target, idx in iteritems(sub_conns):\n\n # If a target of the indexed desvar connection is\n # in the relevant path for this constraint, then\n # those indices are relevant.\n if target in rels:\n rel_idx.update(idx)\n\n nrel = len(rel_idx)\n if nrel > 0:\n\n if jac is None:\n jac = {}\n\n if param not in jac:\n # A coo matrix for the Jacobian\n # mat = {'coo':[row, col, data],\n # 'shape':[nrow, ncols]}\n coo = {}\n coo['shape'] = [consize, len(param_vals[param])]\n jac[param] = coo\n\n row = []\n col = []\n for i in range(consize):\n row.extend([i]*nrel)\n col.extend(rel_idx)\n data = np.ones((len(row), ))\n\n jac[param]['coo'] = [np.array(row), np.array(col), data]\n\n if name not in self.sub_sparsity:\n self.sub_sparsity[name] = {}\n self.sub_sparsity[name][param] = np.array(list(rel_idx))\n\n return jac", "def setDominantSparseSymeig(A, Aadjoint_to_gadjoint):\n global DominantSparseSymeig \n from .CG import setCGSubspaceSparse\n setCGSubspaceSparse(A, Aadjoint_to_gadjoint)\n from .CG import CGSubspaceSparse\n @staticmethod\n def forward(ctx, g, k, dim):\n eigval, eigvector = symeigLanczos(A, k, extreme=\"min\", sparse=True, dim=dim)\n ctx.save_for_backward(g, eigval, eigvector)\n return eigval, eigvector\n @staticmethod\n def backward(ctx, grad_eigval, grad_eigvector):\n cg = CGSubspaceSparse.apply\n g, eigval, eigvector = ctx.saved_tensors\n b = grad_eigvector - torch.matmul(eigvector, grad_eigvector) * eigvector\n lambda0 = cg(g, eigval, b, eigvector)\n grad_A = grad_eigval * eigvector - lambda0, eigvector\n v1, v2 = grad_A\n grad_g = Aadjoint_to_gadjoint(v1, v2)\n grad_k = grad_dim = None\n return grad_g, grad_k, grad_dim\n DominantSparseSymeig = type(\"DominantSparseSymeig\", (torch.autograd.Function, ), \n {\"forward\": forward, \"backward\": backward})", "def set_sparse_signals(self):\n\t\n\t\tparams_dSs = [self.mu_dSs, self.sigma_dSs]\n\t\tparams_Ss0 = [self.mu_Ss0, self.sigma_Ss0]\n\t\tself.dSs, self.idxs = sparse_vector([self.Nn, self.Kk], \n\t\t\t\t\t\t\t\t\t\t\t\tparams_dSs,\tseed=self.seed_dSs)\n\t\t\n\t\t# Replace components with conflicting background odor \n\t\tif self.Kk_split is not None and self.Kk_split != 0:\n\t\t\tassert 0 <= self.Kk_split <= self.Kk, \\\n\t\t\t\t\"Splitting sparse signal into two levels requires Kk_split\" \\\n\t\t\t\t\" to be non-negative and less than or equal to Kk.\"\n\t\t\tassert self.mu_dSs_2 is not None \\\n\t\t\t\tand self.sigma_dSs_2 is not None, \\\n\t\t\t\t\"Splitting sparse signal into two levels requires that\" \\\n\t\t\t\t\" mu_dSs_2 and sigma_dSs_2 are set.\"\n\n\t\t\tsp.random.seed(self.seed_dSs)\n\t\t\tself.idxs_2 = sp.random.choice(self.idxs[0], self.Kk_split, \n\t\t\t\t\t\t\t\t\t\t\treplace=False)\n\t\t\tfor idx_2 in self.idxs_2:\n\t\t\t\tself.dSs[idx_2] = sp.random.normal(self.mu_dSs_2, \n\t\t\t\t\t\t\t\t\t\t\t\t\tself.sigma_dSs_2)\n\t\telse:\n\t\t\tself.idxs_2 = []\n\t\t\tself.Kk_split = 0\n\t\t\t\n\t\t# Ss0 is the ideal (learned) background stimulus without noise\n\t\tself.Ss0, self.Ss0_noisy = sparse_vector_bkgrnd([self.Nn, self.Kk], \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.idxs, params_Ss0,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tseed=self.seed_Ss0)\n\t\t\n\t\tself.Ss = self.dSs + self.Ss0_noisy", "def SymmetriseMatrix(adjmatrix):\n\n if galib.metrics.Reciprocity(adjmatrix) == 1:\n # if Reciprocity(adjmatrix) == 1:\n return adjmatrix\n else:\n return 0.5 * (adjmatrix + adjmatrix.T)", "def dict2sparseMatrix(wDict,std=0,diag=0):\n data = lil_matrix((len(list(wDict.keys())),len(list(wDict.keys()))))\n nAreas = len(list(wDict.keys()))\n for i in wDict:\n data[i,i] = diag\n ne = len(wDict[i])+ diag\n for j in wDict[i]:\n if std:\n data[i,j] = 1 / float(ne)\n else:\n data[i,j] = 1\n return data", "def eval_sparse(self, array_in, array_out, sp_matrix=None):\n if sp_matrix is None:\n sp_matrix = self.to_sparse_matrix(array_in.shape, \"csc\")\n # print(\"usually:\", sp_matrix.todense())\n array_out[:] = sp_matrix.dot(array_in.reshape(-1)).reshape(array_out.shape)", "def sparse_matrix(data, stype=\"csr\", dtype=complex):\n return _SPARSE_CONSTRUCTORS[stype](data, dtype=dtype)", "def set_sparsity(self,use_sparse):\n \n if hasattr(self.problem,'sparse_jac'):\n self.use_sparse = use_sparse\n else:\n raise KINSOL_Exception(\"The problem must have implemented a method 'sparse_jac' for sparsity to by used.\")", "def sparse_matlab(i, j, v, m, n):\n return csr_matrix((v, (i, j)), shape=(m, n))", "def make_sparse(self, fmt='csc', make_method=None):\n if make_method:\n self.sparse = make_method(self.hamiltonian)\n else:\n self.sparse = self.hamiltonian.to_matrix(sparse=fmt)", "def sparsify(W,conn):\n \n N = W.shape[0]\n W_sparse = sparse.lil_matrix((N,N)) \n for row, weights in itertools.izip(conn, W):\n W_sparse[row[0],row[1:]] = weights[1:]\n return W_sparse", "def normalize_adj( adj : np.ndarray, \n sparse : bool = False\n ) -> Union[np.ndarray, sp.spmatrix]:\n if sparse:\n adj = sp.coo_matrix(adj) # [N,N]\n rowsum = np.array(adj.sum(1)) # [N,]\n \n d_inv_sqrt = np.power(rowsum, -0.5) # [N,], may issue runtime warnings (div by zero)\n d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0. # []\n d_mat_inv_sqrt = sp.diags(d_inv_sqrt) if sparse else np.diag(d_inv_sqrt) #[N,N]\n \n if sparse:\n return adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).tocoo()\n else:\n return ((adj @ d_mat_inv_sqrt).transpose() @ d_mat_inv_sqrt) # not quite sure why this order = D^T A^T D, D^T = D, A^T = A - the transpose is unncessary?!", "def sparse_matrix(shape, integer=False):\n dtype = numpy.int_ if integer else numpy.float_\n return scipy.sparse.lil_matrix(shape, dtype=dtype)", "def append(self, symmData: list) -> None:\n newSymm = SymmetryElement(symmData)\n if not newSymm in self._symmcards:\n self._symmcards.append(newSymm)", "def matrix_add():", "def store(self, subj):\n if subj in self.__lst:\n raise ValueError('Disciplina exista deja')\n self.__lst.append(subj)", "def save_sparse_matrix(self,\n artifact_type: str,\n params: Dict[str, Any],\n sparse_matrix: sp.csr_matrix,\n ignore_duplicate: bool = False) -> str:\n ppr_idx = None\n if \"ppr_idx\" in params.keys() and not isinstance(params[\"ppr_idx\"], int):\n ppr_idx = np.array(params[\"ppr_idx\"])\n params[\"ppr_idx\"] = hash(frozenset(params[\"ppr_idx\"]))\n\n if ignore_duplicate:\n # check there's no entry with the exact same config already present\n ids = Storage.locked_call(\n lambda: self._find_meta_by_exact_params(artifact_type, params),\n self._get_lock_path(artifact_type),\n self.lock_timeout\n )\n if len(ids) > 0:\n logging.info(f\"Ignoring duplicate save in save_sparse_matrix call\")\n return self._build_artifact_path(artifact_type, ids[0].doc_id).replace(\".pt\", \".npz\")\n\n ids = Storage.locked_call(\n lambda: self._upsert_meta(artifact_type, params),\n self._get_lock_path(artifact_type),\n self.lock_timeout,\n )\n if len(ids) != 1:\n raise RuntimeError(f'The index contains duplicates (artifact_type={artifact_type}, params={params})')\n\n try:\n path = self._build_artifact_path(artifact_type, ids[0]).replace(\".pt\", \".npz\")\n sp.save_npz(path, sparse_matrix)\n logging.info(f\"Saved sparse matrix to storage\")\n if ppr_idx is not None:\n ppr_path = path.replace(\".npz\", \"idx.npy\")\n np.save(ppr_path, ppr_idx)\n logging.info(f\"Saved ppr index to storage\")\n return path\n except: # noqa: E722\n Storage.locked_call(\n lambda: self._remove_meta(artifact_type, params),\n self._get_lock_path(artifact_type),\n self.lock_timeout\n )\n raise", "def sparse_matrix (base_type=float):\n return defaultdict (lambda: sparse_vector (base_type))", "def _identity_sparse(d, stype=\"csr\", dtype=complex):\n return sp.eye(d, dtype=dtype, format=stype)", "def create_sparse_matrix(self, matrix_df):\n\n print('creating sparse matrix...')\n sparse_seg_tmp_df = matrix_df.groupby(['segment_id','day_of_week','time_idx'])[self.args['cluster_variable']].mean().reset_index()\n sparse_rt_tmp_df = matrix_df.groupby(['road_type','day_of_week','time_idx'])[self.args['cluster_variable']].mean().reset_index()\n time_seg_df = sparse_seg_tmp_df.groupby(['day_of_week','time_idx'])[self.args['cluster_variable']].mean().reset_index()\n time_rt_df = sparse_rt_tmp_df.groupby(['day_of_week','time_idx'])[self.args['cluster_variable']].mean().reset_index()\n #time_seg_df['time_id'] = time_seg_df.index\n #time_rt_df['time_id'] = time_rt_df.index\n times = list(range(24*60/self.args['time_resolution']))\n full_time_idx = pd.DataFrame([i * 30 for i in times],columns = ['time_idx'])\n full_time_idx['key'] = 1\n full_day_of_week = pd.DataFrame(list(range(7)), columns = ['day_of_week'])\n full_day_of_week['key'] = 1\n full_times = pd.merge(full_time_idx, full_day_of_week, on='key')\n full_times['time_id'] = full_times.index\n time_seg_df = pd.merge(time_seg_df, full_times[['time_idx','day_of_week','time_id']], on=['time_idx','day_of_week'])\n time_rt_df = pd.merge(time_rt_df, full_times[['time_idx','day_of_week','time_id']], on=['time_idx','day_of_week'])\n \n matrix_seg_keys_df = pd.merge(sparse_seg_tmp_df, time_seg_df[['time_id','day_of_week','time_idx']], how='left', on=['day_of_week','time_idx'])\n matrix_rt_keys_df = pd.merge(sparse_rt_tmp_df, time_rt_df[['time_id','day_of_week','time_idx']], how='left', on=['day_of_week','time_idx'])\n\n time_seg_array = np.array(matrix_seg_keys_df['time_id'])\n time_rt_array = np.array(matrix_rt_keys_df['time_id'])\n segment_array = np.array(matrix_seg_keys_df['segment_id'])\n rt_array = np.array(matrix_rt_keys_df['road_type'])\n\n uniquesegments = np.array(list(set(segment_array)))\n uniquerts = np.array(list(set(rt_array)))\n keyuniquesegments = np.array(range(len(uniquesegments)))\n keyuniquerts = np.array(range(len(uniquerts)))\n uniquesegments_df = pd.DataFrame({'segmentskey':keyuniquesegments, 'segment_id':uniquesegments})\n uniquerts_df = pd.DataFrame({'roadtypekey':keyuniquerts, 'road_type':uniquerts})\n\n segments_df = pd.DataFrame(segment_array, columns = ['segment_id'])\n rt_df = pd.DataFrame(rt_array, columns = ['road_type'])\n segments_keys_df = pd.merge(segments_df, uniquesegments_df, how='left', on=['segment_id'])\n rt_keys_df = pd.merge(rt_df, uniquerts_df, how='left', on=['road_type'])\n segmentkeys = np.array(segments_keys_df['segmentskey'])\n rtkeys = np.array(rt_keys_df['road_type'])\n\n level_array_seg = np.array(matrix_seg_keys_df['level_max'])\n sparse_matrix_s = csr_matrix((level_array_seg, (segmentkeys,time_seg_array))).toarray()\n sparse_matrix_seg = preprocessing.scale(sparse_matrix_s)\n level_array_rt = np.array(matrix_rt_keys_df['level_max'])\n sparse_matrix_r = csr_matrix((level_array_rt, (rtkeys,time_rt_array))).toarray()\n sparse_matrix_rt = preprocessing.scale(sparse_matrix_r)\n \n if self.args['perform_pca']:\n sparse_matrix_seg, self.pca_model = self.run_PCA(sparse_matrix_seg)\n sparse_matrix_rt, self.pca_model = self.run_PCA(sparse_matrix_rt)\n else:\n sparse_matrix_seg = sparse_matrix_seg\n sparse_matrix_rt = sparse_matrix_rt\n \n sparse_matrix_withsegkey = pd.DataFrame(sparse_matrix_seg)\n sparse_matrix_withrtkey = pd.DataFrame(sparse_matrix_rt)\n sparse_matrix_withsegkey['segmentskey'] = sparse_matrix_withsegkey.index\n sparse_matrix_withseg = pd.merge(uniquesegments_df, sparse_matrix_withsegkey, on=['segmentskey'])\n sparse_matrix_withrtkey['roadtypekey'] = sparse_matrix_withrtkey.index\n sparse_matrix_withrt = pd.merge(uniquerts_df, sparse_matrix_withrtkey, on=['roadtypekey'])\n \n # write sparse_matrix to database as 'clustering' table\n print('writing sparse matrix to db...')\n sqlalchemy_conn_str = open('../conf/sqlalchemy_conn_str.txt', 'r').read()\n engine = create_engine(sqlalchemy_conn_str)\n if self.split_type == 'random':\n sparse_matrix_withseg.to_sql(name='clust_sparse_avebysegment_random', con=engine, if_exists='replace')\n sparse_matrix_withrt.to_sql(name='clust_sparse_avebyrt_random', con=engine, if_exists='replace')\n elif self.split_type == 'date':\n sparse_matrix_withseg.to_sql(name='clust_sparse_avebysegment_date', con=engine, if_exists='replace')\n sparse_matrix_withrt.to_sql(name='clust_sparse_avebyrt_date', con=engine, if_exists='replace')\n \n print('returning train sparse matrix...')\n return (uniquesegments_df, sparse_matrix_seg)", "def coregionalization_sparse(optimize=True, plot=True):\r\n #fetch the data from the non sparse examples\r\n m = coregionalization_toy2(optimize=False, plot=False)\r\n X, Y = m.X, m.likelihood.Y\r\n\r\n #construct a model\r\n m = GPy.models.SparseGPRegression(X,Y)\r\n m.constrain_fixed('iip_\\d+_1') # don't optimize the inducing input indexes\r\n\r\n if optimize:\r\n m.optimize('bfgs', max_iters=100, messages=1)\r\n\r\n if plot:\r\n m.plot(fixed_inputs=[(1,0)])\r\n m.plot(fixed_inputs=[(1,1)], ax=pb.gca())\r\n\r\n return m", "def putaijlist(self,subi_,subj_,valij_):\n num_ = None\n if num_ is None:\n num_ = len(subi_)\n elif num_ != len(subi_):\n raise IndexError(\"Inconsistent length of array subi\")\n if num_ is None:\n num_ = len(subj_)\n elif num_ != len(subj_):\n raise IndexError(\"Inconsistent length of array subj\")\n if num_ is None:\n num_ = len(valij_)\n elif num_ != len(valij_):\n raise IndexError(\"Inconsistent length of array valij\")\n if subi_ is None:\n raise ValueError(\"Argument subi cannot be None\")\n if subi_ is None:\n raise ValueError(\"Argument subi may not be None\")\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\n _subi_copyarray = False\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subi_ is not None:\n _subi_copyarray = True\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\n _subi_np_tmp[:] = subi_\n assert _subi_np_tmp.flags.contiguous\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subi_copyarray = False\n _subi_tmp = None\n \n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n if valij_ is None:\n raise ValueError(\"Argument valij cannot be None\")\n if valij_ is None:\n raise ValueError(\"Argument valij may not be None\")\n if isinstance(valij_, numpy.ndarray) and valij_.dtype is numpy.dtype(numpy.float64) and valij_.flags.contiguous:\n _valij_copyarray = False\n _valij_tmp = ctypes.cast(valij_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif valij_ is not None:\n _valij_copyarray = True\n _valij_np_tmp = numpy.zeros(len(valij_),numpy.dtype(numpy.float64))\n _valij_np_tmp[:] = valij_\n assert _valij_np_tmp.flags.contiguous\n _valij_tmp = ctypes.cast(_valij_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _valij_copyarray = False\n _valij_tmp = None\n \n res = __library__.MSK_XX_putaijlist64(self.__nativep,num_,_subi_tmp,_subj_tmp,_valij_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def build_lhs_matrix(self):\n j=3\n diags1 = self.build_linear_diags()\n diags1 += self.build_dispersion_diags()\n\n # Ones down primary diagonal\n diags2 = np.zeros_like(diags1)\n diags2[j,:] = 1.\n\n cff = self.dt*(1+self.c_im)*0.5 \n diags = diags2 - cff*diags1\n \n # Build the sparse matrix\n cols = [ii for ii in range(-self._j, self._j+1)]\n M = sparse.spdiags(diags, cols, self.N, self.N)\n\n return M, diags", "def __add__(self, other):\n h, w = self.size\n col_indices = self.col_indices + [w + i for i in other.col_indices]\n row_indices = self.row_indices + other.row_indices\n values = self.values + other.values\n oh, ow = other.size\n size = [max(h, oh), w + ow]\n return Sparse(size, row_indices, col_indices, values)" ]

[ "0.7391125", "0.6855938", "0.64830375", "0.6458727", "0.54127806", "0.5294181", "0.51218337", "0.5078287", "0.5072397", "0.5031775", "0.5025988", "0.50101835", "0.50026613", "0.49249858", "0.49118322", "0.48946965", "0.4886756", "0.4860377", "0.48591778", "0.4854623", "0.4850984", "0.48319915", "0.4809785", "0.48010013", "0.47763708", "0.47703344", "0.47443148", "0.47364503", "0.47254178", "0.47132152" ]

[ "def appendsparsesymmat(self,dim_,subi_,subj_,valij_):\n nz_ = None\n if nz_ is None:\n nz_ = len(subi_)\n elif nz_ != len(subi_):\n raise IndexError(\"Inconsistent length of array subi\")\n if nz_ is None:\n nz_ = len(subj_)\n elif nz_ != len(subj_):\n raise IndexError(\"Inconsistent length of array subj\")\n if nz_ is None:\n nz_ = len(valij_)\n elif nz_ != len(valij_):\n raise IndexError(\"Inconsistent length of array valij\")\n if subi_ is None:\n raise ValueError(\"Argument subi cannot be None\")\n if subi_ is None:\n raise ValueError(\"Argument subi may not be None\")\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\n _subi_copyarray = False\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subi_ is not None:\n _subi_copyarray = True\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\n _subi_np_tmp[:] = subi_\n assert _subi_np_tmp.flags.contiguous\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subi_copyarray = False\n _subi_tmp = None\n \n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n if valij_ is None:\n raise ValueError(\"Argument valij cannot be None\")\n if valij_ is None:\n raise ValueError(\"Argument valij may not be None\")\n if isinstance(valij_, numpy.ndarray) and valij_.dtype is numpy.dtype(numpy.float64) and valij_.flags.contiguous:\n _valij_copyarray = False\n _valij_tmp = ctypes.cast(valij_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif valij_ is not None:\n _valij_copyarray = True\n _valij_np_tmp = numpy.zeros(len(valij_),numpy.dtype(numpy.float64))\n _valij_np_tmp[:] = valij_\n assert _valij_np_tmp.flags.contiguous\n _valij_tmp = ctypes.cast(_valij_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _valij_copyarray = False\n _valij_tmp = None\n \n idx_ = ctypes.c_int64()\n res = __library__.MSK_XX_appendsparsesymmat(self.__nativep,dim_,nz_,_subi_tmp,_subj_tmp,_valij_tmp,ctypes.byref(idx_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n idx_ = idx_.value\n _idx_return_value = idx_\n return (_idx_return_value)", "def appendsparsesymmat(self,dim_,subi,subj,valij): # 3\n nz_ = None\n if nz_ is None:\n nz_ = len(subi)\n elif nz_ != len(subi):\n raise IndexError(\"Inconsistent length of array subi\")\n if nz_ is None:\n nz_ = len(subj)\n elif nz_ != len(subj):\n raise IndexError(\"Inconsistent length of array subj\")\n if nz_ is None:\n nz_ = len(valij)\n elif nz_ != len(valij):\n raise IndexError(\"Inconsistent length of array valij\")\n if nz_ is None: nz_ = 0\n if subi is None: raise TypeError(\"Invalid type for argument subi\")\n if subi is None:\n subi_ = None\n else:\n try:\n subi_ = memoryview(subi)\n except TypeError:\n try:\n _tmparr_subi = array.array(\"i\",subi)\n except TypeError:\n raise TypeError(\"Argument subi has wrong type\")\n else:\n subi_ = memoryview(_tmparr_subi)\n \n else:\n if subi_.format != \"i\":\n subi_ = memoryview(array.array(\"i\",subi))\n \n if subj is None: raise TypeError(\"Invalid type for argument subj\")\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n \n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n \n if valij is None: raise TypeError(\"Invalid type for argument valij\")\n if valij is None:\n valij_ = None\n else:\n try:\n valij_ = memoryview(valij)\n except TypeError:\n try:\n _tmparr_valij = array.array(\"d\",valij)\n except TypeError:\n raise TypeError(\"Argument valij has wrong type\")\n else:\n valij_ = memoryview(_tmparr_valij)\n \n else:\n if valij_.format != \"d\":\n valij_ = memoryview(array.array(\"d\",valij))\n \n res,resargs = self.__obj.appendsparsesymmat(dim_,nz_,subi_,subj_,valij_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _idx_return_value = resargs\n return _idx_return_value", "def getsparsesymmat(self,idx_,subi_,subj_,valij_):\n maxlen_ = self.getsymmatinfo((idx_))[1]\n _subi_minlength = (maxlen_)\n if (maxlen_) > 0 and subi_ is not None and len(subi_) != (maxlen_):\n raise ValueError(\"Array argument subi is not long enough: Is %d, expected %d\" % (len(subi_),(maxlen_)))\n if isinstance(subi_,numpy.ndarray) and not subi_.flags.writeable:\n raise ValueError(\"Argument subi must be writable\")\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\n _subi_copyarray = False\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subi_ is not None:\n _subi_copyarray = True\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\n _subi_np_tmp[:] = subi_\n assert _subi_np_tmp.flags.contiguous\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subi_copyarray = False\n _subi_tmp = None\n \n _subj_minlength = (maxlen_)\n if (maxlen_) > 0 and subj_ is not None and len(subj_) != (maxlen_):\n raise ValueError(\"Array argument subj is not long enough: Is %d, expected %d\" % (len(subj_),(maxlen_)))\n if isinstance(subj_,numpy.ndarray) and not subj_.flags.writeable:\n raise ValueError(\"Argument subj must be writable\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n _valij_minlength = (maxlen_)\n if (maxlen_) > 0 and valij_ is not None and len(valij_) != (maxlen_):\n raise ValueError(\"Array argument valij is not long enough: Is %d, expected %d\" % (len(valij_),(maxlen_)))\n if isinstance(valij_,numpy.ndarray) and not valij_.flags.writeable:\n raise ValueError(\"Argument valij must be writable\")\n if isinstance(valij_, numpy.ndarray) and valij_.dtype is numpy.dtype(numpy.float64) and valij_.flags.contiguous:\n _valij_copyarray = False\n _valij_tmp = ctypes.cast(valij_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif valij_ is not None:\n _valij_copyarray = True\n _valij_np_tmp = numpy.zeros(len(valij_),numpy.dtype(numpy.float64))\n _valij_np_tmp[:] = valij_\n assert _valij_np_tmp.flags.contiguous\n _valij_tmp = ctypes.cast(_valij_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _valij_copyarray = False\n _valij_tmp = None\n \n res = __library__.MSK_XX_getsparsesymmat(self.__nativep,idx_,maxlen_,_subi_tmp,_subj_tmp,_valij_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _subi_copyarray:\n subi_[:] = _subi_np_tmp\n if _subj_copyarray:\n subj_[:] = _subj_np_tmp\n if _valij_copyarray:\n valij_[:] = _valij_np_tmp", "def getsparsesymmat(self,idx_,subi,subj,valij): # 3\n maxlen_ = self.getsymmatinfo((idx_))[1]\n _copyback_subi = False\n if subi is None:\n subi_ = None\n else:\n try:\n subi_ = memoryview(subi)\n except TypeError:\n try:\n _tmparr_subi = array.array(\"i\",subi)\n except TypeError:\n raise TypeError(\"Argument subi has wrong type\")\n else:\n subi_ = memoryview(_tmparr_subi)\n _copyback_subi = True\n else:\n if subi_.format != \"i\":\n subi_ = memoryview(array.array(\"i\",subi))\n _copyback_subi = True\n if subi_ is not None and len(subi_) != (maxlen_):\n raise ValueError(\"Array argument subi has wrong length\")\n _copyback_subj = False\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n _copyback_subj = True\n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n _copyback_subj = True\n if subj_ is not None and len(subj_) != (maxlen_):\n raise ValueError(\"Array argument subj has wrong length\")\n _copyback_valij = False\n if valij is None:\n valij_ = None\n else:\n try:\n valij_ = memoryview(valij)\n except TypeError:\n try:\n _tmparr_valij = array.array(\"d\",valij)\n except TypeError:\n raise TypeError(\"Argument valij has wrong type\")\n else:\n valij_ = memoryview(_tmparr_valij)\n _copyback_valij = True\n else:\n if valij_.format != \"d\":\n valij_ = memoryview(array.array(\"d\",valij))\n _copyback_valij = True\n if valij_ is not None and len(valij_) != (maxlen_):\n raise ValueError(\"Array argument valij has wrong length\")\n res = self.__obj.getsparsesymmat(idx_,maxlen_,subi_,subj_,valij_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_valij:\n valij[:] = _tmparr_valij\n if _copyback_subj:\n subj[:] = _tmparr_subj\n if _copyback_subi:\n subi[:] = _tmparr_subi", "def add_sparse(self, key, element):\n self.add(self._sparse2seq(key), element)", "def _build_sparse(self, name, wrt, consize, param_vals, sub_param_conns,\n full_param_conns, rels):\n\n jac = None\n\n # Additional sparsity for index connections\n for param in wrt:\n\n sub_conns = sub_param_conns.get(param)\n if not sub_conns:\n continue\n\n # If we have a simultaneous full connection, then we move on\n full_conns = full_param_conns.get(param)\n if full_conns.intersection(rels):\n continue\n\n rel_idx = set()\n for target, idx in iteritems(sub_conns):\n\n # If a target of the indexed desvar connection is\n # in the relevant path for this constraint, then\n # those indices are relevant.\n if target in rels:\n rel_idx.update(idx)\n\n nrel = len(rel_idx)\n if nrel > 0:\n\n if jac is None:\n jac = {}\n\n if param not in jac:\n # A coo matrix for the Jacobian\n # mat = {'coo':[row, col, data],\n # 'shape':[nrow, ncols]}\n coo = {}\n coo['shape'] = [consize, len(param_vals[param])]\n jac[param] = coo\n\n row = []\n col = []\n for i in range(consize):\n row.extend([i]*nrel)\n col.extend(rel_idx)\n data = np.ones((len(row), ))\n\n jac[param]['coo'] = [np.array(row), np.array(col), data]\n\n if name not in self.sub_sparsity:\n self.sub_sparsity[name] = {}\n self.sub_sparsity[name][param] = np.array(list(rel_idx))\n\n return jac", "def append(self, symmData: list) -> None:\n newSymm = SymmetryElement(symmData)\n if not newSymm in self._symmcards:\n self._symmcards.append(newSymm)", "def matrix_add_symmetric(M, M_sym):\n M[0, 0] += M_sym[0]\n M[0, 1] += M_sym[1]\n M[1, 0] += M_sym[1]\n M[0, 2] += M_sym[2]\n M[2, 0] += M_sym[2]\n\n M[1, 1] += M_sym[3]\n M[1, 2] += M_sym[4]\n M[2, 1] += M_sym[4]\n\n M[2, 2] += M_sym[5]\n\n return M", "def sparsify(W,conn):\n \n N = W.shape[0]\n W_sparse = sparse.lil_matrix((N,N)) \n for row, weights in itertools.izip(conn, W):\n W_sparse[row[0],row[1:]] = weights[1:]\n return W_sparse", "def putaijlist(self,subi_,subj_,valij_):\n num_ = None\n if num_ is None:\n num_ = len(subi_)\n elif num_ != len(subi_):\n raise IndexError(\"Inconsistent length of array subi\")\n if num_ is None:\n num_ = len(subj_)\n elif num_ != len(subj_):\n raise IndexError(\"Inconsistent length of array subj\")\n if num_ is None:\n num_ = len(valij_)\n elif num_ != len(valij_):\n raise IndexError(\"Inconsistent length of array valij\")\n if subi_ is None:\n raise ValueError(\"Argument subi cannot be None\")\n if subi_ is None:\n raise ValueError(\"Argument subi may not be None\")\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\n _subi_copyarray = False\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subi_ is not None:\n _subi_copyarray = True\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\n _subi_np_tmp[:] = subi_\n assert _subi_np_tmp.flags.contiguous\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subi_copyarray = False\n _subi_tmp = None\n \n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n if valij_ is None:\n raise ValueError(\"Argument valij cannot be None\")\n if valij_ is None:\n raise ValueError(\"Argument valij may not be None\")\n if isinstance(valij_, numpy.ndarray) and valij_.dtype is numpy.dtype(numpy.float64) and valij_.flags.contiguous:\n _valij_copyarray = False\n _valij_tmp = ctypes.cast(valij_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif valij_ is not None:\n _valij_copyarray = True\n _valij_np_tmp = numpy.zeros(len(valij_),numpy.dtype(numpy.float64))\n _valij_np_tmp[:] = valij_\n assert _valij_np_tmp.flags.contiguous\n _valij_tmp = ctypes.cast(_valij_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _valij_copyarray = False\n _valij_tmp = None\n \n res = __library__.MSK_XX_putaijlist64(self.__nativep,num_,_subi_tmp,_subj_tmp,_valij_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def store(self, subj):\n if subj in self.__lst:\n raise ValueError('Disciplina exista deja')\n self.__lst.append(subj)", "def set_sparse_signals(self):\n\t\n\t\tparams_dSs = [self.mu_dSs, self.sigma_dSs]\n\t\tparams_Ss0 = [self.mu_Ss0, self.sigma_Ss0]\n\t\tself.dSs, self.idxs = sparse_vector([self.Nn, self.Kk], \n\t\t\t\t\t\t\t\t\t\t\t\tparams_dSs,\tseed=self.seed_dSs)\n\t\t\n\t\t# Replace components with conflicting background odor \n\t\tif self.Kk_split is not None and self.Kk_split != 0:\n\t\t\tassert 0 <= self.Kk_split <= self.Kk, \\\n\t\t\t\t\"Splitting sparse signal into two levels requires Kk_split\" \\\n\t\t\t\t\" to be non-negative and less than or equal to Kk.\"\n\t\t\tassert self.mu_dSs_2 is not None \\\n\t\t\t\tand self.sigma_dSs_2 is not None, \\\n\t\t\t\t\"Splitting sparse signal into two levels requires that\" \\\n\t\t\t\t\" mu_dSs_2 and sigma_dSs_2 are set.\"\n\n\t\t\tsp.random.seed(self.seed_dSs)\n\t\t\tself.idxs_2 = sp.random.choice(self.idxs[0], self.Kk_split, \n\t\t\t\t\t\t\t\t\t\t\treplace=False)\n\t\t\tfor idx_2 in self.idxs_2:\n\t\t\t\tself.dSs[idx_2] = sp.random.normal(self.mu_dSs_2, \n\t\t\t\t\t\t\t\t\t\t\t\t\tself.sigma_dSs_2)\n\t\telse:\n\t\t\tself.idxs_2 = []\n\t\t\tself.Kk_split = 0\n\t\t\t\n\t\t# Ss0 is the ideal (learned) background stimulus without noise\n\t\tself.Ss0, self.Ss0_noisy = sparse_vector_bkgrnd([self.Nn, self.Kk], \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.idxs, params_Ss0,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tseed=self.seed_Ss0)\n\t\t\n\t\tself.Ss = self.dSs + self.Ss0_noisy", "def save_sparse_matrix(self,\n artifact_type: str,\n params: Dict[str, Any],\n sparse_matrix: sp.csr_matrix,\n ignore_duplicate: bool = False) -> str:\n ppr_idx = None\n if \"ppr_idx\" in params.keys() and not isinstance(params[\"ppr_idx\"], int):\n ppr_idx = np.array(params[\"ppr_idx\"])\n params[\"ppr_idx\"] = hash(frozenset(params[\"ppr_idx\"]))\n\n if ignore_duplicate:\n # check there's no entry with the exact same config already present\n ids = Storage.locked_call(\n lambda: self._find_meta_by_exact_params(artifact_type, params),\n self._get_lock_path(artifact_type),\n self.lock_timeout\n )\n if len(ids) > 0:\n logging.info(f\"Ignoring duplicate save in save_sparse_matrix call\")\n return self._build_artifact_path(artifact_type, ids[0].doc_id).replace(\".pt\", \".npz\")\n\n ids = Storage.locked_call(\n lambda: self._upsert_meta(artifact_type, params),\n self._get_lock_path(artifact_type),\n self.lock_timeout,\n )\n if len(ids) != 1:\n raise RuntimeError(f'The index contains duplicates (artifact_type={artifact_type}, params={params})')\n\n try:\n path = self._build_artifact_path(artifact_type, ids[0]).replace(\".pt\", \".npz\")\n sp.save_npz(path, sparse_matrix)\n logging.info(f\"Saved sparse matrix to storage\")\n if ppr_idx is not None:\n ppr_path = path.replace(\".npz\", \"idx.npy\")\n np.save(ppr_path, ppr_idx)\n logging.info(f\"Saved ppr index to storage\")\n return path\n except: # noqa: E722\n Storage.locked_call(\n lambda: self._remove_meta(artifact_type, params),\n self._get_lock_path(artifact_type),\n self.lock_timeout\n )\n raise", "def dict2sparseMatrix(wDict,std=0,diag=0):\n data = lil_matrix((len(list(wDict.keys())),len(list(wDict.keys()))))\n nAreas = len(list(wDict.keys()))\n for i in wDict:\n data[i,i] = diag\n ne = len(wDict[i])+ diag\n for j in wDict[i]:\n if std:\n data[i,j] = 1 / float(ne)\n else:\n data[i,j] = 1\n return data", "def putaijlist(self,subi,subj,valij): # 3\n num_ = None\n if num_ is None:\n num_ = len(subi)\n elif num_ != len(subi):\n raise IndexError(\"Inconsistent length of array subi\")\n if num_ is None:\n num_ = len(subj)\n elif num_ != len(subj):\n raise IndexError(\"Inconsistent length of array subj\")\n if num_ is None:\n num_ = len(valij)\n elif num_ != len(valij):\n raise IndexError(\"Inconsistent length of array valij\")\n if num_ is None: num_ = 0\n if subi is None: raise TypeError(\"Invalid type for argument subi\")\n if subi is None:\n subi_ = None\n else:\n try:\n subi_ = memoryview(subi)\n except TypeError:\n try:\n _tmparr_subi = array.array(\"i\",subi)\n except TypeError:\n raise TypeError(\"Argument subi has wrong type\")\n else:\n subi_ = memoryview(_tmparr_subi)\n \n else:\n if subi_.format != \"i\":\n subi_ = memoryview(array.array(\"i\",subi))\n \n if subj is None: raise TypeError(\"Invalid type for argument subj\")\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n \n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n \n if valij is None: raise TypeError(\"Invalid type for argument valij\")\n if valij is None:\n valij_ = None\n else:\n try:\n valij_ = memoryview(valij)\n except TypeError:\n try:\n _tmparr_valij = array.array(\"d\",valij)\n except TypeError:\n raise TypeError(\"Argument valij has wrong type\")\n else:\n valij_ = memoryview(_tmparr_valij)\n \n else:\n if valij_.format != \"d\":\n valij_ = memoryview(array.array(\"d\",valij))\n \n res = self.__obj.putaijlist64(num_,subi_,subj_,valij_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def add_ss(self, subjects_list, i):\n sub_added = subjects_list[i+1]\n #random.seed(42)\n surface = random.randint(0, len(self.surfaces)-1)\n\n if os.path.isdir(self.data_dir + str(sub_added)):\n skel_file = os.path.join(self.data_dir, str(sub_added), self.cpt_skel_1,\n self.side + self.cpt_skel_2 + str(sub_added) + self.cpt_skel_3)\n self.skel = aims.read(skel_file)\n bck_map = self.surfaces[surface]['aims_ss']\n for voxel in bck_map[0].keys():\n if self.skel.value(voxel[0], voxel[1], voxel[2])!=11:\n self.skel.setValue(60, voxel[0], voxel[1], voxel[2])\n\n bck_map_bottom = self.surfaces[surface]['aims_bottom']\n for voxel in bck_map_bottom[0].keys():\n if self.skel.value(voxel[0], voxel[1], voxel[2])!=11:\n self.skel.setValue(60, voxel[0], voxel[1], voxel[2])\n\n save_subject = sub_added\n return save_subject", "def sparse_matlab(i, j, v, m, n):\n return csr_matrix((v, (i, j)), shape=(m, n))", "def eval_sparse(self, array_in, array_out, sp_matrix=None):\n if sp_matrix is None:\n sp_matrix = self.to_sparse_matrix(array_in.shape, \"csc\")\n # print(\"usually:\", sp_matrix.todense())\n array_out[:] = sp_matrix.dot(array_in.reshape(-1)).reshape(array_out.shape)", "def to_amat(self, node_list=None, sparse=False) -> np.ndarray:\n if not node_list:\n node_list = sorted(self._nodes)\n node2ix = {node: i for i, node in enumerate(node_list)}\n\n if sparse:\n raise NotImplementedError\n # js, ks = [], []\n # for j, k in self._edges:\n # js.append(j)\n # ks.append(k)\n # js.append(k)\n # ks.append(j)\n # return spmatrix(1, js, ks)\n amat = np.zeros([self.num_nodes, self.num_nodes], dtype=int)\n\n for i, j in self._edges:\n amat[node2ix[i], node2ix[j]] = True\n amat[node2ix[j], node2ix[i]] = True\n return amat", "def build_lhs_matrix(self):\n j=3\n diags1 = self.build_linear_diags()\n diags1 += self.build_dispersion_diags()\n\n # Ones down primary diagonal\n diags2 = np.zeros_like(diags1)\n diags2[j,:] = 1.\n\n cff = self.dt*(1+self.c_im)*0.5 \n diags = diags2 - cff*diags1\n \n # Build the sparse matrix\n cols = [ii for ii in range(-self._j, self._j+1)]\n M = sparse.spdiags(diags, cols, self.N, self.N)\n\n return M, diags", "def j_sparse_vector_wrapper_to_scipy_spmatrix(j_obj: JavaObject):\n indices = np.frombuffer(j_obj.getIndicesBytes(), dtype=\"<i4\")\n values = np.frombuffer(j_obj.getValuesBytes(), dtype=\"<f8\")\n size = j_obj.getSize()\n indptr = np.array([0, indices.shape[0]], dtype=np.int32)\n return csr_matrix((values, indices, indptr), shape=(1, size), dtype=np.float64).todok()", "def sparse_matrix(data, stype=\"csr\", dtype=complex):\n return _SPARSE_CONSTRUCTORS[stype](data, dtype=dtype)", "def make_sparse(self, fmt='csc', make_method=None):\n if make_method:\n self.sparse = make_method(self.hamiltonian)\n else:\n self.sparse = self.hamiltonian.to_matrix(sparse=fmt)", "def sparse_matrix(shape, integer=False):\n dtype = numpy.int_ if integer else numpy.float_\n return scipy.sparse.lil_matrix(shape, dtype=dtype)", "def create_sparse_matrix(self, matrix_df):\n\n print('creating sparse matrix...')\n sparse_seg_tmp_df = matrix_df.groupby(['segment_id','day_of_week','time_idx'])[self.args['cluster_variable']].mean().reset_index()\n sparse_rt_tmp_df = matrix_df.groupby(['road_type','day_of_week','time_idx'])[self.args['cluster_variable']].mean().reset_index()\n time_seg_df = sparse_seg_tmp_df.groupby(['day_of_week','time_idx'])[self.args['cluster_variable']].mean().reset_index()\n time_rt_df = sparse_rt_tmp_df.groupby(['day_of_week','time_idx'])[self.args['cluster_variable']].mean().reset_index()\n #time_seg_df['time_id'] = time_seg_df.index\n #time_rt_df['time_id'] = time_rt_df.index\n times = list(range(24*60/self.args['time_resolution']))\n full_time_idx = pd.DataFrame([i * 30 for i in times],columns = ['time_idx'])\n full_time_idx['key'] = 1\n full_day_of_week = pd.DataFrame(list(range(7)), columns = ['day_of_week'])\n full_day_of_week['key'] = 1\n full_times = pd.merge(full_time_idx, full_day_of_week, on='key')\n full_times['time_id'] = full_times.index\n time_seg_df = pd.merge(time_seg_df, full_times[['time_idx','day_of_week','time_id']], on=['time_idx','day_of_week'])\n time_rt_df = pd.merge(time_rt_df, full_times[['time_idx','day_of_week','time_id']], on=['time_idx','day_of_week'])\n \n matrix_seg_keys_df = pd.merge(sparse_seg_tmp_df, time_seg_df[['time_id','day_of_week','time_idx']], how='left', on=['day_of_week','time_idx'])\n matrix_rt_keys_df = pd.merge(sparse_rt_tmp_df, time_rt_df[['time_id','day_of_week','time_idx']], how='left', on=['day_of_week','time_idx'])\n\n time_seg_array = np.array(matrix_seg_keys_df['time_id'])\n time_rt_array = np.array(matrix_rt_keys_df['time_id'])\n segment_array = np.array(matrix_seg_keys_df['segment_id'])\n rt_array = np.array(matrix_rt_keys_df['road_type'])\n\n uniquesegments = np.array(list(set(segment_array)))\n uniquerts = np.array(list(set(rt_array)))\n keyuniquesegments = np.array(range(len(uniquesegments)))\n keyuniquerts = np.array(range(len(uniquerts)))\n uniquesegments_df = pd.DataFrame({'segmentskey':keyuniquesegments, 'segment_id':uniquesegments})\n uniquerts_df = pd.DataFrame({'roadtypekey':keyuniquerts, 'road_type':uniquerts})\n\n segments_df = pd.DataFrame(segment_array, columns = ['segment_id'])\n rt_df = pd.DataFrame(rt_array, columns = ['road_type'])\n segments_keys_df = pd.merge(segments_df, uniquesegments_df, how='left', on=['segment_id'])\n rt_keys_df = pd.merge(rt_df, uniquerts_df, how='left', on=['road_type'])\n segmentkeys = np.array(segments_keys_df['segmentskey'])\n rtkeys = np.array(rt_keys_df['road_type'])\n\n level_array_seg = np.array(matrix_seg_keys_df['level_max'])\n sparse_matrix_s = csr_matrix((level_array_seg, (segmentkeys,time_seg_array))).toarray()\n sparse_matrix_seg = preprocessing.scale(sparse_matrix_s)\n level_array_rt = np.array(matrix_rt_keys_df['level_max'])\n sparse_matrix_r = csr_matrix((level_array_rt, (rtkeys,time_rt_array))).toarray()\n sparse_matrix_rt = preprocessing.scale(sparse_matrix_r)\n \n if self.args['perform_pca']:\n sparse_matrix_seg, self.pca_model = self.run_PCA(sparse_matrix_seg)\n sparse_matrix_rt, self.pca_model = self.run_PCA(sparse_matrix_rt)\n else:\n sparse_matrix_seg = sparse_matrix_seg\n sparse_matrix_rt = sparse_matrix_rt\n \n sparse_matrix_withsegkey = pd.DataFrame(sparse_matrix_seg)\n sparse_matrix_withrtkey = pd.DataFrame(sparse_matrix_rt)\n sparse_matrix_withsegkey['segmentskey'] = sparse_matrix_withsegkey.index\n sparse_matrix_withseg = pd.merge(uniquesegments_df, sparse_matrix_withsegkey, on=['segmentskey'])\n sparse_matrix_withrtkey['roadtypekey'] = sparse_matrix_withrtkey.index\n sparse_matrix_withrt = pd.merge(uniquerts_df, sparse_matrix_withrtkey, on=['roadtypekey'])\n \n # write sparse_matrix to database as 'clustering' table\n print('writing sparse matrix to db...')\n sqlalchemy_conn_str = open('../conf/sqlalchemy_conn_str.txt', 'r').read()\n engine = create_engine(sqlalchemy_conn_str)\n if self.split_type == 'random':\n sparse_matrix_withseg.to_sql(name='clust_sparse_avebysegment_random', con=engine, if_exists='replace')\n sparse_matrix_withrt.to_sql(name='clust_sparse_avebyrt_random', con=engine, if_exists='replace')\n elif self.split_type == 'date':\n sparse_matrix_withseg.to_sql(name='clust_sparse_avebysegment_date', con=engine, if_exists='replace')\n sparse_matrix_withrt.to_sql(name='clust_sparse_avebyrt_date', con=engine, if_exists='replace')\n \n print('returning train sparse matrix...')\n return (uniquesegments_df, sparse_matrix_seg)", "def putbaraijlist(self,subi_,subj_,alphaptrb_,alphaptre_,matidx_,weights_):\n num_ = None\n if num_ is None:\n num_ = len(subi_)\n elif num_ != len(subi_):\n raise IndexError(\"Inconsistent length of array subi\")\n if num_ is None:\n num_ = len(subj_)\n elif num_ != len(subj_):\n raise IndexError(\"Inconsistent length of array subj\")\n if num_ is None:\n num_ = len(alphaptrb_)\n elif num_ != len(alphaptrb_):\n raise IndexError(\"Inconsistent length of array alphaptrb\")\n if num_ is None:\n num_ = len(alphaptre_)\n elif num_ != len(alphaptre_):\n raise IndexError(\"Inconsistent length of array alphaptre\")\n if subi_ is None:\n raise ValueError(\"Argument subi cannot be None\")\n if subi_ is None:\n raise ValueError(\"Argument subi may not be None\")\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\n _subi_copyarray = False\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subi_ is not None:\n _subi_copyarray = True\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\n _subi_np_tmp[:] = subi_\n assert _subi_np_tmp.flags.contiguous\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subi_copyarray = False\n _subi_tmp = None\n \n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n if alphaptrb_ is None:\n raise ValueError(\"Argument alphaptrb cannot be None\")\n if alphaptrb_ is None:\n raise ValueError(\"Argument alphaptrb may not be None\")\n if isinstance(alphaptrb_, numpy.ndarray) and alphaptrb_.dtype is numpy.dtype(numpy.int64) and alphaptrb_.flags.contiguous:\n _alphaptrb_copyarray = False\n _alphaptrb_tmp = ctypes.cast(alphaptrb_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif alphaptrb_ is not None:\n _alphaptrb_copyarray = True\n _alphaptrb_np_tmp = numpy.zeros(len(alphaptrb_),numpy.dtype(numpy.int64))\n _alphaptrb_np_tmp[:] = alphaptrb_\n assert _alphaptrb_np_tmp.flags.contiguous\n _alphaptrb_tmp = ctypes.cast(_alphaptrb_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _alphaptrb_copyarray = False\n _alphaptrb_tmp = None\n \n if alphaptre_ is None:\n raise ValueError(\"Argument alphaptre cannot be None\")\n if alphaptre_ is None:\n raise ValueError(\"Argument alphaptre may not be None\")\n if isinstance(alphaptre_, numpy.ndarray) and alphaptre_.dtype is numpy.dtype(numpy.int64) and alphaptre_.flags.contiguous:\n _alphaptre_copyarray = False\n _alphaptre_tmp = ctypes.cast(alphaptre_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif alphaptre_ is not None:\n _alphaptre_copyarray = True\n _alphaptre_np_tmp = numpy.zeros(len(alphaptre_),numpy.dtype(numpy.int64))\n _alphaptre_np_tmp[:] = alphaptre_\n assert _alphaptre_np_tmp.flags.contiguous\n _alphaptre_tmp = ctypes.cast(_alphaptre_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _alphaptre_copyarray = False\n _alphaptre_tmp = None\n \n if matidx_ is None:\n raise ValueError(\"Argument matidx cannot be None\")\n if matidx_ is None:\n raise ValueError(\"Argument matidx may not be None\")\n if isinstance(matidx_, numpy.ndarray) and matidx_.dtype is numpy.dtype(numpy.int64) and matidx_.flags.contiguous:\n _matidx_copyarray = False\n _matidx_tmp = ctypes.cast(matidx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif matidx_ is not None:\n _matidx_copyarray = True\n _matidx_np_tmp = numpy.zeros(len(matidx_),numpy.dtype(numpy.int64))\n _matidx_np_tmp[:] = matidx_\n assert _matidx_np_tmp.flags.contiguous\n _matidx_tmp = ctypes.cast(_matidx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _matidx_copyarray = False\n _matidx_tmp = None\n \n if weights_ is None:\n raise ValueError(\"Argument weights cannot be None\")\n if weights_ is None:\n raise ValueError(\"Argument weights may not be None\")\n if isinstance(weights_, numpy.ndarray) and weights_.dtype is numpy.dtype(numpy.float64) and weights_.flags.contiguous:\n _weights_copyarray = False\n _weights_tmp = ctypes.cast(weights_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif weights_ is not None:\n _weights_copyarray = True\n _weights_np_tmp = numpy.zeros(len(weights_),numpy.dtype(numpy.float64))\n _weights_np_tmp[:] = weights_\n assert _weights_np_tmp.flags.contiguous\n _weights_tmp = ctypes.cast(_weights_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _weights_copyarray = False\n _weights_tmp = None\n \n res = __library__.MSK_XX_putbaraijlist(self.__nativep,num_,_subi_tmp,_subj_tmp,_alphaptrb_tmp,_alphaptre_tmp,_matidx_tmp,_weights_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def SymmetriseMatrix(adjmatrix):\n\n if galib.metrics.Reciprocity(adjmatrix) == 1:\n # if Reciprocity(adjmatrix) == 1:\n return adjmatrix\n else:\n return 0.5 * (adjmatrix + adjmatrix.T)", "def create_attach_sparsifier(model, **sparse_config):\n data_norm_sparsifier = DataNormSparsifier(**sparse_config)\n for name, parameter in model.named_parameters():\n if 'emb_l' in name:\n valid_name = get_valid_name(name)\n data_norm_sparsifier.add_data(name=valid_name, data=parameter)\n return data_norm_sparsifier", "def write_matfile(Sint_df, outputfilepath='Sint_no_cofactor_20160831.mat'):\n # convert dataframe to matrix\n Smat = Sint_df.as_matrix()\n\n # get all indices for non-zero elements in Smat (row, col)\n Smat_nzr, Smat_nzc = np.nonzero(Smat)\n\n # get all non-zero elements from Smat\n Smat_nze = Smat[Smat_nzr, Smat_nzc]\n\n # Adjust for matlab coordinate\n Smat_nzr = Smat_nzr + 1\n Smat_nzc = Smat_nzc + 1\n\n # This final line gives the size of the S matrix in matlab\n nr, nc = Smat.shape\n\n # Create a 2D array\n sparseMat = np.vstack((Smat_nzr, Smat_nzc, Smat_nze)).T\n sparseMat = np.vstack((sparseMat, np.array([[nr, nc, 0]])))\n\n # Create a numpy object array from dataframe index\n reactionList = Sint_df.columns.ravel()\n\n # Write only one matlab .mat file\n scipy.io.savemat(outputfilepath,\n mdict={'Sint_sparse': sparseMat,\n 'reactionList': np.array(reactionList)}\n )\n\n return sparseMat, reactionList", "def sparse_matrix (base_type=float):\n return defaultdict (lambda: sparse_vector (base_type))" ]

[ "0.70590025", "0.6795725", "0.63318455", "0.6023266", "0.52935266", "0.5206711", "0.5191596", "0.5079819", "0.49437016", "0.49408752", "0.4925097", "0.4889433", "0.4782237", "0.4736962", "0.4685731", "0.467853", "0.46744376", "0.46646318", "0.46621722", "0.46508092", "0.46442583", "0.4643347", "0.46405804", "0.46150428", "0.4612372", "0.4607801", "0.46047214", "0.45903334", "0.45872107", "0.45837086" ]

[ "def getsymmatinfo(self,idx_): # 3\n res,resargs = self.__obj.getsymmatinfo(idx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _dim_return_value,_nz_return_value,_type_return_value = resargs\n _type_return_value = symmattype(_type_return_value)\n return _dim_return_value,_nz_return_value,_type_return_value", "def getnumsymmat(self):\n num_ = ctypes.c_int64()\n res = __library__.MSK_XX_getnumsymmat(self.__nativep,ctypes.byref(num_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n num_ = num_.value\n _num_return_value = num_\n return (_num_return_value)", "def getsparsesymmat(self,idx_,subi,subj,valij): # 3\n maxlen_ = self.getsymmatinfo((idx_))[1]\n _copyback_subi = False\n if subi is None:\n subi_ = None\n else:\n try:\n subi_ = memoryview(subi)\n except TypeError:\n try:\n _tmparr_subi = array.array(\"i\",subi)\n except TypeError:\n raise TypeError(\"Argument subi has wrong type\")\n else:\n subi_ = memoryview(_tmparr_subi)\n _copyback_subi = True\n else:\n if subi_.format != \"i\":\n subi_ = memoryview(array.array(\"i\",subi))\n _copyback_subi = True\n if subi_ is not None and len(subi_) != (maxlen_):\n raise ValueError(\"Array argument subi has wrong length\")\n _copyback_subj = False\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n _copyback_subj = True\n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n _copyback_subj = True\n if subj_ is not None and len(subj_) != (maxlen_):\n raise ValueError(\"Array argument subj has wrong length\")\n _copyback_valij = False\n if valij is None:\n valij_ = None\n else:\n try:\n valij_ = memoryview(valij)\n except TypeError:\n try:\n _tmparr_valij = array.array(\"d\",valij)\n except TypeError:\n raise TypeError(\"Argument valij has wrong type\")\n else:\n valij_ = memoryview(_tmparr_valij)\n _copyback_valij = True\n else:\n if valij_.format != \"d\":\n valij_ = memoryview(array.array(\"d\",valij))\n _copyback_valij = True\n if valij_ is not None and len(valij_) != (maxlen_):\n raise ValueError(\"Array argument valij has wrong length\")\n res = self.__obj.getsparsesymmat(idx_,maxlen_,subi_,subj_,valij_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_valij:\n valij[:] = _tmparr_valij\n if _copyback_subj:\n subj[:] = _tmparr_subj\n if _copyback_subi:\n subi[:] = _tmparr_subi", "def getsparsesymmat(self,idx_,subi_,subj_,valij_):\n maxlen_ = self.getsymmatinfo((idx_))[1]\n _subi_minlength = (maxlen_)\n if (maxlen_) > 0 and subi_ is not None and len(subi_) != (maxlen_):\n raise ValueError(\"Array argument subi is not long enough: Is %d, expected %d\" % (len(subi_),(maxlen_)))\n if isinstance(subi_,numpy.ndarray) and not subi_.flags.writeable:\n raise ValueError(\"Argument subi must be writable\")\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\n _subi_copyarray = False\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subi_ is not None:\n _subi_copyarray = True\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\n _subi_np_tmp[:] = subi_\n assert _subi_np_tmp.flags.contiguous\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subi_copyarray = False\n _subi_tmp = None\n \n _subj_minlength = (maxlen_)\n if (maxlen_) > 0 and subj_ is not None and len(subj_) != (maxlen_):\n raise ValueError(\"Array argument subj is not long enough: Is %d, expected %d\" % (len(subj_),(maxlen_)))\n if isinstance(subj_,numpy.ndarray) and not subj_.flags.writeable:\n raise ValueError(\"Argument subj must be writable\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n _valij_minlength = (maxlen_)\n if (maxlen_) > 0 and valij_ is not None and len(valij_) != (maxlen_):\n raise ValueError(\"Array argument valij is not long enough: Is %d, expected %d\" % (len(valij_),(maxlen_)))\n if isinstance(valij_,numpy.ndarray) and not valij_.flags.writeable:\n raise ValueError(\"Argument valij must be writable\")\n if isinstance(valij_, numpy.ndarray) and valij_.dtype is numpy.dtype(numpy.float64) and valij_.flags.contiguous:\n _valij_copyarray = False\n _valij_tmp = ctypes.cast(valij_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif valij_ is not None:\n _valij_copyarray = True\n _valij_np_tmp = numpy.zeros(len(valij_),numpy.dtype(numpy.float64))\n _valij_np_tmp[:] = valij_\n assert _valij_np_tmp.flags.contiguous\n _valij_tmp = ctypes.cast(_valij_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _valij_copyarray = False\n _valij_tmp = None\n \n res = __library__.MSK_XX_getsparsesymmat(self.__nativep,idx_,maxlen_,_subi_tmp,_subj_tmp,_valij_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _subi_copyarray:\n subi_[:] = _subi_np_tmp\n if _subj_copyarray:\n subj_[:] = _subj_np_tmp\n if _valij_copyarray:\n valij_[:] = _valij_np_tmp", "def getnumsymmat(self): # 3\n res,resargs = self.__obj.getnumsymmat()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _num_return_value = resargs\n return _num_return_value", "def dctSymUserInfo(pdct, symIndex):\n return _dctmcc.dctSymUserInfo(pdct, symIndex)", "def csr_info(mat, name=\"\", non_empy=False):\n if non_empy:\n print(\"%s [nrows %d (%d non-empty), ncols %d (%d non-empty), nnz %d]\" % (\n name, mat.shape[0], \n sum(1 if mat.indptr[i+1] > mat.indptr[i] else 0 \n for i in range(mat.shape[0])), \n mat.shape[1], len(np.unique(mat.indices)), \n len(mat.data)))\n else:\n print( \"%s [nrows %d, ncols %d, nnz %d]\" % (name, \n mat.shape[0], mat.shape[1], len(mat.data)) )", "def csr_info(mat, name=\"\", non_empy=False):\n if non_empy:\n print(\"%s [nrows %d (%d non-empty), ncols %d (%d non-empty), nnz %d]\" % (\n name, mat.shape[0], \n sum(1 if mat.indptr[i+1] > mat.indptr[i] else 0 \n for i in range(mat.shape[0])), \n mat.shape[1], len(np.unique(mat.indices)), \n len(mat.data)))\n else:\n print( \"%s [nrows %d, ncols %d, nnz %d]\" % (name, \n mat.shape[0], mat.shape[1], len(mat.data)) )", "def csr_info(mat, name=\"\", non_empy=False):\n if non_empy:\n print(\"%s [nrows %d (%d non-empty), ncols %d (%d non-empty), nnz %d]\" % (\n name, mat.shape[0], \n sum(1 if mat.indptr[i+1] > mat.indptr[i] else 0 \n for i in range(mat.shape[0])), \n mat.shape[1], len(np.unique(mat.indices)), \n len(mat.data)))\n else:\n print( \"%s [nrows %d, ncols %d, nnz %d]\" % (name, \n mat.shape[0], mat.shape[1], len(mat.data)) )", "def csr_info(mat, name=\"\", non_empy=False):\n if non_empy:\n print(\"%s [nrows %d (%d non-empty), ncols %d (%d non-empty), nnz %d]\" % (\n name, mat.shape[0], \n sum(1 if mat.indptr[i+1] > mat.indptr[i] else 0 \n for i in range(mat.shape[0])), \n mat.shape[1], len(np.unique(mat.indices)), \n len(mat.data)))\n else:\n print( \"%s [nrows %d, ncols %d, nnz %d]\" % (name, \n mat.shape[0], mat.shape[1], len(mat.data)) )", "def get_sym_matrix(self):\n temp_T = Matrix.eye(3)\n for i in range(len(self.lengths)):\n angle_mat = self.T_a.subs(self.q,self.angles[i]).evalf()\n len_mat = self.T_x.subs(self.l,self.lengths[i]).evalf()\n temp_T = temp_T * angle_mat * len_mat\n \n return temp_T", "def SymmetriseMatrix(adjmatrix):\n\n if galib.metrics.Reciprocity(adjmatrix) == 1:\n # if Reciprocity(adjmatrix) == 1:\n return adjmatrix\n else:\n return 0.5 * (adjmatrix + adjmatrix.T)", "def sym_adj(adj):\n adj = sp.coo_matrix(adj)\n rowsum = np.array(adj.sum(1))\n d_inv_sqrt = np.power(rowsum, -0.5).flatten()\n d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.\n d_mat_inv_sqrt = sp.diags(d_inv_sqrt)\n return adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).astype(np.float32).todense()", "def sym_adj(adj):\n adj = ss.coo_matrix(adj)\n rowsum = np.array(adj.sum(1))\n d_inv_sqrt = np.power(rowsum, -0.5).flatten()\n d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.\n d_mat_inv_sqrt = ss.diags(d_inv_sqrt)\n return np.array(adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).astype(np.float32).todense())", "def symbolic_mutual_information(symX, symY):\n\n if len(symX) != len(symY):\n raise ValueError('All arrays must have same length')\n \n symX = np.array(symX)\n symY = np.array(symY)\n \n symbols = np.unique(np.concatenate((symX,symY))).tolist()\n \n jp = symbolic_joint_probabilities(symX, symY)\n pX = symbolic_probabilities(symX)\n pY = symbolic_probabilities(symY)\n \n MI = 0\n\n for yi, b in pY.items():\n for xi, a in pX.items():\n try:\n c = jp[yi,xi]\n MI += c * np.log(c /(a * b)) / np.log(len(symbols))\n except KeyError:\n continue\n except:\n print(\"Unexpected Error\")\n raise\n \n return MI", "def _print_matrix_info(mtrx, name):\r\n pr = lambda t: print(\"ht3_solver:\\t\" + t)\r\n pr(\"MATRIX INFO:\")\r\n pr(\"Matrix:\\t\" + name)\r\n pr(\"Description:\\t\" + str(mtrx.description))\r\n pr(\"Shape:\\t\" + str(mtrx.shape))", "def dctSymIndex(pdct, symName):\n return _dctmcc.dctSymIndex(pdct, symName)", "def find_symmetry(self):\n from spglib import get_spacegroup\n cell = ( self.lattice, self.fractional_coordinates, self.atomic_nos )\n self.spacegroup = get_spacegroup(cell, symmprec=1e-5)\n print(\"Symmetry space group is\", self.spacegroup)", "def getMibSymbol(self):\n if self.__state & self.stClean:\n return self.__modName, self.__symName, self.__indices\n else:\n raise SmiError('%s object not fully initialized' % self.__class__.__name__)", "def information_matrix(self):\n return self._cov.inv()", "def get_connection_mat(self, idx):\n\n try:\n return self.weights[idx]\n except:\n print(\"\"\"Could not find layer {0} in network.\\nNetwork has {1} layers.\"\"\".format(idx, self.size))", "def get_stain_matrix(I):", "def sym(self) -> np.ndarray:\n if self._sym is None:\n self._sym = symmetrize_discrete_vector_field(self.F, mode=\"sym\")\n return self._sym", "def dctSymEntries(pdct, symIndex):\n return _dctmcc.dctSymEntries(pdct, symIndex)", "def get_sol_mat(self, clust):\r\n\r\n return self.__model_data[clust]", "def jmat(ind: int):\n return _jm[ind - 1]", "def get_solver_mats(self, x_nd, rot_coef):\n raise NotImplementedError", "def symmeterize(self):\n A = self.to_coo_matrix()\n symg = wgraph_from_adjacency((A + A.T) / 2)\n self.E = symg.E\n self.edges = symg.edges\n self.weights = symg.weights\n return self", "def get_symbolic_model(self):\n return self.sym_func", "def get_symbolic_model(self):\n return self.sym_func" ]

[ "0.8472209", "0.61438686", "0.60188043", "0.59274673", "0.5842971", "0.57795507", "0.57204145", "0.57204145", "0.57204145", "0.57204145", "0.5698095", "0.5413046", "0.5405503", "0.5376912", "0.5374786", "0.531883", "0.5297684", "0.523657", "0.51200414", "0.51060134", "0.5081327", "0.5045709", "0.50061107", "0.49957713", "0.4995688", "0.49688283", "0.4937803", "0.49020213", "0.48640984", "0.48640984" ]

[ "def getnumsymmat(self): # 3\n res,resargs = self.__obj.getnumsymmat()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _num_return_value = resargs\n return _num_return_value", "def getsymmatinfo(self,idx_): # 3\n res,resargs = self.__obj.getsymmatinfo(idx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _dim_return_value,_nz_return_value,_type_return_value = resargs\n _type_return_value = symmattype(_type_return_value)\n return _dim_return_value,_nz_return_value,_type_return_value", "def getsymmatinfo(self,idx_):\n dim_ = ctypes.c_int32()\n nz_ = ctypes.c_int64()\n type_ = ctypes.c_int32()\n res = __library__.MSK_XX_getsymmatinfo(self.__nativep,idx_,ctypes.byref(dim_),ctypes.byref(nz_),ctypes.byref(type_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n dim_ = dim_.value\n _dim_return_value = dim_\n nz_ = nz_.value\n _nz_return_value = nz_\n _type_return_value = symmattype(type_.value)\n return (_dim_return_value,_nz_return_value,_type_return_value)", "def get_symmetry(self):\n\n # used to build the symmetry string\n symmetry = self.get_atoms()[0].get_symmetry_class()\n\n # used to count how many atoms there are of the current symmetry\n symmetric_atom_count = 1\n\n for atom in self.get_atoms()[1:]:\n\n # if this atom has a different symmetry than the one before it\n if atom.get_symmetry_class() != symmetry[-1]:\n\n # record the number of atoms with the previous symmetry\n symmetry += str(symmetric_atom_count) + atom.get_symmetry_class()\n\n # reset the atom counter\n symmetric_atom_count = 1\n\n # if this atom has the same symmetry than the one before it\n else:\n\n # record this atom in the atom count\n symmetric_atom_count += 1\n\n # record the number of atoms with the last symmetry\n symmetry += str(symmetric_atom_count)\n\n return symmetry", "def get_conn_matrix_len(self):\n\n return len(self.connection_matrix) * self.brain[\"n_osc\"]", "def sym_K(self):\n raise NotImplementedError", "def axes_of_symmetry(self):\n if self.number_axes_of_symmetry is None: # distinguish from Falsy 0\n raise NotImplementedError(self.message_unknown)\n return self.number_axes_of_symmetry", "def get_sym_matrix(self):\n temp_T = Matrix.eye(3)\n for i in range(len(self.lengths)):\n angle_mat = self.T_a.subs(self.q,self.angles[i]).evalf()\n len_mat = self.T_x.subs(self.l,self.lengths[i]).evalf()\n temp_T = temp_T * angle_mat * len_mat\n \n return temp_T", "def shape(self):\n return self.symbolic.shape", "def size(adj_mat):\n return adj_mat.shape[0]", "def num_syls(syls):\n\treturn len([c for c in syls if c in ['0','1','2']])", "def get_num_connections(self):\n\n synapses = 0\n for mat in self.weights:\n synapses += mat.size\n return synapses", "def getSize(self):\n if self.sym != None:\n return self.sym.getSize()\n return self.define.getSize()", "def sym(self) -> np.ndarray:\n if self._sym is None:\n self._sym = symmetrize_discrete_vector_field(self.F, mode=\"sym\")\n return self._sym", "def find_symmetry(self):\n from spglib import get_spacegroup\n cell = ( self.lattice, self.fractional_coordinates, self.atomic_nos )\n self.spacegroup = get_spacegroup(cell, symmprec=1e-5)\n print(\"Symmetry space group is\", self.spacegroup)", "def get_number_of_atoms_to_optimize(self):\n v = self.c.get(simulation_cell=True)\n return len(v.data.stoichiometry)", "def N(self):\n return _hypre.HypreParMatrix_N(self)", "def GetGlobalNumRows(self):\n return _hypre.HypreParMatrix_GetGlobalNumRows(self)", "def SymmetriseMatrix(adjmatrix):\n\n if galib.metrics.Reciprocity(adjmatrix) == 1:\n # if Reciprocity(adjmatrix) == 1:\n return adjmatrix\n else:\n return 0.5 * (adjmatrix + adjmatrix.T)", "def is_symmetric(mat):\n return np.allclose(mat.T, mat)", "def getSize(self):\n if self.subsym == None:\n if self.size == 0:\n return 1\n else:\n return self.size\n else:\n if self.size == 0:\n return self.subsym.getSize()\n else:\n return self.size * self.subsym.getSize()", "def sym_adj(adj):\n adj = sp.coo_matrix(adj)\n rowsum = np.array(adj.sum(1))\n d_inv_sqrt = np.power(rowsum, -0.5).flatten()\n d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.\n d_mat_inv_sqrt = sp.diags(d_inv_sqrt)\n return adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).astype(np.float32).todense()", "def GetGlobalNumCols(self):\n return _hypre.HypreParMatrix_GetGlobalNumCols(self)", "def numSpecial(self, mat: list[list[int]]) -> int:\n ans = 0\n col_cache = {}\n for row in mat:\n # print(row)\n ones = []\n for i, n in enumerate(row):\n if n == 1:\n ones.append(i)\n # print(ones)\n if len(ones) == 1:\n j = ones[0]\n cols = [row[j] for row in mat]\n s = col_cache.get(j, sum(cols))\n col_cache[j] = s\n if s == 1:\n ans += 1\n return ans", "def symmetric(matrix):\n return sp.allclose(matrix, matrix.T)", "def sym_adj(adj):\n adj = ss.coo_matrix(adj)\n rowsum = np.array(adj.sum(1))\n d_inv_sqrt = np.power(rowsum, -0.5).flatten()\n d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.\n d_mat_inv_sqrt = ss.diags(d_inv_sqrt)\n return np.array(adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).astype(np.float32).todense())", "def confirm_symmetry(mat: numpy.ndarray, symmetry: List[Any]) -> None:\n is_unity = validate_unity(symmetry[0])\n if len(symmetry) == 1 and is_unity:\n return\n build_symmetry_operations(symmetry)\n validate_matrix_symmetry(mat, symmetry)", "def getlen(self):\n if self.onlydiag():\n return self.lendiag()\n else:\n return len(self)", "def global_symbols_size(self):\n size = 0\n for s in self.global_symbols:\n if self.global_symbols[s].type == 'procedure': continue\n size += self.global_symbols[s].size\n return size", "def expected_size(self):\n return self.nsym * self.symbol_len_per_byte" ]

[ "0.7524286", "0.6255513", "0.62463", "0.6031631", "0.5934353", "0.58589876", "0.5748166", "0.5643437", "0.5588444", "0.5525005", "0.551974", "0.5482156", "0.547971", "0.54718024", "0.54672223", "0.5427831", "0.54226774", "0.5403771", "0.5391538", "0.5375537", "0.53549564", "0.53485143", "0.5328631", "0.53220564", "0.5302894", "0.52880394", "0.5283709", "0.52629054", "0.5262857", "0.5261725" ]

[ "def getsparsesymmat(self,idx_,subi,subj,valij): # 3\n maxlen_ = self.getsymmatinfo((idx_))[1]\n _copyback_subi = False\n if subi is None:\n subi_ = None\n else:\n try:\n subi_ = memoryview(subi)\n except TypeError:\n try:\n _tmparr_subi = array.array(\"i\",subi)\n except TypeError:\n raise TypeError(\"Argument subi has wrong type\")\n else:\n subi_ = memoryview(_tmparr_subi)\n _copyback_subi = True\n else:\n if subi_.format != \"i\":\n subi_ = memoryview(array.array(\"i\",subi))\n _copyback_subi = True\n if subi_ is not None and len(subi_) != (maxlen_):\n raise ValueError(\"Array argument subi has wrong length\")\n _copyback_subj = False\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n _copyback_subj = True\n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n _copyback_subj = True\n if subj_ is not None and len(subj_) != (maxlen_):\n raise ValueError(\"Array argument subj has wrong length\")\n _copyback_valij = False\n if valij is None:\n valij_ = None\n else:\n try:\n valij_ = memoryview(valij)\n except TypeError:\n try:\n _tmparr_valij = array.array(\"d\",valij)\n except TypeError:\n raise TypeError(\"Argument valij has wrong type\")\n else:\n valij_ = memoryview(_tmparr_valij)\n _copyback_valij = True\n else:\n if valij_.format != \"d\":\n valij_ = memoryview(array.array(\"d\",valij))\n _copyback_valij = True\n if valij_ is not None and len(valij_) != (maxlen_):\n raise ValueError(\"Array argument valij has wrong length\")\n res = self.__obj.getsparsesymmat(idx_,maxlen_,subi_,subj_,valij_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_valij:\n valij[:] = _tmparr_valij\n if _copyback_subj:\n subj[:] = _tmparr_subj\n if _copyback_subi:\n subi[:] = _tmparr_subi", "def appendsparsesymmat(self,dim_,subi_,subj_,valij_):\n nz_ = None\n if nz_ is None:\n nz_ = len(subi_)\n elif nz_ != len(subi_):\n raise IndexError(\"Inconsistent length of array subi\")\n if nz_ is None:\n nz_ = len(subj_)\n elif nz_ != len(subj_):\n raise IndexError(\"Inconsistent length of array subj\")\n if nz_ is None:\n nz_ = len(valij_)\n elif nz_ != len(valij_):\n raise IndexError(\"Inconsistent length of array valij\")\n if subi_ is None:\n raise ValueError(\"Argument subi cannot be None\")\n if subi_ is None:\n raise ValueError(\"Argument subi may not be None\")\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\n _subi_copyarray = False\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subi_ is not None:\n _subi_copyarray = True\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\n _subi_np_tmp[:] = subi_\n assert _subi_np_tmp.flags.contiguous\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subi_copyarray = False\n _subi_tmp = None\n \n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n if valij_ is None:\n raise ValueError(\"Argument valij cannot be None\")\n if valij_ is None:\n raise ValueError(\"Argument valij may not be None\")\n if isinstance(valij_, numpy.ndarray) and valij_.dtype is numpy.dtype(numpy.float64) and valij_.flags.contiguous:\n _valij_copyarray = False\n _valij_tmp = ctypes.cast(valij_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif valij_ is not None:\n _valij_copyarray = True\n _valij_np_tmp = numpy.zeros(len(valij_),numpy.dtype(numpy.float64))\n _valij_np_tmp[:] = valij_\n assert _valij_np_tmp.flags.contiguous\n _valij_tmp = ctypes.cast(_valij_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _valij_copyarray = False\n _valij_tmp = None\n \n idx_ = ctypes.c_int64()\n res = __library__.MSK_XX_appendsparsesymmat(self.__nativep,dim_,nz_,_subi_tmp,_subj_tmp,_valij_tmp,ctypes.byref(idx_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n idx_ = idx_.value\n _idx_return_value = idx_\n return (_idx_return_value)", "def appendsparsesymmat(self,dim_,subi,subj,valij): # 3\n nz_ = None\n if nz_ is None:\n nz_ = len(subi)\n elif nz_ != len(subi):\n raise IndexError(\"Inconsistent length of array subi\")\n if nz_ is None:\n nz_ = len(subj)\n elif nz_ != len(subj):\n raise IndexError(\"Inconsistent length of array subj\")\n if nz_ is None:\n nz_ = len(valij)\n elif nz_ != len(valij):\n raise IndexError(\"Inconsistent length of array valij\")\n if nz_ is None: nz_ = 0\n if subi is None: raise TypeError(\"Invalid type for argument subi\")\n if subi is None:\n subi_ = None\n else:\n try:\n subi_ = memoryview(subi)\n except TypeError:\n try:\n _tmparr_subi = array.array(\"i\",subi)\n except TypeError:\n raise TypeError(\"Argument subi has wrong type\")\n else:\n subi_ = memoryview(_tmparr_subi)\n \n else:\n if subi_.format != \"i\":\n subi_ = memoryview(array.array(\"i\",subi))\n \n if subj is None: raise TypeError(\"Invalid type for argument subj\")\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n \n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n \n if valij is None: raise TypeError(\"Invalid type for argument valij\")\n if valij is None:\n valij_ = None\n else:\n try:\n valij_ = memoryview(valij)\n except TypeError:\n try:\n _tmparr_valij = array.array(\"d\",valij)\n except TypeError:\n raise TypeError(\"Argument valij has wrong type\")\n else:\n valij_ = memoryview(_tmparr_valij)\n \n else:\n if valij_.format != \"d\":\n valij_ = memoryview(array.array(\"d\",valij))\n \n res,resargs = self.__obj.appendsparsesymmat(dim_,nz_,subi_,subj_,valij_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _idx_return_value = resargs\n return _idx_return_value", "def getsymmatinfo(self,idx_):\n dim_ = ctypes.c_int32()\n nz_ = ctypes.c_int64()\n type_ = ctypes.c_int32()\n res = __library__.MSK_XX_getsymmatinfo(self.__nativep,idx_,ctypes.byref(dim_),ctypes.byref(nz_),ctypes.byref(type_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n dim_ = dim_.value\n _dim_return_value = dim_\n nz_ = nz_.value\n _nz_return_value = nz_\n _type_return_value = symmattype(type_.value)\n return (_dim_return_value,_nz_return_value,_type_return_value)", "def getsymmatinfo(self,idx_): # 3\n res,resargs = self.__obj.getsymmatinfo(idx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _dim_return_value,_nz_return_value,_type_return_value = resargs\n _type_return_value = symmattype(_type_return_value)\n return _dim_return_value,_nz_return_value,_type_return_value", "def j_sparse_vector_wrapper_to_scipy_spmatrix(j_obj: JavaObject):\n indices = np.frombuffer(j_obj.getIndicesBytes(), dtype=\"<i4\")\n values = np.frombuffer(j_obj.getValuesBytes(), dtype=\"<f8\")\n size = j_obj.getSize()\n indptr = np.array([0, indices.shape[0]], dtype=np.int32)\n return csr_matrix((values, indices, indptr), shape=(1, size), dtype=np.float64).todok()", "def sparse_matlab(i, j, v, m, n):\n return csr_matrix((v, (i, j)), shape=(m, n))", "def SymmetriseMatrix(adjmatrix):\n\n if galib.metrics.Reciprocity(adjmatrix) == 1:\n # if Reciprocity(adjmatrix) == 1:\n return adjmatrix\n else:\n return 0.5 * (adjmatrix + adjmatrix.T)", "def get_stain_matrix(I):", "def sparse_matrix(data, stype=\"csr\", dtype=complex):\n return _SPARSE_CONSTRUCTORS[stype](data, dtype=dtype)", "def jmat(ind: int):\n return _jm[ind - 1]", "def to_sparse(self, method='csr_matrix'):\r\n data = self.data.values\r\n if method == 'csr_matrix':\r\n data_sp = sps.csr_matrix(data)\r\n elif method == 'bsr_matrix':\r\n data_sp = sps.bsr_matrix(data)\r\n elif method == 'coo_matrix':\r\n data_sp = sps.coo_matrix(data)\r\n elif method == 'csc_matrix':\r\n data_sp = sps.csc_matrix(data)\r\n elif method == 'dia_matrix':\r\n data_sp = sps.dia_matrix(data)\r\n elif method == 'dok_matrix':\r\n data_sp = sps.dok_matrix(data)\r\n elif method == 'lil_matrix':\r\n data_sp = sps.lil_matrix(data)\r\n else:\r\n raise ValueError('The method does not exist in scipy.sparse')\r\n return data_sp", "def get_solver_mats(self, x_nd, rot_coef):\n raise NotImplementedError", "def get_sparse(self, key, element):\n return self.get(self._sparse2seq(key), element)", "def scipy_sparse_to_spmatrix(A):\n coo = A.tocoo()\n SP = spmatrix(coo.data.tolist(), coo.row.tolist(), coo.col.tolist(), size=A.shape)\n return SP", "def appendsparsesymmatlist(self,dims_,nz_,subi_,subj_,valij_,idx_):\n num_ = None\n if num_ is None:\n num_ = len(dims_)\n elif num_ != len(dims_):\n raise IndexError(\"Inconsistent length of array dims\")\n if num_ is None:\n num_ = len(nz_)\n elif num_ != len(nz_):\n raise IndexError(\"Inconsistent length of array nz\")\n if dims_ is None:\n raise ValueError(\"Argument dims cannot be None\")\n if dims_ is None:\n raise ValueError(\"Argument dims may not be None\")\n if isinstance(dims_, numpy.ndarray) and dims_.dtype is numpy.dtype(numpy.int32) and dims_.flags.contiguous:\n _dims_copyarray = False\n _dims_tmp = ctypes.cast(dims_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif dims_ is not None:\n _dims_copyarray = True\n _dims_np_tmp = numpy.zeros(len(dims_),numpy.dtype(numpy.int32))\n _dims_np_tmp[:] = dims_\n assert _dims_np_tmp.flags.contiguous\n _dims_tmp = ctypes.cast(_dims_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _dims_copyarray = False\n _dims_tmp = None\n \n if nz_ is None:\n raise ValueError(\"Argument nz cannot be None\")\n if nz_ is None:\n raise ValueError(\"Argument nz may not be None\")\n if isinstance(nz_, numpy.ndarray) and nz_.dtype is numpy.dtype(numpy.int64) and nz_.flags.contiguous:\n _nz_copyarray = False\n _nz_tmp = ctypes.cast(nz_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif nz_ is not None:\n _nz_copyarray = True\n _nz_np_tmp = numpy.zeros(len(nz_),numpy.dtype(numpy.int64))\n _nz_np_tmp[:] = nz_\n assert _nz_np_tmp.flags.contiguous\n _nz_tmp = ctypes.cast(_nz_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _nz_copyarray = False\n _nz_tmp = None\n \n _subi_minlength = sum((nz_))\n if sum((nz_)) > 0 and subi_ is not None and len(subi_) != sum((nz_)):\n raise ValueError(\"Array argument subi is not long enough: Is %d, expected %d\" % (len(subi_),sum((nz_))))\n if subi_ is None:\n raise ValueError(\"Argument subi cannot be None\")\n if subi_ is None:\n raise ValueError(\"Argument subi may not be None\")\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\n _subi_copyarray = False\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subi_ is not None:\n _subi_copyarray = True\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\n _subi_np_tmp[:] = subi_\n assert _subi_np_tmp.flags.contiguous\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subi_copyarray = False\n _subi_tmp = None\n \n _subj_minlength = sum((nz_))\n if sum((nz_)) > 0 and subj_ is not None and len(subj_) != sum((nz_)):\n raise ValueError(\"Array argument subj is not long enough: Is %d, expected %d\" % (len(subj_),sum((nz_))))\n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n _valij_minlength = sum((nz_))\n if sum((nz_)) > 0 and valij_ is not None and len(valij_) != sum((nz_)):\n raise ValueError(\"Array argument valij is not long enough: Is %d, expected %d\" % (len(valij_),sum((nz_))))\n if valij_ is None:\n raise ValueError(\"Argument valij cannot be None\")\n if valij_ is None:\n raise ValueError(\"Argument valij may not be None\")\n if isinstance(valij_, numpy.ndarray) and valij_.dtype is numpy.dtype(numpy.float64) and valij_.flags.contiguous:\n _valij_copyarray = False\n _valij_tmp = ctypes.cast(valij_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif valij_ is not None:\n _valij_copyarray = True\n _valij_np_tmp = numpy.zeros(len(valij_),numpy.dtype(numpy.float64))\n _valij_np_tmp[:] = valij_\n assert _valij_np_tmp.flags.contiguous\n _valij_tmp = ctypes.cast(_valij_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _valij_copyarray = False\n _valij_tmp = None\n \n _idx_minlength = (num_)\n if (num_) > 0 and idx_ is not None and len(idx_) != (num_):\n raise ValueError(\"Array argument idx is not long enough: Is %d, expected %d\" % (len(idx_),(num_)))\n if isinstance(idx_,numpy.ndarray) and not idx_.flags.writeable:\n raise ValueError(\"Argument idx must be writable\")\n if idx_ is None:\n raise ValueError(\"Argument idx may not be None\")\n if isinstance(idx_, numpy.ndarray) and idx_.dtype is numpy.dtype(numpy.int64) and idx_.flags.contiguous:\n _idx_copyarray = False\n _idx_tmp = ctypes.cast(idx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n elif idx_ is not None:\n _idx_copyarray = True\n _idx_np_tmp = numpy.zeros(len(idx_),numpy.dtype(numpy.int64))\n _idx_np_tmp[:] = idx_\n assert _idx_np_tmp.flags.contiguous\n _idx_tmp = ctypes.cast(_idx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int64))\n else:\n _idx_copyarray = False\n _idx_tmp = None\n \n res = __library__.MSK_XX_appendsparsesymmatlist(self.__nativep,num_,_dims_tmp,_nz_tmp,_subi_tmp,_subj_tmp,_valij_tmp,_idx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _idx_copyarray:\n idx_[:] = _idx_np_tmp", "def to_s_matrix(w,v):\n pass", "def save_sparse_matrix(self,\n artifact_type: str,\n params: Dict[str, Any],\n sparse_matrix: sp.csr_matrix,\n ignore_duplicate: bool = False) -> str:\n ppr_idx = None\n if \"ppr_idx\" in params.keys() and not isinstance(params[\"ppr_idx\"], int):\n ppr_idx = np.array(params[\"ppr_idx\"])\n params[\"ppr_idx\"] = hash(frozenset(params[\"ppr_idx\"]))\n\n if ignore_duplicate:\n # check there's no entry with the exact same config already present\n ids = Storage.locked_call(\n lambda: self._find_meta_by_exact_params(artifact_type, params),\n self._get_lock_path(artifact_type),\n self.lock_timeout\n )\n if len(ids) > 0:\n logging.info(f\"Ignoring duplicate save in save_sparse_matrix call\")\n return self._build_artifact_path(artifact_type, ids[0].doc_id).replace(\".pt\", \".npz\")\n\n ids = Storage.locked_call(\n lambda: self._upsert_meta(artifact_type, params),\n self._get_lock_path(artifact_type),\n self.lock_timeout,\n )\n if len(ids) != 1:\n raise RuntimeError(f'The index contains duplicates (artifact_type={artifact_type}, params={params})')\n\n try:\n path = self._build_artifact_path(artifact_type, ids[0]).replace(\".pt\", \".npz\")\n sp.save_npz(path, sparse_matrix)\n logging.info(f\"Saved sparse matrix to storage\")\n if ppr_idx is not None:\n ppr_path = path.replace(\".npz\", \"idx.npy\")\n np.save(ppr_path, ppr_idx)\n logging.info(f\"Saved ppr index to storage\")\n return path\n except: # noqa: E722\n Storage.locked_call(\n lambda: self._remove_meta(artifact_type, params),\n self._get_lock_path(artifact_type),\n self.lock_timeout\n )\n raise", "def sparse_matrix(shape, integer=False):\n dtype = numpy.int_ if integer else numpy.float_\n return scipy.sparse.lil_matrix(shape, dtype=dtype)", "def generate_direct_solver(self, grid=None):\n if grid is None:\n # LOG.debug(\"Generate Solver for internal Spare Matrix: %s\" % self.sp_matrix)\n solver = spla.factorized(self.sp_matrix)\n else:\n # LOG.debug(\"Generate Solver for given Grid %s\" % (grid,))\n sp_matrix = self.to_sparse_matrix(grid, \"csc\")\n # LOG.debug(\" with Sparse Matrix: %s\" % sp_matrix.todense())\n # print(\"Jahier\\n\", sp_matrix.todense())\n # print(\"Jahier.shape\\n\", sp_matrix.todense().shape)\n solver = spla.factorized(sp_matrix)\n return solver", "def wrapDBMatrix(self,mat):\n return mat.todense()", "def sparsify(W,conn):\n \n N = W.shape[0]\n W_sparse = sparse.lil_matrix((N,N)) \n for row, weights in itertools.izip(conn, W):\n W_sparse[row[0],row[1:]] = weights[1:]\n return W_sparse", "def get_sym_matrix(self):\n temp_T = Matrix.eye(3)\n for i in range(len(self.lengths)):\n angle_mat = self.T_a.subs(self.q,self.angles[i]).evalf()\n len_mat = self.T_x.subs(self.l,self.lengths[i]).evalf()\n temp_T = temp_T * angle_mat * len_mat\n \n return temp_T", "def dict2sparseMatrix(wDict,std=0,diag=0):\n data = lil_matrix((len(list(wDict.keys())),len(list(wDict.keys()))))\n nAreas = len(list(wDict.keys()))\n for i in wDict:\n data[i,i] = diag\n ne = len(wDict[i])+ diag\n for j in wDict[i]:\n if std:\n data[i,j] = 1 / float(ne)\n else:\n data[i,j] = 1\n return data", "def get_cvxopt_sparse_intf():\n import cvxpy.interface.cvxopt_interface.sparse_matrix_interface as smi\n return smi.SparseMatrixInterface()", "def to_sparse(self):\n if self.rep.fmt == 'sparse':\n return self\n\n return self.from_rep(self.rep.to_sdm())", "def to_sparse(self):\n from divisi2.sparse import SparseMatrix\n return SparseMatrix(self, self.row_labels, self.col_labels)", "def getSparse(self): # as opposed to makeSparse which keeps the same form and return nothing\n return copy.deepcopy(self.makeSparse())", "def make_sparse(self, fmt='csc', make_method=None):\n if make_method:\n self.sparse = make_method(self.hamiltonian)\n else:\n self.sparse = self.hamiltonian.to_matrix(sparse=fmt)", "def return_adjacencyMatrix(self):\n return self.__mat" ]

[ "0.75738734", "0.6238482", "0.6137533", "0.56444985", "0.5625718", "0.558371", "0.54747504", "0.52915025", "0.52799106", "0.52322876", "0.5228932", "0.51636285", "0.5140221", "0.5121791", "0.50425875", "0.49852064", "0.4978507", "0.49779502", "0.49698326", "0.4951648", "0.49289328", "0.49200913", "0.48945662", "0.48862562", "0.4880411", "0.48706338", "0.48516077", "0.48509476", "0.48347786", "0.48201743" ]

[ "def putintparam(self,param_,parvalue_): # 3\n if not isinstance(param_,iparam): raise TypeError(\"Argument param has wrong type\")\n res = self.__obj.putintparam(param_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def getintparam(self,param_): # 3\n if not isinstance(param_,iparam): raise TypeError(\"Argument param has wrong type\")\n res,resargs = self.__obj.getintparam(param_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _parvalue_return_value = resargs\n return _parvalue_return_value", "def set_param(self, param, value):\n self._set_param_client(param, value)", "def getintparam(self,param_):\n parvalue_ = ctypes.c_int32()\n res = __library__.MSK_XX_getintparam(self.__nativep,param_,ctypes.byref(parvalue_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n parvalue_ = parvalue_.value\n _parvalue_return_value = parvalue_\n return (_parvalue_return_value)", "def setInteger(self, value):", "def setInteger(self, value):", "def setInt(self, addr: ghidra.program.model.address.Address, value: int) -> None:\n ...", "def set_param(param, num, set_val):\n param[0][num] = set_val", "def setParam(self,param,value):\n if param in self.params.keys():\n self.params[param] = value", "def putparam(self,parname_,parvalue_): # 3\n res = self.__obj.putparam(parname_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "async def set_param(self, param: str, value: int) -> ArchonCommand:\n cmd = await self.send_command(f\"FASTLOADPARAM {param} {value}\")\n if not cmd.succeeded():\n raise ArchonError(\n f\"Failed setting parameter {param!r} ({cmd.status.name}).\"\n )\n return cmd", "def setInt(self, address: ghidra.program.model.address.Address, value: int) -> None:\n ...", "def setInteger(self, value):\n assert self._is_int is True\n self._value = value", "def setInteger(self, value: int):\n self.value = value", "def _mn_set_par_ ( self , i , val , fix = False ) :\n if not i in self : raise IndexError\n #\n if hasattr ( val , 'value' ) : val = val.value()\n #\n ierr = _mn_exec_ ( self , \"SET PAR\" , i + 1 , val )\n #\n if fix : self.FixParameter ( i ) \n #\n return ierr", "def setInt(self, addr: ghidra.program.model.address.Address, value: int, bigEndian: bool) -> None:\n ...", "def set_param(self, label, val):\n assert type(label) is str, 'Parameter name \"%s\" is not string' % label\n assert type(val) is float or type(val) is int, 'Fixed parameter value is not numeric for %s' % label\n self.params[label] = val", "def set_parameter(self, params, name, val):\n raise NotImplementedError()", "def putparam(self,parname_,parvalue_):\n if isinstance(parname_,unicode):\n parname_ = parname_.encode(\"utf-8\",errors=\"replace\")\n if isinstance(parvalue_,unicode):\n parvalue_ = parvalue_.encode(\"utf-8\",errors=\"replace\")\n res = __library__.MSK_XX_putparam(self.__nativep,parname_,parvalue_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def set_param(self, param_key, value):\n return self._params.set_param_value(param_key, value)", "def setParameter(self, name, value):", "def putnaintparam(self,paramname_,parvalue_): # 3\n res = self.__obj.putnaintparam(paramname_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def getIntParam(self, paramkey, default=None):\n value = self.request.getParameter(paramkey)\n if value is None: return default\n try: return int(value)\n except: return default", "def set_parameter_value(self, parameter, value):\n pass", "def set_parameter(doc, o_path, param, value, mn_consts):\n if isinstance(value, str):\n doc.setParameter(o_path, param, value, mn_consts.infoStringParameter)\n elif isinstance(value, int):\n doc.setParameter(o_path, param, str(value), mn_consts.infoNumberParameter)\n elif isinstance(value, list):\n doc.setParameter(o_path, param, str(value), mn_consts.infoArrayParameter)", "def putnaintparam(self,paramname_,parvalue_):\n if isinstance(paramname_,unicode):\n paramname_ = paramname_.encode(\"utf-8\",errors=\"replace\")\n res = __library__.MSK_XX_putnaintparam(self.__nativep,paramname_,parvalue_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def setIntValue(self, *args):\n return _libsbml.ConversionProperties_setIntValue(self, *args)", "def integer(self, integer):\n\n self._integer = integer", "def setInt(self, key, value):\n self.__config.setValue(key, QtCore.QVariant(value))\n self.__saved = False", "def setParameter(self,arg,value):\n self._params[arg] = value\n return self._params" ]

[ "0.8837676", "0.71326786", "0.6860121", "0.68544596", "0.6800701", "0.6800701", "0.6740805", "0.6733184", "0.6618874", "0.658969", "0.6509817", "0.6441833", "0.6363545", "0.6344362", "0.633649", "0.6328604", "0.63135433", "0.62845325", "0.6263861", "0.62351775", "0.6199873", "0.60748166", "0.60120517", "0.59740245", "0.59688926", "0.5924039", "0.59031224", "0.58957654", "0.5817367", "0.5803042" ]

[ "def putmaxnumcon(self,maxnumcon_): # 3\n res = self.__obj.putmaxnumcon(maxnumcon_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putmaxnumcone(self,maxnumcone_):\n res = __library__.MSK_XX_putmaxnumcone(self.__nativep,maxnumcone_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putmaxnumcone(self,maxnumcone_): # 3\n res = self.__obj.putmaxnumcone(maxnumcone_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putmaxnumvar(self,maxnumvar_): # 3\n res = self.__obj.putmaxnumvar(maxnumvar_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putmaxnumvar(self,maxnumvar_):\n res = __library__.MSK_XX_putmaxnumvar(self.__nativep,maxnumvar_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def set_max_nb_instructions(nb): #py:set_max_nb_instructions\n RUR._set_max_nb_instructions_(nb)", "def getmaxnumcon(self): # 3\n res,resargs = self.__obj.getmaxnumcon()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _maxnumcon_return_value = resargs\n return _maxnumcon_return_value", "def putmaxnumbarvar(self,maxnumbarvar_):\n res = __library__.MSK_XX_putmaxnumbarvar(self.__nativep,maxnumbarvar_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putmaxnumbarvar(self,maxnumbarvar_): # 3\n res = self.__obj.putmaxnumbarvar(maxnumbarvar_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def resizetask(self,maxnumcon_,maxnumvar_,maxnumcone_,maxnumanz_,maxnumqnz_):\n res = __library__.MSK_XX_resizetask(self.__nativep,maxnumcon_,maxnumvar_,maxnumcone_,maxnumanz_,maxnumqnz_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putmaxnumqnz(self,maxnumqnz_): # 3\n res = self.__obj.putmaxnumqnz(maxnumqnz_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def setNumWorkers(self, num):\r\n self.numWorkers = num", "def putmaxnumanz(self,maxnumanz_):\n res = __library__.MSK_XX_putmaxnumanz(self.__nativep,maxnumanz_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def max_num_neighbors(self, max_num_neighbors):\n self._max_num_neighbors = max_num_neighbors", "def putmaxnumanz(self,maxnumanz_): # 3\n res = self.__obj.putmaxnumanz(maxnumanz_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def set_max_nb_robots(nb): #py:set_max_nb_robots\n RUR._set_max_nb_robots_(nb)", "def putmaxnumqnz(self,maxnumqnz_):\n res = __library__.MSK_XX_putmaxnumqnz(self.__nativep,maxnumqnz_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def set_workers(self, nworkers):\n\n self.max_workers = nworkers", "def getmaxnumcon(self):\n maxnumcon_ = ctypes.c_int32()\n res = __library__.MSK_XX_getmaxnumcon(self.__nativep,ctypes.byref(maxnumcon_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n maxnumcon_ = maxnumcon_.value\n _maxnumcon_return_value = maxnumcon_\n return (_maxnumcon_return_value)", "def _set_constraint(self):\n pass", "def resizetask(self,maxnumcon_,maxnumvar_,maxnumcone_,maxnumanz_,maxnumqnz_): # 3\n res = self.__obj.resizetask(maxnumcon_,maxnumvar_,maxnumcone_,maxnumanz_,maxnumqnz_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def non_heap_max(self, non_heap_max):\n\n self._non_heap_max = non_heap_max", "def set_max_eval_concurrency(self, max_eval_concurrency):\n self.max_eval_concurrency = max_eval_concurrency", "def max_pool_size(self, max_pool_size: ConfigNodePropertyInteger):\n\n self._max_pool_size = max_pool_size", "def _process_threadpool_limits_initializier():\n import numpy # required for loky's autodetection\n from threadpoolctl import threadpool_limits\n\n threadpool_limits(limits=1)", "def max_num_links(self, max_num_links):\n self._max_num_links = max_num_links", "def max_in_gbps(self, max_in_gbps):\n self._max_in_gbps = max_in_gbps", "def set_numpins(self, n):\n self.numpins = n", "def set_constraint_scaling_factor(self, con):\n condata = self.get_representative_data_object(con)\n vardata = self.con2var[condata]\n scaling_factor = self.scaling_factor\n\n var_factor = scaling_factor[vardata]\n if self.dim == 0:\n scaling_factor[con] = var_factor\n else:\n for c in con.values():\n scaling_factor[c] = var_factor", "def SetConstraint(self, model) :\n if 'pp' in self.__type : self.SetPPConstraint( model )\n elif self.__type == 'prBin' and self.bound!=0 : self.SetPRBinConstraint( model )\n elif self.__type == 'prCat' and self.bound != 0 : self.SetPRCatConstraint(model)\n elif self.__type == 'prBinCat' and self.bound != 0 : self.SetPRBinCatConstraint(model)\n elif self.bound == 0 : return\n else : raise RuntimeError( 'SetConstraint : Unknown type for Constraint : ', self.__type )" ]

[ "0.7620724", "0.6740477", "0.6646557", "0.60926807", "0.5997314", "0.59877884", "0.59074914", "0.589972", "0.5885956", "0.57590777", "0.5751454", "0.5703682", "0.5699466", "0.56882566", "0.5686642", "0.568195", "0.56570315", "0.5625261", "0.5607635", "0.5532643", "0.5513424", "0.5498084", "0.53858757", "0.5384151", "0.5372652", "0.53134924", "0.5308602", "0.5308152", "0.52772665", "0.5263952" ]

[ "def putmaxnumcone(self,maxnumcone_): # 3\n res = self.__obj.putmaxnumcone(maxnumcone_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putmaxnumcon(self,maxnumcon_): # 3\n res = self.__obj.putmaxnumcon(maxnumcon_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def getmaxnumcone(self): # 3\n res,resargs = self.__obj.getmaxnumcone()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _maxnumcone_return_value = resargs\n return _maxnumcone_return_value", "def putmaxnumcon(self,maxnumcon_):\n res = __library__.MSK_XX_putmaxnumcon(self.__nativep,maxnumcon_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def getmaxnumcone(self):\n maxnumcone_ = ctypes.c_int32()\n res = __library__.MSK_XX_getmaxnumcone(self.__nativep,ctypes.byref(maxnumcone_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n maxnumcone_ = maxnumcone_.value\n _maxnumcone_return_value = maxnumcone_\n return (_maxnumcone_return_value)", "def resizetask(self,maxnumcon_,maxnumvar_,maxnumcone_,maxnumanz_,maxnumqnz_): # 3\n res = self.__obj.resizetask(maxnumcon_,maxnumvar_,maxnumcone_,maxnumanz_,maxnumqnz_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def set_max_nb_robots(nb): #py:set_max_nb_robots\n RUR._set_max_nb_robots_(nb)", "def resizetask(self,maxnumcon_,maxnumvar_,maxnumcone_,maxnumanz_,maxnumqnz_):\n res = __library__.MSK_XX_resizetask(self.__nativep,maxnumcon_,maxnumvar_,maxnumcone_,maxnumanz_,maxnumqnz_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def set_max_eval_concurrency(self, max_eval_concurrency):\n self.max_eval_concurrency = max_eval_concurrency", "def set_workers(self, nworkers):\n\n self.max_workers = nworkers", "def max_num_neighbors(self, max_num_neighbors):\n self._max_num_neighbors = max_num_neighbors", "def getmaxnumcon(self): # 3\n res,resargs = self.__obj.getmaxnumcon()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _maxnumcon_return_value = resargs\n return _maxnumcon_return_value", "def set_max_nb_instructions(nb): #py:set_max_nb_instructions\n RUR._set_max_nb_instructions_(nb)", "def getnumcone(self): # 3\n res,resargs = self.__obj.getnumcone()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _numcone_return_value = resargs\n return _numcone_return_value", "def set_number_of_neurons_per_core(neuronType, maxPermitted):\n if not inspect.isclass(neuronType):\n neuronType = globals()[neuronType]\n if neuronType is None:\n raise Exception(\"Unknown Vertex Type {}\".format(neuronType))\n \n if hasattr(neuronType, \"custom_max_atoms_per_core\"):\n neuronType.custom_max_atoms_per_core = maxPermitted\n else:\n raise Exception(\"{} is not a Vertex type\".format(neuronType))", "def putmaxnumanz(self,maxnumanz_): # 3\n res = self.__obj.putmaxnumanz(maxnumanz_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def max_concurrent_requests(self, max_concurrent_requests):\n\n self._max_concurrent_requests = max_concurrent_requests", "def limit_num_clients(self, limit_num_clients):\n\n self._limit_num_clients = limit_num_clients", "def putmaxnumqnz(self,maxnumqnz_): # 3\n res = self.__obj.putmaxnumqnz(maxnumqnz_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def setNumWorkers(self, num):\r\n self.numWorkers = num", "def putmaxnumvar(self,maxnumvar_): # 3\n res = self.__obj.putmaxnumvar(maxnumvar_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putmaxnumanz(self,maxnumanz_):\n res = __library__.MSK_XX_putmaxnumanz(self.__nativep,maxnumanz_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def maxCODQty(self, maxCODQty):\n\n self._maxCODQty = maxCODQty", "def abs_max_cool_setpoint_limit(self) -> int:\n return self.cluster.get(\"abs_max_cool_setpoint_limit\", 3200)", "def getnumcone(self):\n numcone_ = ctypes.c_int32()\n res = __library__.MSK_XX_getnumcone(self.__nativep,ctypes.byref(numcone_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n numcone_ = numcone_.value\n _numcone_return_value = numcone_\n return (_numcone_return_value)", "def getmaxnumcon(self):\n maxnumcon_ = ctypes.c_int32()\n res = __library__.MSK_XX_getmaxnumcon(self.__nativep,ctypes.byref(maxnumcon_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n maxnumcon_ = maxnumcon_.value\n _maxnumcon_return_value = maxnumcon_\n return (_maxnumcon_return_value)", "def set_max_noutput_items(self, m):\n return _spacegrant_swig.DeNRZI_sptr_set_max_noutput_items(self, m)", "def __init__(self, qubit_count: int, max_depth: int, **kwargs) -> None:\n self._constraints = constraints.QuantumCircuitConstraints(\n qubit_count=qubit_count, max_depth=max_depth, **kwargs\n )", "def num_conll(self):\n pass", "def constraints_max_offer_per_cust(n_row, n_col):\n constraints = np.identity(n_row * n_col)\n return constraints" ]

[ "0.7859737", "0.687435", "0.6576984", "0.6516009", "0.6250021", "0.5742838", "0.57224095", "0.5664653", "0.5662323", "0.5644606", "0.5565795", "0.55509084", "0.55272174", "0.54718906", "0.5445443", "0.54382503", "0.5431226", "0.5375135", "0.53488725", "0.5343298", "0.5334908", "0.52925533", "0.5292211", "0.52714384", "0.52018744", "0.5191569", "0.5190401", "0.51797", "0.5178937", "0.5178857" ]

[ "def getmaxnumcone(self): # 3\n res,resargs = self.__obj.getmaxnumcone()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _maxnumcone_return_value = resargs\n return _maxnumcone_return_value", "def getnumcone(self): # 3\n res,resargs = self.__obj.getnumcone()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _numcone_return_value = resargs\n return _numcone_return_value", "def getmaxnumcon(self): # 3\n res,resargs = self.__obj.getmaxnumcon()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _maxnumcon_return_value = resargs\n return _maxnumcon_return_value", "def getnumcone(self):\n numcone_ = ctypes.c_int32()\n res = __library__.MSK_XX_getnumcone(self.__nativep,ctypes.byref(numcone_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n numcone_ = numcone_.value\n _numcone_return_value = numcone_\n return (_numcone_return_value)", "def num_cones(self):\n return self._shape_count(_sff.cone)", "def putmaxnumcone(self,maxnumcone_): # 3\n res = self.__obj.putmaxnumcone(maxnumcone_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def num_conll(self):\n pass", "def getmaxnumcon(self):\n maxnumcon_ = ctypes.c_int32()\n res = __library__.MSK_XX_getmaxnumcon(self.__nativep,ctypes.byref(maxnumcon_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n maxnumcon_ = maxnumcon_.value\n _maxnumcon_return_value = maxnumcon_\n return (_maxnumcon_return_value)", "def control_edge_count_max(self) -> int:\n return int(self.graph_tuple_stats.control_edge_count_max or 0)", "def number_of_electrodes(self):\n return self._pre_kernel.shape[1]", "def max_cardinality():\r\n #create a list containing the number of each vertex involvement.\r\n array = []\r\n for i in adj:\r\n array += [i[0],i[1]]\r\n\r\n #compute the degree by counting the involment\r\n degree = Counter(array).most_common()\r\n\r\n #retrieve the degree only\r\n degree_ = [ i[1] for i in degree]\r\n\r\n degree_ = np.array(degree_)\r\n \r\n max_m = None\r\n \r\n #check if m is valid\r\n for i in range(degree[0][1]+2)[2:]:\r\n \r\n #valid if there are at least m vertex with degree equals to at least m-1 \r\n if i < len(np.where(degree_>=i-1)[0]):\r\n max_m = i\r\n else:\r\n break\r\n max_m += 1\r\n print(f'maximum possible clique cardinality :{max_m}')\r\n return max_m", "def maximum_number_of_workers(self) -> pulumi.Output[int]:\n return pulumi.get(self, \"maximum_number_of_workers\")", "def putmaxnumcone(self,maxnumcone_):\n res = __library__.MSK_XX_putmaxnumcone(self.__nativep,maxnumcone_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def maximum_number_of_workers(self) -> pulumi.Output[Optional[int]]:\n return pulumi.get(self, \"maximum_number_of_workers\")", "def concurrent_tasks_limit(self):\n return self._concurrent_tasks_limit", "def largest_cc_size(ugraph):\n\tconnected = cc_visited(ugraph)\n\tmaxnum = 0\n\tfor content in connected:\n\t\tmaxnum = max(maxnum,len(content))\n\treturn maxnum", "def n_cs(self):\n return self._configurations[0].n_cs", "def num_cochains(self) -> int:\n if self.__num_cochains__ is not None:\n return self.__num_cochains__\n return self.ptr.numel() + 1", "def getnumconemem(self,k_): # 3\n res,resargs = self.__obj.getnumconemem(k_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _nummem_return_value = resargs\n return _nummem_return_value", "def n_cs(self):\n return np.size(self._cs, 0)", "def get_max_cleverbot_requests(self):\n return int(self.bot_data_file[\"maxCleverbotRequests\"])", "def cmax(self):\n return self['cmax']", "def cmax(self):\n return self[\"cmax\"]", "def max_node_count(self) -> int:\n return pulumi.get(self, \"max_node_count\")", "def getnumcon(self): # 3\n res,resargs = self.__obj.getnumcon()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _numcon_return_value = resargs\n return _numcon_return_value", "def call_edge_count_max(self) -> int:\n return int(self.graph_tuple_stats.call_edge_count_max or 0)", "def max_concurrency(self) -> Optional[int]:\n result = get_feature(self.vm, \"qubes-vm-update-max-concurrency\", None)\n if result is None:\n return result\n return int(result)", "def nClumps(self):\n \n return len(self)", "def maxclients(self) -> Optional[int]:\n return pulumi.get(self, \"maxclients\")", "def maxContigLength(self):\n\t\tstats = self.scores()\n\t\treturn stats['largestContig']" ]

[ "0.82559127", "0.74485886", "0.7001308", "0.6936185", "0.6920529", "0.67586905", "0.6735455", "0.64610845", "0.64500237", "0.64104486", "0.6385375", "0.6377788", "0.63512313", "0.6300954", "0.6270852", "0.6235373", "0.6222689", "0.6206008", "0.62018204", "0.61789495", "0.617264", "0.6168127", "0.60945046", "0.60941845", "0.6088023", "0.60830295", "0.6073222", "0.6049861", "0.60468364", "0.60220975" ]

[ "def putmaxnumvar(self,maxnumvar_): # 3\n res = self.__obj.putmaxnumvar(maxnumvar_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putmaxnumbarvar(self,maxnumbarvar_): # 3\n res = self.__obj.putmaxnumbarvar(maxnumbarvar_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putmaxnumbarvar(self,maxnumbarvar_):\n res = __library__.MSK_XX_putmaxnumbarvar(self.__nativep,maxnumbarvar_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def getmaxnumvar(self): # 3\n res,resargs = self.__obj.getmaxnumvar()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _maxnumvar_return_value = resargs\n return _maxnumvar_return_value", "def putmaxnumqnz(self,maxnumqnz_): # 3\n res = self.__obj.putmaxnumqnz(maxnumqnz_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putmaxnumcon(self,maxnumcon_): # 3\n res = self.__obj.putmaxnumcon(maxnumcon_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def getmaxnumbarvar(self): # 3\n res,resargs = self.__obj.getmaxnumbarvar()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _maxnumbarvar_return_value = resargs\n return _maxnumbarvar_return_value", "def putmaxnumqnz(self,maxnumqnz_):\n res = __library__.MSK_XX_putmaxnumqnz(self.__nativep,maxnumqnz_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def getmaxnumvar(self):\n maxnumvar_ = ctypes.c_int32()\n res = __library__.MSK_XX_getmaxnumvar(self.__nativep,ctypes.byref(maxnumvar_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n maxnumvar_ = maxnumvar_.value\n _maxnumvar_return_value = maxnumvar_\n return (_maxnumvar_return_value)", "def putmaxnumanz(self,maxnumanz_): # 3\n res = self.__obj.putmaxnumanz(maxnumanz_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def max_num_neighbors(self, max_num_neighbors):\n self._max_num_neighbors = max_num_neighbors", "def putmaxnumcon(self,maxnumcon_):\n res = __library__.MSK_XX_putmaxnumcon(self.__nativep,maxnumcon_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putmaxnumanz(self,maxnumanz_):\n res = __library__.MSK_XX_putmaxnumanz(self.__nativep,maxnumanz_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def set_max_nb_instructions(nb): #py:set_max_nb_instructions\n RUR._set_max_nb_instructions_(nb)", "def getmaxnumbarvar(self):\n maxnumbarvar_ = ctypes.c_int32()\n res = __library__.MSK_XX_getmaxnumbarvar(self.__nativep,ctypes.byref(maxnumbarvar_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n maxnumbarvar_ = maxnumbarvar_.value\n _maxnumbarvar_return_value = maxnumbarvar_\n return (_maxnumbarvar_return_value)", "def putmaxnumcone(self,maxnumcone_): # 3\n res = self.__obj.putmaxnumcone(maxnumcone_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def set_max_nb_robots(nb): #py:set_max_nb_robots\n RUR._set_max_nb_robots_(nb)", "def update_maxp(self) -> None:\n maxp = self.otf[\"maxp\"]\n ttdata = self.ufo.lib.get(TRUETYPE_INSTRUCTIONS_KEY, None)\n if ttdata:\n for name in (\n \"maxStorage\",\n \"maxFunctionDefs\",\n \"maxInstructionDefs\",\n \"maxStackElements\",\n # \"maxSizeOfInstructions\", # Is recalculated below\n \"maxZones\",\n \"maxTwilightPoints\",\n ):\n value = ttdata.get(name, None)\n if value is not None:\n setattr(maxp, name, value)\n\n # Recalculate maxp.maxSizeOfInstructions\n sizes = [\n len(ttglyph.program.getBytecode())\n for ttglyph in self.otf[\"glyf\"].glyphs.values()\n if hasattr(ttglyph, \"program\")\n ]\n maxp.maxSizeOfInstructions = max(sizes, default=0)", "def putmaxnumcone(self,maxnumcone_):\n res = __library__.MSK_XX_putmaxnumcone(self.__nativep,maxnumcone_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def non_heap_max(self, non_heap_max):\n\n self._non_heap_max = non_heap_max", "def setOutMax(self, out_max):\n\t\tself.out_max = out_max", "def set_workers(self, nworkers):\n\n self.max_workers = nworkers", "def set_max(self, val):\n self._max = val", "def set_max(self, max):\n self.set_val((self.val[0], max))", "def set_max_noutput_items(self, m):\n return _spacegrant_swig.DeNRZI_sptr_set_max_noutput_items(self, m)", "def appendvars(self,num_):\n res = __library__.MSK_XX_appendvars(self.__nativep,num_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def set_maxVal(self, val):\n self.maxVal = val", "def set_max_noutput_items(self, m):\n return _spacegrant_swig.NRZI_sptr_set_max_noutput_items(self, m)", "def set_max_noutput_items(self, m: \"int\") -> \"void\":\n return _beamforming_swig.phasedarray_sptr_set_max_noutput_items(self, m)", "def set_max_reps(self, max_reps):\n self.max_reps = int(max_reps)" ]

[ "0.80868816", "0.7560464", "0.73389435", "0.65560055", "0.65400666", "0.6336814", "0.63105714", "0.62413776", "0.61817116", "0.6158278", "0.60976154", "0.60662615", "0.6011411", "0.5994649", "0.5986585", "0.5917469", "0.58999217", "0.58730257", "0.58647376", "0.58646524", "0.57081693", "0.5631341", "0.5595974", "0.5582642", "0.55472887", "0.5540637", "0.5538168", "0.5523251", "0.54823864", "0.5444105" ]

[ "def putmaxnumbarvar(self,maxnumbarvar_): # 3\n res = self.__obj.putmaxnumbarvar(maxnumbarvar_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putmaxnumvar(self,maxnumvar_): # 3\n res = self.__obj.putmaxnumvar(maxnumvar_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putmaxnumvar(self,maxnumvar_):\n res = __library__.MSK_XX_putmaxnumvar(self.__nativep,maxnumvar_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def getmaxnumbarvar(self): # 3\n res,resargs = self.__obj.getmaxnumbarvar()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _maxnumbarvar_return_value = resargs\n return _maxnumbarvar_return_value", "def getmaxnumbarvar(self):\n maxnumbarvar_ = ctypes.c_int32()\n res = __library__.MSK_XX_getmaxnumbarvar(self.__nativep,ctypes.byref(maxnumbarvar_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n maxnumbarvar_ = maxnumbarvar_.value\n _maxnumbarvar_return_value = maxnumbarvar_\n return (_maxnumbarvar_return_value)", "def putmaxnumqnz(self,maxnumqnz_): # 3\n res = self.__obj.putmaxnumqnz(maxnumqnz_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putmaxnumcon(self,maxnumcon_): # 3\n res = self.__obj.putmaxnumcon(maxnumcon_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putmaxnumcon(self,maxnumcon_):\n res = __library__.MSK_XX_putmaxnumcon(self.__nativep,maxnumcon_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putmaxnumqnz(self,maxnumqnz_):\n res = __library__.MSK_XX_putmaxnumqnz(self.__nativep,maxnumqnz_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def max_num_neighbors(self, max_num_neighbors):\n self._max_num_neighbors = max_num_neighbors", "def set_max_noutput_items(self, m):\n return _spacegrant_swig.NRZI_sptr_set_max_noutput_items(self, m)", "def getnumbarvar(self): # 3\n res,resargs = self.__obj.getnumbarvar()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _numbarvar_return_value = resargs\n return _numbarvar_return_value", "def set_max_noutput_items(self, m: \"int\") -> \"void\":\n return _beamforming_swig.phasedarray_sptr_set_max_noutput_items(self, m)", "def set_max_noutput_items(self, m):\n return _spacegrant_swig.DeNRZI_sptr_set_max_noutput_items(self, m)", "def getnumbarvar(self):\n numbarvar_ = ctypes.c_int32()\n res = __library__.MSK_XX_getnumbarvar(self.__nativep,ctypes.byref(numbarvar_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n numbarvar_ = numbarvar_.value\n _numbarvar_return_value = numbarvar_\n return (_numbarvar_return_value)", "def set_max_noutput_items(self, m):\n return _spacegrant_swig.general_burster_2_sptr_set_max_noutput_items(self, m)", "def getmaxnumvar(self): # 3\n res,resargs = self.__obj.getmaxnumvar()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _maxnumvar_return_value = resargs\n return _maxnumvar_return_value", "def set_max_nb_instructions(nb): #py:set_max_nb_instructions\n RUR._set_max_nb_instructions_(nb)", "def set_max_nb_robots(nb): #py:set_max_nb_robots\n RUR._set_max_nb_robots_(nb)", "def set_max_noutput_items(self, m: \"int\") -> \"void\":\n return _beamforming_swig.beamformer_sptr_set_max_noutput_items(self, m)", "def maxsize(self, maxsize):\n self.shape = (int(maxsize), ) + self.shape[1:]\n self.clear()", "def getmaxnumvar(self):\n maxnumvar_ = ctypes.c_int32()\n res = __library__.MSK_XX_getmaxnumvar(self.__nativep,ctypes.byref(maxnumvar_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n maxnumvar_ = maxnumvar_.value\n _maxnumvar_return_value = maxnumvar_\n return (_maxnumvar_return_value)", "def set_max_noutput_items(self, m):\n return _spacegrant_swig.invert_bit_sptr_set_max_noutput_items(self, m)", "def setImax(self, I_max):\n\t\tself.I_max = I_max", "def update_maxp(self) -> None:\n maxp = self.otf[\"maxp\"]\n ttdata = self.ufo.lib.get(TRUETYPE_INSTRUCTIONS_KEY, None)\n if ttdata:\n for name in (\n \"maxStorage\",\n \"maxFunctionDefs\",\n \"maxInstructionDefs\",\n \"maxStackElements\",\n # \"maxSizeOfInstructions\", # Is recalculated below\n \"maxZones\",\n \"maxTwilightPoints\",\n ):\n value = ttdata.get(name, None)\n if value is not None:\n setattr(maxp, name, value)\n\n # Recalculate maxp.maxSizeOfInstructions\n sizes = [\n len(ttglyph.program.getBytecode())\n for ttglyph in self.otf[\"glyf\"].glyphs.values()\n if hasattr(ttglyph, \"program\")\n ]\n maxp.maxSizeOfInstructions = max(sizes, default=0)", "def non_heap_max(self, non_heap_max):\n\n self._non_heap_max = non_heap_max", "def putmaxnumcone(self,maxnumcone_):\n res = __library__.MSK_XX_putmaxnumcone(self.__nativep,maxnumcone_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def set_max_noutput_items(self, m):\n return _spacegrant_swig.binary_sink_sptr_set_max_noutput_items(self, m)", "def set_max_noutput_items(self, m):\n return _spacegrant_swig.hdlc_framer_sptr_set_max_noutput_items(self, m)", "def set_max_output_buffer(self, *args):\n return _spacegrant_swig.NRZI_sptr_set_max_output_buffer(self, *args)" ]

[ "0.7951648", "0.6959753", "0.6795598", "0.65727705", "0.6438065", "0.6048308", "0.5996996", "0.5852882", "0.5841968", "0.5822205", "0.5682764", "0.5667983", "0.5649398", "0.5640302", "0.5615086", "0.56006753", "0.55580956", "0.5552524", "0.5495012", "0.54564613", "0.5411751", "0.53988004", "0.53824806", "0.53719604", "0.5364293", "0.53622717", "0.53509885", "0.53323233", "0.532731", "0.53160566" ]

[ "def putmaxnumanz(self,maxnumanz_): # 3\n res = self.__obj.putmaxnumanz(maxnumanz_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putmaxnumqnz(self,maxnumqnz_):\n res = __library__.MSK_XX_putmaxnumqnz(self.__nativep,maxnumqnz_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putmaxnumqnz(self,maxnumqnz_): # 3\n res = self.__obj.putmaxnumqnz(maxnumqnz_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putmaxnumcone(self,maxnumcone_):\n res = __library__.MSK_XX_putmaxnumcone(self.__nativep,maxnumcone_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def getmaxnumanz(self):\n maxnumanz_ = ctypes.c_int64()\n res = __library__.MSK_XX_getmaxnumanz64(self.__nativep,ctypes.byref(maxnumanz_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n maxnumanz_ = maxnumanz_.value\n _maxnumanz_return_value = maxnumanz_\n return (_maxnumanz_return_value)", "def getmaxnumanz(self): # 3\n res,resargs = self.__obj.getmaxnumanz64()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _maxnumanz_return_value = resargs\n return _maxnumanz_return_value", "def putmaxnumcone(self,maxnumcone_): # 3\n res = self.__obj.putmaxnumcone(maxnumcone_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putmaxnumvar(self,maxnumvar_):\n res = __library__.MSK_XX_putmaxnumvar(self.__nativep,maxnumvar_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putmaxnumcon(self,maxnumcon_):\n res = __library__.MSK_XX_putmaxnumcon(self.__nativep,maxnumcon_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def max_num_neighbors(self, max_num_neighbors):\n self._max_num_neighbors = max_num_neighbors", "def putmaxnumbarvar(self,maxnumbarvar_):\n res = __library__.MSK_XX_putmaxnumbarvar(self.__nativep,maxnumbarvar_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putmaxnumvar(self,maxnumvar_): # 3\n res = self.__obj.putmaxnumvar(maxnumvar_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putmaxnumcon(self,maxnumcon_): # 3\n res = self.__obj.putmaxnumcon(maxnumcon_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def getmaxnumqnz(self):\n maxnumqnz_ = ctypes.c_int64()\n res = __library__.MSK_XX_getmaxnumqnz64(self.__nativep,ctypes.byref(maxnumqnz_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n maxnumqnz_ = maxnumqnz_.value\n _maxnumqnz_return_value = maxnumqnz_\n return (_maxnumqnz_return_value)", "def getnumbaranz(self):\n nz_ = ctypes.c_int64()\n res = __library__.MSK_XX_getnumbaranz(self.__nativep,ctypes.byref(nz_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n nz_ = nz_.value\n _nz_return_value = nz_\n return (_nz_return_value)", "def putmaxnumbarvar(self,maxnumbarvar_): # 3\n res = self.__obj.putmaxnumbarvar(maxnumbarvar_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def setOutMax(self, out_max):\n\t\tself.out_max = out_max", "def Max_ner(self, lst, max_ner):\r\n for i in range(len(lst)):\r\n if len(lst[i]) >= max_ner:\r\n lst[i] = lst[i][:max_ner]\r\n else:\r\n length = len(lst[i])\r\n for _ in range(max_ner - length):\r\n lst[i].append(0)\r\n return lst", "def getnumanz(self):\n numanz_ = ctypes.c_int32()\n res = __library__.MSK_XX_getnumanz(self.__nativep,ctypes.byref(numanz_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n numanz_ = numanz_.value\n _numanz_return_value = numanz_\n return (_numanz_return_value)", "def N_z(self) -> int:\n return self.params.N_z", "def set_max_nb_instructions(nb): #py:set_max_nb_instructions\n RUR._set_max_nb_instructions_(nb)", "def getmaxnumqnz(self): # 3\n res,resargs = self.__obj.getmaxnumqnz64()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _maxnumqnz_return_value = resargs\n return _maxnumqnz_return_value", "def set_max_output_buffer(self, *args):\n return _spacegrant_swig.DeNRZI_sptr_set_max_output_buffer(self, *args)", "def set_max(self, val):\n self._max = val", "def non_heap_max(self, non_heap_max):\n\n self._non_heap_max = non_heap_max", "def NNZ(self):\n return _hypre.HypreParMatrix_NNZ(self)", "def set_max(self, max):\n self.set_val((self.val[0], max))", "def setImax(self, I_max):\n\t\tself.I_max = I_max", "def nnz(self):", "def set_max_position(self, max_us):\n raise NotImplementedError()" ]

[ "0.7441124", "0.68018794", "0.66798395", "0.6399272", "0.6247705", "0.61643946", "0.6043132", "0.59559965", "0.57927316", "0.57495356", "0.57457256", "0.56957024", "0.5552648", "0.5549431", "0.5518782", "0.55130196", "0.55069184", "0.54949814", "0.53994", "0.5381273", "0.5353293", "0.53387547", "0.53298783", "0.5319342", "0.5309368", "0.5306598", "0.53033555", "0.52742773", "0.5223341", "0.52227163" ]

[ "def putmaxnumqnz(self,maxnumqnz_): # 3\n res = self.__obj.putmaxnumqnz(maxnumqnz_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def getmaxnumqnz(self): # 3\n res,resargs = self.__obj.getmaxnumqnz64()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _maxnumqnz_return_value = resargs\n return _maxnumqnz_return_value", "def getmaxnumqnz(self):\n maxnumqnz_ = ctypes.c_int64()\n res = __library__.MSK_XX_getmaxnumqnz64(self.__nativep,ctypes.byref(maxnumqnz_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n maxnumqnz_ = maxnumqnz_.value\n _maxnumqnz_return_value = maxnumqnz_\n return (_maxnumqnz_return_value)", "def getnumqobjnz(self): # 3\n res,resargs = self.__obj.getnumqobjnz64()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _numqonz_return_value = resargs\n return _numqonz_return_value", "def getnumqobjnz(self):\n numqonz_ = ctypes.c_int64()\n res = __library__.MSK_XX_getnumqobjnz64(self.__nativep,ctypes.byref(numqonz_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n numqonz_ = numqonz_.value\n _numqonz_return_value = numqonz_\n return (_numqonz_return_value)", "def putmaxnumbarvar(self,maxnumbarvar_):\n res = __library__.MSK_XX_putmaxnumbarvar(self.__nativep,maxnumbarvar_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putmaxnumanz(self,maxnumanz_):\n res = __library__.MSK_XX_putmaxnumanz(self.__nativep,maxnumanz_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putmaxnumvar(self,maxnumvar_):\n res = __library__.MSK_XX_putmaxnumvar(self.__nativep,maxnumvar_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putmaxnumbarvar(self,maxnumbarvar_): # 3\n res = self.__obj.putmaxnumbarvar(maxnumbarvar_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def num_qubits(self, num_qubits: int) -> None:\n if self.num_qubits != num_qubits:\n # invalidate the circuit\n self._invalidate()\n self.qregs = []\n if num_qubits is not None and num_qubits > 0:\n self.qregs = [QuantumRegister(num_qubits, name=\"q\")]", "def putmaxnumvar(self,maxnumvar_): # 3\n res = self.__obj.putmaxnumvar(maxnumvar_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putmaxnumanz(self,maxnumanz_): # 3\n res = self.__obj.putmaxnumanz(maxnumanz_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def N_z(self) -> int:\n return self.params.N_z", "def num_qubits(self) -> int:\n raise NotImplementedError()", "def nnz(self):", "def num_qubits(self) -> int:\n return super().num_qubits", "def putmaxnumcone(self,maxnumcone_):\n res = __library__.MSK_XX_putmaxnumcone(self.__nativep,maxnumcone_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putmaxnumcon(self,maxnumcon_):\n res = __library__.MSK_XX_putmaxnumcon(self.__nativep,maxnumcon_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def NNZ(self):\n return _hypre.HypreParMatrix_NNZ(self)", "def nnz(self):\n return len(self.value)", "def n_qubits(self):\n return int(np.log2(len(self.mat)))", "def getnumbarcnz(self):\n nz_ = ctypes.c_int64()\n res = __library__.MSK_XX_getnumbarcnz(self.__nativep,ctypes.byref(nz_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n nz_ = nz_.value\n _nz_return_value = nz_\n return (_nz_return_value)", "def qsize(self) -> int:\n pass", "def getnumbarcnz(self): # 3\n res,resargs = self.__obj.getnumbarcnz()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _nz_return_value = resargs\n return _nz_return_value", "def nnz(self):\n return len(self.data)", "def getnumqconknz(self,k_): # 3\n res,resargs = self.__obj.getnumqconknz64(k_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _numqcnz_return_value = resargs\n return _numqcnz_return_value", "def num_qubits(self) -> int:\n return self._circuit.num_qubits", "def putmaxnumcone(self,maxnumcone_): # 3\n res = self.__obj.putmaxnumcone(maxnumcone_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putmaxnumcon(self,maxnumcon_): # 3\n res = self.__obj.putmaxnumcon(maxnumcon_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def k_space_grad_fq_allocation(n, qmax_bin, mem):\n return int(math.floor(\n float(.8 * mem - 16 * qmax_bin * n - 12 * n) / (\n 16 * (2 * qmax_bin + 1))))" ]

[ "0.8385307", "0.7157034", "0.6808096", "0.6252767", "0.6003535", "0.6001192", "0.5939193", "0.59272075", "0.5830991", "0.5783531", "0.5719293", "0.5708783", "0.56221986", "0.5621964", "0.5618641", "0.5608385", "0.55962783", "0.55303234", "0.54971045", "0.5450261", "0.54395735", "0.54374975", "0.5437029", "0.5387193", "0.5310357", "0.5301221", "0.5273116", "0.52507025", "0.525045", "0.5206621" ]

[ "def putnastrparam(self,paramname_,parvalue_): # 3\n res = self.__obj.putnastrparam(paramname_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putstrparam(self,param_,parvalue_): # 3\n if not isinstance(param_,sparam): raise TypeError(\"Argument param has wrong type\")\n res = self.__obj.putstrparam(param_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putstrparam(self,param_,parvalue_):\n if isinstance(parvalue_,unicode):\n parvalue_ = parvalue_.encode(\"utf-8\",errors=\"replace\")\n res = __library__.MSK_XX_putstrparam(self.__nativep,param_,parvalue_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putnaintparam(self,paramname_,parvalue_):\n if isinstance(paramname_,unicode):\n paramname_ = paramname_.encode(\"utf-8\",errors=\"replace\")\n res = __library__.MSK_XX_putnaintparam(self.__nativep,paramname_,parvalue_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def set_param(self, name, value):\n name = str(name)\n value = str(value)\n cxnlib.CXNNetSetParam(self.handle,\n ctypes.c_char_p(name.encode('utf-8')),\n ctypes.c_char_p(value.encode('utf-8')))", "def putparam(self,parname_,parvalue_):\n if isinstance(parname_,unicode):\n parname_ = parname_.encode(\"utf-8\",errors=\"replace\")\n if isinstance(parvalue_,unicode):\n parvalue_ = parvalue_.encode(\"utf-8\",errors=\"replace\")\n res = __library__.MSK_XX_putparam(self.__nativep,parname_,parvalue_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putnaintparam(self,paramname_,parvalue_): # 3\n res = self.__obj.putnaintparam(paramname_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def isstrparname(self,parname_):\n if isinstance(parname_,unicode):\n parname_ = parname_.encode(\"utf-8\",errors=\"replace\")\n param_ = ctypes.c_int32()\n res = __library__.MSK_XX_isstrparname(self.__nativep,parname_,ctypes.byref(param_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _param_return_value = sparam(param_.value)\n return (_param_return_value)", "def setString(self, name: unicode, value: unicode) -> None:\n ...", "def isstrparname(self,parname_): # 3\n res,resargs = self.__obj.isstrparname(parname_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _param_return_value = resargs\n _param_return_value = sparam(_param_return_value)\n return _param_return_value", "def putparam(self,parname_,parvalue_): # 3\n res = self.__obj.putparam(parname_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def setParameter(self, name, value):", "def _set_string_value_pair(self, parameter, value=None):\n if type(parameter) is str:\n if value==None:\n raise Exception(\"Error: No value given in set() function for population parameter. Exiting.\")\n self.parameters[parameter] = value\n return\n if type(parameter) is not dict:\n raise Exception(\"Error: invalid parameter type for set() function for population parameter. Exiting.\")\n # Add a dictionary-structured set of new parameters to the current set:\n self.parameters.update(parameter)", "def set_parameter(self, params, name, val):\n raise NotImplementedError()", "def getstrparam(self,param_):\n maxlen_ = (1 + self.getstrparamlen((param_)))\n len_ = ctypes.c_int32()\n parvalue_ = (ctypes.c_char * (maxlen_))()\n res = __library__.MSK_XX_getstrparam(self.__nativep,param_,maxlen_,ctypes.byref(len_),parvalue_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n len_ = len_.value\n _len_return_value = len_\n _parvalue_retval = parvalue_.value.decode(\"utf-8\",errors=\"replace\")\n return (_len_return_value,_parvalue_retval)", "def _put_ssm_param(self, parameter, parameter_name):\n self.ssm_client.put_parameter(\n Name=parameter_name,\n Type=\"String\",\n Value=json.dumps(parameter),\n Overwrite=True,\n Tier=\"Intelligent-Tiering\",\n )", "def param_name(self, value):\n self._param_name = value", "def setParam(self,param,value):\n if param in self.params.keys():\n self.params[param] = value", "def putnadouparam(self,paramname_,parvalue_):\n if isinstance(paramname_,unicode):\n paramname_ = paramname_.encode(\"utf-8\",errors=\"replace\")\n res = __library__.MSK_XX_putnadouparam(self.__nativep,paramname_,parvalue_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putnadouparam(self,paramname_,parvalue_): # 3\n res = self.__obj.putnadouparam(paramname_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def getstrparam(self,param_): # 3\n if not isinstance(param_,sparam): raise TypeError(\"Argument param has wrong type\")\n maxlen_ = (1 + self.getstrparamlen((param_)))\n arr_parvalue = array.array(\"b\",[0]*((maxlen_)))\n memview_arr_parvalue = memoryview(arr_parvalue)\n res,resargs = self.__obj.getstrparam(param_,maxlen_,memview_arr_parvalue)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _len_return_value,retarg_parvalue = resargs\n retarg_parvalue = arr_parvalue.tobytes()[:-1].decode(\"utf-8\",errors=\"ignore\")\n return _len_return_value,retarg_parvalue", "def 置项目文本(self, n, string): # real signature unknown; restored from __doc__\n self.SetString(n, string)", "def set_param(self, name, value, *, distrib=None, ref=None):\n raise NotImplementedError", "def set_param(self, label, val):\n assert type(label) is str, 'Parameter name \"%s\" is not string' % label\n assert type(val) is float or type(val) is int, 'Fixed parameter value is not numeric for %s' % label\n self.params[label] = val", "def put_param(self, attr_name, val):\n self._params[attr_name] = val", "def set_parameter(doc, o_path, param, value, mn_consts):\n if isinstance(value, str):\n doc.setParameter(o_path, param, value, mn_consts.infoStringParameter)\n elif isinstance(value, int):\n doc.setParameter(o_path, param, str(value), mn_consts.infoNumberParameter)\n elif isinstance(value, list):\n doc.setParameter(o_path, param, str(value), mn_consts.infoArrayParameter)", "def getStrParam(self, paramkey, default=None):\n value = self.request.getParameter(paramkey)\n if value is None: return default\n return value", "def set_param(self, param, value):\n self._set_param_client(param, value)", "def set_param(command):\n namespace = app.main(command)\n assert namespace.command == 'sp' or namespace.command == \"setparam\"\n assert namespace.name == \"test\"\n assert namespace.value == \"test\"", "def string_check(param, name):\n\tif not isinstance(param, strcomp):\n\t\traise TypeError(\"Keyword arg '%s' must be of type string. Got: %s\" % (\n\t\t\tname, type(param)))\n\telse:\n\t\tpass" ]

[ "0.900503", "0.8041969", "0.7825734", "0.6532771", "0.64951867", "0.64323235", "0.63091147", "0.62664294", "0.62081355", "0.6152447", "0.61227375", "0.5890109", "0.58600485", "0.58454996", "0.5755116", "0.5754159", "0.575312", "0.5688754", "0.5685935", "0.5649547", "0.56411093", "0.56101125", "0.55851966", "0.5574769", "0.5546487", "0.5542524", "0.5528479", "0.5432164", "0.54304594", "0.5417621" ]

[ "def putobjname(self,objname_): # 3\n res = self.__obj.putobjname(objname_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def set_object_name(self, object_name = \"DefaultObject\"):\n self.obj_name = object_name", "def set_object_name(self, agent, Name):\n\n self.send_ObjectName(agent, agent.agent_id, agent.session_id, {1:[self.LocalID, Name]})", "def set_object_name(remote, object_id, new_name):\n cmd = mmapi.StoredCommands()\n cmd.AppendSceneCommand_SetObjectName(object_id, new_name)\n remote.runCommand(cmd)", "def setName(self, *args):\n return _libsbml.Objective_setName(self, *args)", "def setObjectName( self, name ):\n scene = self.scene()\n if ( scene ):\n name = scene.uniqueNodeName(name)\n self._objectName = name\n self._titleFont = None\n self.update()", "def objective_metric_name(self, objective_metric_name):\n\n self._objective_metric_name = objective_metric_name", "def setName(self, *args):\n return _libsbml.FluxObjective_setName(self, *args)", "def new_name(self,new_name):\n self.name = new_name", "def set_name(self,name):\r\n self._name = __name", "def update_object(self, name: str) -> None:", "def setname(self, name):\n self.__name = name", "def update_name(self, new_name):\r\n self.__name = new_name", "def update_name(self, new_name):\r\n self.__name = new_name", "def name(self, name):\n self.__name = name", "def set_name(self, name):\r\n self.__name = name", "def set_name(self, name):\n self.name = name # overwrite the existing name with the input name", "def set_name(self, name):\n self.name = name # overwrite the existing name with the input name", "def set_name(self, name):\n\t\tself.name_ = name", "def replace(name, newobject):", "def putvarname(self,j_,name_): # 3\n res = self.__obj.putvarname(j_,name_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def name(self, name: str):\n self.inst['targetname'] = name", "def updateName(self,name):\n self.name = name", "def rename(self, objkey, objname, newname):\n\n refnames = eppy.modeleditor.getrefnames(self, objkey)\n for refname in refnames:\n objlists = eppy.modeleditor.getallobjlists(self, refname)\n # [('OBJKEY', refname, fieldindexlist), ...]\n for robjkey, refname, fieldindexlist in objlists:\n idfobjects = self.idfobjects[robjkey]\n for idfobject in idfobjects:\n for findex in fieldindexlist: # for each field\n if (\n idfobject[idfobject.objls[findex]].lower()\n == objname.lower()\n ):\n idfobject[idfobject.objls[findex]] = newname\n theobject = self.getobject(objkey, objname)\n fieldname = [item for item in theobject.objls if item.endswith(\"Name\")][0]\n theobject[fieldname] = newname\n return theobject", "def set_name(self, newname=\"\"):\n self.name = newname", "def set_name(self, name):\n self.__name = name", "def set_name(self, name):\n self.__name = name", "def set_name(self, PersonName):\r\n self.name = PersonName", "def name(self, name):\n self._name = name", "def name(self, name):\n self._name = name" ]

[ "0.8106175", "0.71860224", "0.70278543", "0.6894341", "0.68561333", "0.66999173", "0.663241", "0.6498052", "0.6482429", "0.64018923", "0.6332358", "0.63283575", "0.6323521", "0.6323521", "0.6271868", "0.62309307", "0.6210313", "0.6210313", "0.62092906", "0.61988294", "0.6191593", "0.61654073", "0.6160827", "0.61594695", "0.61172014", "0.6107862", "0.6107862", "0.60988474", "0.6094943", "0.6094943" ]

[ "def putparam(self,parname_,parvalue_): # 3\n res = self.__obj.putparam(parname_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def setParam(self,param,value):\n if param in self.params.keys():\n self.params[param] = value", "def setParameter(self, name, value):", "def set_parameter(self, params, name, val):\n raise NotImplementedError()", "def put_param(self, attr_name, val):\n self._params[attr_name] = val", "def putnaintparam(self,paramname_,parvalue_): # 3\n res = self.__obj.putnaintparam(paramname_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def set(self,name,val):\n matches = self.grep_param_names(name)\n if len(matches):\n x = self._get_params()\n x[matches] = val\n self._set_params(x)\n else:\n raise AttributeError, \"no parameter matches %s\"%name", "def set_param(self, name, value, *, distrib=None, ref=None):\n raise NotImplementedError", "def log_param(self, name: str, value):\n self.params[name] = value\n\n self._sync_log_event()", "def put_par(self, parname, value, sep=\".\"):\n pv = self.get_pvname(parname, sep=sep)\n return Pv.put(pv, value)", "def __setitem__(self, name: str, value):\n super(Parameter, self).__setitem__(name, value)", "def set_param(self, name, value):\n name = str(name)\n value = str(value)\n cxnlib.CXNNetSetParam(self.handle,\n ctypes.c_char_p(name.encode('utf-8')),\n ctypes.c_char_p(value.encode('utf-8')))", "def putnaintparam(self,paramname_,parvalue_):\n if isinstance(paramname_,unicode):\n paramname_ = paramname_.encode(\"utf-8\",errors=\"replace\")\n res = __library__.MSK_XX_putnaintparam(self.__nativep,paramname_,parvalue_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def setParameter(self,arg,value):\n self._params[arg] = value\n return self._params", "def set_param(self, param, value):\n self._set_param_client(param, value)", "def set_param(params, pname, value=None, bounds=None):\n if value is not None:\n for p in params.flattened():\n if p.name == pname:\n p.value = value\n break\n\n if bounds is not None:\n for p in params.flattened():\n if p.name == pname:\n p.bounds = bounds\n p.vary = True\n break", "def set_parameter_value(self, parameter, value):\n pass", "def SetParameterValue(self, paramName, value):\n UnitOperations.UnitOperation.SetParameterValue(self, paramName, value)\n if paramName == ISENTROPIC_PAR:\n if (self.ideal != None):\n self.ideal.SetParameterValue(paramName, value)", "def update_parameter(self, param, val, force=False):\n self._update_dict[param] = val\n if force:\n self._cur_val[param] = None", "def putstrparam(self,param_,parvalue_): # 3\n if not isinstance(param_,sparam): raise TypeError(\"Argument param has wrong type\")\n res = self.__obj.putstrparam(param_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putnadouparam(self,paramname_,parvalue_): # 3\n res = self.__obj.putnadouparam(paramname_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putintparam(self,param_,parvalue_): # 3\n if not isinstance(param_,iparam): raise TypeError(\"Argument param has wrong type\")\n res = self.__obj.putintparam(param_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def _set_param(self, name, value):\n self._frozenjson._data[name] = value", "def set_param(self, param_key, value):\n return self._params.set_param_value(param_key, value)", "def putintparam(self,param_,parvalue_):\n res = __library__.MSK_XX_putintparam(self.__nativep,param_,parvalue_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def set_param(self, label, val):\n assert type(label) is str, 'Parameter name \"%s\" is not string' % label\n assert type(val) is float or type(val) is int, 'Fixed parameter value is not numeric for %s' % label\n self.params[label] = val", "def param_name(self, value):\n self._param_name = value", "def write_parameter(self, parameter_name: str, parameter_value: Union[str, float, int]):\n self._parameters.append(Parameter(parameter_name, parameter_value))", "def setTemplateParameter(self,name,value):\n self.tplparam[name] = value", "def _set_par(vid, par, value):\n traci.vehicle.setParameter(vid, \"carFollowModel.%s\" % par, str(value))" ]

[ "0.89312124", "0.8022478", "0.7980035", "0.7786411", "0.77297336", "0.75346786", "0.746019", "0.7395771", "0.7331477", "0.7268502", "0.7261241", "0.72502863", "0.72311014", "0.72027457", "0.7178884", "0.7141566", "0.70621306", "0.7020081", "0.7006386", "0.69946426", "0.69921887", "0.6981263", "0.69764525", "0.69764125", "0.6957367", "0.6941811", "0.6940353", "0.6910571", "0.6903306", "0.68879795" ]

[ "def putqconk(self,k_,qcsubi_,qcsubj_,qcval_):\n numqcnz_ = None\n if numqcnz_ is None:\n numqcnz_ = len(qcsubi_)\n elif numqcnz_ != len(qcsubi_):\n raise IndexError(\"Inconsistent length of array qcsubi\")\n if numqcnz_ is None:\n numqcnz_ = len(qcsubj_)\n elif numqcnz_ != len(qcsubj_):\n raise IndexError(\"Inconsistent length of array qcsubj\")\n if numqcnz_ is None:\n numqcnz_ = len(qcval_)\n elif numqcnz_ != len(qcval_):\n raise IndexError(\"Inconsistent length of array qcval\")\n if qcsubi_ is None:\n raise ValueError(\"Argument qcsubi cannot be None\")\n if qcsubi_ is None:\n raise ValueError(\"Argument qcsubi may not be None\")\n if isinstance(qcsubi_, numpy.ndarray) and qcsubi_.dtype is numpy.dtype(numpy.int32) and qcsubi_.flags.contiguous:\n _qcsubi_copyarray = False\n _qcsubi_tmp = ctypes.cast(qcsubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubi_ is not None:\n _qcsubi_copyarray = True\n _qcsubi_np_tmp = numpy.zeros(len(qcsubi_),numpy.dtype(numpy.int32))\n _qcsubi_np_tmp[:] = qcsubi_\n assert _qcsubi_np_tmp.flags.contiguous\n _qcsubi_tmp = ctypes.cast(_qcsubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubi_copyarray = False\n _qcsubi_tmp = None\n \n if qcsubj_ is None:\n raise ValueError(\"Argument qcsubj cannot be None\")\n if qcsubj_ is None:\n raise ValueError(\"Argument qcsubj may not be None\")\n if isinstance(qcsubj_, numpy.ndarray) and qcsubj_.dtype is numpy.dtype(numpy.int32) and qcsubj_.flags.contiguous:\n _qcsubj_copyarray = False\n _qcsubj_tmp = ctypes.cast(qcsubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubj_ is not None:\n _qcsubj_copyarray = True\n _qcsubj_np_tmp = numpy.zeros(len(qcsubj_),numpy.dtype(numpy.int32))\n _qcsubj_np_tmp[:] = qcsubj_\n assert _qcsubj_np_tmp.flags.contiguous\n _qcsubj_tmp = ctypes.cast(_qcsubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubj_copyarray = False\n _qcsubj_tmp = None\n \n if qcval_ is None:\n raise ValueError(\"Argument qcval cannot be None\")\n if qcval_ is None:\n raise ValueError(\"Argument qcval may not be None\")\n if isinstance(qcval_, numpy.ndarray) and qcval_.dtype is numpy.dtype(numpy.float64) and qcval_.flags.contiguous:\n _qcval_copyarray = False\n _qcval_tmp = ctypes.cast(qcval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif qcval_ is not None:\n _qcval_copyarray = True\n _qcval_np_tmp = numpy.zeros(len(qcval_),numpy.dtype(numpy.float64))\n _qcval_np_tmp[:] = qcval_\n assert _qcval_np_tmp.flags.contiguous\n _qcval_tmp = ctypes.cast(_qcval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _qcval_copyarray = False\n _qcval_tmp = None\n \n res = __library__.MSK_XX_putqconk(self.__nativep,k_,numqcnz_,_qcsubi_tmp,_qcsubj_tmp,_qcval_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putqcon(self,qcsubk,qcsubi,qcsubj,qcval): # 3\n numqcnz_ = None\n if numqcnz_ is None:\n numqcnz_ = len(qcsubi)\n elif numqcnz_ != len(qcsubi):\n raise IndexError(\"Inconsistent length of array qcsubi\")\n if numqcnz_ is None:\n numqcnz_ = len(qcsubj)\n elif numqcnz_ != len(qcsubj):\n raise IndexError(\"Inconsistent length of array qcsubj\")\n if numqcnz_ is None:\n numqcnz_ = len(qcval)\n elif numqcnz_ != len(qcval):\n raise IndexError(\"Inconsistent length of array qcval\")\n if numqcnz_ is None: numqcnz_ = 0\n if qcsubk is None: raise TypeError(\"Invalid type for argument qcsubk\")\n if qcsubk is None:\n qcsubk_ = None\n else:\n try:\n qcsubk_ = memoryview(qcsubk)\n except TypeError:\n try:\n _tmparr_qcsubk = array.array(\"i\",qcsubk)\n except TypeError:\n raise TypeError(\"Argument qcsubk has wrong type\")\n else:\n qcsubk_ = memoryview(_tmparr_qcsubk)\n \n else:\n if qcsubk_.format != \"i\":\n qcsubk_ = memoryview(array.array(\"i\",qcsubk))\n \n if qcsubi is None: raise TypeError(\"Invalid type for argument qcsubi\")\n if qcsubi is None:\n qcsubi_ = None\n else:\n try:\n qcsubi_ = memoryview(qcsubi)\n except TypeError:\n try:\n _tmparr_qcsubi = array.array(\"i\",qcsubi)\n except TypeError:\n raise TypeError(\"Argument qcsubi has wrong type\")\n else:\n qcsubi_ = memoryview(_tmparr_qcsubi)\n \n else:\n if qcsubi_.format != \"i\":\n qcsubi_ = memoryview(array.array(\"i\",qcsubi))\n \n if qcsubj is None: raise TypeError(\"Invalid type for argument qcsubj\")\n if qcsubj is None:\n qcsubj_ = None\n else:\n try:\n qcsubj_ = memoryview(qcsubj)\n except TypeError:\n try:\n _tmparr_qcsubj = array.array(\"i\",qcsubj)\n except TypeError:\n raise TypeError(\"Argument qcsubj has wrong type\")\n else:\n qcsubj_ = memoryview(_tmparr_qcsubj)\n \n else:\n if qcsubj_.format != \"i\":\n qcsubj_ = memoryview(array.array(\"i\",qcsubj))\n \n if qcval is None: raise TypeError(\"Invalid type for argument qcval\")\n if qcval is None:\n qcval_ = None\n else:\n try:\n qcval_ = memoryview(qcval)\n except TypeError:\n try:\n _tmparr_qcval = array.array(\"d\",qcval)\n except TypeError:\n raise TypeError(\"Argument qcval has wrong type\")\n else:\n qcval_ = memoryview(_tmparr_qcval)\n \n else:\n if qcval_.format != \"d\":\n qcval_ = memoryview(array.array(\"d\",qcval))\n \n res = self.__obj.putqcon(numqcnz_,qcsubk_,qcsubi_,qcsubj_,qcval_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putqconk(self,k_,qcsubi,qcsubj,qcval): # 3\n numqcnz_ = None\n if numqcnz_ is None:\n numqcnz_ = len(qcsubi)\n elif numqcnz_ != len(qcsubi):\n raise IndexError(\"Inconsistent length of array qcsubi\")\n if numqcnz_ is None:\n numqcnz_ = len(qcsubj)\n elif numqcnz_ != len(qcsubj):\n raise IndexError(\"Inconsistent length of array qcsubj\")\n if numqcnz_ is None:\n numqcnz_ = len(qcval)\n elif numqcnz_ != len(qcval):\n raise IndexError(\"Inconsistent length of array qcval\")\n if numqcnz_ is None: numqcnz_ = 0\n if qcsubi is None: raise TypeError(\"Invalid type for argument qcsubi\")\n if qcsubi is None:\n qcsubi_ = None\n else:\n try:\n qcsubi_ = memoryview(qcsubi)\n except TypeError:\n try:\n _tmparr_qcsubi = array.array(\"i\",qcsubi)\n except TypeError:\n raise TypeError(\"Argument qcsubi has wrong type\")\n else:\n qcsubi_ = memoryview(_tmparr_qcsubi)\n \n else:\n if qcsubi_.format != \"i\":\n qcsubi_ = memoryview(array.array(\"i\",qcsubi))\n \n if qcsubj is None: raise TypeError(\"Invalid type for argument qcsubj\")\n if qcsubj is None:\n qcsubj_ = None\n else:\n try:\n qcsubj_ = memoryview(qcsubj)\n except TypeError:\n try:\n _tmparr_qcsubj = array.array(\"i\",qcsubj)\n except TypeError:\n raise TypeError(\"Argument qcsubj has wrong type\")\n else:\n qcsubj_ = memoryview(_tmparr_qcsubj)\n \n else:\n if qcsubj_.format != \"i\":\n qcsubj_ = memoryview(array.array(\"i\",qcsubj))\n \n if qcval is None: raise TypeError(\"Invalid type for argument qcval\")\n if qcval is None:\n qcval_ = None\n else:\n try:\n qcval_ = memoryview(qcval)\n except TypeError:\n try:\n _tmparr_qcval = array.array(\"d\",qcval)\n except TypeError:\n raise TypeError(\"Argument qcval has wrong type\")\n else:\n qcval_ = memoryview(_tmparr_qcval)\n \n else:\n if qcval_.format != \"d\":\n qcval_ = memoryview(array.array(\"d\",qcval))\n \n res = self.__obj.putqconk(k_,numqcnz_,qcsubi_,qcsubj_,qcval_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def getqconk(self,k_,qcsubi_,qcsubj_,qcval_):\n maxnumqcnz_ = self.getnumqconknz((k_))\n numqcnz_ = ctypes.c_int64()\n _qcsubi_minlength = self.getnumqconknz((k_))\n if self.getnumqconknz((k_)) > 0 and qcsubi_ is not None and len(qcsubi_) != self.getnumqconknz((k_)):\n raise ValueError(\"Array argument qcsubi is not long enough: Is %d, expected %d\" % (len(qcsubi_),self.getnumqconknz((k_))))\n if isinstance(qcsubi_,numpy.ndarray) and not qcsubi_.flags.writeable:\n raise ValueError(\"Argument qcsubi must be writable\")\n if qcsubi_ is None:\n raise ValueError(\"Argument qcsubi may not be None\")\n if isinstance(qcsubi_, numpy.ndarray) and qcsubi_.dtype is numpy.dtype(numpy.int32) and qcsubi_.flags.contiguous:\n _qcsubi_copyarray = False\n _qcsubi_tmp = ctypes.cast(qcsubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubi_ is not None:\n _qcsubi_copyarray = True\n _qcsubi_np_tmp = numpy.zeros(len(qcsubi_),numpy.dtype(numpy.int32))\n _qcsubi_np_tmp[:] = qcsubi_\n assert _qcsubi_np_tmp.flags.contiguous\n _qcsubi_tmp = ctypes.cast(_qcsubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubi_copyarray = False\n _qcsubi_tmp = None\n \n _qcsubj_minlength = self.getnumqconknz((k_))\n if self.getnumqconknz((k_)) > 0 and qcsubj_ is not None and len(qcsubj_) != self.getnumqconknz((k_)):\n raise ValueError(\"Array argument qcsubj is not long enough: Is %d, expected %d\" % (len(qcsubj_),self.getnumqconknz((k_))))\n if isinstance(qcsubj_,numpy.ndarray) and not qcsubj_.flags.writeable:\n raise ValueError(\"Argument qcsubj must be writable\")\n if qcsubj_ is None:\n raise ValueError(\"Argument qcsubj may not be None\")\n if isinstance(qcsubj_, numpy.ndarray) and qcsubj_.dtype is numpy.dtype(numpy.int32) and qcsubj_.flags.contiguous:\n _qcsubj_copyarray = False\n _qcsubj_tmp = ctypes.cast(qcsubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubj_ is not None:\n _qcsubj_copyarray = True\n _qcsubj_np_tmp = numpy.zeros(len(qcsubj_),numpy.dtype(numpy.int32))\n _qcsubj_np_tmp[:] = qcsubj_\n assert _qcsubj_np_tmp.flags.contiguous\n _qcsubj_tmp = ctypes.cast(_qcsubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubj_copyarray = False\n _qcsubj_tmp = None\n \n _qcval_minlength = self.getnumqconknz((k_))\n if self.getnumqconknz((k_)) > 0 and qcval_ is not None and len(qcval_) != self.getnumqconknz((k_)):\n raise ValueError(\"Array argument qcval is not long enough: Is %d, expected %d\" % (len(qcval_),self.getnumqconknz((k_))))\n if isinstance(qcval_,numpy.ndarray) and not qcval_.flags.writeable:\n raise ValueError(\"Argument qcval must be writable\")\n if qcval_ is None:\n raise ValueError(\"Argument qcval may not be None\")\n if isinstance(qcval_, numpy.ndarray) and qcval_.dtype is numpy.dtype(numpy.float64) and qcval_.flags.contiguous:\n _qcval_copyarray = False\n _qcval_tmp = ctypes.cast(qcval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif qcval_ is not None:\n _qcval_copyarray = True\n _qcval_np_tmp = numpy.zeros(len(qcval_),numpy.dtype(numpy.float64))\n _qcval_np_tmp[:] = qcval_\n assert _qcval_np_tmp.flags.contiguous\n _qcval_tmp = ctypes.cast(_qcval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _qcval_copyarray = False\n _qcval_tmp = None\n \n qcsurp_ = ctypes.c_int64(_qcsubi_minlength)\n res = __library__.MSK_XX_getqconk64(self.__nativep,k_,maxnumqcnz_,ctypes.byref(qcsurp_),ctypes.byref(numqcnz_),_qcsubi_tmp,_qcsubj_tmp,_qcval_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n numqcnz_ = numqcnz_.value\n _numqcnz_return_value = numqcnz_\n if _qcsubi_copyarray:\n qcsubi_[:] = _qcsubi_np_tmp\n if _qcsubj_copyarray:\n qcsubj_[:] = _qcsubj_np_tmp\n if _qcval_copyarray:\n qcval_[:] = _qcval_np_tmp\n return (_numqcnz_return_value)", "def getqconk(self,k_,qcsubi,qcsubj,qcval): # 3\n maxnumqcnz_ = self.getnumqconknz((k_))\n if qcsubi is None: raise TypeError(\"Invalid type for argument qcsubi\")\n _copyback_qcsubi = False\n if qcsubi is None:\n qcsubi_ = None\n else:\n try:\n qcsubi_ = memoryview(qcsubi)\n except TypeError:\n try:\n _tmparr_qcsubi = array.array(\"i\",qcsubi)\n except TypeError:\n raise TypeError(\"Argument qcsubi has wrong type\")\n else:\n qcsubi_ = memoryview(_tmparr_qcsubi)\n _copyback_qcsubi = True\n else:\n if qcsubi_.format != \"i\":\n qcsubi_ = memoryview(array.array(\"i\",qcsubi))\n _copyback_qcsubi = True\n if qcsubi_ is not None and len(qcsubi_) != self.getnumqconknz((k_)):\n raise ValueError(\"Array argument qcsubi has wrong length\")\n if qcsubj is None: raise TypeError(\"Invalid type for argument qcsubj\")\n _copyback_qcsubj = False\n if qcsubj is None:\n qcsubj_ = None\n else:\n try:\n qcsubj_ = memoryview(qcsubj)\n except TypeError:\n try:\n _tmparr_qcsubj = array.array(\"i\",qcsubj)\n except TypeError:\n raise TypeError(\"Argument qcsubj has wrong type\")\n else:\n qcsubj_ = memoryview(_tmparr_qcsubj)\n _copyback_qcsubj = True\n else:\n if qcsubj_.format != \"i\":\n qcsubj_ = memoryview(array.array(\"i\",qcsubj))\n _copyback_qcsubj = True\n if qcsubj_ is not None and len(qcsubj_) != self.getnumqconknz((k_)):\n raise ValueError(\"Array argument qcsubj has wrong length\")\n if qcval is None: raise TypeError(\"Invalid type for argument qcval\")\n _copyback_qcval = False\n if qcval is None:\n qcval_ = None\n else:\n try:\n qcval_ = memoryview(qcval)\n except TypeError:\n try:\n _tmparr_qcval = array.array(\"d\",qcval)\n except TypeError:\n raise TypeError(\"Argument qcval has wrong type\")\n else:\n qcval_ = memoryview(_tmparr_qcval)\n _copyback_qcval = True\n else:\n if qcval_.format != \"d\":\n qcval_ = memoryview(array.array(\"d\",qcval))\n _copyback_qcval = True\n if qcval_ is not None and len(qcval_) != self.getnumqconknz((k_)):\n raise ValueError(\"Array argument qcval has wrong length\")\n res,resargs = self.__obj.getqconk64(k_,maxnumqcnz_,len(qcsubi),qcsubi_,qcsubj_,qcval_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _numqcnz_return_value = resargs\n if _copyback_qcval:\n qcval[:] = _tmparr_qcval\n if _copyback_qcsubj:\n qcsubj[:] = _tmparr_qcsubj\n if _copyback_qcsubi:\n qcsubi[:] = _tmparr_qcsubi\n return _numqcnz_return_value", "def qmincon(self, q=None):\n\n def sumsqr(A):\n return np.sum(A**2)\n\n def cost(x, ub, lb, qm, N):\n return sumsqr(\n (2 * (N @ x + qm) - ub - lb) / (ub - lb))\n\n q = getmatrix(q, (None, self.n))\n\n qstar = np.zeros((q.shape[0], self.n))\n error = np.zeros(q.shape[0])\n success = np.zeros(q.shape[0])\n\n lb = self.qlim[0, :]\n ub = self.qlim[1, :]\n\n for k, qk in enumerate(q):\n\n J = self.jacobe(qk)\n\n N = null(J)\n\n x0 = np.zeros(N.shape[1])\n A = np.r_[N, -N]\n b = np.r_[ub - qk, qk - lb].reshape(A.shape[0],)\n\n con = LinearConstraint(A, -np.inf, b)\n\n res = minimize(\n lambda x: cost(x, ub, lb, qk, N),\n x0, constraints=con)\n\n qstar[k, :] = qk + N @ res.x\n error[k] = res.fun\n success[k] = res.success\n\n if q.shape[0] == 1:\n return qstar[0, :], success[0], error[0]\n else:\n return qstar, success, error", "def add_cp_qe_RBM_terms(K, cons_pot_mesh, quad_geo_mesh):\n num_faces = cons_pot_mesh.get_faces().shape[0]\n x_c = quad_geo_mesh.get_centroid()\n w = quad_geo_mesh.get_w()\n A_m = quad_geo_mesh.get_A_m()\n S_D = quad_geo_mesh.get_surface_area()\n\n for face_num in range(num_faces):\n face_nodes = quad_geo_mesh.get_tri_nodes(face_num)\n face_hs = quad_geo_mesh.get_hs(face_num)\n def v_quad(_xi, _eta, _nodes):\n return np.identity(3)\n v_sub_mat = (1. / S_D) * gq.int_over_tri_quad(v_quad, face_nodes, face_hs)\n def omega_quad(xi, eta, nodes):\n pos = geo.quadratic_interp(xi, eta, nodes)\n X = pos - x_c\n return np.einsum(\"lrs,s->lr\", geo.LC_3, X)\n tmp_omega = gq.int_over_tri_quad(\n omega_quad,\n face_nodes,\n face_hs,\n )\n tmp_arr = []\n for m in range(3):\n tmp_arr.append((1./ A_m[m]) * np.outer(w[m], np.einsum(\"l,ls\", w[m], tmp_omega)))\n tmp_arr = np.array(tmp_arr)\n tmp_omega_mat = np.sum(tmp_arr, axis=0)\n for src_num in range(num_faces):\n K[(3 * src_num):(3 * src_num + 3),\n (3 * face_num):(3 * face_num + 3)] += v_sub_mat\n src_center = cons_pot_mesh.get_node(src_num)\n X_0 = src_center - x_c\n omega_mat = np.einsum(\"ijk,js,k->is\", geo.LC_3, tmp_omega_mat, X_0)\n K[(3 * src_num):(3 * src_num + 3),\n (3 * face_num):(3 * face_num + 3)] += omega_mat", "def putconboundlistconst(self,sub_,bkc_,blc_,buc_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n res = __library__.MSK_XX_putconboundlistconst(self.__nativep,num_,_sub_tmp,bkc_,blc_,buc_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def custom_constr(x, qr, inverse, depth):\n qc = QuantumCircuit(qr)\n maxi, mini = max(x), min(x)\n n = x.shape[0]\n #qc_wv = Wavelets(n).construct_circuit(register=qr)\n for _ in range(depth):\n qc.h(qr)\n for i in range(n):\n qc.u2(np.pi*(x[(i+1) % n]-mini)/(maxi-mini), 2*np.pi*(x[i]-mini)/(maxi-mini), qr[i])\n for i in range(n):\n qc.cx(qr[i], qr[(i + 1) % n])\n qc.u2(np.pi*(x[(i+1) % n]-mini)/(maxi-mini),\n ((2*np.pi)**2*(x[i]-mini)*(x[(i+1) % n]-mini)/(maxi-mini)**2) % 2*np.pi,\n qr[(i + 1) % n])\n qc.cx(qr[i], qr[(i + 1) % n])\n #qc = qc + qc_wv\n if inverse:\n return qc.inverse()\n return qc", "def putconsolutioni(self,i_,whichsol_,sk_,x_,sl_,su_):\n res = __library__.MSK_XX_putconsolutioni(self.__nativep,i_,whichsol_,sk_,x_,sl_,su_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def conj(q):\n q = np.array([q[0]])\n q[0,1]=-q[0,1]\n q[0,2]=-q[0,2]\n q[0,3]=-q[0,3]\n complexconjugate = quatreal(q)\n return complexconjugate", "def ikcon(self, T, q0=None):\n\n if not isinstance(T, SE3):\n T = SE3(T)\n\n trajn = len(T)\n\n try:\n if q0 is not None:\n q0 = getvector(q0, self.n, 'row')\n else:\n q0 = np.zeros((trajn, self.n))\n except ValueError:\n verifymatrix(q0, (trajn, self.n))\n\n # create output variables\n qstar = np.zeros((trajn, self.n))\n error = []\n exitflag = []\n\n omega = np.diag([1, 1, 1, 3 / self.reach])\n\n def cost(q, T, omega):\n return np.sum(\n (\n (np.linalg.pinv(T.A) @ self.fkine(q).A - np.eye(4)) @\n omega) ** 2\n )\n\n bnds = Bounds(self.qlim[0, :], self.qlim[1, :])\n\n for i in range(trajn):\n Ti = T[i]\n res = minimize(\n lambda q: cost(q, Ti, omega),\n q0[i, :], bounds=bnds, options={'gtol': 1e-6})\n qstar[i, :] = res.x\n error.append(res.fun)\n exitflag.append(res.success)\n\n if trajn > 1:\n return qstar, exitflag, error\n else:\n return qstar[0, :], exitflag[0], error[0]", "def add_cp_qe_DL_terms(K, cons_pot_mesh, quad_geo_mesh):\n geo_faces = quad_geo_mesh.get_faces()\n pot_faces = cons_pot_mesh.get_faces()\n assert geo_faces.shape[0] == pot_faces.shape[0]\n num_faces = geo_faces.shape[0]\n c_0 = 1. / (4. * np.pi)\n for face_num in range(num_faces): # field points\n face_nodes = quad_geo_mesh.get_tri_nodes(face_num)\n face_n = quad_geo_mesh.get_quad_n(face_num)\n face_hs = quad_geo_mesh.get_hs(face_num)\n for src_num in range(num_faces): # source points\n src_center = cons_pot_mesh.get_node(src_num)\n if face_num != src_num:\n sub_mat = gq.int_over_tri_quad_n(\n make_cp_qe_quad_func(src_center),\n face_nodes,\n face_n,\n face_hs\n )\n K[(3 * src_num):(3 * src_num + 3),\n (3 * face_num):(3 * face_num + 3)] += sub_mat\n K[(3 * src_num):(3 * src_num + 3),\n (3 * src_num):(3 * src_num + 3)] -= sub_mat\n # do nothing face_num == src_num, how it works out for constant elements\n\n for src_num in range(num_faces):\n K[(3 * src_num):(3 * src_num + 3),\n (3 * src_num):(3 * src_num + 3)] -= 4. * np.pi * np.identity(3)\n K *= c_0", "def do_reduction_placzek_corrections(q,sqfg,bgd,rescale_bgd=1.0,plaz_type=None,\n gauss_damp=False,gw=20.0,qmax=None,qmin=None,\n rmin=0.0,rmax=20.0,delr=.02\n ,qminpla=10.0,qmaxpla=30.0,ndeg=2, return_correction = False,\n skip_bgd = False, return_final_sq = False, force_qmax_type='Off'):\n #first, make netsq if bgd and/or damping is present\n q = np.array(q)\n sqfg = np.array(sqfg)\n bgd = np.array(bgd)\n\n if skip_bgd:\n netsq = sqfg\n else:\n netsq = sqfg - bgd*rescale_bgd\n\n\n if gauss_damp:\n netsq = netsq*gauss(q,gw,0)\n\n\n if force_qmax_type == 'Force Data (PreCorrection)':\n qcut, sqcut = cut_data(q,netsq,qmax-.5,qmax)\n mean_sqmax = np.mean(sqcut)\n netsq -= mean_sqmax\n\n #now, apply a correction if requested\n if plaz_type != None:\n if plaz_type == 'Polynomial' or plaz_type == 'poly' or plaz_type == 'ndeg':\n sq_poly_fit = fit_ndeg_to_sq(q,netsq,ndeg=ndeg,qmin=qminpla,qmax=qmaxpla)\n this_fit = sq_poly_fit\n elif plaz_type == 'Pseudo-Voight' or plaz_type == 'pv' or plaz_type == 'hydro':\n pv_fit = fit_pv_to_sq(q,netsq,qmin=qminpla,qmax=qmaxpla)\n this_fit = pv_fit\n elif plaz_type == 'PVoight + n0' or plaz_type == 'pvndeg0':\n pv_n0_fit = fit_pv_n0_to_sq(q,netsq,qmin=qminpla,qmax=qmaxpla)\n this_fit = pv_n0_fit\n elif plaz_type == 'PVoight + n1' or plaz_type == 'pvndeg1':\n pv_n1_fit = fit_pv_n1_to_sq(q,netsq,qmin=qminpla,qmax=qmaxpla)\n this_fit = pv_n1_fit\n elif plaz_type == 'PVoight + n2' or plaz_type == 'pvndeg2':\n pv_n2_fit = fit_pv_n2_to_sq(q,netsq,qmin=qminpla,qmax=qmaxpla)\n this_fit = pv_n2_fit\n else:\n print (\"I don't know that correction type, sorry\")\n this_fit = np.zeros(len(q))\n else:\n this_fit = np.zeros(len(q))\n\n if force_qmax_type == 'Force Data' or force_qmax_type == 'Force Both (Independent)':\n qcut, sqcut = cut_data(q,netsq,qmax-.5,qmax)\n mean_sqmax = np.mean(sqcut)\n netsq -= mean_sqmax\n if force_qmax_type == 'Force Correction' or force_qmax_type == 'Force Both (Independent)':\n qcut, sqcut = cut_data(q,this_fit,qmax-.5,qmax)\n mean_sqmax = np.mean(sqcut)\n this_fit -= mean_sqmax\n if force_qmax_type == 'ReCorrection':\n qcut, sqcut = cut_data(q,netsq-this_fit,qmax-.5,qmax)\n mean_sqmax = np.mean(sqcut)\n this_fit += mean_sqmax\n\n netsq = netsq - this_fit\n\n if return_correction:\n return this_fit\n\n if return_final_sq:\n return netsq\n\n #finally, generate PDF\n r,gr = make_gr_from_sq(q,netsq,qmin=qmin,qmax=qmax,rmin=rmin,rmax=rmax,delr=delr)\n\n return r,gr", "def putcone(self,k_,ct_,conepar_,submem_):\n nummem_ = None\n if nummem_ is None:\n nummem_ = len(submem_)\n elif nummem_ != len(submem_):\n raise IndexError(\"Inconsistent length of array submem\")\n if submem_ is None:\n raise ValueError(\"Argument submem cannot be None\")\n if submem_ is None:\n raise ValueError(\"Argument submem may not be None\")\n if isinstance(submem_, numpy.ndarray) and submem_.dtype is numpy.dtype(numpy.int32) and submem_.flags.contiguous:\n _submem_copyarray = False\n _submem_tmp = ctypes.cast(submem_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif submem_ is not None:\n _submem_copyarray = True\n _submem_np_tmp = numpy.zeros(len(submem_),numpy.dtype(numpy.int32))\n _submem_np_tmp[:] = submem_\n assert _submem_np_tmp.flags.contiguous\n _submem_tmp = ctypes.cast(_submem_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _submem_copyarray = False\n _submem_tmp = None\n \n res = __library__.MSK_XX_putcone(self.__nativep,k_,ct_,conepar_,nummem_,_submem_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putcone(self,k_,ct_,conepar_,submem): # 3\n if not isinstance(ct_,conetype): raise TypeError(\"Argument ct has wrong type\")\n nummem_ = None\n if nummem_ is None:\n nummem_ = len(submem)\n elif nummem_ != len(submem):\n raise IndexError(\"Inconsistent length of array submem\")\n if nummem_ is None: nummem_ = 0\n if submem is None: raise TypeError(\"Invalid type for argument submem\")\n if submem is None:\n submem_ = None\n else:\n try:\n submem_ = memoryview(submem)\n except TypeError:\n try:\n _tmparr_submem = array.array(\"i\",submem)\n except TypeError:\n raise TypeError(\"Argument submem has wrong type\")\n else:\n submem_ = memoryview(_tmparr_submem)\n \n else:\n if submem_.format != \"i\":\n submem_ = memoryview(array.array(\"i\",submem))\n \n res = self.__obj.putcone(k_,ct_,conepar_,nummem_,submem_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putconbound(self,i_,bkc_,blc_,buc_):\n res = __library__.MSK_XX_putconbound(self.__nativep,i_,bkc_,blc_,buc_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def _ect_qrs_tconst(pattern, qrs):\n beats = pattern.evidence[o.QRS]\n idx = beats.index(qrs)\n tnet = pattern.last_tnet\n hyp = pattern.hypothesis\n if idx > 0:\n prev = beats[idx - 1]\n # After the second couplet, every ectopic beat introduces a new temporal\n # network in the pattern to make it easier the minimization.\n if idx > 3:\n tnet.remove_constraint(hyp.end, prev.time)\n # We create a new temporal network for the cyclic observations\n tnet = ConstraintNetwork()\n pattern.temporal_constraints.append(tnet)\n # The duration of each couplet should not have high instantaneous\n # variations.\n refrr = beats[idx - 2].time.end - beats[idx - 3].time.start\n tnet.add_constraint(prev.time, qrs.time, Iv(refrr - C.RR_MAX_DIFF, refrr + C.RR_MAX_DIFF))\n # We guide the morphology search to be similar to the previous\n # ectopic QRS complex.\n qrs.shape = beats[idx - 2].shape\n # The reference RR varies from an upper limit to the last measurement,\n # through the contextual previous rhythm.\n refrr = C.BRADY_RR.end\n stdrr = 0.1 * refrr\n if pattern.evidence[o.Cardiac_Rhythm] and idx == 1:\n mrr, srr = pattern.evidence[o.Cardiac_Rhythm][0].meas.rr\n if mrr > 0:\n refrr, stdrr = mrr, srr\n elif idx > 1:\n refrr, stdrr = hyp.meas.rr\n # Ectopic beats must be advanced wrt the reference RR\n tnet.add_constraint(prev.time, qrs.time, Iv(C.TACHY_RR.start, max(C.TACHY_RR.start, refrr - stdrr)))\n # Beats cannot overlap\n tnet.add_constraint(prev.end, qrs.start, Iv(C.TQ_INTERVAL_MIN, np.Inf))\n BASIC_TCONST(pattern, qrs)\n tnet.add_constraint(qrs.start, qrs.end, C.QRS_DUR)\n tnet.set_before(qrs.time, hyp.end)\n # Constraints with the precedent T Wave\n _qrs_after_twave(pattern, qrs)", "def vahlen_conj(q_1: Q, conj_type: str = \"-\", q_type: str = \"vc\") -> Q:\n\n vc_t, vc_x, vc_y, vc_z = q_1.t, q_1.x, q_1.y, q_1.z\n c_q = Q()\n\n if conj_type == \"-\":\n c_q.t = vc_t\n if vc_x != 0:\n c_q.x = -1 * vc_x\n if vc_y != 0:\n c_q.y = -1 * vc_y\n if vc_z != 0:\n c_q.z = -1 * vc_z\n q_type += \"*-\"\n\n if conj_type == \"'\":\n c_q.t = vc_t\n if vc_x != 0:\n c_q.x = -1 * vc_x\n if vc_y != 0:\n c_q.y = -1 * vc_y\n c_q.z = vc_z\n q_type += \"*'\"\n\n if conj_type == \"*\":\n c_q.t = vc_t\n c_q.x = vc_x\n c_q.y = vc_y\n if vc_z != 0:\n c_q.z = -1 * vc_z\n q_type += \"*\"\n\n c_q.q_type = f\"{q_1.q_type}{q_type}\"\n c_q.representation = q_1.representation\n\n return c_q", "def mk_q(self, xc: list, yc: list):\n for i in range(len(xc) - 1):\n cur = cor(xc[i], xc[i + 1])\n self.xq.put((-cur.dist, cur))\n cur = cor(yc[i], yc[i + 1])\n self.yq.put((-cur.dist, cur))\n self.rnd_mk()", "def putbarcblocktriplet(self,num_,subj_,subk_,subl_,valjkl_):\n _subj_minlength = (num_)\n if (num_) > 0 and subj_ is not None and len(subj_) != (num_):\n raise ValueError(\"Array argument subj is not long enough: Is %d, expected %d\" % (len(subj_),(num_)))\n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n _subk_minlength = (num_)\n if (num_) > 0 and subk_ is not None and len(subk_) != (num_):\n raise ValueError(\"Array argument subk is not long enough: Is %d, expected %d\" % (len(subk_),(num_)))\n if subk_ is None:\n raise ValueError(\"Argument subk cannot be None\")\n if subk_ is None:\n raise ValueError(\"Argument subk may not be None\")\n if isinstance(subk_, numpy.ndarray) and subk_.dtype is numpy.dtype(numpy.int32) and subk_.flags.contiguous:\n _subk_copyarray = False\n _subk_tmp = ctypes.cast(subk_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subk_ is not None:\n _subk_copyarray = True\n _subk_np_tmp = numpy.zeros(len(subk_),numpy.dtype(numpy.int32))\n _subk_np_tmp[:] = subk_\n assert _subk_np_tmp.flags.contiguous\n _subk_tmp = ctypes.cast(_subk_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subk_copyarray = False\n _subk_tmp = None\n \n _subl_minlength = (num_)\n if (num_) > 0 and subl_ is not None and len(subl_) != (num_):\n raise ValueError(\"Array argument subl is not long enough: Is %d, expected %d\" % (len(subl_),(num_)))\n if subl_ is None:\n raise ValueError(\"Argument subl cannot be None\")\n if subl_ is None:\n raise ValueError(\"Argument subl may not be None\")\n if isinstance(subl_, numpy.ndarray) and subl_.dtype is numpy.dtype(numpy.int32) and subl_.flags.contiguous:\n _subl_copyarray = False\n _subl_tmp = ctypes.cast(subl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subl_ is not None:\n _subl_copyarray = True\n _subl_np_tmp = numpy.zeros(len(subl_),numpy.dtype(numpy.int32))\n _subl_np_tmp[:] = subl_\n assert _subl_np_tmp.flags.contiguous\n _subl_tmp = ctypes.cast(_subl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subl_copyarray = False\n _subl_tmp = None\n \n _valjkl_minlength = (num_)\n if (num_) > 0 and valjkl_ is not None and len(valjkl_) != (num_):\n raise ValueError(\"Array argument valjkl is not long enough: Is %d, expected %d\" % (len(valjkl_),(num_)))\n if valjkl_ is None:\n raise ValueError(\"Argument valjkl cannot be None\")\n if valjkl_ is None:\n raise ValueError(\"Argument valjkl may not be None\")\n if isinstance(valjkl_, numpy.ndarray) and valjkl_.dtype is numpy.dtype(numpy.float64) and valjkl_.flags.contiguous:\n _valjkl_copyarray = False\n _valjkl_tmp = ctypes.cast(valjkl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif valjkl_ is not None:\n _valjkl_copyarray = True\n _valjkl_np_tmp = numpy.zeros(len(valjkl_),numpy.dtype(numpy.float64))\n _valjkl_np_tmp[:] = valjkl_\n assert _valjkl_np_tmp.flags.contiguous\n _valjkl_tmp = ctypes.cast(_valjkl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _valjkl_copyarray = False\n _valjkl_tmp = None\n \n res = __library__.MSK_XX_putbarcblocktriplet(self.__nativep,num_,_subj_tmp,_subk_tmp,_subl_tmp,_valjkl_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def set_equality_constraint_multipliers(self, eq_con_multiplier_values):\n # we should check these for efficiency\n assert self.n_equality_constraints() == len(eq_con_multiplier_values)\n if (\n not hasattr(self, 'evaluate_hessian_equality_constraints')\n or self.n_equality_constraints() == 0\n ):\n return\n\n raise NotImplementedError(\n 'Derived ExternalGreyBoxModel classes need to implement'\n ' set_equality_constraint_multipliers when they'\n ' support Hessian computations.'\n )", "def Q(self, k, x):\n g = np.asarray(self.g(k, x))\n Q = g @ g.T\n return Q", "def update_q(self):\n beta = self.EC_beta\n self.gamma_q = (self.gamma_s - self.gamma_r) * beta + (1 - beta) * self.gamma_q\n self.Sigma_q = (self.Sigma_s - self.Sigma_r) * beta + (1 - beta) * self.Sigma_q\n try:\n assert np.all(np.logical_not(np.isnan(self.gamma_q)))\n except:\n print(\"Invalid update encountered...\")", "def SetPRCatConstraint(self, model ) :\n tot = np.multiply(self.wish, self.dispo)\n for line in tot :\n for val in line :\n if not val : continue\n if self.bound>0 : model += val <= self.valBound\n elif self.bound<0 : model += val >= self.valBound", "def putqobj(self,qosubi_,qosubj_,qoval_):\n numqonz_ = None\n if numqonz_ is None:\n numqonz_ = len(qosubi_)\n elif numqonz_ != len(qosubi_):\n raise IndexError(\"Inconsistent length of array qosubi\")\n if numqonz_ is None:\n numqonz_ = len(qosubj_)\n elif numqonz_ != len(qosubj_):\n raise IndexError(\"Inconsistent length of array qosubj\")\n if numqonz_ is None:\n numqonz_ = len(qoval_)\n elif numqonz_ != len(qoval_):\n raise IndexError(\"Inconsistent length of array qoval\")\n if qosubi_ is None:\n raise ValueError(\"Argument qosubi cannot be None\")\n if qosubi_ is None:\n raise ValueError(\"Argument qosubi may not be None\")\n if isinstance(qosubi_, numpy.ndarray) and qosubi_.dtype is numpy.dtype(numpy.int32) and qosubi_.flags.contiguous:\n _qosubi_copyarray = False\n _qosubi_tmp = ctypes.cast(qosubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qosubi_ is not None:\n _qosubi_copyarray = True\n _qosubi_np_tmp = numpy.zeros(len(qosubi_),numpy.dtype(numpy.int32))\n _qosubi_np_tmp[:] = qosubi_\n assert _qosubi_np_tmp.flags.contiguous\n _qosubi_tmp = ctypes.cast(_qosubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qosubi_copyarray = False\n _qosubi_tmp = None\n \n if qosubj_ is None:\n raise ValueError(\"Argument qosubj cannot be None\")\n if qosubj_ is None:\n raise ValueError(\"Argument qosubj may not be None\")\n if isinstance(qosubj_, numpy.ndarray) and qosubj_.dtype is numpy.dtype(numpy.int32) and qosubj_.flags.contiguous:\n _qosubj_copyarray = False\n _qosubj_tmp = ctypes.cast(qosubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qosubj_ is not None:\n _qosubj_copyarray = True\n _qosubj_np_tmp = numpy.zeros(len(qosubj_),numpy.dtype(numpy.int32))\n _qosubj_np_tmp[:] = qosubj_\n assert _qosubj_np_tmp.flags.contiguous\n _qosubj_tmp = ctypes.cast(_qosubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qosubj_copyarray = False\n _qosubj_tmp = None\n \n if qoval_ is None:\n raise ValueError(\"Argument qoval cannot be None\")\n if qoval_ is None:\n raise ValueError(\"Argument qoval may not be None\")\n if isinstance(qoval_, numpy.ndarray) and qoval_.dtype is numpy.dtype(numpy.float64) and qoval_.flags.contiguous:\n _qoval_copyarray = False\n _qoval_tmp = ctypes.cast(qoval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif qoval_ is not None:\n _qoval_copyarray = True\n _qoval_np_tmp = numpy.zeros(len(qoval_),numpy.dtype(numpy.float64))\n _qoval_np_tmp[:] = qoval_\n assert _qoval_np_tmp.flags.contiguous\n _qoval_tmp = ctypes.cast(_qoval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _qoval_copyarray = False\n _qoval_tmp = None\n \n res = __library__.MSK_XX_putqobj(self.__nativep,numqonz_,_qosubi_tmp,_qosubj_tmp,_qoval_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def conj_q(q_1: Q, q_2: Q) -> Q:\n\n _conj = deepcopy(q_1)\n\n if q_2.t:\n _conj = conj(_conj, conj_type=0)\n\n if q_2.x:\n _conj = conj(_conj, conj_type=1)\n\n if q_2.y:\n _conj = conj(_conj, conj_type=2)\n\n if q_2.z:\n _conj = flip_sign(_conj)\n\n return _conj", "def _update_tarsqidoc(self, cp):\n self.tarsqidoc.remove_tlinks()\n for n1, rest in cp.graph.edges.items():\n for n2, edge in cp.graph.edges[n1].items():\n if edge.constraint is not None:\n if edge.constraint.has_simple_relation():\n self._add_constraint_to_tarsqidoc(edge)", "def eqconstr(x, problem):\n x, t_final = matrify(x, problem)\n return np.concatenate([problem['dynamics'](x[:, :, i], t_final, problem) for i in range(problem['Nv'])])", "def quadratic_strain(x, dof):\n base = np.zeros([6, dof])\n base[1, 0] = 1 # initial y-bending\n if dof > 2:\n base[1, 1] = x**2 # quadratic y-bending term\n base[2, dof-1] = x**2 # quadratic z-bending term\n return base" ]

[ "0.7356916", "0.7274804", "0.7158734", "0.66391945", "0.6536458", "0.5380898", "0.5333184", "0.5304116", "0.52284986", "0.52283263", "0.5153612", "0.5121988", "0.50708705", "0.50423145", "0.4997849", "0.49975076", "0.49906054", "0.49511495", "0.4933693", "0.4864164", "0.48633", "0.48333412", "0.48287615", "0.48150796", "0.48011255", "0.47780675", "0.47725552", "0.47551745", "0.47496006", "0.4739018" ]

[ "def putqconk(self,k_,qcsubi,qcsubj,qcval): # 3\n numqcnz_ = None\n if numqcnz_ is None:\n numqcnz_ = len(qcsubi)\n elif numqcnz_ != len(qcsubi):\n raise IndexError(\"Inconsistent length of array qcsubi\")\n if numqcnz_ is None:\n numqcnz_ = len(qcsubj)\n elif numqcnz_ != len(qcsubj):\n raise IndexError(\"Inconsistent length of array qcsubj\")\n if numqcnz_ is None:\n numqcnz_ = len(qcval)\n elif numqcnz_ != len(qcval):\n raise IndexError(\"Inconsistent length of array qcval\")\n if numqcnz_ is None: numqcnz_ = 0\n if qcsubi is None: raise TypeError(\"Invalid type for argument qcsubi\")\n if qcsubi is None:\n qcsubi_ = None\n else:\n try:\n qcsubi_ = memoryview(qcsubi)\n except TypeError:\n try:\n _tmparr_qcsubi = array.array(\"i\",qcsubi)\n except TypeError:\n raise TypeError(\"Argument qcsubi has wrong type\")\n else:\n qcsubi_ = memoryview(_tmparr_qcsubi)\n \n else:\n if qcsubi_.format != \"i\":\n qcsubi_ = memoryview(array.array(\"i\",qcsubi))\n \n if qcsubj is None: raise TypeError(\"Invalid type for argument qcsubj\")\n if qcsubj is None:\n qcsubj_ = None\n else:\n try:\n qcsubj_ = memoryview(qcsubj)\n except TypeError:\n try:\n _tmparr_qcsubj = array.array(\"i\",qcsubj)\n except TypeError:\n raise TypeError(\"Argument qcsubj has wrong type\")\n else:\n qcsubj_ = memoryview(_tmparr_qcsubj)\n \n else:\n if qcsubj_.format != \"i\":\n qcsubj_ = memoryview(array.array(\"i\",qcsubj))\n \n if qcval is None: raise TypeError(\"Invalid type for argument qcval\")\n if qcval is None:\n qcval_ = None\n else:\n try:\n qcval_ = memoryview(qcval)\n except TypeError:\n try:\n _tmparr_qcval = array.array(\"d\",qcval)\n except TypeError:\n raise TypeError(\"Argument qcval has wrong type\")\n else:\n qcval_ = memoryview(_tmparr_qcval)\n \n else:\n if qcval_.format != \"d\":\n qcval_ = memoryview(array.array(\"d\",qcval))\n \n res = self.__obj.putqconk(k_,numqcnz_,qcsubi_,qcsubj_,qcval_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putqcon(self,qcsubk_,qcsubi_,qcsubj_,qcval_):\n numqcnz_ = None\n if numqcnz_ is None:\n numqcnz_ = len(qcsubi_)\n elif numqcnz_ != len(qcsubi_):\n raise IndexError(\"Inconsistent length of array qcsubi\")\n if numqcnz_ is None:\n numqcnz_ = len(qcsubj_)\n elif numqcnz_ != len(qcsubj_):\n raise IndexError(\"Inconsistent length of array qcsubj\")\n if numqcnz_ is None:\n numqcnz_ = len(qcval_)\n elif numqcnz_ != len(qcval_):\n raise IndexError(\"Inconsistent length of array qcval\")\n if qcsubk_ is None:\n raise ValueError(\"Argument qcsubk cannot be None\")\n if qcsubk_ is None:\n raise ValueError(\"Argument qcsubk may not be None\")\n if isinstance(qcsubk_, numpy.ndarray) and qcsubk_.dtype is numpy.dtype(numpy.int32) and qcsubk_.flags.contiguous:\n _qcsubk_copyarray = False\n _qcsubk_tmp = ctypes.cast(qcsubk_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubk_ is not None:\n _qcsubk_copyarray = True\n _qcsubk_np_tmp = numpy.zeros(len(qcsubk_),numpy.dtype(numpy.int32))\n _qcsubk_np_tmp[:] = qcsubk_\n assert _qcsubk_np_tmp.flags.contiguous\n _qcsubk_tmp = ctypes.cast(_qcsubk_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubk_copyarray = False\n _qcsubk_tmp = None\n \n if qcsubi_ is None:\n raise ValueError(\"Argument qcsubi cannot be None\")\n if qcsubi_ is None:\n raise ValueError(\"Argument qcsubi may not be None\")\n if isinstance(qcsubi_, numpy.ndarray) and qcsubi_.dtype is numpy.dtype(numpy.int32) and qcsubi_.flags.contiguous:\n _qcsubi_copyarray = False\n _qcsubi_tmp = ctypes.cast(qcsubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubi_ is not None:\n _qcsubi_copyarray = True\n _qcsubi_np_tmp = numpy.zeros(len(qcsubi_),numpy.dtype(numpy.int32))\n _qcsubi_np_tmp[:] = qcsubi_\n assert _qcsubi_np_tmp.flags.contiguous\n _qcsubi_tmp = ctypes.cast(_qcsubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubi_copyarray = False\n _qcsubi_tmp = None\n \n if qcsubj_ is None:\n raise ValueError(\"Argument qcsubj cannot be None\")\n if qcsubj_ is None:\n raise ValueError(\"Argument qcsubj may not be None\")\n if isinstance(qcsubj_, numpy.ndarray) and qcsubj_.dtype is numpy.dtype(numpy.int32) and qcsubj_.flags.contiguous:\n _qcsubj_copyarray = False\n _qcsubj_tmp = ctypes.cast(qcsubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubj_ is not None:\n _qcsubj_copyarray = True\n _qcsubj_np_tmp = numpy.zeros(len(qcsubj_),numpy.dtype(numpy.int32))\n _qcsubj_np_tmp[:] = qcsubj_\n assert _qcsubj_np_tmp.flags.contiguous\n _qcsubj_tmp = ctypes.cast(_qcsubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubj_copyarray = False\n _qcsubj_tmp = None\n \n if qcval_ is None:\n raise ValueError(\"Argument qcval cannot be None\")\n if qcval_ is None:\n raise ValueError(\"Argument qcval may not be None\")\n if isinstance(qcval_, numpy.ndarray) and qcval_.dtype is numpy.dtype(numpy.float64) and qcval_.flags.contiguous:\n _qcval_copyarray = False\n _qcval_tmp = ctypes.cast(qcval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif qcval_ is not None:\n _qcval_copyarray = True\n _qcval_np_tmp = numpy.zeros(len(qcval_),numpy.dtype(numpy.float64))\n _qcval_np_tmp[:] = qcval_\n assert _qcval_np_tmp.flags.contiguous\n _qcval_tmp = ctypes.cast(_qcval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _qcval_copyarray = False\n _qcval_tmp = None\n \n res = __library__.MSK_XX_putqcon(self.__nativep,numqcnz_,_qcsubk_tmp,_qcsubi_tmp,_qcsubj_tmp,_qcval_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putqcon(self,qcsubk,qcsubi,qcsubj,qcval): # 3\n numqcnz_ = None\n if numqcnz_ is None:\n numqcnz_ = len(qcsubi)\n elif numqcnz_ != len(qcsubi):\n raise IndexError(\"Inconsistent length of array qcsubi\")\n if numqcnz_ is None:\n numqcnz_ = len(qcsubj)\n elif numqcnz_ != len(qcsubj):\n raise IndexError(\"Inconsistent length of array qcsubj\")\n if numqcnz_ is None:\n numqcnz_ = len(qcval)\n elif numqcnz_ != len(qcval):\n raise IndexError(\"Inconsistent length of array qcval\")\n if numqcnz_ is None: numqcnz_ = 0\n if qcsubk is None: raise TypeError(\"Invalid type for argument qcsubk\")\n if qcsubk is None:\n qcsubk_ = None\n else:\n try:\n qcsubk_ = memoryview(qcsubk)\n except TypeError:\n try:\n _tmparr_qcsubk = array.array(\"i\",qcsubk)\n except TypeError:\n raise TypeError(\"Argument qcsubk has wrong type\")\n else:\n qcsubk_ = memoryview(_tmparr_qcsubk)\n \n else:\n if qcsubk_.format != \"i\":\n qcsubk_ = memoryview(array.array(\"i\",qcsubk))\n \n if qcsubi is None: raise TypeError(\"Invalid type for argument qcsubi\")\n if qcsubi is None:\n qcsubi_ = None\n else:\n try:\n qcsubi_ = memoryview(qcsubi)\n except TypeError:\n try:\n _tmparr_qcsubi = array.array(\"i\",qcsubi)\n except TypeError:\n raise TypeError(\"Argument qcsubi has wrong type\")\n else:\n qcsubi_ = memoryview(_tmparr_qcsubi)\n \n else:\n if qcsubi_.format != \"i\":\n qcsubi_ = memoryview(array.array(\"i\",qcsubi))\n \n if qcsubj is None: raise TypeError(\"Invalid type for argument qcsubj\")\n if qcsubj is None:\n qcsubj_ = None\n else:\n try:\n qcsubj_ = memoryview(qcsubj)\n except TypeError:\n try:\n _tmparr_qcsubj = array.array(\"i\",qcsubj)\n except TypeError:\n raise TypeError(\"Argument qcsubj has wrong type\")\n else:\n qcsubj_ = memoryview(_tmparr_qcsubj)\n \n else:\n if qcsubj_.format != \"i\":\n qcsubj_ = memoryview(array.array(\"i\",qcsubj))\n \n if qcval is None: raise TypeError(\"Invalid type for argument qcval\")\n if qcval is None:\n qcval_ = None\n else:\n try:\n qcval_ = memoryview(qcval)\n except TypeError:\n try:\n _tmparr_qcval = array.array(\"d\",qcval)\n except TypeError:\n raise TypeError(\"Argument qcval has wrong type\")\n else:\n qcval_ = memoryview(_tmparr_qcval)\n \n else:\n if qcval_.format != \"d\":\n qcval_ = memoryview(array.array(\"d\",qcval))\n \n res = self.__obj.putqcon(numqcnz_,qcsubk_,qcsubi_,qcsubj_,qcval_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def getqconk(self,k_,qcsubi_,qcsubj_,qcval_):\n maxnumqcnz_ = self.getnumqconknz((k_))\n numqcnz_ = ctypes.c_int64()\n _qcsubi_minlength = self.getnumqconknz((k_))\n if self.getnumqconknz((k_)) > 0 and qcsubi_ is not None and len(qcsubi_) != self.getnumqconknz((k_)):\n raise ValueError(\"Array argument qcsubi is not long enough: Is %d, expected %d\" % (len(qcsubi_),self.getnumqconknz((k_))))\n if isinstance(qcsubi_,numpy.ndarray) and not qcsubi_.flags.writeable:\n raise ValueError(\"Argument qcsubi must be writable\")\n if qcsubi_ is None:\n raise ValueError(\"Argument qcsubi may not be None\")\n if isinstance(qcsubi_, numpy.ndarray) and qcsubi_.dtype is numpy.dtype(numpy.int32) and qcsubi_.flags.contiguous:\n _qcsubi_copyarray = False\n _qcsubi_tmp = ctypes.cast(qcsubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubi_ is not None:\n _qcsubi_copyarray = True\n _qcsubi_np_tmp = numpy.zeros(len(qcsubi_),numpy.dtype(numpy.int32))\n _qcsubi_np_tmp[:] = qcsubi_\n assert _qcsubi_np_tmp.flags.contiguous\n _qcsubi_tmp = ctypes.cast(_qcsubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubi_copyarray = False\n _qcsubi_tmp = None\n \n _qcsubj_minlength = self.getnumqconknz((k_))\n if self.getnumqconknz((k_)) > 0 and qcsubj_ is not None and len(qcsubj_) != self.getnumqconknz((k_)):\n raise ValueError(\"Array argument qcsubj is not long enough: Is %d, expected %d\" % (len(qcsubj_),self.getnumqconknz((k_))))\n if isinstance(qcsubj_,numpy.ndarray) and not qcsubj_.flags.writeable:\n raise ValueError(\"Argument qcsubj must be writable\")\n if qcsubj_ is None:\n raise ValueError(\"Argument qcsubj may not be None\")\n if isinstance(qcsubj_, numpy.ndarray) and qcsubj_.dtype is numpy.dtype(numpy.int32) and qcsubj_.flags.contiguous:\n _qcsubj_copyarray = False\n _qcsubj_tmp = ctypes.cast(qcsubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubj_ is not None:\n _qcsubj_copyarray = True\n _qcsubj_np_tmp = numpy.zeros(len(qcsubj_),numpy.dtype(numpy.int32))\n _qcsubj_np_tmp[:] = qcsubj_\n assert _qcsubj_np_tmp.flags.contiguous\n _qcsubj_tmp = ctypes.cast(_qcsubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubj_copyarray = False\n _qcsubj_tmp = None\n \n _qcval_minlength = self.getnumqconknz((k_))\n if self.getnumqconknz((k_)) > 0 and qcval_ is not None and len(qcval_) != self.getnumqconknz((k_)):\n raise ValueError(\"Array argument qcval is not long enough: Is %d, expected %d\" % (len(qcval_),self.getnumqconknz((k_))))\n if isinstance(qcval_,numpy.ndarray) and not qcval_.flags.writeable:\n raise ValueError(\"Argument qcval must be writable\")\n if qcval_ is None:\n raise ValueError(\"Argument qcval may not be None\")\n if isinstance(qcval_, numpy.ndarray) and qcval_.dtype is numpy.dtype(numpy.float64) and qcval_.flags.contiguous:\n _qcval_copyarray = False\n _qcval_tmp = ctypes.cast(qcval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif qcval_ is not None:\n _qcval_copyarray = True\n _qcval_np_tmp = numpy.zeros(len(qcval_),numpy.dtype(numpy.float64))\n _qcval_np_tmp[:] = qcval_\n assert _qcval_np_tmp.flags.contiguous\n _qcval_tmp = ctypes.cast(_qcval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _qcval_copyarray = False\n _qcval_tmp = None\n \n qcsurp_ = ctypes.c_int64(_qcsubi_minlength)\n res = __library__.MSK_XX_getqconk64(self.__nativep,k_,maxnumqcnz_,ctypes.byref(qcsurp_),ctypes.byref(numqcnz_),_qcsubi_tmp,_qcsubj_tmp,_qcval_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n numqcnz_ = numqcnz_.value\n _numqcnz_return_value = numqcnz_\n if _qcsubi_copyarray:\n qcsubi_[:] = _qcsubi_np_tmp\n if _qcsubj_copyarray:\n qcsubj_[:] = _qcsubj_np_tmp\n if _qcval_copyarray:\n qcval_[:] = _qcval_np_tmp\n return (_numqcnz_return_value)", "def getqconk(self,k_,qcsubi,qcsubj,qcval): # 3\n maxnumqcnz_ = self.getnumqconknz((k_))\n if qcsubi is None: raise TypeError(\"Invalid type for argument qcsubi\")\n _copyback_qcsubi = False\n if qcsubi is None:\n qcsubi_ = None\n else:\n try:\n qcsubi_ = memoryview(qcsubi)\n except TypeError:\n try:\n _tmparr_qcsubi = array.array(\"i\",qcsubi)\n except TypeError:\n raise TypeError(\"Argument qcsubi has wrong type\")\n else:\n qcsubi_ = memoryview(_tmparr_qcsubi)\n _copyback_qcsubi = True\n else:\n if qcsubi_.format != \"i\":\n qcsubi_ = memoryview(array.array(\"i\",qcsubi))\n _copyback_qcsubi = True\n if qcsubi_ is not None and len(qcsubi_) != self.getnumqconknz((k_)):\n raise ValueError(\"Array argument qcsubi has wrong length\")\n if qcsubj is None: raise TypeError(\"Invalid type for argument qcsubj\")\n _copyback_qcsubj = False\n if qcsubj is None:\n qcsubj_ = None\n else:\n try:\n qcsubj_ = memoryview(qcsubj)\n except TypeError:\n try:\n _tmparr_qcsubj = array.array(\"i\",qcsubj)\n except TypeError:\n raise TypeError(\"Argument qcsubj has wrong type\")\n else:\n qcsubj_ = memoryview(_tmparr_qcsubj)\n _copyback_qcsubj = True\n else:\n if qcsubj_.format != \"i\":\n qcsubj_ = memoryview(array.array(\"i\",qcsubj))\n _copyback_qcsubj = True\n if qcsubj_ is not None and len(qcsubj_) != self.getnumqconknz((k_)):\n raise ValueError(\"Array argument qcsubj has wrong length\")\n if qcval is None: raise TypeError(\"Invalid type for argument qcval\")\n _copyback_qcval = False\n if qcval is None:\n qcval_ = None\n else:\n try:\n qcval_ = memoryview(qcval)\n except TypeError:\n try:\n _tmparr_qcval = array.array(\"d\",qcval)\n except TypeError:\n raise TypeError(\"Argument qcval has wrong type\")\n else:\n qcval_ = memoryview(_tmparr_qcval)\n _copyback_qcval = True\n else:\n if qcval_.format != \"d\":\n qcval_ = memoryview(array.array(\"d\",qcval))\n _copyback_qcval = True\n if qcval_ is not None and len(qcval_) != self.getnumqconknz((k_)):\n raise ValueError(\"Array argument qcval has wrong length\")\n res,resargs = self.__obj.getqconk64(k_,maxnumqcnz_,len(qcsubi),qcsubi_,qcsubj_,qcval_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _numqcnz_return_value = resargs\n if _copyback_qcval:\n qcval[:] = _tmparr_qcval\n if _copyback_qcsubj:\n qcsubj[:] = _tmparr_qcsubj\n if _copyback_qcsubi:\n qcsubi[:] = _tmparr_qcsubi\n return _numqcnz_return_value", "def Q(self, k, x):\n g = np.asarray(self.g(k, x))\n Q = g @ g.T\n return Q", "def add_cp_qe_RBM_terms(K, cons_pot_mesh, quad_geo_mesh):\n num_faces = cons_pot_mesh.get_faces().shape[0]\n x_c = quad_geo_mesh.get_centroid()\n w = quad_geo_mesh.get_w()\n A_m = quad_geo_mesh.get_A_m()\n S_D = quad_geo_mesh.get_surface_area()\n\n for face_num in range(num_faces):\n face_nodes = quad_geo_mesh.get_tri_nodes(face_num)\n face_hs = quad_geo_mesh.get_hs(face_num)\n def v_quad(_xi, _eta, _nodes):\n return np.identity(3)\n v_sub_mat = (1. / S_D) * gq.int_over_tri_quad(v_quad, face_nodes, face_hs)\n def omega_quad(xi, eta, nodes):\n pos = geo.quadratic_interp(xi, eta, nodes)\n X = pos - x_c\n return np.einsum(\"lrs,s->lr\", geo.LC_3, X)\n tmp_omega = gq.int_over_tri_quad(\n omega_quad,\n face_nodes,\n face_hs,\n )\n tmp_arr = []\n for m in range(3):\n tmp_arr.append((1./ A_m[m]) * np.outer(w[m], np.einsum(\"l,ls\", w[m], tmp_omega)))\n tmp_arr = np.array(tmp_arr)\n tmp_omega_mat = np.sum(tmp_arr, axis=0)\n for src_num in range(num_faces):\n K[(3 * src_num):(3 * src_num + 3),\n (3 * face_num):(3 * face_num + 3)] += v_sub_mat\n src_center = cons_pot_mesh.get_node(src_num)\n X_0 = src_center - x_c\n omega_mat = np.einsum(\"ijk,js,k->is\", geo.LC_3, tmp_omega_mat, X_0)\n K[(3 * src_num):(3 * src_num + 3),\n (3 * face_num):(3 * face_num + 3)] += omega_mat", "def putcone(self,k_,ct_,conepar_,submem_):\n nummem_ = None\n if nummem_ is None:\n nummem_ = len(submem_)\n elif nummem_ != len(submem_):\n raise IndexError(\"Inconsistent length of array submem\")\n if submem_ is None:\n raise ValueError(\"Argument submem cannot be None\")\n if submem_ is None:\n raise ValueError(\"Argument submem may not be None\")\n if isinstance(submem_, numpy.ndarray) and submem_.dtype is numpy.dtype(numpy.int32) and submem_.flags.contiguous:\n _submem_copyarray = False\n _submem_tmp = ctypes.cast(submem_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif submem_ is not None:\n _submem_copyarray = True\n _submem_np_tmp = numpy.zeros(len(submem_),numpy.dtype(numpy.int32))\n _submem_np_tmp[:] = submem_\n assert _submem_np_tmp.flags.contiguous\n _submem_tmp = ctypes.cast(_submem_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _submem_copyarray = False\n _submem_tmp = None\n \n res = __library__.MSK_XX_putcone(self.__nativep,k_,ct_,conepar_,nummem_,_submem_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putconsolutioni(self,i_,whichsol_,sk_,x_,sl_,su_):\n res = __library__.MSK_XX_putconsolutioni(self.__nativep,i_,whichsol_,sk_,x_,sl_,su_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putbarcblocktriplet(self,num_,subj_,subk_,subl_,valjkl_):\n _subj_minlength = (num_)\n if (num_) > 0 and subj_ is not None and len(subj_) != (num_):\n raise ValueError(\"Array argument subj is not long enough: Is %d, expected %d\" % (len(subj_),(num_)))\n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n _subk_minlength = (num_)\n if (num_) > 0 and subk_ is not None and len(subk_) != (num_):\n raise ValueError(\"Array argument subk is not long enough: Is %d, expected %d\" % (len(subk_),(num_)))\n if subk_ is None:\n raise ValueError(\"Argument subk cannot be None\")\n if subk_ is None:\n raise ValueError(\"Argument subk may not be None\")\n if isinstance(subk_, numpy.ndarray) and subk_.dtype is numpy.dtype(numpy.int32) and subk_.flags.contiguous:\n _subk_copyarray = False\n _subk_tmp = ctypes.cast(subk_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subk_ is not None:\n _subk_copyarray = True\n _subk_np_tmp = numpy.zeros(len(subk_),numpy.dtype(numpy.int32))\n _subk_np_tmp[:] = subk_\n assert _subk_np_tmp.flags.contiguous\n _subk_tmp = ctypes.cast(_subk_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subk_copyarray = False\n _subk_tmp = None\n \n _subl_minlength = (num_)\n if (num_) > 0 and subl_ is not None and len(subl_) != (num_):\n raise ValueError(\"Array argument subl is not long enough: Is %d, expected %d\" % (len(subl_),(num_)))\n if subl_ is None:\n raise ValueError(\"Argument subl cannot be None\")\n if subl_ is None:\n raise ValueError(\"Argument subl may not be None\")\n if isinstance(subl_, numpy.ndarray) and subl_.dtype is numpy.dtype(numpy.int32) and subl_.flags.contiguous:\n _subl_copyarray = False\n _subl_tmp = ctypes.cast(subl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subl_ is not None:\n _subl_copyarray = True\n _subl_np_tmp = numpy.zeros(len(subl_),numpy.dtype(numpy.int32))\n _subl_np_tmp[:] = subl_\n assert _subl_np_tmp.flags.contiguous\n _subl_tmp = ctypes.cast(_subl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subl_copyarray = False\n _subl_tmp = None\n \n _valjkl_minlength = (num_)\n if (num_) > 0 and valjkl_ is not None and len(valjkl_) != (num_):\n raise ValueError(\"Array argument valjkl is not long enough: Is %d, expected %d\" % (len(valjkl_),(num_)))\n if valjkl_ is None:\n raise ValueError(\"Argument valjkl cannot be None\")\n if valjkl_ is None:\n raise ValueError(\"Argument valjkl may not be None\")\n if isinstance(valjkl_, numpy.ndarray) and valjkl_.dtype is numpy.dtype(numpy.float64) and valjkl_.flags.contiguous:\n _valjkl_copyarray = False\n _valjkl_tmp = ctypes.cast(valjkl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif valjkl_ is not None:\n _valjkl_copyarray = True\n _valjkl_np_tmp = numpy.zeros(len(valjkl_),numpy.dtype(numpy.float64))\n _valjkl_np_tmp[:] = valjkl_\n assert _valjkl_np_tmp.flags.contiguous\n _valjkl_tmp = ctypes.cast(_valjkl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _valjkl_copyarray = False\n _valjkl_tmp = None\n \n res = __library__.MSK_XX_putbarcblocktriplet(self.__nativep,num_,_subj_tmp,_subk_tmp,_subl_tmp,_valjkl_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def add_cp_qe_DL_terms(K, cons_pot_mesh, quad_geo_mesh):\n geo_faces = quad_geo_mesh.get_faces()\n pot_faces = cons_pot_mesh.get_faces()\n assert geo_faces.shape[0] == pot_faces.shape[0]\n num_faces = geo_faces.shape[0]\n c_0 = 1. / (4. * np.pi)\n for face_num in range(num_faces): # field points\n face_nodes = quad_geo_mesh.get_tri_nodes(face_num)\n face_n = quad_geo_mesh.get_quad_n(face_num)\n face_hs = quad_geo_mesh.get_hs(face_num)\n for src_num in range(num_faces): # source points\n src_center = cons_pot_mesh.get_node(src_num)\n if face_num != src_num:\n sub_mat = gq.int_over_tri_quad_n(\n make_cp_qe_quad_func(src_center),\n face_nodes,\n face_n,\n face_hs\n )\n K[(3 * src_num):(3 * src_num + 3),\n (3 * face_num):(3 * face_num + 3)] += sub_mat\n K[(3 * src_num):(3 * src_num + 3),\n (3 * src_num):(3 * src_num + 3)] -= sub_mat\n # do nothing face_num == src_num, how it works out for constant elements\n\n for src_num in range(num_faces):\n K[(3 * src_num):(3 * src_num + 3),\n (3 * src_num):(3 * src_num + 3)] -= 4. * np.pi * np.identity(3)\n K *= c_0", "def ikcon(self, T, q0=None):\n\n if not isinstance(T, SE3):\n T = SE3(T)\n\n trajn = len(T)\n\n try:\n if q0 is not None:\n q0 = getvector(q0, self.n, 'row')\n else:\n q0 = np.zeros((trajn, self.n))\n except ValueError:\n verifymatrix(q0, (trajn, self.n))\n\n # create output variables\n qstar = np.zeros((trajn, self.n))\n error = []\n exitflag = []\n\n omega = np.diag([1, 1, 1, 3 / self.reach])\n\n def cost(q, T, omega):\n return np.sum(\n (\n (np.linalg.pinv(T.A) @ self.fkine(q).A - np.eye(4)) @\n omega) ** 2\n )\n\n bnds = Bounds(self.qlim[0, :], self.qlim[1, :])\n\n for i in range(trajn):\n Ti = T[i]\n res = minimize(\n lambda q: cost(q, Ti, omega),\n q0[i, :], bounds=bnds, options={'gtol': 1e-6})\n qstar[i, :] = res.x\n error.append(res.fun)\n exitflag.append(res.success)\n\n if trajn > 1:\n return qstar, exitflag, error\n else:\n return qstar[0, :], exitflag[0], error[0]", "def putconbound(self,i_,bkc_,blc_,buc_):\n res = __library__.MSK_XX_putconbound(self.__nativep,i_,bkc_,blc_,buc_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putcone(self,k_,ct_,conepar_,submem): # 3\n if not isinstance(ct_,conetype): raise TypeError(\"Argument ct has wrong type\")\n nummem_ = None\n if nummem_ is None:\n nummem_ = len(submem)\n elif nummem_ != len(submem):\n raise IndexError(\"Inconsistent length of array submem\")\n if nummem_ is None: nummem_ = 0\n if submem is None: raise TypeError(\"Invalid type for argument submem\")\n if submem is None:\n submem_ = None\n else:\n try:\n submem_ = memoryview(submem)\n except TypeError:\n try:\n _tmparr_submem = array.array(\"i\",submem)\n except TypeError:\n raise TypeError(\"Argument submem has wrong type\")\n else:\n submem_ = memoryview(_tmparr_submem)\n \n else:\n if submem_.format != \"i\":\n submem_ = memoryview(array.array(\"i\",submem))\n \n res = self.__obj.putcone(k_,ct_,conepar_,nummem_,submem_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def _append_phase(self, k, i):\n if not 0 <= i < self.num_qubits:\n raise QiskitError(\"phase qubit out of bounds.\")\n # If the kth bit is flipped, conjugate this gate\n if self.shift[i] == 1:\n k = (7 * k) % 8\n # Take all subsets \\alpha of the support of row i\n # of weight up to 3 and add k*(-2)**(|\\alpha| - 1) mod 8\n # to the corresponding term.\n support = np.arange(self.num_qubits)[np.nonzero(self.linear[i])]\n subsets_2 = itertools.combinations(support, 2)\n subsets_3 = itertools.combinations(support, 3)\n for j in support:\n value = self.poly.get_term([j])\n self.poly.set_term([j], (value + k) % 8)\n for j in subsets_2:\n value = self.poly.get_term(list(j))\n self.poly.set_term(list(j), (value + -2 * k) % 8)\n for j in subsets_3:\n value = self.poly.get_term(list(j))\n self.poly.set_term(list(j), (value + 4 * k) % 8)", "def ket(self: Qs) -> Qs:\n\n if self.qs_type == \"ket\":\n return self\n\n ket = conjs(deepcopy(self))\n ket.rows = self.dim\n ket.columns = 1\n\n ket.qs_type = \"ket\" if self.dim > 1 else \"scalar_q\"\n\n return ket", "def ALIGNF(km_list, ky):\n n_feat = len(km_list)\n\n #km_list_copy = []\n # center the kernel first\n #for i in range(n_feat):\n # km_list_copy.append(center(km_list[i].copy()))\n #ky_copy = center(ky.copy())\n\n\n a = np.zeros(n_feat)\n for i in range(n_feat):\n a[i] = f_dot(km_list[i], ky)\n\n M = np.zeros((n_feat, n_feat))\n for i in range(n_feat):\n for j in range(i,n_feat):\n M[i,j] = f_dot(km_list[i],km_list[j])\n M[j,i] = M[i,j]\n\n Q = 2*M\n C = -2*a\n\n Q = Q + np.diag(np.ones(n_feat)*1e-8)\n\n ################################################\n # Using mosek to solve the quadratice programming\n\n # Set upper diagonal element to zeros, mosek only accept lower triangle\n iu = np.triu_indices(n_feat,1)\n Q[iu] = 0\n\n # start solving with mosek\n inf = 0.0\n env = mosek.Env()\n env.set_Stream(mosek.streamtype.log, streamprinter)\n\n # Create a task \n task = env.Task()\n task.set_Stream(mosek.streamtype.log, streamprinter)\n\n # Set up bound for variables \n bkx = [mosek.boundkey.lo]* n_feat\n blx = [0.0] * n_feat\n #bkx = [mosek.boundkey.fr]* n_feat\n #blx = [-inf] * n_feat\n bux = [+inf] * n_feat\n\n numvar = len(bkx)\n\n task.appendvars(numvar)\n\n for j in range(numvar):\n task.putcj(j,C[j])\n task.putvarbound(j,bkx[j],blx[j],bux[j])\n\n # Set up quadratic objective \n inds = np.nonzero(Q)\n qsubi = inds[0].tolist()\n qsubj = inds[1].tolist()\n qval = Q[inds].tolist()\n\n # Input quadratic objective \n task.putqobj(qsubi,qsubj,qval)\n\n # Input objective sense (minimize/mximize) \n task.putobjsense(mosek.objsense.minimize)\n\n task.optimize()\n\n # Print a summary containing information \n # about the solution for debugging purposes \n task.solutionsummary(mosek.streamtype.msg)\n\n solsta = task.getsolsta(mosek.soltype.itr)\n if (solsta == mosek.solsta.optimal or\n solsta == mosek.solsta.near_optimal):\n # Output a solution \n xx = np.zeros(numvar, float)\n task.getxx(mosek.soltype.itr, xx)\n #xx = xx/np.linalg.norm(xx)\n return xx\n else:\n print solsta\n xx = np.zeros(numvar, float)\n task.getxx(mosek.soltype.itr, xx)\n #xx = xx/np.linalg.norm(xx)\n return xx", "def putskc(self,whichsol_,skc_):\n _skc_minlength = self.getnumcon()\n if self.getnumcon() > 0 and skc_ is not None and len(skc_) != self.getnumcon():\n raise ValueError(\"Array argument skc is not long enough: Is %d, expected %d\" % (len(skc_),self.getnumcon()))\n if skc_ is None:\n raise ValueError(\"Argument skc cannot be None\")\n if skc_ is None:\n raise ValueError(\"Argument skc may not be None\")\n if skc_ is not None:\n _skc_tmp = (ctypes.c_int32 * len(skc_))(*skc_)\n else:\n _skc_tmp = None\n res = __library__.MSK_XX_putskc(self.__nativep,whichsol_,_skc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def additional_equations(self, k):\n return", "def putconboundlistconst(self,sub_,bkc_,blc_,buc_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n res = __library__.MSK_XX_putconboundlistconst(self.__nativep,num_,_sub_tmp,bkc_,blc_,buc_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putqobjij(self,i_,j_,qoij_):\n res = __library__.MSK_XX_putqobjij(self.__nativep,i_,j_,qoij_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putbarablocktriplet(self,num_,subi_,subj_,subk_,subl_,valijkl_):\n _subi_minlength = (num_)\n if (num_) > 0 and subi_ is not None and len(subi_) != (num_):\n raise ValueError(\"Array argument subi is not long enough: Is %d, expected %d\" % (len(subi_),(num_)))\n if subi_ is None:\n raise ValueError(\"Argument subi cannot be None\")\n if subi_ is None:\n raise ValueError(\"Argument subi may not be None\")\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\n _subi_copyarray = False\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subi_ is not None:\n _subi_copyarray = True\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\n _subi_np_tmp[:] = subi_\n assert _subi_np_tmp.flags.contiguous\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subi_copyarray = False\n _subi_tmp = None\n \n _subj_minlength = (num_)\n if (num_) > 0 and subj_ is not None and len(subj_) != (num_):\n raise ValueError(\"Array argument subj is not long enough: Is %d, expected %d\" % (len(subj_),(num_)))\n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n _subk_minlength = (num_)\n if (num_) > 0 and subk_ is not None and len(subk_) != (num_):\n raise ValueError(\"Array argument subk is not long enough: Is %d, expected %d\" % (len(subk_),(num_)))\n if subk_ is None:\n raise ValueError(\"Argument subk cannot be None\")\n if subk_ is None:\n raise ValueError(\"Argument subk may not be None\")\n if isinstance(subk_, numpy.ndarray) and subk_.dtype is numpy.dtype(numpy.int32) and subk_.flags.contiguous:\n _subk_copyarray = False\n _subk_tmp = ctypes.cast(subk_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subk_ is not None:\n _subk_copyarray = True\n _subk_np_tmp = numpy.zeros(len(subk_),numpy.dtype(numpy.int32))\n _subk_np_tmp[:] = subk_\n assert _subk_np_tmp.flags.contiguous\n _subk_tmp = ctypes.cast(_subk_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subk_copyarray = False\n _subk_tmp = None\n \n _subl_minlength = (num_)\n if (num_) > 0 and subl_ is not None and len(subl_) != (num_):\n raise ValueError(\"Array argument subl is not long enough: Is %d, expected %d\" % (len(subl_),(num_)))\n if subl_ is None:\n raise ValueError(\"Argument subl cannot be None\")\n if subl_ is None:\n raise ValueError(\"Argument subl may not be None\")\n if isinstance(subl_, numpy.ndarray) and subl_.dtype is numpy.dtype(numpy.int32) and subl_.flags.contiguous:\n _subl_copyarray = False\n _subl_tmp = ctypes.cast(subl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subl_ is not None:\n _subl_copyarray = True\n _subl_np_tmp = numpy.zeros(len(subl_),numpy.dtype(numpy.int32))\n _subl_np_tmp[:] = subl_\n assert _subl_np_tmp.flags.contiguous\n _subl_tmp = ctypes.cast(_subl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subl_copyarray = False\n _subl_tmp = None\n \n _valijkl_minlength = (num_)\n if (num_) > 0 and valijkl_ is not None and len(valijkl_) != (num_):\n raise ValueError(\"Array argument valijkl is not long enough: Is %d, expected %d\" % (len(valijkl_),(num_)))\n if valijkl_ is None:\n raise ValueError(\"Argument valijkl cannot be None\")\n if valijkl_ is None:\n raise ValueError(\"Argument valijkl may not be None\")\n if isinstance(valijkl_, numpy.ndarray) and valijkl_.dtype is numpy.dtype(numpy.float64) and valijkl_.flags.contiguous:\n _valijkl_copyarray = False\n _valijkl_tmp = ctypes.cast(valijkl_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif valijkl_ is not None:\n _valijkl_copyarray = True\n _valijkl_np_tmp = numpy.zeros(len(valijkl_),numpy.dtype(numpy.float64))\n _valijkl_np_tmp[:] = valijkl_\n assert _valijkl_np_tmp.flags.contiguous\n _valijkl_tmp = ctypes.cast(_valijkl_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _valijkl_copyarray = False\n _valijkl_tmp = None\n \n res = __library__.MSK_XX_putbarablocktriplet(self.__nativep,num_,_subi_tmp,_subj_tmp,_subk_tmp,_subl_tmp,_valijkl_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putqobjij(self,i_,j_,qoij_): # 3\n res = self.__obj.putqobjij(i_,j_,qoij_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def qmincon(self, q=None):\n\n def sumsqr(A):\n return np.sum(A**2)\n\n def cost(x, ub, lb, qm, N):\n return sumsqr(\n (2 * (N @ x + qm) - ub - lb) / (ub - lb))\n\n q = getmatrix(q, (None, self.n))\n\n qstar = np.zeros((q.shape[0], self.n))\n error = np.zeros(q.shape[0])\n success = np.zeros(q.shape[0])\n\n lb = self.qlim[0, :]\n ub = self.qlim[1, :]\n\n for k, qk in enumerate(q):\n\n J = self.jacobe(qk)\n\n N = null(J)\n\n x0 = np.zeros(N.shape[1])\n A = np.r_[N, -N]\n b = np.r_[ub - qk, qk - lb].reshape(A.shape[0],)\n\n con = LinearConstraint(A, -np.inf, b)\n\n res = minimize(\n lambda x: cost(x, ub, lb, qk, N),\n x0, constraints=con)\n\n qstar[k, :] = qk + N @ res.x\n error[k] = res.fun\n success[k] = res.success\n\n if q.shape[0] == 1:\n return qstar[0, :], success[0], error[0]\n else:\n return qstar, success, error", "def putconbound(self,i_,bk_,bl_,bu_): # 3\n if not isinstance(bk_,boundkey): raise TypeError(\"Argument bk has wrong type\")\n res = self.__obj.putconbound(i_,bk_,bl_,bu_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putbarcblocktriplet(self,num_,subj,subk,subl,valjkl): # 3\n if subj is None: raise TypeError(\"Invalid type for argument subj\")\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n \n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n \n if subj_ is not None and len(subj_) != (num_):\n raise ValueError(\"Array argument subj has wrong length\")\n if subk is None: raise TypeError(\"Invalid type for argument subk\")\n if subk is None:\n subk_ = None\n else:\n try:\n subk_ = memoryview(subk)\n except TypeError:\n try:\n _tmparr_subk = array.array(\"i\",subk)\n except TypeError:\n raise TypeError(\"Argument subk has wrong type\")\n else:\n subk_ = memoryview(_tmparr_subk)\n \n else:\n if subk_.format != \"i\":\n subk_ = memoryview(array.array(\"i\",subk))\n \n if subk_ is not None and len(subk_) != (num_):\n raise ValueError(\"Array argument subk has wrong length\")\n if subl is None: raise TypeError(\"Invalid type for argument subl\")\n if subl is None:\n subl_ = None\n else:\n try:\n subl_ = memoryview(subl)\n except TypeError:\n try:\n _tmparr_subl = array.array(\"i\",subl)\n except TypeError:\n raise TypeError(\"Argument subl has wrong type\")\n else:\n subl_ = memoryview(_tmparr_subl)\n \n else:\n if subl_.format != \"i\":\n subl_ = memoryview(array.array(\"i\",subl))\n \n if subl_ is not None and len(subl_) != (num_):\n raise ValueError(\"Array argument subl has wrong length\")\n if valjkl is None: raise TypeError(\"Invalid type for argument valjkl\")\n if valjkl is None:\n valjkl_ = None\n else:\n try:\n valjkl_ = memoryview(valjkl)\n except TypeError:\n try:\n _tmparr_valjkl = array.array(\"d\",valjkl)\n except TypeError:\n raise TypeError(\"Argument valjkl has wrong type\")\n else:\n valjkl_ = memoryview(_tmparr_valjkl)\n \n else:\n if valjkl_.format != \"d\":\n valjkl_ = memoryview(array.array(\"d\",valjkl))\n \n if valjkl_ is not None and len(valjkl_) != (num_):\n raise ValueError(\"Array argument valjkl has wrong length\")\n res = self.__obj.putbarcblocktriplet(num_,subj_,subk_,subl_,valjkl_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def Qc_fit(x, a, b, c, d, e, f, g, h, i, k):\n x1 = x[0] # I\n x2 = x[1] # dT\n m = (i * x1 ** 4 + a * x1 ** 3 + b * x1 ** 2 + c * x1 + d)\n b = (k * x1 ** 4 + e * x1 ** 3 + f * x1 ** 2 + g * x1 + h)\n return m * x2 + b", "def putcj(self,j_,cj_):\n res = __library__.MSK_XX_putcj(self.__nativep,j_,cj_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def quaternion_conjugate(self, q):\n\n \"\"\"\n in-place operation is an operation that changes directly the content of a given Tensor without making a copy.\n ALL operations on the tensor that operate in-place on it will have an _ postfix.\n \"\"\"\n q_star = q.new(4).fill_(-1)\n\n # leave the scalar unchanged and change signs of i, j, k number parts\n q_star[0] = 1.0\n\n return q * q_star", "def conj(q):\n q = np.array([q[0]])\n q[0,1]=-q[0,1]\n q[0,2]=-q[0,2]\n q[0,3]=-q[0,3]\n complexconjugate = quatreal(q)\n return complexconjugate" ]

[ "0.7407992", "0.73707557", "0.7119913", "0.67798036", "0.6661862", "0.5547101", "0.55400264", "0.5332845", "0.5304892", "0.5226392", "0.5216021", "0.5155784", "0.5152715", "0.51131207", "0.50926286", "0.50507396", "0.5048387", "0.5019227", "0.4992005", "0.497944", "0.49701524", "0.4944257", "0.4915807", "0.48931497", "0.48842365", "0.4846689", "0.4778596", "0.47593817", "0.47248182", "0.47172788" ]

[ "def putqobj(self,qosubi,qosubj,qoval): # 3\n numqonz_ = None\n if numqonz_ is None:\n numqonz_ = len(qosubi)\n elif numqonz_ != len(qosubi):\n raise IndexError(\"Inconsistent length of array qosubi\")\n if numqonz_ is None:\n numqonz_ = len(qosubj)\n elif numqonz_ != len(qosubj):\n raise IndexError(\"Inconsistent length of array qosubj\")\n if numqonz_ is None:\n numqonz_ = len(qoval)\n elif numqonz_ != len(qoval):\n raise IndexError(\"Inconsistent length of array qoval\")\n if numqonz_ is None: numqonz_ = 0\n if qosubi is None: raise TypeError(\"Invalid type for argument qosubi\")\n if qosubi is None:\n qosubi_ = None\n else:\n try:\n qosubi_ = memoryview(qosubi)\n except TypeError:\n try:\n _tmparr_qosubi = array.array(\"i\",qosubi)\n except TypeError:\n raise TypeError(\"Argument qosubi has wrong type\")\n else:\n qosubi_ = memoryview(_tmparr_qosubi)\n \n else:\n if qosubi_.format != \"i\":\n qosubi_ = memoryview(array.array(\"i\",qosubi))\n \n if qosubj is None: raise TypeError(\"Invalid type for argument qosubj\")\n if qosubj is None:\n qosubj_ = None\n else:\n try:\n qosubj_ = memoryview(qosubj)\n except TypeError:\n try:\n _tmparr_qosubj = array.array(\"i\",qosubj)\n except TypeError:\n raise TypeError(\"Argument qosubj has wrong type\")\n else:\n qosubj_ = memoryview(_tmparr_qosubj)\n \n else:\n if qosubj_.format != \"i\":\n qosubj_ = memoryview(array.array(\"i\",qosubj))\n \n if qoval is None: raise TypeError(\"Invalid type for argument qoval\")\n if qoval is None:\n qoval_ = None\n else:\n try:\n qoval_ = memoryview(qoval)\n except TypeError:\n try:\n _tmparr_qoval = array.array(\"d\",qoval)\n except TypeError:\n raise TypeError(\"Argument qoval has wrong type\")\n else:\n qoval_ = memoryview(_tmparr_qoval)\n \n else:\n if qoval_.format != \"d\":\n qoval_ = memoryview(array.array(\"d\",qoval))\n \n res = self.__obj.putqobj(numqonz_,qosubi_,qosubj_,qoval_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putqobjij(self,i_,j_,qoij_):\n res = __library__.MSK_XX_putqobjij(self.__nativep,i_,j_,qoij_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putqobjij(self,i_,j_,qoij_): # 3\n res = self.__obj.putqobjij(i_,j_,qoij_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def getqobj(self,qosubi,qosubj,qoval): # 3\n maxnumqonz_ = self.getnumqobjnz()\n if qosubi is None: raise TypeError(\"Invalid type for argument qosubi\")\n _copyback_qosubi = False\n if qosubi is None:\n qosubi_ = None\n else:\n try:\n qosubi_ = memoryview(qosubi)\n except TypeError:\n try:\n _tmparr_qosubi = array.array(\"i\",qosubi)\n except TypeError:\n raise TypeError(\"Argument qosubi has wrong type\")\n else:\n qosubi_ = memoryview(_tmparr_qosubi)\n _copyback_qosubi = True\n else:\n if qosubi_.format != \"i\":\n qosubi_ = memoryview(array.array(\"i\",qosubi))\n _copyback_qosubi = True\n if qosubi_ is not None and len(qosubi_) != (maxnumqonz_):\n raise ValueError(\"Array argument qosubi has wrong length\")\n if qosubj is None: raise TypeError(\"Invalid type for argument qosubj\")\n _copyback_qosubj = False\n if qosubj is None:\n qosubj_ = None\n else:\n try:\n qosubj_ = memoryview(qosubj)\n except TypeError:\n try:\n _tmparr_qosubj = array.array(\"i\",qosubj)\n except TypeError:\n raise TypeError(\"Argument qosubj has wrong type\")\n else:\n qosubj_ = memoryview(_tmparr_qosubj)\n _copyback_qosubj = True\n else:\n if qosubj_.format != \"i\":\n qosubj_ = memoryview(array.array(\"i\",qosubj))\n _copyback_qosubj = True\n if qosubj_ is not None and len(qosubj_) != (maxnumqonz_):\n raise ValueError(\"Array argument qosubj has wrong length\")\n if qoval is None: raise TypeError(\"Invalid type for argument qoval\")\n _copyback_qoval = False\n if qoval is None:\n qoval_ = None\n else:\n try:\n qoval_ = memoryview(qoval)\n except TypeError:\n try:\n _tmparr_qoval = array.array(\"d\",qoval)\n except TypeError:\n raise TypeError(\"Argument qoval has wrong type\")\n else:\n qoval_ = memoryview(_tmparr_qoval)\n _copyback_qoval = True\n else:\n if qoval_.format != \"d\":\n qoval_ = memoryview(array.array(\"d\",qoval))\n _copyback_qoval = True\n if qoval_ is not None and len(qoval_) != (maxnumqonz_):\n raise ValueError(\"Array argument qoval has wrong length\")\n res,resargs = self.__obj.getqobj64(maxnumqonz_,len(qosubi),qosubi_,qosubj_,qoval_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _numqonz_return_value = resargs\n if _copyback_qoval:\n qoval[:] = _tmparr_qoval\n if _copyback_qosubj:\n qosubj[:] = _tmparr_qosubj\n if _copyback_qosubi:\n qosubi[:] = _tmparr_qosubi\n return _numqonz_return_value", "def getqobj(self,qosubi_,qosubj_,qoval_):\n maxnumqonz_ = self.getnumqobjnz()\n numqonz_ = ctypes.c_int64()\n _qosubi_minlength = (maxnumqonz_)\n if (maxnumqonz_) > 0 and qosubi_ is not None and len(qosubi_) != (maxnumqonz_):\n raise ValueError(\"Array argument qosubi is not long enough: Is %d, expected %d\" % (len(qosubi_),(maxnumqonz_)))\n if isinstance(qosubi_,numpy.ndarray) and not qosubi_.flags.writeable:\n raise ValueError(\"Argument qosubi must be writable\")\n if qosubi_ is None:\n raise ValueError(\"Argument qosubi may not be None\")\n if isinstance(qosubi_, numpy.ndarray) and qosubi_.dtype is numpy.dtype(numpy.int32) and qosubi_.flags.contiguous:\n _qosubi_copyarray = False\n _qosubi_tmp = ctypes.cast(qosubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qosubi_ is not None:\n _qosubi_copyarray = True\n _qosubi_np_tmp = numpy.zeros(len(qosubi_),numpy.dtype(numpy.int32))\n _qosubi_np_tmp[:] = qosubi_\n assert _qosubi_np_tmp.flags.contiguous\n _qosubi_tmp = ctypes.cast(_qosubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qosubi_copyarray = False\n _qosubi_tmp = None\n \n _qosubj_minlength = (maxnumqonz_)\n if (maxnumqonz_) > 0 and qosubj_ is not None and len(qosubj_) != (maxnumqonz_):\n raise ValueError(\"Array argument qosubj is not long enough: Is %d, expected %d\" % (len(qosubj_),(maxnumqonz_)))\n if isinstance(qosubj_,numpy.ndarray) and not qosubj_.flags.writeable:\n raise ValueError(\"Argument qosubj must be writable\")\n if qosubj_ is None:\n raise ValueError(\"Argument qosubj may not be None\")\n if isinstance(qosubj_, numpy.ndarray) and qosubj_.dtype is numpy.dtype(numpy.int32) and qosubj_.flags.contiguous:\n _qosubj_copyarray = False\n _qosubj_tmp = ctypes.cast(qosubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qosubj_ is not None:\n _qosubj_copyarray = True\n _qosubj_np_tmp = numpy.zeros(len(qosubj_),numpy.dtype(numpy.int32))\n _qosubj_np_tmp[:] = qosubj_\n assert _qosubj_np_tmp.flags.contiguous\n _qosubj_tmp = ctypes.cast(_qosubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qosubj_copyarray = False\n _qosubj_tmp = None\n \n _qoval_minlength = (maxnumqonz_)\n if (maxnumqonz_) > 0 and qoval_ is not None and len(qoval_) != (maxnumqonz_):\n raise ValueError(\"Array argument qoval is not long enough: Is %d, expected %d\" % (len(qoval_),(maxnumqonz_)))\n if isinstance(qoval_,numpy.ndarray) and not qoval_.flags.writeable:\n raise ValueError(\"Argument qoval must be writable\")\n if qoval_ is None:\n raise ValueError(\"Argument qoval may not be None\")\n if isinstance(qoval_, numpy.ndarray) and qoval_.dtype is numpy.dtype(numpy.float64) and qoval_.flags.contiguous:\n _qoval_copyarray = False\n _qoval_tmp = ctypes.cast(qoval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif qoval_ is not None:\n _qoval_copyarray = True\n _qoval_np_tmp = numpy.zeros(len(qoval_),numpy.dtype(numpy.float64))\n _qoval_np_tmp[:] = qoval_\n assert _qoval_np_tmp.flags.contiguous\n _qoval_tmp = ctypes.cast(_qoval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _qoval_copyarray = False\n _qoval_tmp = None\n \n qosurp_ = ctypes.c_int64(_qosubi_minlength)\n res = __library__.MSK_XX_getqobj64(self.__nativep,maxnumqonz_,ctypes.byref(qosurp_),ctypes.byref(numqonz_),_qosubi_tmp,_qosubj_tmp,_qoval_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n numqonz_ = numqonz_.value\n _numqonz_return_value = numqonz_\n if _qosubi_copyarray:\n qosubi_[:] = _qosubi_np_tmp\n if _qosubj_copyarray:\n qosubj_[:] = _qosubj_np_tmp\n if _qoval_copyarray:\n qoval_[:] = _qoval_np_tmp\n return (_numqonz_return_value)", "def putqconk(self,k_,qcsubi_,qcsubj_,qcval_):\n numqcnz_ = None\n if numqcnz_ is None:\n numqcnz_ = len(qcsubi_)\n elif numqcnz_ != len(qcsubi_):\n raise IndexError(\"Inconsistent length of array qcsubi\")\n if numqcnz_ is None:\n numqcnz_ = len(qcsubj_)\n elif numqcnz_ != len(qcsubj_):\n raise IndexError(\"Inconsistent length of array qcsubj\")\n if numqcnz_ is None:\n numqcnz_ = len(qcval_)\n elif numqcnz_ != len(qcval_):\n raise IndexError(\"Inconsistent length of array qcval\")\n if qcsubi_ is None:\n raise ValueError(\"Argument qcsubi cannot be None\")\n if qcsubi_ is None:\n raise ValueError(\"Argument qcsubi may not be None\")\n if isinstance(qcsubi_, numpy.ndarray) and qcsubi_.dtype is numpy.dtype(numpy.int32) and qcsubi_.flags.contiguous:\n _qcsubi_copyarray = False\n _qcsubi_tmp = ctypes.cast(qcsubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubi_ is not None:\n _qcsubi_copyarray = True\n _qcsubi_np_tmp = numpy.zeros(len(qcsubi_),numpy.dtype(numpy.int32))\n _qcsubi_np_tmp[:] = qcsubi_\n assert _qcsubi_np_tmp.flags.contiguous\n _qcsubi_tmp = ctypes.cast(_qcsubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubi_copyarray = False\n _qcsubi_tmp = None\n \n if qcsubj_ is None:\n raise ValueError(\"Argument qcsubj cannot be None\")\n if qcsubj_ is None:\n raise ValueError(\"Argument qcsubj may not be None\")\n if isinstance(qcsubj_, numpy.ndarray) and qcsubj_.dtype is numpy.dtype(numpy.int32) and qcsubj_.flags.contiguous:\n _qcsubj_copyarray = False\n _qcsubj_tmp = ctypes.cast(qcsubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubj_ is not None:\n _qcsubj_copyarray = True\n _qcsubj_np_tmp = numpy.zeros(len(qcsubj_),numpy.dtype(numpy.int32))\n _qcsubj_np_tmp[:] = qcsubj_\n assert _qcsubj_np_tmp.flags.contiguous\n _qcsubj_tmp = ctypes.cast(_qcsubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubj_copyarray = False\n _qcsubj_tmp = None\n \n if qcval_ is None:\n raise ValueError(\"Argument qcval cannot be None\")\n if qcval_ is None:\n raise ValueError(\"Argument qcval may not be None\")\n if isinstance(qcval_, numpy.ndarray) and qcval_.dtype is numpy.dtype(numpy.float64) and qcval_.flags.contiguous:\n _qcval_copyarray = False\n _qcval_tmp = ctypes.cast(qcval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif qcval_ is not None:\n _qcval_copyarray = True\n _qcval_np_tmp = numpy.zeros(len(qcval_),numpy.dtype(numpy.float64))\n _qcval_np_tmp[:] = qcval_\n assert _qcval_np_tmp.flags.contiguous\n _qcval_tmp = ctypes.cast(_qcval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _qcval_copyarray = False\n _qcval_tmp = None\n \n res = __library__.MSK_XX_putqconk(self.__nativep,k_,numqcnz_,_qcsubi_tmp,_qcsubj_tmp,_qcval_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putqcon(self,qcsubk,qcsubi,qcsubj,qcval): # 3\n numqcnz_ = None\n if numqcnz_ is None:\n numqcnz_ = len(qcsubi)\n elif numqcnz_ != len(qcsubi):\n raise IndexError(\"Inconsistent length of array qcsubi\")\n if numqcnz_ is None:\n numqcnz_ = len(qcsubj)\n elif numqcnz_ != len(qcsubj):\n raise IndexError(\"Inconsistent length of array qcsubj\")\n if numqcnz_ is None:\n numqcnz_ = len(qcval)\n elif numqcnz_ != len(qcval):\n raise IndexError(\"Inconsistent length of array qcval\")\n if numqcnz_ is None: numqcnz_ = 0\n if qcsubk is None: raise TypeError(\"Invalid type for argument qcsubk\")\n if qcsubk is None:\n qcsubk_ = None\n else:\n try:\n qcsubk_ = memoryview(qcsubk)\n except TypeError:\n try:\n _tmparr_qcsubk = array.array(\"i\",qcsubk)\n except TypeError:\n raise TypeError(\"Argument qcsubk has wrong type\")\n else:\n qcsubk_ = memoryview(_tmparr_qcsubk)\n \n else:\n if qcsubk_.format != \"i\":\n qcsubk_ = memoryview(array.array(\"i\",qcsubk))\n \n if qcsubi is None: raise TypeError(\"Invalid type for argument qcsubi\")\n if qcsubi is None:\n qcsubi_ = None\n else:\n try:\n qcsubi_ = memoryview(qcsubi)\n except TypeError:\n try:\n _tmparr_qcsubi = array.array(\"i\",qcsubi)\n except TypeError:\n raise TypeError(\"Argument qcsubi has wrong type\")\n else:\n qcsubi_ = memoryview(_tmparr_qcsubi)\n \n else:\n if qcsubi_.format != \"i\":\n qcsubi_ = memoryview(array.array(\"i\",qcsubi))\n \n if qcsubj is None: raise TypeError(\"Invalid type for argument qcsubj\")\n if qcsubj is None:\n qcsubj_ = None\n else:\n try:\n qcsubj_ = memoryview(qcsubj)\n except TypeError:\n try:\n _tmparr_qcsubj = array.array(\"i\",qcsubj)\n except TypeError:\n raise TypeError(\"Argument qcsubj has wrong type\")\n else:\n qcsubj_ = memoryview(_tmparr_qcsubj)\n \n else:\n if qcsubj_.format != \"i\":\n qcsubj_ = memoryview(array.array(\"i\",qcsubj))\n \n if qcval is None: raise TypeError(\"Invalid type for argument qcval\")\n if qcval is None:\n qcval_ = None\n else:\n try:\n qcval_ = memoryview(qcval)\n except TypeError:\n try:\n _tmparr_qcval = array.array(\"d\",qcval)\n except TypeError:\n raise TypeError(\"Argument qcval has wrong type\")\n else:\n qcval_ = memoryview(_tmparr_qcval)\n \n else:\n if qcval_.format != \"d\":\n qcval_ = memoryview(array.array(\"d\",qcval))\n \n res = self.__obj.putqcon(numqcnz_,qcsubk_,qcsubi_,qcsubj_,qcval_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putqconk(self,k_,qcsubi,qcsubj,qcval): # 3\n numqcnz_ = None\n if numqcnz_ is None:\n numqcnz_ = len(qcsubi)\n elif numqcnz_ != len(qcsubi):\n raise IndexError(\"Inconsistent length of array qcsubi\")\n if numqcnz_ is None:\n numqcnz_ = len(qcsubj)\n elif numqcnz_ != len(qcsubj):\n raise IndexError(\"Inconsistent length of array qcsubj\")\n if numqcnz_ is None:\n numqcnz_ = len(qcval)\n elif numqcnz_ != len(qcval):\n raise IndexError(\"Inconsistent length of array qcval\")\n if numqcnz_ is None: numqcnz_ = 0\n if qcsubi is None: raise TypeError(\"Invalid type for argument qcsubi\")\n if qcsubi is None:\n qcsubi_ = None\n else:\n try:\n qcsubi_ = memoryview(qcsubi)\n except TypeError:\n try:\n _tmparr_qcsubi = array.array(\"i\",qcsubi)\n except TypeError:\n raise TypeError(\"Argument qcsubi has wrong type\")\n else:\n qcsubi_ = memoryview(_tmparr_qcsubi)\n \n else:\n if qcsubi_.format != \"i\":\n qcsubi_ = memoryview(array.array(\"i\",qcsubi))\n \n if qcsubj is None: raise TypeError(\"Invalid type for argument qcsubj\")\n if qcsubj is None:\n qcsubj_ = None\n else:\n try:\n qcsubj_ = memoryview(qcsubj)\n except TypeError:\n try:\n _tmparr_qcsubj = array.array(\"i\",qcsubj)\n except TypeError:\n raise TypeError(\"Argument qcsubj has wrong type\")\n else:\n qcsubj_ = memoryview(_tmparr_qcsubj)\n \n else:\n if qcsubj_.format != \"i\":\n qcsubj_ = memoryview(array.array(\"i\",qcsubj))\n \n if qcval is None: raise TypeError(\"Invalid type for argument qcval\")\n if qcval is None:\n qcval_ = None\n else:\n try:\n qcval_ = memoryview(qcval)\n except TypeError:\n try:\n _tmparr_qcval = array.array(\"d\",qcval)\n except TypeError:\n raise TypeError(\"Argument qcval has wrong type\")\n else:\n qcval_ = memoryview(_tmparr_qcval)\n \n else:\n if qcval_.format != \"d\":\n qcval_ = memoryview(array.array(\"d\",qcval))\n \n res = self.__obj.putqconk(k_,numqcnz_,qcsubi_,qcsubj_,qcval_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putqcon(self,qcsubk_,qcsubi_,qcsubj_,qcval_):\n numqcnz_ = None\n if numqcnz_ is None:\n numqcnz_ = len(qcsubi_)\n elif numqcnz_ != len(qcsubi_):\n raise IndexError(\"Inconsistent length of array qcsubi\")\n if numqcnz_ is None:\n numqcnz_ = len(qcsubj_)\n elif numqcnz_ != len(qcsubj_):\n raise IndexError(\"Inconsistent length of array qcsubj\")\n if numqcnz_ is None:\n numqcnz_ = len(qcval_)\n elif numqcnz_ != len(qcval_):\n raise IndexError(\"Inconsistent length of array qcval\")\n if qcsubk_ is None:\n raise ValueError(\"Argument qcsubk cannot be None\")\n if qcsubk_ is None:\n raise ValueError(\"Argument qcsubk may not be None\")\n if isinstance(qcsubk_, numpy.ndarray) and qcsubk_.dtype is numpy.dtype(numpy.int32) and qcsubk_.flags.contiguous:\n _qcsubk_copyarray = False\n _qcsubk_tmp = ctypes.cast(qcsubk_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubk_ is not None:\n _qcsubk_copyarray = True\n _qcsubk_np_tmp = numpy.zeros(len(qcsubk_),numpy.dtype(numpy.int32))\n _qcsubk_np_tmp[:] = qcsubk_\n assert _qcsubk_np_tmp.flags.contiguous\n _qcsubk_tmp = ctypes.cast(_qcsubk_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubk_copyarray = False\n _qcsubk_tmp = None\n \n if qcsubi_ is None:\n raise ValueError(\"Argument qcsubi cannot be None\")\n if qcsubi_ is None:\n raise ValueError(\"Argument qcsubi may not be None\")\n if isinstance(qcsubi_, numpy.ndarray) and qcsubi_.dtype is numpy.dtype(numpy.int32) and qcsubi_.flags.contiguous:\n _qcsubi_copyarray = False\n _qcsubi_tmp = ctypes.cast(qcsubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubi_ is not None:\n _qcsubi_copyarray = True\n _qcsubi_np_tmp = numpy.zeros(len(qcsubi_),numpy.dtype(numpy.int32))\n _qcsubi_np_tmp[:] = qcsubi_\n assert _qcsubi_np_tmp.flags.contiguous\n _qcsubi_tmp = ctypes.cast(_qcsubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubi_copyarray = False\n _qcsubi_tmp = None\n \n if qcsubj_ is None:\n raise ValueError(\"Argument qcsubj cannot be None\")\n if qcsubj_ is None:\n raise ValueError(\"Argument qcsubj may not be None\")\n if isinstance(qcsubj_, numpy.ndarray) and qcsubj_.dtype is numpy.dtype(numpy.int32) and qcsubj_.flags.contiguous:\n _qcsubj_copyarray = False\n _qcsubj_tmp = ctypes.cast(qcsubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubj_ is not None:\n _qcsubj_copyarray = True\n _qcsubj_np_tmp = numpy.zeros(len(qcsubj_),numpy.dtype(numpy.int32))\n _qcsubj_np_tmp[:] = qcsubj_\n assert _qcsubj_np_tmp.flags.contiguous\n _qcsubj_tmp = ctypes.cast(_qcsubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubj_copyarray = False\n _qcsubj_tmp = None\n \n if qcval_ is None:\n raise ValueError(\"Argument qcval cannot be None\")\n if qcval_ is None:\n raise ValueError(\"Argument qcval may not be None\")\n if isinstance(qcval_, numpy.ndarray) and qcval_.dtype is numpy.dtype(numpy.float64) and qcval_.flags.contiguous:\n _qcval_copyarray = False\n _qcval_tmp = ctypes.cast(qcval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif qcval_ is not None:\n _qcval_copyarray = True\n _qcval_np_tmp = numpy.zeros(len(qcval_),numpy.dtype(numpy.float64))\n _qcval_np_tmp[:] = qcval_\n assert _qcval_np_tmp.flags.contiguous\n _qcval_tmp = ctypes.cast(_qcval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _qcval_copyarray = False\n _qcval_tmp = None\n \n res = __library__.MSK_XX_putqcon(self.__nativep,numqcnz_,_qcsubk_tmp,_qcsubi_tmp,_qcsubj_tmp,_qcval_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def convert_to_q(self):\n if self.measure == 'Q':\n warnings.warn('Parameters are already converted to Q!')\n else:\n kappa_sp = self.kappa_s\n kappa_yp = self.kappa_y\n self.kappa_s = self.kappa_s - self.lmbd_s * self.eta_s\n self.kappa_y = self.kappa_y - self.lmbd_y * self.eta_y\n self.scale = kappa_sp / self.kappa_s\n self.mean_v *= (kappa_yp / self.kappa_y * self.scale)\n self.lmbd = 0\n self.eta_y *= (self.scale**.5)\n self.measure = 'Q'\n self.update_ajd()", "def putSCeval(self,\n opro = None,\n oprjo = None,\n oprfo = None,\n oprgo = None,\n oprho = None,\n oprc = None,\n opric = None,\n oprjc = None,\n oprfc = None,\n oprgc = None,\n oprhc = None):\n\n if ( opro is not None\n and oprjo is not None\n and oprfo is not None\n and oprgo is not None\n and oprho is not None):\n # we have objective.\n try:\n numnlov = len(opro)\n if ( numnlov != len(oprjo)\n or numnlov != len(oprfo)\n or numnlov != len(oprgo)\n or numnlov != len(oprho)):\n raise SCoptException(\"Arguments opro, oprjo, oprfo, oprgo and oprho have different lengths\")\n if not all([ isinstance(i,scopr) for i in opro ]):\n raise SCoptException(\"Argument opro must be an array of mosek.scopr\")\n\n _opro = array.array('i',opro)\n _oprjo = array.array('i',oprjo)\n _oprfo = array.array('d',oprfo)\n _oprgo = array.array('d',oprgo)\n _oprho = array.array('d',oprho)\n except TypeError:\n raise ValueError(\"Arguments opro, oprjo, oprfo, oprgo and oprho must be arrays\")\n else:\n numnlov = 0\n\n if ( oprc is not None\n and opric is not None\n and oprjc is not None\n and oprfc is not None\n and oprgc is not None\n and oprhc is not None):\n # we have objective.\n try:\n numnlcv = len(oprc)\n if ( numnlcv != len(opric)\n or numnlcv != len(oprjc)\n or numnlcv != len(oprfc)\n or numnlcv != len(oprgc)\n or numnlcv != len(oprhc)):\n raise ValueError(\"Arguments oprc, opric, oprjc, oprfc, oprgc and oprhc have different lengths\") \n if not all([isinstance(i,scopr) for i in oprc]):\n raise ValieError(\"Argument oprc must be an array of mosek.scopr\")\n _oprc = array.array('i',oprc)\n _opric = array.array('i',opric)\n _oprjc = array.array('i',oprjc)\n _oprfc = array.array('d',oprfc)\n _oprgc = array.array('d',oprgc)\n _oprhc = array.array('d',oprhc)\n except TypeError:\n # not 'len' operation\n raise ValueError(\"Arguments oprc, opric, oprjc, oprfc, oprgc and oprhc must be arrays\") \n else:\n numnlcv = 0\n\n if numnlov > 0 or numnlcv > 0:\n args = []\n if numnlov > 0:\n args.append(memoryview(_opro))\n args.append(memoryview(_oprjo))\n args.append(memoryview(_oprfo))\n args.append(memoryview(_oprgo))\n args.append(memoryview(_oprho))\n else:\n args.extend([ None, None, None, None, None ])\n\n if numnlcv > 0:\n args.append(memoryview(_oprc))\n args.append(memoryview(_opric))\n args.append(memoryview(_oprjc))\n args.append(memoryview(_oprfc))\n args.append(memoryview(_oprgc))\n args.append(memoryview(_oprhc))\n else:\n args.extend([ None, None, None, None, None, None ])\n\n print(len(args))\n res = self.__obj.putSCeval(*args)", "def putvarsolutionj(self,j_,whichsol_,sk_,x_,sl_,su_,sn_):\n res = __library__.MSK_XX_putvarsolutionj(self.__nativep,j_,whichsol_,sk_,x_,sl_,su_,sn_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def update_q(self):\n beta = self.EC_beta\n self.gamma_q = (self.gamma_s - self.gamma_r) * beta + (1 - beta) * self.gamma_q\n self.Sigma_q = (self.Sigma_s - self.Sigma_r) * beta + (1 - beta) * self.Sigma_q\n try:\n assert np.all(np.logical_not(np.isnan(self.gamma_q)))\n except:\n print(\"Invalid update encountered...\")", "def _update_objective(self):\n # rewrap the cost if the solver has been run\n self.Finalize()\n return", "def Optimize_Sqq(qj,dt):\n\n # find the signal psd\n fs=1.0/dt\n M,L = 256,512\n wxx,Sxx=sa.Welch_estimator(qj,fs=fs,M=M,L=L)\n w_threshold = fs*np.pi/2\n Sxx=Sxx[wxx<w_threshold]\n wxx=wxx[wxx<w_threshold]\n\n # the psd of oscillator model is computed using Wiener-Khinchin relation\n # and called in the objective function\n print('S_qq matching objective fun: ls error of spectrum')\n spec_ls_distance = lambda params: np.linalg.norm(Sxx*(Sxx - sa.Oscillator_Spectrum(params[0],params[1]/params[0],params[1],wxx)),ord=1)\n\n # optimize via pyswarm - v1\n lb = [0.001, 0.001]\n ub = [100, 500]\n xopt, fopt = pso(spec_ls_distance, lb, ub, maxiter=10000,swarmsize=10000,minfunc=1e-10)\n k,D = xopt[0],xopt[1]\n\n b = D/k\n print('result: k='+str(k)+' b='+str(b)+' D='+str(D))\n\n return k,D", "def putsolutionyi(self,i_,whichsol_,y_):\n res = __library__.MSK_XX_putsolutionyi(self.__nativep,i_,whichsol_,y_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def objective(self, objective):\n\n self._objective = objective", "def puty(self,whichsol_,y_):\n _y_minlength = self.getnumcon()\n if self.getnumcon() > 0 and y_ is not None and len(y_) != self.getnumcon():\n raise ValueError(\"Array argument y is not long enough: Is %d, expected %d\" % (len(y_),self.getnumcon()))\n if y_ is None:\n raise ValueError(\"Argument y cannot be None\")\n if y_ is None:\n raise ValueError(\"Argument y may not be None\")\n if isinstance(y_, numpy.ndarray) and y_.dtype is numpy.dtype(numpy.float64) and y_.flags.contiguous:\n _y_copyarray = False\n _y_tmp = ctypes.cast(y_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif y_ is not None:\n _y_copyarray = True\n _y_np_tmp = numpy.zeros(len(y_),numpy.dtype(numpy.float64))\n _y_np_tmp[:] = y_\n assert _y_np_tmp.flags.contiguous\n _y_tmp = ctypes.cast(_y_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _y_copyarray = False\n _y_tmp = None\n \n res = __library__.MSK_XX_puty(self.__nativep,whichsol_,_y_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def tweak_q(self, q):\n self._q = q\n self.reset()", "def putbarsj(self,whichsol_,j_,barsj_):\n _barsj_minlength = self.getlenbarvarj((j_))\n if self.getlenbarvarj((j_)) > 0 and barsj_ is not None and len(barsj_) != self.getlenbarvarj((j_)):\n raise ValueError(\"Array argument barsj is not long enough: Is %d, expected %d\" % (len(barsj_),self.getlenbarvarj((j_))))\n if barsj_ is None:\n raise ValueError(\"Argument barsj cannot be None\")\n if barsj_ is None:\n raise ValueError(\"Argument barsj may not be None\")\n if isinstance(barsj_, numpy.ndarray) and barsj_.dtype is numpy.dtype(numpy.float64) and barsj_.flags.contiguous:\n _barsj_copyarray = False\n _barsj_tmp = ctypes.cast(barsj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif barsj_ is not None:\n _barsj_copyarray = True\n _barsj_np_tmp = numpy.zeros(len(barsj_),numpy.dtype(numpy.float64))\n _barsj_np_tmp[:] = barsj_\n assert _barsj_np_tmp.flags.contiguous\n _barsj_tmp = ctypes.cast(_barsj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _barsj_copyarray = False\n _barsj_tmp = None\n \n res = __library__.MSK_XX_putbarsj(self.__nativep,whichsol_,j_,_barsj_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def updateIMU(self, q: np.ndarray, gyr: np.ndarray, acc: np.ndarray) -> np.ndarray:\n if gyr is None or not np.linalg.norm(gyr)>0:\n return q\n qEst = 0.5 * q_prod(q, [0, *gyr]) # (eq. 12)\n a_norm = np.linalg.norm(acc)\n if a_norm>0:\n a = acc/a_norm\n qw, qx, qy, qz = q/np.linalg.norm(q)\n # Gradient objective function (eq. 25) and Jacobian (eq. 26)\n f = np.array([2.0*(qx*qz - qw*qy) - a[0],\n 2.0*(qw*qx + qy*qz) - a[1],\n 2.0*(0.5-qx**2-qy**2) - a[2]]) # (eq. 25)\n J = np.array([[-2.0*qy, 2.0*qz, -2.0*qw, 2.0*qx],\n [ 2.0*qx, 2.0*qw, 2.0*qz, 2.0*qy],\n [ 0.0, -4.0*qx, -4.0*qy, 0.0 ]]) # (eq. 26)\n # Objective Function Gradient\n gradient = J.T@f # (eq. 34)\n gradient /= np.linalg.norm(gradient)\n qEst -= self.gain*gradient # (eq. 33)\n q += qEst*self.Dt # (eq. 13)\n q /= np.linalg.norm(q)\n return q", "def getqobjij(self,i_,j_):\n qoij_ = ctypes.c_double()\n res = __library__.MSK_XX_getqobjij(self.__nativep,i_,j_,ctypes.byref(qoij_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n qoij_ = qoij_.value\n _qoij_return_value = qoij_\n return (_qoij_return_value)", "def update_Q(self):", "def calc_q_square(self):\n return self._q_x()**2 + self._q_z()**2", "def update(self, q=None, l=None, u=None,\n Px=None, Px_idx=np.array([]), Ax=None, Ax_idx=np.array([])):\n\n # get problem dimensions\n (n, m) = self._model.dimensions()\n\n # check consistency of the input arguments\n if q is not None and len(q) != n:\n raise ValueError(\"q must have length n\")\n if l is not None:\n if not isinstance(l, np.ndarray):\n raise TypeError(\"l must be numpy.ndarray, not %s\" %\n type(l).__name__)\n elif len(l) != m:\n raise ValueError(\"l must have length m\")\n # Convert values to -OSQP_INFTY\n l = np.maximum(l, -_osqp.constant('OSQP_INFTY'))\n if u is not None:\n if not isinstance(u, np.ndarray):\n raise TypeError(\"u must be numpy.ndarray, not %s\" %\n type(u).__name__)\n elif len(u) != m:\n raise ValueError(\"u must have length m\")\n # Convert values to OSQP_INFTY\n u = np.minimum(u, _osqp.constant('OSQP_INFTY'))\n if Ax is None:\n if len(Ax_idx) > 0:\n raise ValueError(\"Vector Ax has not been specified\")\n else:\n if len(Ax_idx) > 0 and len(Ax) != len(Ax_idx):\n raise ValueError(\"Ax and Ax_idx must have the same lengths\")\n if Px is None:\n if len(Px_idx) > 0:\n raise ValueError(\"Vector Px has not been specified\")\n else:\n if len(Px_idx) > 0 and len(Px) != len(Px_idx):\n raise ValueError(\"Px and Px_idx must have the same lengths\")\n if q is None and l is None and u is None and Px is None and Ax is None:\n raise ValueError(\"No updatable data has been specified\")\n\n # update linear cost\n if q is not None:\n self._model.update_lin_cost(q)\n\n # update lower bound\n if l is not None and u is None:\n self._model.update_lower_bound(l)\n\n # update upper bound\n if u is not None and l is None:\n self._model.update_upper_bound(u)\n\n # update bounds\n if l is not None and u is not None:\n self._model.update_bounds(l, u)\n\n # update matrix P\n if Px is not None and Ax is None:\n self._model.update_P(Px, Px_idx, len(Px))\n\n # update matrix A\n if Ax is not None and Px is None:\n self._model.update_A(Ax, Ax_idx, len(Ax))\n\n # update matrices P and A\n if Px is not None and Ax is not None:\n self._model.update_P_A(Px, Px_idx, len(Px), Ax, Ax_idx, len(Ax))\n\n\n # TODO(bart): this will be unnecessary when the derivative will be in C\n # update problem data in self._derivative_cache\n if q is not None:\n self._derivative_cache[\"q\"] = q\n\n if l is not None:\n self._derivative_cache[\"l\"] = l\n\n if u is not None:\n self._derivative_cache[\"u\"] = u\n\n if Px is not None:\n if Px_idx.size == 0:\n self._derivative_cache[\"P\"].data = Px\n else:\n self._derivative_cache[\"P\"].data[Px_idx] = Px\n\n if Ax is not None:\n if Ax_idx.size == 0:\n self._derivative_cache[\"A\"].data = Ax\n else:\n self._derivative_cache[\"A\"].data[Ax_idx] = Ax\n\n # delete results from self._derivative_cache to prohibit\n # taking the derivative of unsolved problems\n if \"results\" in self._derivative_cache.keys():\n del self._derivative_cache[\"results\"]", "def objective(self):\n pass", "def test_qing(self):\n fun = get_problem('qing', self.dimension, -500, 500)\n self.assertAlmostEqual(fun(self.array10), 584.0, delta=1e-4)", "def do_reduction_placzek_corrections(q,sqfg,bgd,rescale_bgd=1.0,plaz_type=None,\n gauss_damp=False,gw=20.0,qmax=None,qmin=None,\n rmin=0.0,rmax=20.0,delr=.02\n ,qminpla=10.0,qmaxpla=30.0,ndeg=2, return_correction = False,\n skip_bgd = False, return_final_sq = False, force_qmax_type='Off'):\n #first, make netsq if bgd and/or damping is present\n q = np.array(q)\n sqfg = np.array(sqfg)\n bgd = np.array(bgd)\n\n if skip_bgd:\n netsq = sqfg\n else:\n netsq = sqfg - bgd*rescale_bgd\n\n\n if gauss_damp:\n netsq = netsq*gauss(q,gw,0)\n\n\n if force_qmax_type == 'Force Data (PreCorrection)':\n qcut, sqcut = cut_data(q,netsq,qmax-.5,qmax)\n mean_sqmax = np.mean(sqcut)\n netsq -= mean_sqmax\n\n #now, apply a correction if requested\n if plaz_type != None:\n if plaz_type == 'Polynomial' or plaz_type == 'poly' or plaz_type == 'ndeg':\n sq_poly_fit = fit_ndeg_to_sq(q,netsq,ndeg=ndeg,qmin=qminpla,qmax=qmaxpla)\n this_fit = sq_poly_fit\n elif plaz_type == 'Pseudo-Voight' or plaz_type == 'pv' or plaz_type == 'hydro':\n pv_fit = fit_pv_to_sq(q,netsq,qmin=qminpla,qmax=qmaxpla)\n this_fit = pv_fit\n elif plaz_type == 'PVoight + n0' or plaz_type == 'pvndeg0':\n pv_n0_fit = fit_pv_n0_to_sq(q,netsq,qmin=qminpla,qmax=qmaxpla)\n this_fit = pv_n0_fit\n elif plaz_type == 'PVoight + n1' or plaz_type == 'pvndeg1':\n pv_n1_fit = fit_pv_n1_to_sq(q,netsq,qmin=qminpla,qmax=qmaxpla)\n this_fit = pv_n1_fit\n elif plaz_type == 'PVoight + n2' or plaz_type == 'pvndeg2':\n pv_n2_fit = fit_pv_n2_to_sq(q,netsq,qmin=qminpla,qmax=qmaxpla)\n this_fit = pv_n2_fit\n else:\n print (\"I don't know that correction type, sorry\")\n this_fit = np.zeros(len(q))\n else:\n this_fit = np.zeros(len(q))\n\n if force_qmax_type == 'Force Data' or force_qmax_type == 'Force Both (Independent)':\n qcut, sqcut = cut_data(q,netsq,qmax-.5,qmax)\n mean_sqmax = np.mean(sqcut)\n netsq -= mean_sqmax\n if force_qmax_type == 'Force Correction' or force_qmax_type == 'Force Both (Independent)':\n qcut, sqcut = cut_data(q,this_fit,qmax-.5,qmax)\n mean_sqmax = np.mean(sqcut)\n this_fit -= mean_sqmax\n if force_qmax_type == 'ReCorrection':\n qcut, sqcut = cut_data(q,netsq-this_fit,qmax-.5,qmax)\n mean_sqmax = np.mean(sqcut)\n this_fit += mean_sqmax\n\n netsq = netsq - this_fit\n\n if return_correction:\n return this_fit\n\n if return_final_sq:\n return netsq\n\n #finally, generate PDF\n r,gr = make_gr_from_sq(q,netsq,qmin=qmin,qmax=qmax,rmin=rmin,rmax=rmax,delr=delr)\n\n return r,gr", "def initial_Q(self, negative):\n \n ##get each values in the Q, and change their content to given number, plan to use in Q5\n for key in self.Q.iterkeys():\n self.Q[key] = float(negative)", "def quo(self, a, b):\n raise NotImplementedError" ]

[ "0.71988887", "0.67377245", "0.66842896", "0.6546956", "0.642287", "0.5680763", "0.565179", "0.5649305", "0.5616514", "0.55763125", "0.536642", "0.5291595", "0.5255819", "0.51279503", "0.51187503", "0.51012385", "0.50656164", "0.50490975", "0.5015812", "0.5012745", "0.50038093", "0.50020003", "0.49785858", "0.4954533", "0.49522758", "0.49517247", "0.4938055", "0.49255264", "0.49179035", "0.48810005" ]

[ "def putqobjij(self,i_,j_,qoij_): # 3\n res = self.__obj.putqobjij(i_,j_,qoij_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def getqobjij(self,i_,j_): # 3\n res,resargs = self.__obj.getqobjij(i_,j_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _qoij_return_value = resargs\n return _qoij_return_value", "def getqobjij(self,i_,j_):\n qoij_ = ctypes.c_double()\n res = __library__.MSK_XX_getqobjij(self.__nativep,i_,j_,ctypes.byref(qoij_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n qoij_ = qoij_.value\n _qoij_return_value = qoij_\n return (_qoij_return_value)", "def _qij_minus(i: int, j: int):\n ib = i * 2 + 1\n jb = j * 2 + 1\n term = FermionOperator(((jb, 0), (ib, 0)), 1.0)\n return term", "def _qij_0(i: int, j: int):\n ia = i * 2 + 0\n ib = i * 2 + 1\n ja = j * 2 + 0\n jb = j * 2 + 1\n term1 = FermionOperator(((ja, 0), (ib, 0)), 1.0)\n term2 = FermionOperator(((ia, 0), (jb, 0)), 1.0)\n return numpy.sqrt(0.5) * (term1 - term2)", "def putsolutionyi(self,i_,whichsol_,y_):\n res = __library__.MSK_XX_putsolutionyi(self.__nativep,i_,whichsol_,y_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def _qij_plus(i: int, j: int):\n ia = i * 2 + 0\n ja = j * 2 + 0\n term = FermionOperator(((ja, 0), (ia, 0)), 1.0)\n return term", "def putaij(self,i_,j_,aij_): # 3\n res = self.__obj.putaij(i_,j_,aij_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putaij(self,i_,j_,aij_):\n res = __library__.MSK_XX_putaij(self.__nativep,i_,j_,aij_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def substitute_cost(self, i, j):\n raise NotImplementedError", "def putsolutionyi(self,i_,whichsol_,y_): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res = self.__obj.putsolutionyi(i_,whichsol_,y_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putqobj(self,qosubi_,qosubj_,qoval_):\n numqonz_ = None\n if numqonz_ is None:\n numqonz_ = len(qosubi_)\n elif numqonz_ != len(qosubi_):\n raise IndexError(\"Inconsistent length of array qosubi\")\n if numqonz_ is None:\n numqonz_ = len(qosubj_)\n elif numqonz_ != len(qosubj_):\n raise IndexError(\"Inconsistent length of array qosubj\")\n if numqonz_ is None:\n numqonz_ = len(qoval_)\n elif numqonz_ != len(qoval_):\n raise IndexError(\"Inconsistent length of array qoval\")\n if qosubi_ is None:\n raise ValueError(\"Argument qosubi cannot be None\")\n if qosubi_ is None:\n raise ValueError(\"Argument qosubi may not be None\")\n if isinstance(qosubi_, numpy.ndarray) and qosubi_.dtype is numpy.dtype(numpy.int32) and qosubi_.flags.contiguous:\n _qosubi_copyarray = False\n _qosubi_tmp = ctypes.cast(qosubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qosubi_ is not None:\n _qosubi_copyarray = True\n _qosubi_np_tmp = numpy.zeros(len(qosubi_),numpy.dtype(numpy.int32))\n _qosubi_np_tmp[:] = qosubi_\n assert _qosubi_np_tmp.flags.contiguous\n _qosubi_tmp = ctypes.cast(_qosubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qosubi_copyarray = False\n _qosubi_tmp = None\n \n if qosubj_ is None:\n raise ValueError(\"Argument qosubj cannot be None\")\n if qosubj_ is None:\n raise ValueError(\"Argument qosubj may not be None\")\n if isinstance(qosubj_, numpy.ndarray) and qosubj_.dtype is numpy.dtype(numpy.int32) and qosubj_.flags.contiguous:\n _qosubj_copyarray = False\n _qosubj_tmp = ctypes.cast(qosubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qosubj_ is not None:\n _qosubj_copyarray = True\n _qosubj_np_tmp = numpy.zeros(len(qosubj_),numpy.dtype(numpy.int32))\n _qosubj_np_tmp[:] = qosubj_\n assert _qosubj_np_tmp.flags.contiguous\n _qosubj_tmp = ctypes.cast(_qosubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qosubj_copyarray = False\n _qosubj_tmp = None\n \n if qoval_ is None:\n raise ValueError(\"Argument qoval cannot be None\")\n if qoval_ is None:\n raise ValueError(\"Argument qoval may not be None\")\n if isinstance(qoval_, numpy.ndarray) and qoval_.dtype is numpy.dtype(numpy.float64) and qoval_.flags.contiguous:\n _qoval_copyarray = False\n _qoval_tmp = ctypes.cast(qoval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif qoval_ is not None:\n _qoval_copyarray = True\n _qoval_np_tmp = numpy.zeros(len(qoval_),numpy.dtype(numpy.float64))\n _qoval_np_tmp[:] = qoval_\n assert _qoval_np_tmp.flags.contiguous\n _qoval_tmp = ctypes.cast(_qoval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _qoval_copyarray = False\n _qoval_tmp = None\n \n res = __library__.MSK_XX_putqobj(self.__nativep,numqonz_,_qosubi_tmp,_qosubj_tmp,_qoval_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def fixA(self,i,j,value):\n if self.coeffPattern[0] == None:\n m,n=self.m,self.n\n self.coeffPattern[0] = [[None]*m for i in range(m)]\n self.coeffPattern[0][i][j]=value\n self._updateEstimatorSize(i)", "def fixB(self,i,j,value):\n if self.coeffPattern[1] == None:\n m,n=self.m,self.n\n self.coeffPattern = [[None]*n for i in range(m)]\n self.coeffPattern[1][i][j]=value\n self._updateEstimatorSize(i)", "def putvarsolutionj(self,j_,whichsol_,sk_,x_,sl_,su_,sn_):\n res = __library__.MSK_XX_putvarsolutionj(self.__nativep,j_,whichsol_,sk_,x_,sl_,su_,sn_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putconsolutioni(self,i_,whichsol_,sk_,x_,sl_,su_):\n res = __library__.MSK_XX_putconsolutioni(self.__nativep,i_,whichsol_,sk_,x_,sl_,su_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putqconk(self,k_,qcsubi_,qcsubj_,qcval_):\n numqcnz_ = None\n if numqcnz_ is None:\n numqcnz_ = len(qcsubi_)\n elif numqcnz_ != len(qcsubi_):\n raise IndexError(\"Inconsistent length of array qcsubi\")\n if numqcnz_ is None:\n numqcnz_ = len(qcsubj_)\n elif numqcnz_ != len(qcsubj_):\n raise IndexError(\"Inconsistent length of array qcsubj\")\n if numqcnz_ is None:\n numqcnz_ = len(qcval_)\n elif numqcnz_ != len(qcval_):\n raise IndexError(\"Inconsistent length of array qcval\")\n if qcsubi_ is None:\n raise ValueError(\"Argument qcsubi cannot be None\")\n if qcsubi_ is None:\n raise ValueError(\"Argument qcsubi may not be None\")\n if isinstance(qcsubi_, numpy.ndarray) and qcsubi_.dtype is numpy.dtype(numpy.int32) and qcsubi_.flags.contiguous:\n _qcsubi_copyarray = False\n _qcsubi_tmp = ctypes.cast(qcsubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubi_ is not None:\n _qcsubi_copyarray = True\n _qcsubi_np_tmp = numpy.zeros(len(qcsubi_),numpy.dtype(numpy.int32))\n _qcsubi_np_tmp[:] = qcsubi_\n assert _qcsubi_np_tmp.flags.contiguous\n _qcsubi_tmp = ctypes.cast(_qcsubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubi_copyarray = False\n _qcsubi_tmp = None\n \n if qcsubj_ is None:\n raise ValueError(\"Argument qcsubj cannot be None\")\n if qcsubj_ is None:\n raise ValueError(\"Argument qcsubj may not be None\")\n if isinstance(qcsubj_, numpy.ndarray) and qcsubj_.dtype is numpy.dtype(numpy.int32) and qcsubj_.flags.contiguous:\n _qcsubj_copyarray = False\n _qcsubj_tmp = ctypes.cast(qcsubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubj_ is not None:\n _qcsubj_copyarray = True\n _qcsubj_np_tmp = numpy.zeros(len(qcsubj_),numpy.dtype(numpy.int32))\n _qcsubj_np_tmp[:] = qcsubj_\n assert _qcsubj_np_tmp.flags.contiguous\n _qcsubj_tmp = ctypes.cast(_qcsubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubj_copyarray = False\n _qcsubj_tmp = None\n \n if qcval_ is None:\n raise ValueError(\"Argument qcval cannot be None\")\n if qcval_ is None:\n raise ValueError(\"Argument qcval may not be None\")\n if isinstance(qcval_, numpy.ndarray) and qcval_.dtype is numpy.dtype(numpy.float64) and qcval_.flags.contiguous:\n _qcval_copyarray = False\n _qcval_tmp = ctypes.cast(qcval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif qcval_ is not None:\n _qcval_copyarray = True\n _qcval_np_tmp = numpy.zeros(len(qcval_),numpy.dtype(numpy.float64))\n _qcval_np_tmp[:] = qcval_\n assert _qcval_np_tmp.flags.contiguous\n _qcval_tmp = ctypes.cast(_qcval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _qcval_copyarray = False\n _qcval_tmp = None\n \n res = __library__.MSK_XX_putqconk(self.__nativep,k_,numqcnz_,_qcsubi_tmp,_qcsubj_tmp,_qcval_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putqobj(self,qosubi,qosubj,qoval): # 3\n numqonz_ = None\n if numqonz_ is None:\n numqonz_ = len(qosubi)\n elif numqonz_ != len(qosubi):\n raise IndexError(\"Inconsistent length of array qosubi\")\n if numqonz_ is None:\n numqonz_ = len(qosubj)\n elif numqonz_ != len(qosubj):\n raise IndexError(\"Inconsistent length of array qosubj\")\n if numqonz_ is None:\n numqonz_ = len(qoval)\n elif numqonz_ != len(qoval):\n raise IndexError(\"Inconsistent length of array qoval\")\n if numqonz_ is None: numqonz_ = 0\n if qosubi is None: raise TypeError(\"Invalid type for argument qosubi\")\n if qosubi is None:\n qosubi_ = None\n else:\n try:\n qosubi_ = memoryview(qosubi)\n except TypeError:\n try:\n _tmparr_qosubi = array.array(\"i\",qosubi)\n except TypeError:\n raise TypeError(\"Argument qosubi has wrong type\")\n else:\n qosubi_ = memoryview(_tmparr_qosubi)\n \n else:\n if qosubi_.format != \"i\":\n qosubi_ = memoryview(array.array(\"i\",qosubi))\n \n if qosubj is None: raise TypeError(\"Invalid type for argument qosubj\")\n if qosubj is None:\n qosubj_ = None\n else:\n try:\n qosubj_ = memoryview(qosubj)\n except TypeError:\n try:\n _tmparr_qosubj = array.array(\"i\",qosubj)\n except TypeError:\n raise TypeError(\"Argument qosubj has wrong type\")\n else:\n qosubj_ = memoryview(_tmparr_qosubj)\n \n else:\n if qosubj_.format != \"i\":\n qosubj_ = memoryview(array.array(\"i\",qosubj))\n \n if qoval is None: raise TypeError(\"Invalid type for argument qoval\")\n if qoval is None:\n qoval_ = None\n else:\n try:\n qoval_ = memoryview(qoval)\n except TypeError:\n try:\n _tmparr_qoval = array.array(\"d\",qoval)\n except TypeError:\n raise TypeError(\"Argument qoval has wrong type\")\n else:\n qoval_ = memoryview(_tmparr_qoval)\n \n else:\n if qoval_.format != \"d\":\n qoval_ = memoryview(array.array(\"d\",qoval))\n \n res = self.__obj.putqobj(numqonz_,qosubi_,qosubj_,qoval_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putbarxj(self,whichsol_,j_,barxj_):\n _barxj_minlength = self.getlenbarvarj((j_))\n if self.getlenbarvarj((j_)) > 0 and barxj_ is not None and len(barxj_) != self.getlenbarvarj((j_)):\n raise ValueError(\"Array argument barxj is not long enough: Is %d, expected %d\" % (len(barxj_),self.getlenbarvarj((j_))))\n if barxj_ is None:\n raise ValueError(\"Argument barxj cannot be None\")\n if barxj_ is None:\n raise ValueError(\"Argument barxj may not be None\")\n if isinstance(barxj_, numpy.ndarray) and barxj_.dtype is numpy.dtype(numpy.float64) and barxj_.flags.contiguous:\n _barxj_copyarray = False\n _barxj_tmp = ctypes.cast(barxj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif barxj_ is not None:\n _barxj_copyarray = True\n _barxj_np_tmp = numpy.zeros(len(barxj_),numpy.dtype(numpy.float64))\n _barxj_np_tmp[:] = barxj_\n assert _barxj_np_tmp.flags.contiguous\n _barxj_tmp = ctypes.cast(_barxj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _barxj_copyarray = False\n _barxj_tmp = None\n \n res = __library__.MSK_XX_putbarxj(self.__nativep,whichsol_,j_,_barxj_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def ij(ij, pol, ant) :\n s.ij(pol, ij, ant)", "def fixC(self,i,value):\n if self.coeffPattern[2] == None:\n m,n=self.m,self.n\n self.coeffPattern[2] = [None]*m\n self.coeffPattern[2][i]=value\n self._updateEstimatorSize(i)", "def _Q(self, x, y):\n\n # Calculate the [Q] coefficient matrix\n Q = array([[0, 0, 0, -2, 0, 0, -6*x, -2*y, 0, 0, -6*x*y, 0],\n [0, 0, 0, 0, 0, -2, 0, 0, -2*x, -6*y, 0, -6*x*y],\n [0, 0, 0, 0, -2, 0, 0, -4*x, -4*y, 0, -6*x**2, -6*y**2]])\n \n # Return the [Q] coefficient matrix\n return Q", "def SetIJ(self, newI, newJ, newsize=-1):\n return _table.Table_SetIJ(self, newI, newJ, newsize)", "def replace_q_indices(circuit, q_nums, qr):\n\n new_circuit = qiskit.QuantumCircuit(qr)\n for op in circuit.data:\n original_qubits = op.qargs\n new_op = copy.deepcopy(op)\n new_op.qargs = [(qr, q_nums[x]) for x in [arg[1] for arg in original_qubits]]\n new_circuit.data.append(new_op)\n\n return new_circuit", "def putbarsj(self,whichsol_,j_,barsj_):\n _barsj_minlength = self.getlenbarvarj((j_))\n if self.getlenbarvarj((j_)) > 0 and barsj_ is not None and len(barsj_) != self.getlenbarvarj((j_)):\n raise ValueError(\"Array argument barsj is not long enough: Is %d, expected %d\" % (len(barsj_),self.getlenbarvarj((j_))))\n if barsj_ is None:\n raise ValueError(\"Argument barsj cannot be None\")\n if barsj_ is None:\n raise ValueError(\"Argument barsj may not be None\")\n if isinstance(barsj_, numpy.ndarray) and barsj_.dtype is numpy.dtype(numpy.float64) and barsj_.flags.contiguous:\n _barsj_copyarray = False\n _barsj_tmp = ctypes.cast(barsj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif barsj_ is not None:\n _barsj_copyarray = True\n _barsj_np_tmp = numpy.zeros(len(barsj_),numpy.dtype(numpy.float64))\n _barsj_np_tmp[:] = barsj_\n assert _barsj_np_tmp.flags.contiguous\n _barsj_tmp = ctypes.cast(_barsj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _barsj_copyarray = False\n _barsj_tmp = None\n \n res = __library__.MSK_XX_putbarsj(self.__nativep,whichsol_,j_,_barsj_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def J(self, name, q, x=None):\n\n x = self.x_zeros if x is None else x\n funcname = name + '[0,0,0]' if np.allclose(x, 0) else name\n # check for function in dictionary\n if self._J.get(funcname, None) is None:\n self._J[funcname] = self._calc_J(name=name, x=x)\n parameters = tuple(q) + tuple(x)\n return np.array(self._J[funcname](*parameters), dtype='float32')", "def putbarxj(self,whichsol_,j_,barxj): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if barxj is None: raise TypeError(\"Invalid type for argument barxj\")\n if barxj is None:\n barxj_ = None\n else:\n try:\n barxj_ = memoryview(barxj)\n except TypeError:\n try:\n _tmparr_barxj = array.array(\"d\",barxj)\n except TypeError:\n raise TypeError(\"Argument barxj has wrong type\")\n else:\n barxj_ = memoryview(_tmparr_barxj)\n \n else:\n if barxj_.format != \"d\":\n barxj_ = memoryview(array.array(\"d\",barxj))\n \n if barxj_ is not None and len(barxj_) != self.getlenbarvarj((j_)):\n raise ValueError(\"Array argument barxj has wrong length\")\n res = self.__obj.putbarxj(whichsol_,j_,barxj_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putcj(self,j_,cj_):\n res = __library__.MSK_XX_putcj(self.__nativep,j_,cj_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def quaternion_conjugate(self, q):\n\n \"\"\"\n in-place operation is an operation that changes directly the content of a given Tensor without making a copy.\n ALL operations on the tensor that operate in-place on it will have an _ postfix.\n \"\"\"\n q_star = q.new(4).fill_(-1)\n\n # leave the scalar unchanged and change signs of i, j, k number parts\n q_star[0] = 1.0\n\n return q * q_star", "def putqcon(self,qcsubk_,qcsubi_,qcsubj_,qcval_):\n numqcnz_ = None\n if numqcnz_ is None:\n numqcnz_ = len(qcsubi_)\n elif numqcnz_ != len(qcsubi_):\n raise IndexError(\"Inconsistent length of array qcsubi\")\n if numqcnz_ is None:\n numqcnz_ = len(qcsubj_)\n elif numqcnz_ != len(qcsubj_):\n raise IndexError(\"Inconsistent length of array qcsubj\")\n if numqcnz_ is None:\n numqcnz_ = len(qcval_)\n elif numqcnz_ != len(qcval_):\n raise IndexError(\"Inconsistent length of array qcval\")\n if qcsubk_ is None:\n raise ValueError(\"Argument qcsubk cannot be None\")\n if qcsubk_ is None:\n raise ValueError(\"Argument qcsubk may not be None\")\n if isinstance(qcsubk_, numpy.ndarray) and qcsubk_.dtype is numpy.dtype(numpy.int32) and qcsubk_.flags.contiguous:\n _qcsubk_copyarray = False\n _qcsubk_tmp = ctypes.cast(qcsubk_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubk_ is not None:\n _qcsubk_copyarray = True\n _qcsubk_np_tmp = numpy.zeros(len(qcsubk_),numpy.dtype(numpy.int32))\n _qcsubk_np_tmp[:] = qcsubk_\n assert _qcsubk_np_tmp.flags.contiguous\n _qcsubk_tmp = ctypes.cast(_qcsubk_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubk_copyarray = False\n _qcsubk_tmp = None\n \n if qcsubi_ is None:\n raise ValueError(\"Argument qcsubi cannot be None\")\n if qcsubi_ is None:\n raise ValueError(\"Argument qcsubi may not be None\")\n if isinstance(qcsubi_, numpy.ndarray) and qcsubi_.dtype is numpy.dtype(numpy.int32) and qcsubi_.flags.contiguous:\n _qcsubi_copyarray = False\n _qcsubi_tmp = ctypes.cast(qcsubi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubi_ is not None:\n _qcsubi_copyarray = True\n _qcsubi_np_tmp = numpy.zeros(len(qcsubi_),numpy.dtype(numpy.int32))\n _qcsubi_np_tmp[:] = qcsubi_\n assert _qcsubi_np_tmp.flags.contiguous\n _qcsubi_tmp = ctypes.cast(_qcsubi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubi_copyarray = False\n _qcsubi_tmp = None\n \n if qcsubj_ is None:\n raise ValueError(\"Argument qcsubj cannot be None\")\n if qcsubj_ is None:\n raise ValueError(\"Argument qcsubj may not be None\")\n if isinstance(qcsubj_, numpy.ndarray) and qcsubj_.dtype is numpy.dtype(numpy.int32) and qcsubj_.flags.contiguous:\n _qcsubj_copyarray = False\n _qcsubj_tmp = ctypes.cast(qcsubj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif qcsubj_ is not None:\n _qcsubj_copyarray = True\n _qcsubj_np_tmp = numpy.zeros(len(qcsubj_),numpy.dtype(numpy.int32))\n _qcsubj_np_tmp[:] = qcsubj_\n assert _qcsubj_np_tmp.flags.contiguous\n _qcsubj_tmp = ctypes.cast(_qcsubj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _qcsubj_copyarray = False\n _qcsubj_tmp = None\n \n if qcval_ is None:\n raise ValueError(\"Argument qcval cannot be None\")\n if qcval_ is None:\n raise ValueError(\"Argument qcval may not be None\")\n if isinstance(qcval_, numpy.ndarray) and qcval_.dtype is numpy.dtype(numpy.float64) and qcval_.flags.contiguous:\n _qcval_copyarray = False\n _qcval_tmp = ctypes.cast(qcval_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif qcval_ is not None:\n _qcval_copyarray = True\n _qcval_np_tmp = numpy.zeros(len(qcval_),numpy.dtype(numpy.float64))\n _qcval_np_tmp[:] = qcval_\n assert _qcval_np_tmp.flags.contiguous\n _qcval_tmp = ctypes.cast(_qcval_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _qcval_copyarray = False\n _qcval_tmp = None\n \n res = __library__.MSK_XX_putqcon(self.__nativep,numqcnz_,_qcsubk_tmp,_qcsubi_tmp,_qcsubj_tmp,_qcval_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)" ]

[ "0.8022443", "0.67813957", "0.659472", "0.62387276", "0.6219436", "0.61874425", "0.6092477", "0.6030711", "0.6007447", "0.58423615", "0.58029187", "0.5546479", "0.5543163", "0.54854363", "0.54592514", "0.54140776", "0.5403207", "0.54002947", "0.537518", "0.5340782", "0.5334495", "0.53016627", "0.527868", "0.52648073", "0.52392924", "0.52378243", "0.5234838", "0.52327704", "0.5232115", "0.52215046" ]

[ "def putsolutioni(self,accmode_,i_,whichsol_,sk_,x_,sl_,su_,sn_): # 3\n if not isinstance(accmode_,accmode): raise TypeError(\"Argument accmode has wrong type\")\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if not isinstance(sk_,stakey): raise TypeError(\"Argument sk has wrong type\")\n res = self.__obj.putsolutioni(accmode_,i_,whichsol_,sk_,x_,sl_,su_,sn_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putvarsolutionj(self,j_,whichsol_,sk_,x_,sl_,su_,sn_):\n res = __library__.MSK_XX_putvarsolutionj(self.__nativep,j_,whichsol_,sk_,x_,sl_,su_,sn_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putsolution(self,whichsol_,skc,skx,skn,xc,xx,y,slc,suc,slx,sux,snx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if skc is None:\n skc_ = None\n else:\n try:\n skc_ = memoryview(skc)\n except TypeError:\n try:\n _tmparr_skc = array.array(\"i\",skc)\n except TypeError:\n raise TypeError(\"Argument skc has wrong type\")\n else:\n skc_ = memoryview(_tmparr_skc)\n \n else:\n if skc_.format != \"i\":\n skc_ = memoryview(array.array(\"i\",skc))\n \n if skx is None:\n skx_ = None\n else:\n try:\n skx_ = memoryview(skx)\n except TypeError:\n try:\n _tmparr_skx = array.array(\"i\",skx)\n except TypeError:\n raise TypeError(\"Argument skx has wrong type\")\n else:\n skx_ = memoryview(_tmparr_skx)\n \n else:\n if skx_.format != \"i\":\n skx_ = memoryview(array.array(\"i\",skx))\n \n if skn is None:\n skn_ = None\n else:\n try:\n skn_ = memoryview(skn)\n except TypeError:\n try:\n _tmparr_skn = array.array(\"i\",skn)\n except TypeError:\n raise TypeError(\"Argument skn has wrong type\")\n else:\n skn_ = memoryview(_tmparr_skn)\n \n else:\n if skn_.format != \"i\":\n skn_ = memoryview(array.array(\"i\",skn))\n \n if xc is None:\n xc_ = None\n else:\n try:\n xc_ = memoryview(xc)\n except TypeError:\n try:\n _tmparr_xc = array.array(\"d\",xc)\n except TypeError:\n raise TypeError(\"Argument xc has wrong type\")\n else:\n xc_ = memoryview(_tmparr_xc)\n \n else:\n if xc_.format != \"d\":\n xc_ = memoryview(array.array(\"d\",xc))\n \n if xx is None:\n xx_ = None\n else:\n try:\n xx_ = memoryview(xx)\n except TypeError:\n try:\n _tmparr_xx = array.array(\"d\",xx)\n except TypeError:\n raise TypeError(\"Argument xx has wrong type\")\n else:\n xx_ = memoryview(_tmparr_xx)\n \n else:\n if xx_.format != \"d\":\n xx_ = memoryview(array.array(\"d\",xx))\n \n if y is None:\n y_ = None\n else:\n try:\n y_ = memoryview(y)\n except TypeError:\n try:\n _tmparr_y = array.array(\"d\",y)\n except TypeError:\n raise TypeError(\"Argument y has wrong type\")\n else:\n y_ = memoryview(_tmparr_y)\n \n else:\n if y_.format != \"d\":\n y_ = memoryview(array.array(\"d\",y))\n \n if slc is None:\n slc_ = None\n else:\n try:\n slc_ = memoryview(slc)\n except TypeError:\n try:\n _tmparr_slc = array.array(\"d\",slc)\n except TypeError:\n raise TypeError(\"Argument slc has wrong type\")\n else:\n slc_ = memoryview(_tmparr_slc)\n \n else:\n if slc_.format != \"d\":\n slc_ = memoryview(array.array(\"d\",slc))\n \n if suc is None:\n suc_ = None\n else:\n try:\n suc_ = memoryview(suc)\n except TypeError:\n try:\n _tmparr_suc = array.array(\"d\",suc)\n except TypeError:\n raise TypeError(\"Argument suc has wrong type\")\n else:\n suc_ = memoryview(_tmparr_suc)\n \n else:\n if suc_.format != \"d\":\n suc_ = memoryview(array.array(\"d\",suc))\n \n if slx is None:\n slx_ = None\n else:\n try:\n slx_ = memoryview(slx)\n except TypeError:\n try:\n _tmparr_slx = array.array(\"d\",slx)\n except TypeError:\n raise TypeError(\"Argument slx has wrong type\")\n else:\n slx_ = memoryview(_tmparr_slx)\n \n else:\n if slx_.format != \"d\":\n slx_ = memoryview(array.array(\"d\",slx))\n \n if sux is None:\n sux_ = None\n else:\n try:\n sux_ = memoryview(sux)\n except TypeError:\n try:\n _tmparr_sux = array.array(\"d\",sux)\n except TypeError:\n raise TypeError(\"Argument sux has wrong type\")\n else:\n sux_ = memoryview(_tmparr_sux)\n \n else:\n if sux_.format != \"d\":\n sux_ = memoryview(array.array(\"d\",sux))\n \n if snx is None:\n snx_ = None\n else:\n try:\n snx_ = memoryview(snx)\n except TypeError:\n try:\n _tmparr_snx = array.array(\"d\",snx)\n except TypeError:\n raise TypeError(\"Argument snx has wrong type\")\n else:\n snx_ = memoryview(_tmparr_snx)\n \n else:\n if snx_.format != \"d\":\n snx_ = memoryview(array.array(\"d\",snx))\n \n res = self.__obj.putsolution(whichsol_,skc_,skx_,skn_,xc_,xx_,y_,slc_,suc_,slx_,sux_,snx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def getsolution(self,whichsol_,skc_,skx_,skn_,xc_,xx_,y_,slc_,suc_,slx_,sux_,snx_):\n prosta_ = ctypes.c_int32()\n solsta_ = ctypes.c_int32()\n _skc_minlength = self.getnumcon()\n if self.getnumcon() > 0 and skc_ is not None and len(skc_) != self.getnumcon():\n raise ValueError(\"Array argument skc is not long enough: Is %d, expected %d\" % (len(skc_),self.getnumcon()))\n if isinstance(skc_,numpy.ndarray) and not skc_.flags.writeable:\n raise ValueError(\"Argument skc must be writable\")\n if skc_ is not None:\n _skc_tmp = (ctypes.c_int32 * len(skc_))()\n else:\n _skc_tmp = None\n _skx_minlength = self.getnumvar()\n if self.getnumvar() > 0 and skx_ is not None and len(skx_) != self.getnumvar():\n raise ValueError(\"Array argument skx is not long enough: Is %d, expected %d\" % (len(skx_),self.getnumvar()))\n if isinstance(skx_,numpy.ndarray) and not skx_.flags.writeable:\n raise ValueError(\"Argument skx must be writable\")\n if skx_ is not None:\n _skx_tmp = (ctypes.c_int32 * len(skx_))()\n else:\n _skx_tmp = None\n _skn_minlength = self.getnumcone()\n if self.getnumcone() > 0 and skn_ is not None and len(skn_) != self.getnumcone():\n raise ValueError(\"Array argument skn is not long enough: Is %d, expected %d\" % (len(skn_),self.getnumcone()))\n if isinstance(skn_,numpy.ndarray) and not skn_.flags.writeable:\n raise ValueError(\"Argument skn must be writable\")\n if skn_ is not None:\n _skn_tmp = (ctypes.c_int32 * len(skn_))()\n else:\n _skn_tmp = None\n _xc_minlength = self.getnumcon()\n if self.getnumcon() > 0 and xc_ is not None and len(xc_) != self.getnumcon():\n raise ValueError(\"Array argument xc is not long enough: Is %d, expected %d\" % (len(xc_),self.getnumcon()))\n if isinstance(xc_,numpy.ndarray) and not xc_.flags.writeable:\n raise ValueError(\"Argument xc must be writable\")\n if isinstance(xc_, numpy.ndarray) and xc_.dtype is numpy.dtype(numpy.float64) and xc_.flags.contiguous:\n _xc_copyarray = False\n _xc_tmp = ctypes.cast(xc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif xc_ is not None:\n _xc_copyarray = True\n _xc_np_tmp = numpy.zeros(len(xc_),numpy.dtype(numpy.float64))\n _xc_np_tmp[:] = xc_\n assert _xc_np_tmp.flags.contiguous\n _xc_tmp = ctypes.cast(_xc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _xc_copyarray = False\n _xc_tmp = None\n \n _xx_minlength = self.getnumvar()\n if self.getnumvar() > 0 and xx_ is not None and len(xx_) != self.getnumvar():\n raise ValueError(\"Array argument xx is not long enough: Is %d, expected %d\" % (len(xx_),self.getnumvar()))\n if isinstance(xx_,numpy.ndarray) and not xx_.flags.writeable:\n raise ValueError(\"Argument xx must be writable\")\n if isinstance(xx_, numpy.ndarray) and xx_.dtype is numpy.dtype(numpy.float64) and xx_.flags.contiguous:\n _xx_copyarray = False\n _xx_tmp = ctypes.cast(xx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif xx_ is not None:\n _xx_copyarray = True\n _xx_np_tmp = numpy.zeros(len(xx_),numpy.dtype(numpy.float64))\n _xx_np_tmp[:] = xx_\n assert _xx_np_tmp.flags.contiguous\n _xx_tmp = ctypes.cast(_xx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _xx_copyarray = False\n _xx_tmp = None\n \n _y_minlength = self.getnumcon()\n if self.getnumcon() > 0 and y_ is not None and len(y_) != self.getnumcon():\n raise ValueError(\"Array argument y is not long enough: Is %d, expected %d\" % (len(y_),self.getnumcon()))\n if isinstance(y_,numpy.ndarray) and not y_.flags.writeable:\n raise ValueError(\"Argument y must be writable\")\n if isinstance(y_, numpy.ndarray) and y_.dtype is numpy.dtype(numpy.float64) and y_.flags.contiguous:\n _y_copyarray = False\n _y_tmp = ctypes.cast(y_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif y_ is not None:\n _y_copyarray = True\n _y_np_tmp = numpy.zeros(len(y_),numpy.dtype(numpy.float64))\n _y_np_tmp[:] = y_\n assert _y_np_tmp.flags.contiguous\n _y_tmp = ctypes.cast(_y_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _y_copyarray = False\n _y_tmp = None\n \n _slc_minlength = self.getnumcon()\n if self.getnumcon() > 0 and slc_ is not None and len(slc_) != self.getnumcon():\n raise ValueError(\"Array argument slc is not long enough: Is %d, expected %d\" % (len(slc_),self.getnumcon()))\n if isinstance(slc_,numpy.ndarray) and not slc_.flags.writeable:\n raise ValueError(\"Argument slc must be writable\")\n if isinstance(slc_, numpy.ndarray) and slc_.dtype is numpy.dtype(numpy.float64) and slc_.flags.contiguous:\n _slc_copyarray = False\n _slc_tmp = ctypes.cast(slc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slc_ is not None:\n _slc_copyarray = True\n _slc_np_tmp = numpy.zeros(len(slc_),numpy.dtype(numpy.float64))\n _slc_np_tmp[:] = slc_\n assert _slc_np_tmp.flags.contiguous\n _slc_tmp = ctypes.cast(_slc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slc_copyarray = False\n _slc_tmp = None\n \n _suc_minlength = self.getnumcon()\n if self.getnumcon() > 0 and suc_ is not None and len(suc_) != self.getnumcon():\n raise ValueError(\"Array argument suc is not long enough: Is %d, expected %d\" % (len(suc_),self.getnumcon()))\n if isinstance(suc_,numpy.ndarray) and not suc_.flags.writeable:\n raise ValueError(\"Argument suc must be writable\")\n if isinstance(suc_, numpy.ndarray) and suc_.dtype is numpy.dtype(numpy.float64) and suc_.flags.contiguous:\n _suc_copyarray = False\n _suc_tmp = ctypes.cast(suc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif suc_ is not None:\n _suc_copyarray = True\n _suc_np_tmp = numpy.zeros(len(suc_),numpy.dtype(numpy.float64))\n _suc_np_tmp[:] = suc_\n assert _suc_np_tmp.flags.contiguous\n _suc_tmp = ctypes.cast(_suc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _suc_copyarray = False\n _suc_tmp = None\n \n _slx_minlength = self.getnumvar()\n if self.getnumvar() > 0 and slx_ is not None and len(slx_) != self.getnumvar():\n raise ValueError(\"Array argument slx is not long enough: Is %d, expected %d\" % (len(slx_),self.getnumvar()))\n if isinstance(slx_,numpy.ndarray) and not slx_.flags.writeable:\n raise ValueError(\"Argument slx must be writable\")\n if isinstance(slx_, numpy.ndarray) and slx_.dtype is numpy.dtype(numpy.float64) and slx_.flags.contiguous:\n _slx_copyarray = False\n _slx_tmp = ctypes.cast(slx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slx_ is not None:\n _slx_copyarray = True\n _slx_np_tmp = numpy.zeros(len(slx_),numpy.dtype(numpy.float64))\n _slx_np_tmp[:] = slx_\n assert _slx_np_tmp.flags.contiguous\n _slx_tmp = ctypes.cast(_slx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slx_copyarray = False\n _slx_tmp = None\n \n _sux_minlength = self.getnumvar()\n if self.getnumvar() > 0 and sux_ is not None and len(sux_) != self.getnumvar():\n raise ValueError(\"Array argument sux is not long enough: Is %d, expected %d\" % (len(sux_),self.getnumvar()))\n if isinstance(sux_,numpy.ndarray) and not sux_.flags.writeable:\n raise ValueError(\"Argument sux must be writable\")\n if isinstance(sux_, numpy.ndarray) and sux_.dtype is numpy.dtype(numpy.float64) and sux_.flags.contiguous:\n _sux_copyarray = False\n _sux_tmp = ctypes.cast(sux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif sux_ is not None:\n _sux_copyarray = True\n _sux_np_tmp = numpy.zeros(len(sux_),numpy.dtype(numpy.float64))\n _sux_np_tmp[:] = sux_\n assert _sux_np_tmp.flags.contiguous\n _sux_tmp = ctypes.cast(_sux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _sux_copyarray = False\n _sux_tmp = None\n \n _snx_minlength = self.getnumvar()\n if self.getnumvar() > 0 and snx_ is not None and len(snx_) != self.getnumvar():\n raise ValueError(\"Array argument snx is not long enough: Is %d, expected %d\" % (len(snx_),self.getnumvar()))\n if isinstance(snx_,numpy.ndarray) and not snx_.flags.writeable:\n raise ValueError(\"Argument snx must be writable\")\n if isinstance(snx_, numpy.ndarray) and snx_.dtype is numpy.dtype(numpy.float64) and snx_.flags.contiguous:\n _snx_copyarray = False\n _snx_tmp = ctypes.cast(snx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif snx_ is not None:\n _snx_copyarray = True\n _snx_np_tmp = numpy.zeros(len(snx_),numpy.dtype(numpy.float64))\n _snx_np_tmp[:] = snx_\n assert _snx_np_tmp.flags.contiguous\n _snx_tmp = ctypes.cast(_snx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _snx_copyarray = False\n _snx_tmp = None\n \n res = __library__.MSK_XX_getsolution(self.__nativep,whichsol_,ctypes.byref(prosta_),ctypes.byref(solsta_),_skc_tmp,_skx_tmp,_skn_tmp,_xc_tmp,_xx_tmp,_y_tmp,_slc_tmp,_suc_tmp,_slx_tmp,_sux_tmp,_snx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _prosta_return_value = prosta(prosta_.value)\n _solsta_return_value = solsta(solsta_.value)\n if skc_ is not None: skc_[:] = [ stakey(v) for v in _skc_tmp[0:len(skc_)] ]\n if skx_ is not None: skx_[:] = [ stakey(v) for v in _skx_tmp[0:len(skx_)] ]\n if skn_ is not None: skn_[:] = [ stakey(v) for v in _skn_tmp[0:len(skn_)] ]\n if _xc_copyarray:\n xc_[:] = _xc_np_tmp\n if _xx_copyarray:\n xx_[:] = _xx_np_tmp\n if _y_copyarray:\n y_[:] = _y_np_tmp\n if _slc_copyarray:\n slc_[:] = _slc_np_tmp\n if _suc_copyarray:\n suc_[:] = _suc_np_tmp\n if _slx_copyarray:\n slx_[:] = _slx_np_tmp\n if _sux_copyarray:\n sux_[:] = _sux_np_tmp\n if _snx_copyarray:\n snx_[:] = _snx_np_tmp\n return (_prosta_return_value,_solsta_return_value)", "def putsolutionyi(self,i_,whichsol_,y_):\n res = __library__.MSK_XX_putsolutionyi(self.__nativep,i_,whichsol_,y_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putsolution(self,whichsol_,skc_,skx_,skn_,xc_,xx_,y_,slc_,suc_,slx_,sux_,snx_):\n if skc_ is not None:\n _skc_tmp = (ctypes.c_int32 * len(skc_))(*skc_)\n else:\n _skc_tmp = None\n if skx_ is not None:\n _skx_tmp = (ctypes.c_int32 * len(skx_))(*skx_)\n else:\n _skx_tmp = None\n if skn_ is not None:\n _skn_tmp = (ctypes.c_int32 * len(skn_))(*skn_)\n else:\n _skn_tmp = None\n if isinstance(xc_, numpy.ndarray) and xc_.dtype is numpy.dtype(numpy.float64) and xc_.flags.contiguous:\n _xc_copyarray = False\n _xc_tmp = ctypes.cast(xc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif xc_ is not None:\n _xc_copyarray = True\n _xc_np_tmp = numpy.zeros(len(xc_),numpy.dtype(numpy.float64))\n _xc_np_tmp[:] = xc_\n assert _xc_np_tmp.flags.contiguous\n _xc_tmp = ctypes.cast(_xc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _xc_copyarray = False\n _xc_tmp = None\n \n if isinstance(xx_, numpy.ndarray) and xx_.dtype is numpy.dtype(numpy.float64) and xx_.flags.contiguous:\n _xx_copyarray = False\n _xx_tmp = ctypes.cast(xx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif xx_ is not None:\n _xx_copyarray = True\n _xx_np_tmp = numpy.zeros(len(xx_),numpy.dtype(numpy.float64))\n _xx_np_tmp[:] = xx_\n assert _xx_np_tmp.flags.contiguous\n _xx_tmp = ctypes.cast(_xx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _xx_copyarray = False\n _xx_tmp = None\n \n if isinstance(y_, numpy.ndarray) and y_.dtype is numpy.dtype(numpy.float64) and y_.flags.contiguous:\n _y_copyarray = False\n _y_tmp = ctypes.cast(y_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif y_ is not None:\n _y_copyarray = True\n _y_np_tmp = numpy.zeros(len(y_),numpy.dtype(numpy.float64))\n _y_np_tmp[:] = y_\n assert _y_np_tmp.flags.contiguous\n _y_tmp = ctypes.cast(_y_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _y_copyarray = False\n _y_tmp = None\n \n if isinstance(slc_, numpy.ndarray) and slc_.dtype is numpy.dtype(numpy.float64) and slc_.flags.contiguous:\n _slc_copyarray = False\n _slc_tmp = ctypes.cast(slc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slc_ is not None:\n _slc_copyarray = True\n _slc_np_tmp = numpy.zeros(len(slc_),numpy.dtype(numpy.float64))\n _slc_np_tmp[:] = slc_\n assert _slc_np_tmp.flags.contiguous\n _slc_tmp = ctypes.cast(_slc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slc_copyarray = False\n _slc_tmp = None\n \n if isinstance(suc_, numpy.ndarray) and suc_.dtype is numpy.dtype(numpy.float64) and suc_.flags.contiguous:\n _suc_copyarray = False\n _suc_tmp = ctypes.cast(suc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif suc_ is not None:\n _suc_copyarray = True\n _suc_np_tmp = numpy.zeros(len(suc_),numpy.dtype(numpy.float64))\n _suc_np_tmp[:] = suc_\n assert _suc_np_tmp.flags.contiguous\n _suc_tmp = ctypes.cast(_suc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _suc_copyarray = False\n _suc_tmp = None\n \n if isinstance(slx_, numpy.ndarray) and slx_.dtype is numpy.dtype(numpy.float64) and slx_.flags.contiguous:\n _slx_copyarray = False\n _slx_tmp = ctypes.cast(slx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slx_ is not None:\n _slx_copyarray = True\n _slx_np_tmp = numpy.zeros(len(slx_),numpy.dtype(numpy.float64))\n _slx_np_tmp[:] = slx_\n assert _slx_np_tmp.flags.contiguous\n _slx_tmp = ctypes.cast(_slx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slx_copyarray = False\n _slx_tmp = None\n \n if isinstance(sux_, numpy.ndarray) and sux_.dtype is numpy.dtype(numpy.float64) and sux_.flags.contiguous:\n _sux_copyarray = False\n _sux_tmp = ctypes.cast(sux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif sux_ is not None:\n _sux_copyarray = True\n _sux_np_tmp = numpy.zeros(len(sux_),numpy.dtype(numpy.float64))\n _sux_np_tmp[:] = sux_\n assert _sux_np_tmp.flags.contiguous\n _sux_tmp = ctypes.cast(_sux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _sux_copyarray = False\n _sux_tmp = None\n \n if isinstance(snx_, numpy.ndarray) and snx_.dtype is numpy.dtype(numpy.float64) and snx_.flags.contiguous:\n _snx_copyarray = False\n _snx_tmp = ctypes.cast(snx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif snx_ is not None:\n _snx_copyarray = True\n _snx_np_tmp = numpy.zeros(len(snx_),numpy.dtype(numpy.float64))\n _snx_np_tmp[:] = snx_\n assert _snx_np_tmp.flags.contiguous\n _snx_tmp = ctypes.cast(_snx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _snx_copyarray = False\n _snx_tmp = None\n \n res = __library__.MSK_XX_putsolution(self.__nativep,whichsol_,_skc_tmp,_skx_tmp,_skn_tmp,_xc_tmp,_xx_tmp,_y_tmp,_slc_tmp,_suc_tmp,_slx_tmp,_sux_tmp,_snx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def getsolution(self,whichsol_,skc,skx,skn,xc,xx,y,slc,suc,slx,sux,snx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n _copyback_skc = False\n if skc is None:\n skc_ = None\n else:\n try:\n skc_ = memoryview(skc)\n except TypeError:\n try:\n _tmparr_skc = array.array(\"i\",skc)\n except TypeError:\n raise TypeError(\"Argument skc has wrong type\")\n else:\n skc_ = memoryview(_tmparr_skc)\n _copyback_skc = True\n else:\n if skc_.format != \"i\":\n skc_ = memoryview(array.array(\"i\",skc))\n _copyback_skc = True\n if skc_ is not None and len(skc_) != self.getnumcon():\n raise ValueError(\"Array argument skc has wrong length\")\n _copyback_skx = False\n if skx is None:\n skx_ = None\n else:\n try:\n skx_ = memoryview(skx)\n except TypeError:\n try:\n _tmparr_skx = array.array(\"i\",skx)\n except TypeError:\n raise TypeError(\"Argument skx has wrong type\")\n else:\n skx_ = memoryview(_tmparr_skx)\n _copyback_skx = True\n else:\n if skx_.format != \"i\":\n skx_ = memoryview(array.array(\"i\",skx))\n _copyback_skx = True\n if skx_ is not None and len(skx_) != self.getnumvar():\n raise ValueError(\"Array argument skx has wrong length\")\n _copyback_skn = False\n if skn is None:\n skn_ = None\n else:\n try:\n skn_ = memoryview(skn)\n except TypeError:\n try:\n _tmparr_skn = array.array(\"i\",skn)\n except TypeError:\n raise TypeError(\"Argument skn has wrong type\")\n else:\n skn_ = memoryview(_tmparr_skn)\n _copyback_skn = True\n else:\n if skn_.format != \"i\":\n skn_ = memoryview(array.array(\"i\",skn))\n _copyback_skn = True\n if skn_ is not None and len(skn_) != self.getnumcone():\n raise ValueError(\"Array argument skn has wrong length\")\n _copyback_xc = False\n if xc is None:\n xc_ = None\n else:\n try:\n xc_ = memoryview(xc)\n except TypeError:\n try:\n _tmparr_xc = array.array(\"d\",xc)\n except TypeError:\n raise TypeError(\"Argument xc has wrong type\")\n else:\n xc_ = memoryview(_tmparr_xc)\n _copyback_xc = True\n else:\n if xc_.format != \"d\":\n xc_ = memoryview(array.array(\"d\",xc))\n _copyback_xc = True\n if xc_ is not None and len(xc_) != self.getnumcon():\n raise ValueError(\"Array argument xc has wrong length\")\n _copyback_xx = False\n if xx is None:\n xx_ = None\n else:\n try:\n xx_ = memoryview(xx)\n except TypeError:\n try:\n _tmparr_xx = array.array(\"d\",xx)\n except TypeError:\n raise TypeError(\"Argument xx has wrong type\")\n else:\n xx_ = memoryview(_tmparr_xx)\n _copyback_xx = True\n else:\n if xx_.format != \"d\":\n xx_ = memoryview(array.array(\"d\",xx))\n _copyback_xx = True\n if xx_ is not None and len(xx_) != self.getnumvar():\n raise ValueError(\"Array argument xx has wrong length\")\n _copyback_y = False\n if y is None:\n y_ = None\n else:\n try:\n y_ = memoryview(y)\n except TypeError:\n try:\n _tmparr_y = array.array(\"d\",y)\n except TypeError:\n raise TypeError(\"Argument y has wrong type\")\n else:\n y_ = memoryview(_tmparr_y)\n _copyback_y = True\n else:\n if y_.format != \"d\":\n y_ = memoryview(array.array(\"d\",y))\n _copyback_y = True\n if y_ is not None and len(y_) != self.getnumcon():\n raise ValueError(\"Array argument y has wrong length\")\n _copyback_slc = False\n if slc is None:\n slc_ = None\n else:\n try:\n slc_ = memoryview(slc)\n except TypeError:\n try:\n _tmparr_slc = array.array(\"d\",slc)\n except TypeError:\n raise TypeError(\"Argument slc has wrong type\")\n else:\n slc_ = memoryview(_tmparr_slc)\n _copyback_slc = True\n else:\n if slc_.format != \"d\":\n slc_ = memoryview(array.array(\"d\",slc))\n _copyback_slc = True\n if slc_ is not None and len(slc_) != self.getnumcon():\n raise ValueError(\"Array argument slc has wrong length\")\n _copyback_suc = False\n if suc is None:\n suc_ = None\n else:\n try:\n suc_ = memoryview(suc)\n except TypeError:\n try:\n _tmparr_suc = array.array(\"d\",suc)\n except TypeError:\n raise TypeError(\"Argument suc has wrong type\")\n else:\n suc_ = memoryview(_tmparr_suc)\n _copyback_suc = True\n else:\n if suc_.format != \"d\":\n suc_ = memoryview(array.array(\"d\",suc))\n _copyback_suc = True\n if suc_ is not None and len(suc_) != self.getnumcon():\n raise ValueError(\"Array argument suc has wrong length\")\n _copyback_slx = False\n if slx is None:\n slx_ = None\n else:\n try:\n slx_ = memoryview(slx)\n except TypeError:\n try:\n _tmparr_slx = array.array(\"d\",slx)\n except TypeError:\n raise TypeError(\"Argument slx has wrong type\")\n else:\n slx_ = memoryview(_tmparr_slx)\n _copyback_slx = True\n else:\n if slx_.format != \"d\":\n slx_ = memoryview(array.array(\"d\",slx))\n _copyback_slx = True\n if slx_ is not None and len(slx_) != self.getnumvar():\n raise ValueError(\"Array argument slx has wrong length\")\n _copyback_sux = False\n if sux is None:\n sux_ = None\n else:\n try:\n sux_ = memoryview(sux)\n except TypeError:\n try:\n _tmparr_sux = array.array(\"d\",sux)\n except TypeError:\n raise TypeError(\"Argument sux has wrong type\")\n else:\n sux_ = memoryview(_tmparr_sux)\n _copyback_sux = True\n else:\n if sux_.format != \"d\":\n sux_ = memoryview(array.array(\"d\",sux))\n _copyback_sux = True\n if sux_ is not None and len(sux_) != self.getnumvar():\n raise ValueError(\"Array argument sux has wrong length\")\n _copyback_snx = False\n if snx is None:\n snx_ = None\n else:\n try:\n snx_ = memoryview(snx)\n except TypeError:\n try:\n _tmparr_snx = array.array(\"d\",snx)\n except TypeError:\n raise TypeError(\"Argument snx has wrong type\")\n else:\n snx_ = memoryview(_tmparr_snx)\n _copyback_snx = True\n else:\n if snx_.format != \"d\":\n snx_ = memoryview(array.array(\"d\",snx))\n _copyback_snx = True\n if snx_ is not None and len(snx_) != self.getnumvar():\n raise ValueError(\"Array argument snx has wrong length\")\n res,resargs = self.__obj.getsolution(whichsol_,skc_,skx_,skn_,xc_,xx_,y_,slc_,suc_,slx_,sux_,snx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _prosta_return_value,_solsta_return_value = resargs\n if _copyback_snx:\n snx[:] = _tmparr_snx\n if _copyback_sux:\n sux[:] = _tmparr_sux\n if _copyback_slx:\n slx[:] = _tmparr_slx\n if _copyback_suc:\n suc[:] = _tmparr_suc\n if _copyback_slc:\n slc[:] = _tmparr_slc\n if _copyback_y:\n y[:] = _tmparr_y\n if _copyback_xx:\n xx[:] = _tmparr_xx\n if _copyback_xc:\n xc[:] = _tmparr_xc\n if _copyback_skn:\n for __tmp_var_2 in range(len(skn_)): skn[__tmp_var_2] = stakey(_tmparr_skn[__tmp_var_2])\n if _copyback_skx:\n for __tmp_var_1 in range(len(skx_)): skx[__tmp_var_1] = stakey(_tmparr_skx[__tmp_var_1])\n if _copyback_skc:\n for __tmp_var_0 in range(len(skc_)): skc[__tmp_var_0] = stakey(_tmparr_skc[__tmp_var_0])\n _solsta_return_value = solsta(_solsta_return_value)\n _prosta_return_value = prosta(_prosta_return_value)\n return _prosta_return_value,_solsta_return_value", "def putsolutionyi(self,i_,whichsol_,y_): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res = self.__obj.putsolutionyi(i_,whichsol_,y_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def updatesolutioninfo(self,whichsol_):\n res = __library__.MSK_XX_updatesolutioninfo(self.__nativep,whichsol_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putskc(self,whichsol_,skc): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if skc is None: raise TypeError(\"Invalid type for argument skc\")\n if skc is None:\n skc_ = None\n else:\n try:\n skc_ = memoryview(skc)\n except TypeError:\n try:\n _tmparr_skc = array.array(\"i\",skc)\n except TypeError:\n raise TypeError(\"Argument skc has wrong type\")\n else:\n skc_ = memoryview(_tmparr_skc)\n \n else:\n if skc_.format != \"i\":\n skc_ = memoryview(array.array(\"i\",skc))\n \n if skc_ is not None and len(skc_) != self.getnumcon():\n raise ValueError(\"Array argument skc has wrong length\")\n res = self.__obj.putskc(whichsol_,skc_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def updatesolutioninfo(self,whichsol_): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res = self.__obj.updatesolutioninfo(whichsol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putskc(self,whichsol_,skc_):\n _skc_minlength = self.getnumcon()\n if self.getnumcon() > 0 and skc_ is not None and len(skc_) != self.getnumcon():\n raise ValueError(\"Array argument skc is not long enough: Is %d, expected %d\" % (len(skc_),self.getnumcon()))\n if skc_ is None:\n raise ValueError(\"Argument skc cannot be None\")\n if skc_ is None:\n raise ValueError(\"Argument skc may not be None\")\n if skc_ is not None:\n _skc_tmp = (ctypes.c_int32 * len(skc_))(*skc_)\n else:\n _skc_tmp = None\n res = __library__.MSK_XX_putskc(self.__nativep,whichsol_,_skc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def sketch_of_solution(self,sol=None):\n raise NotImplementedError", "def putslc(self,whichsol_,slc_):\n _slc_minlength = self.getnumcon()\n if self.getnumcon() > 0 and slc_ is not None and len(slc_) != self.getnumcon():\n raise ValueError(\"Array argument slc is not long enough: Is %d, expected %d\" % (len(slc_),self.getnumcon()))\n if slc_ is None:\n raise ValueError(\"Argument slc cannot be None\")\n if slc_ is None:\n raise ValueError(\"Argument slc may not be None\")\n if isinstance(slc_, numpy.ndarray) and slc_.dtype is numpy.dtype(numpy.float64) and slc_.flags.contiguous:\n _slc_copyarray = False\n _slc_tmp = ctypes.cast(slc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slc_ is not None:\n _slc_copyarray = True\n _slc_np_tmp = numpy.zeros(len(slc_),numpy.dtype(numpy.float64))\n _slc_np_tmp[:] = slc_\n assert _slc_np_tmp.flags.contiguous\n _slc_tmp = ctypes.cast(_slc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slc_copyarray = False\n _slc_tmp = None\n \n res = __library__.MSK_XX_putslc(self.__nativep,whichsol_,_slc_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putskx(self,whichsol_,skx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if skx is None: raise TypeError(\"Invalid type for argument skx\")\n if skx is None:\n skx_ = None\n else:\n try:\n skx_ = memoryview(skx)\n except TypeError:\n try:\n _tmparr_skx = array.array(\"i\",skx)\n except TypeError:\n raise TypeError(\"Argument skx has wrong type\")\n else:\n skx_ = memoryview(_tmparr_skx)\n \n else:\n if skx_.format != \"i\":\n skx_ = memoryview(array.array(\"i\",skx))\n \n if skx_ is not None and len(skx_) != self.getnumvar():\n raise ValueError(\"Array argument skx has wrong length\")\n res = self.__obj.putskx(whichsol_,skx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putslc(self,whichsol_,slc): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if slc is None: raise TypeError(\"Invalid type for argument slc\")\n if slc is None:\n slc_ = None\n else:\n try:\n slc_ = memoryview(slc)\n except TypeError:\n try:\n _tmparr_slc = array.array(\"d\",slc)\n except TypeError:\n raise TypeError(\"Argument slc has wrong type\")\n else:\n slc_ = memoryview(_tmparr_slc)\n \n else:\n if slc_.format != \"d\":\n slc_ = memoryview(array.array(\"d\",slc))\n \n if slc_ is not None and len(slc_) != self.getnumcon():\n raise ValueError(\"Array argument slc has wrong length\")\n res = self.__obj.putslc(whichsol_,slc_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def solve(self):\n print(\"Attempting to solve problem instance with {} constraints\".format(len(self.constraints)))\n self.formulation.solve(solver='SCS')\n print(self.formulation.status)", "def solve(self):\n print(\"Attempting to solve problem instance with {} constraints\".format(len(self.constraints)))\n self.formulation.solve(solver='SCS')\n print(self.formulation.status)", "def solve(self):\n print(\"Attempting to solve problem instance with {} constraints\".format(len(self.constraints)))\n self.formulation.solve(solver='SCS')\n print(self.formulation.status)", "def getsolutioni(self,accmode_,i_,whichsol_): # 3\n if not isinstance(accmode_,accmode): raise TypeError(\"Argument accmode has wrong type\")\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res,resargs = self.__obj.getsolutioni(accmode_,i_,whichsol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _sk_return_value,_x_return_value,_sl_return_value,_su_return_value,_sn_return_value = resargs\n _sk_return_value = stakey(_sk_return_value)\n return _sk_return_value,_x_return_value,_sl_return_value,_su_return_value,_sn_return_value", "def putskx(self,whichsol_,skx_):\n _skx_minlength = self.getnumvar()\n if self.getnumvar() > 0 and skx_ is not None and len(skx_) != self.getnumvar():\n raise ValueError(\"Array argument skx is not long enough: Is %d, expected %d\" % (len(skx_),self.getnumvar()))\n if skx_ is None:\n raise ValueError(\"Argument skx cannot be None\")\n if skx_ is None:\n raise ValueError(\"Argument skx may not be None\")\n if skx_ is not None:\n _skx_tmp = (ctypes.c_int32 * len(skx_))(*skx_)\n else:\n _skx_tmp = None\n res = __library__.MSK_XX_putskx(self.__nativep,whichsol_,_skx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def setup_solver(self):\n option = Options()\n if logger.getEffectiveLevel() == logging.DEBUG:\n # option.printLevel = PrintLevel.HIGH\n option.printLevel = PrintLevel.NONE\n else:\n option.printLevel = PrintLevel.NONE\n self.solver_minimizing = SQProblem(self.nV, self.nC)\n self.solver_minimizing.setOptions(option)\n self.solver_maximizing = SQProblem(self.nV, self.nC)\n self.solver_maximizing.setOptions(option)\n\n self.solver_minimizing_recent_index = -2\n self.solver_maximizing_recent_index = -2", "def putslx(self,whichsol_,slx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if slx is None: raise TypeError(\"Invalid type for argument slx\")\n if slx is None:\n slx_ = None\n else:\n try:\n slx_ = memoryview(slx)\n except TypeError:\n try:\n _tmparr_slx = array.array(\"d\",slx)\n except TypeError:\n raise TypeError(\"Argument slx has wrong type\")\n else:\n slx_ = memoryview(_tmparr_slx)\n \n else:\n if slx_.format != \"d\":\n slx_ = memoryview(array.array(\"d\",slx))\n \n if slx_ is not None and len(slx_) != self.getnumvar():\n raise ValueError(\"Array argument slx has wrong length\")\n res = self.__obj.putslx(whichsol_,slx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def getsolutioninfo(self,whichsol_):\n pobj_ = ctypes.c_double()\n pviolcon_ = ctypes.c_double()\n pviolvar_ = ctypes.c_double()\n pviolbarvar_ = ctypes.c_double()\n pviolcone_ = ctypes.c_double()\n pviolitg_ = ctypes.c_double()\n dobj_ = ctypes.c_double()\n dviolcon_ = ctypes.c_double()\n dviolvar_ = ctypes.c_double()\n dviolbarvar_ = ctypes.c_double()\n dviolcone_ = ctypes.c_double()\n res = __library__.MSK_XX_getsolutioninfo(self.__nativep,whichsol_,ctypes.byref(pobj_),ctypes.byref(pviolcon_),ctypes.byref(pviolvar_),ctypes.byref(pviolbarvar_),ctypes.byref(pviolcone_),ctypes.byref(pviolitg_),ctypes.byref(dobj_),ctypes.byref(dviolcon_),ctypes.byref(dviolvar_),ctypes.byref(dviolbarvar_),ctypes.byref(dviolcone_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n pobj_ = pobj_.value\n _pobj_return_value = pobj_\n pviolcon_ = pviolcon_.value\n _pviolcon_return_value = pviolcon_\n pviolvar_ = pviolvar_.value\n _pviolvar_return_value = pviolvar_\n pviolbarvar_ = pviolbarvar_.value\n _pviolbarvar_return_value = pviolbarvar_\n pviolcone_ = pviolcone_.value\n _pviolcone_return_value = pviolcone_\n pviolitg_ = pviolitg_.value\n _pviolitg_return_value = pviolitg_\n dobj_ = dobj_.value\n _dobj_return_value = dobj_\n dviolcon_ = dviolcon_.value\n _dviolcon_return_value = dviolcon_\n dviolvar_ = dviolvar_.value\n _dviolvar_return_value = dviolvar_\n dviolbarvar_ = dviolbarvar_.value\n _dviolbarvar_return_value = dviolbarvar_\n dviolcone_ = dviolcone_.value\n _dviolcone_return_value = dviolcone_\n return (_pobj_return_value,_pviolcon_return_value,_pviolvar_return_value,_pviolbarvar_return_value,_pviolcone_return_value,_pviolitg_return_value,_dobj_return_value,_dviolcon_return_value,_dviolvar_return_value,_dviolbarvar_return_value,_dviolcone_return_value)", "def getsolutioninfo(self,whichsol_): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res,resargs = self.__obj.getsolutioninfo(whichsol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _pobj_return_value,_pviolcon_return_value,_pviolvar_return_value,_pviolbarvar_return_value,_pviolcone_return_value,_pviolitg_return_value,_dobj_return_value,_dviolcon_return_value,_dviolvar_return_value,_dviolbarvar_return_value,_dviolcone_return_value = resargs\n return _pobj_return_value,_pviolcon_return_value,_pviolvar_return_value,_pviolbarvar_return_value,_pviolcone_return_value,_pviolitg_return_value,_dobj_return_value,_dviolcon_return_value,_dviolvar_return_value,_dviolbarvar_return_value,_dviolcone_return_value", "def putslx(self,whichsol_,slx_):\n _slx_minlength = self.getnumvar()\n if self.getnumvar() > 0 and slx_ is not None and len(slx_) != self.getnumvar():\n raise ValueError(\"Array argument slx is not long enough: Is %d, expected %d\" % (len(slx_),self.getnumvar()))\n if slx_ is None:\n raise ValueError(\"Argument slx cannot be None\")\n if slx_ is None:\n raise ValueError(\"Argument slx may not be None\")\n if isinstance(slx_, numpy.ndarray) and slx_.dtype is numpy.dtype(numpy.float64) and slx_.flags.contiguous:\n _slx_copyarray = False\n _slx_tmp = ctypes.cast(slx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slx_ is not None:\n _slx_copyarray = True\n _slx_np_tmp = numpy.zeros(len(slx_),numpy.dtype(numpy.float64))\n _slx_np_tmp[:] = slx_\n assert _slx_np_tmp.flags.contiguous\n _slx_tmp = ctypes.cast(_slx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slx_copyarray = False\n _slx_tmp = None\n \n res = __library__.MSK_XX_putslx(self.__nativep,whichsol_,_slx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def solutiondef(self,whichsol_):\n isdef_ = ctypes.c_int32()\n res = __library__.MSK_XX_solutiondef(self.__nativep,whichsol_,ctypes.byref(isdef_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n isdef_ = isdef_.value\n _isdef_return_value = isdef_\n return (_isdef_return_value)", "def update_current_sol_and_cost(self,sol=None):\n\n # Update current sol if argument given\n if sol is not None:\n self.current_sol = sol\n \n # Update residual and cost\n try:\n self.residual = self.sketch_reweighted - self.sketch_of_solution(self.current_sol)\n self.current_sol_cost = np.linalg.norm(self.residual)\n except AttributeError: # We are here if self.current_sol does not exist yet\n self.current_sol, self.residual = None, self.sketch_reweighted\n self.current_sol_cost = np.inf", "def notify_solution(self, sol):\n pass # pragma: no cover", "def getskc(self,whichsol_,skc): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n _copyback_skc = False\n if skc is None:\n skc_ = None\n else:\n try:\n skc_ = memoryview(skc)\n except TypeError:\n try:\n _tmparr_skc = array.array(\"i\",skc)\n except TypeError:\n raise TypeError(\"Argument skc has wrong type\")\n else:\n skc_ = memoryview(_tmparr_skc)\n _copyback_skc = True\n else:\n if skc_.format != \"i\":\n skc_ = memoryview(array.array(\"i\",skc))\n _copyback_skc = True\n if skc_ is not None and len(skc_) != self.getnumcon():\n raise ValueError(\"Array argument skc has wrong length\")\n res = self.__obj.getskc(whichsol_,skc_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_skc:\n for __tmp_var_0 in range(len(skc_)): skc[__tmp_var_0] = stakey(_tmparr_skc[__tmp_var_0])" ]

[ "0.7256664", "0.7085556", "0.70574284", "0.67001754", "0.66156536", "0.6531177", "0.6481275", "0.63412815", "0.63247937", "0.6043454", "0.5931599", "0.5860473", "0.58587015", "0.57669955", "0.57536566", "0.5601438", "0.55334353", "0.55334353", "0.55334353", "0.5502079", "0.54629934", "0.5458374", "0.542206", "0.5412008", "0.5379738", "0.53786117", "0.53472835", "0.5208965", "0.51494455", "0.51446486" ]

[ "def putsolution(self,whichsol_,skc,skx,skn,xc,xx,y,slc,suc,slx,sux,snx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if skc is None:\n skc_ = None\n else:\n try:\n skc_ = memoryview(skc)\n except TypeError:\n try:\n _tmparr_skc = array.array(\"i\",skc)\n except TypeError:\n raise TypeError(\"Argument skc has wrong type\")\n else:\n skc_ = memoryview(_tmparr_skc)\n \n else:\n if skc_.format != \"i\":\n skc_ = memoryview(array.array(\"i\",skc))\n \n if skx is None:\n skx_ = None\n else:\n try:\n skx_ = memoryview(skx)\n except TypeError:\n try:\n _tmparr_skx = array.array(\"i\",skx)\n except TypeError:\n raise TypeError(\"Argument skx has wrong type\")\n else:\n skx_ = memoryview(_tmparr_skx)\n \n else:\n if skx_.format != \"i\":\n skx_ = memoryview(array.array(\"i\",skx))\n \n if skn is None:\n skn_ = None\n else:\n try:\n skn_ = memoryview(skn)\n except TypeError:\n try:\n _tmparr_skn = array.array(\"i\",skn)\n except TypeError:\n raise TypeError(\"Argument skn has wrong type\")\n else:\n skn_ = memoryview(_tmparr_skn)\n \n else:\n if skn_.format != \"i\":\n skn_ = memoryview(array.array(\"i\",skn))\n \n if xc is None:\n xc_ = None\n else:\n try:\n xc_ = memoryview(xc)\n except TypeError:\n try:\n _tmparr_xc = array.array(\"d\",xc)\n except TypeError:\n raise TypeError(\"Argument xc has wrong type\")\n else:\n xc_ = memoryview(_tmparr_xc)\n \n else:\n if xc_.format != \"d\":\n xc_ = memoryview(array.array(\"d\",xc))\n \n if xx is None:\n xx_ = None\n else:\n try:\n xx_ = memoryview(xx)\n except TypeError:\n try:\n _tmparr_xx = array.array(\"d\",xx)\n except TypeError:\n raise TypeError(\"Argument xx has wrong type\")\n else:\n xx_ = memoryview(_tmparr_xx)\n \n else:\n if xx_.format != \"d\":\n xx_ = memoryview(array.array(\"d\",xx))\n \n if y is None:\n y_ = None\n else:\n try:\n y_ = memoryview(y)\n except TypeError:\n try:\n _tmparr_y = array.array(\"d\",y)\n except TypeError:\n raise TypeError(\"Argument y has wrong type\")\n else:\n y_ = memoryview(_tmparr_y)\n \n else:\n if y_.format != \"d\":\n y_ = memoryview(array.array(\"d\",y))\n \n if slc is None:\n slc_ = None\n else:\n try:\n slc_ = memoryview(slc)\n except TypeError:\n try:\n _tmparr_slc = array.array(\"d\",slc)\n except TypeError:\n raise TypeError(\"Argument slc has wrong type\")\n else:\n slc_ = memoryview(_tmparr_slc)\n \n else:\n if slc_.format != \"d\":\n slc_ = memoryview(array.array(\"d\",slc))\n \n if suc is None:\n suc_ = None\n else:\n try:\n suc_ = memoryview(suc)\n except TypeError:\n try:\n _tmparr_suc = array.array(\"d\",suc)\n except TypeError:\n raise TypeError(\"Argument suc has wrong type\")\n else:\n suc_ = memoryview(_tmparr_suc)\n \n else:\n if suc_.format != \"d\":\n suc_ = memoryview(array.array(\"d\",suc))\n \n if slx is None:\n slx_ = None\n else:\n try:\n slx_ = memoryview(slx)\n except TypeError:\n try:\n _tmparr_slx = array.array(\"d\",slx)\n except TypeError:\n raise TypeError(\"Argument slx has wrong type\")\n else:\n slx_ = memoryview(_tmparr_slx)\n \n else:\n if slx_.format != \"d\":\n slx_ = memoryview(array.array(\"d\",slx))\n \n if sux is None:\n sux_ = None\n else:\n try:\n sux_ = memoryview(sux)\n except TypeError:\n try:\n _tmparr_sux = array.array(\"d\",sux)\n except TypeError:\n raise TypeError(\"Argument sux has wrong type\")\n else:\n sux_ = memoryview(_tmparr_sux)\n \n else:\n if sux_.format != \"d\":\n sux_ = memoryview(array.array(\"d\",sux))\n \n if snx is None:\n snx_ = None\n else:\n try:\n snx_ = memoryview(snx)\n except TypeError:\n try:\n _tmparr_snx = array.array(\"d\",snx)\n except TypeError:\n raise TypeError(\"Argument snx has wrong type\")\n else:\n snx_ = memoryview(_tmparr_snx)\n \n else:\n if snx_.format != \"d\":\n snx_ = memoryview(array.array(\"d\",snx))\n \n res = self.__obj.putsolution(whichsol_,skc_,skx_,skn_,xc_,xx_,y_,slc_,suc_,slx_,sux_,snx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putconsolutioni(self,i_,whichsol_,sk_,x_,sl_,su_):\n res = __library__.MSK_XX_putconsolutioni(self.__nativep,i_,whichsol_,sk_,x_,sl_,su_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putsolutioni(self,accmode_,i_,whichsol_,sk_,x_,sl_,su_,sn_): # 3\n if not isinstance(accmode_,accmode): raise TypeError(\"Argument accmode has wrong type\")\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if not isinstance(sk_,stakey): raise TypeError(\"Argument sk has wrong type\")\n res = self.__obj.putsolutioni(accmode_,i_,whichsol_,sk_,x_,sl_,su_,sn_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putsolutionyi(self,i_,whichsol_,y_): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res = self.__obj.putsolutionyi(i_,whichsol_,y_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putsolutionyi(self,i_,whichsol_,y_):\n res = __library__.MSK_XX_putsolutionyi(self.__nativep,i_,whichsol_,y_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putsolution(self,whichsol_,skc_,skx_,skn_,xc_,xx_,y_,slc_,suc_,slx_,sux_,snx_):\n if skc_ is not None:\n _skc_tmp = (ctypes.c_int32 * len(skc_))(*skc_)\n else:\n _skc_tmp = None\n if skx_ is not None:\n _skx_tmp = (ctypes.c_int32 * len(skx_))(*skx_)\n else:\n _skx_tmp = None\n if skn_ is not None:\n _skn_tmp = (ctypes.c_int32 * len(skn_))(*skn_)\n else:\n _skn_tmp = None\n if isinstance(xc_, numpy.ndarray) and xc_.dtype is numpy.dtype(numpy.float64) and xc_.flags.contiguous:\n _xc_copyarray = False\n _xc_tmp = ctypes.cast(xc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif xc_ is not None:\n _xc_copyarray = True\n _xc_np_tmp = numpy.zeros(len(xc_),numpy.dtype(numpy.float64))\n _xc_np_tmp[:] = xc_\n assert _xc_np_tmp.flags.contiguous\n _xc_tmp = ctypes.cast(_xc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _xc_copyarray = False\n _xc_tmp = None\n \n if isinstance(xx_, numpy.ndarray) and xx_.dtype is numpy.dtype(numpy.float64) and xx_.flags.contiguous:\n _xx_copyarray = False\n _xx_tmp = ctypes.cast(xx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif xx_ is not None:\n _xx_copyarray = True\n _xx_np_tmp = numpy.zeros(len(xx_),numpy.dtype(numpy.float64))\n _xx_np_tmp[:] = xx_\n assert _xx_np_tmp.flags.contiguous\n _xx_tmp = ctypes.cast(_xx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _xx_copyarray = False\n _xx_tmp = None\n \n if isinstance(y_, numpy.ndarray) and y_.dtype is numpy.dtype(numpy.float64) and y_.flags.contiguous:\n _y_copyarray = False\n _y_tmp = ctypes.cast(y_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif y_ is not None:\n _y_copyarray = True\n _y_np_tmp = numpy.zeros(len(y_),numpy.dtype(numpy.float64))\n _y_np_tmp[:] = y_\n assert _y_np_tmp.flags.contiguous\n _y_tmp = ctypes.cast(_y_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _y_copyarray = False\n _y_tmp = None\n \n if isinstance(slc_, numpy.ndarray) and slc_.dtype is numpy.dtype(numpy.float64) and slc_.flags.contiguous:\n _slc_copyarray = False\n _slc_tmp = ctypes.cast(slc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slc_ is not None:\n _slc_copyarray = True\n _slc_np_tmp = numpy.zeros(len(slc_),numpy.dtype(numpy.float64))\n _slc_np_tmp[:] = slc_\n assert _slc_np_tmp.flags.contiguous\n _slc_tmp = ctypes.cast(_slc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slc_copyarray = False\n _slc_tmp = None\n \n if isinstance(suc_, numpy.ndarray) and suc_.dtype is numpy.dtype(numpy.float64) and suc_.flags.contiguous:\n _suc_copyarray = False\n _suc_tmp = ctypes.cast(suc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif suc_ is not None:\n _suc_copyarray = True\n _suc_np_tmp = numpy.zeros(len(suc_),numpy.dtype(numpy.float64))\n _suc_np_tmp[:] = suc_\n assert _suc_np_tmp.flags.contiguous\n _suc_tmp = ctypes.cast(_suc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _suc_copyarray = False\n _suc_tmp = None\n \n if isinstance(slx_, numpy.ndarray) and slx_.dtype is numpy.dtype(numpy.float64) and slx_.flags.contiguous:\n _slx_copyarray = False\n _slx_tmp = ctypes.cast(slx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slx_ is not None:\n _slx_copyarray = True\n _slx_np_tmp = numpy.zeros(len(slx_),numpy.dtype(numpy.float64))\n _slx_np_tmp[:] = slx_\n assert _slx_np_tmp.flags.contiguous\n _slx_tmp = ctypes.cast(_slx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slx_copyarray = False\n _slx_tmp = None\n \n if isinstance(sux_, numpy.ndarray) and sux_.dtype is numpy.dtype(numpy.float64) and sux_.flags.contiguous:\n _sux_copyarray = False\n _sux_tmp = ctypes.cast(sux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif sux_ is not None:\n _sux_copyarray = True\n _sux_np_tmp = numpy.zeros(len(sux_),numpy.dtype(numpy.float64))\n _sux_np_tmp[:] = sux_\n assert _sux_np_tmp.flags.contiguous\n _sux_tmp = ctypes.cast(_sux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _sux_copyarray = False\n _sux_tmp = None\n \n if isinstance(snx_, numpy.ndarray) and snx_.dtype is numpy.dtype(numpy.float64) and snx_.flags.contiguous:\n _snx_copyarray = False\n _snx_tmp = ctypes.cast(snx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif snx_ is not None:\n _snx_copyarray = True\n _snx_np_tmp = numpy.zeros(len(snx_),numpy.dtype(numpy.float64))\n _snx_np_tmp[:] = snx_\n assert _snx_np_tmp.flags.contiguous\n _snx_tmp = ctypes.cast(_snx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _snx_copyarray = False\n _snx_tmp = None\n \n res = __library__.MSK_XX_putsolution(self.__nativep,whichsol_,_skc_tmp,_skx_tmp,_skn_tmp,_xc_tmp,_xx_tmp,_y_tmp,_slc_tmp,_suc_tmp,_slx_tmp,_sux_tmp,_snx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putskx(self,whichsol_,skx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if skx is None: raise TypeError(\"Invalid type for argument skx\")\n if skx is None:\n skx_ = None\n else:\n try:\n skx_ = memoryview(skx)\n except TypeError:\n try:\n _tmparr_skx = array.array(\"i\",skx)\n except TypeError:\n raise TypeError(\"Argument skx has wrong type\")\n else:\n skx_ = memoryview(_tmparr_skx)\n \n else:\n if skx_.format != \"i\":\n skx_ = memoryview(array.array(\"i\",skx))\n \n if skx_ is not None and len(skx_) != self.getnumvar():\n raise ValueError(\"Array argument skx has wrong length\")\n res = self.__obj.putskx(whichsol_,skx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putbarxj(self,whichsol_,j_,barxj): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if barxj is None: raise TypeError(\"Invalid type for argument barxj\")\n if barxj is None:\n barxj_ = None\n else:\n try:\n barxj_ = memoryview(barxj)\n except TypeError:\n try:\n _tmparr_barxj = array.array(\"d\",barxj)\n except TypeError:\n raise TypeError(\"Argument barxj has wrong type\")\n else:\n barxj_ = memoryview(_tmparr_barxj)\n \n else:\n if barxj_.format != \"d\":\n barxj_ = memoryview(array.array(\"d\",barxj))\n \n if barxj_ is not None and len(barxj_) != self.getlenbarvarj((j_)):\n raise ValueError(\"Array argument barxj has wrong length\")\n res = self.__obj.putbarxj(whichsol_,j_,barxj_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putbarsj(self,whichsol_,j_,barsj_):\n _barsj_minlength = self.getlenbarvarj((j_))\n if self.getlenbarvarj((j_)) > 0 and barsj_ is not None and len(barsj_) != self.getlenbarvarj((j_)):\n raise ValueError(\"Array argument barsj is not long enough: Is %d, expected %d\" % (len(barsj_),self.getlenbarvarj((j_))))\n if barsj_ is None:\n raise ValueError(\"Argument barsj cannot be None\")\n if barsj_ is None:\n raise ValueError(\"Argument barsj may not be None\")\n if isinstance(barsj_, numpy.ndarray) and barsj_.dtype is numpy.dtype(numpy.float64) and barsj_.flags.contiguous:\n _barsj_copyarray = False\n _barsj_tmp = ctypes.cast(barsj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif barsj_ is not None:\n _barsj_copyarray = True\n _barsj_np_tmp = numpy.zeros(len(barsj_),numpy.dtype(numpy.float64))\n _barsj_np_tmp[:] = barsj_\n assert _barsj_np_tmp.flags.contiguous\n _barsj_tmp = ctypes.cast(_barsj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _barsj_copyarray = False\n _barsj_tmp = None\n \n res = __library__.MSK_XX_putbarsj(self.__nativep,whichsol_,j_,_barsj_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def updatesolutioninfo(self,whichsol_):\n res = __library__.MSK_XX_updatesolutioninfo(self.__nativep,whichsol_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putbarsj(self,whichsol_,j_,barsj): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if barsj is None: raise TypeError(\"Invalid type for argument barsj\")\n if barsj is None:\n barsj_ = None\n else:\n try:\n barsj_ = memoryview(barsj)\n except TypeError:\n try:\n _tmparr_barsj = array.array(\"d\",barsj)\n except TypeError:\n raise TypeError(\"Argument barsj has wrong type\")\n else:\n barsj_ = memoryview(_tmparr_barsj)\n \n else:\n if barsj_.format != \"d\":\n barsj_ = memoryview(array.array(\"d\",barsj))\n \n if barsj_ is not None and len(barsj_) != self.getlenbarvarj((j_)):\n raise ValueError(\"Array argument barsj has wrong length\")\n res = self.__obj.putbarsj(whichsol_,j_,barsj_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putsnx(self,whichsol_,sux): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if sux is None: raise TypeError(\"Invalid type for argument sux\")\n if sux is None:\n sux_ = None\n else:\n try:\n sux_ = memoryview(sux)\n except TypeError:\n try:\n _tmparr_sux = array.array(\"d\",sux)\n except TypeError:\n raise TypeError(\"Argument sux has wrong type\")\n else:\n sux_ = memoryview(_tmparr_sux)\n \n else:\n if sux_.format != \"d\":\n sux_ = memoryview(array.array(\"d\",sux))\n \n if sux_ is not None and len(sux_) != self.getnumvar():\n raise ValueError(\"Array argument sux has wrong length\")\n res = self.__obj.putsnx(whichsol_,sux_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def getsolution(self,whichsol_,skc,skx,skn,xc,xx,y,slc,suc,slx,sux,snx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n _copyback_skc = False\n if skc is None:\n skc_ = None\n else:\n try:\n skc_ = memoryview(skc)\n except TypeError:\n try:\n _tmparr_skc = array.array(\"i\",skc)\n except TypeError:\n raise TypeError(\"Argument skc has wrong type\")\n else:\n skc_ = memoryview(_tmparr_skc)\n _copyback_skc = True\n else:\n if skc_.format != \"i\":\n skc_ = memoryview(array.array(\"i\",skc))\n _copyback_skc = True\n if skc_ is not None and len(skc_) != self.getnumcon():\n raise ValueError(\"Array argument skc has wrong length\")\n _copyback_skx = False\n if skx is None:\n skx_ = None\n else:\n try:\n skx_ = memoryview(skx)\n except TypeError:\n try:\n _tmparr_skx = array.array(\"i\",skx)\n except TypeError:\n raise TypeError(\"Argument skx has wrong type\")\n else:\n skx_ = memoryview(_tmparr_skx)\n _copyback_skx = True\n else:\n if skx_.format != \"i\":\n skx_ = memoryview(array.array(\"i\",skx))\n _copyback_skx = True\n if skx_ is not None and len(skx_) != self.getnumvar():\n raise ValueError(\"Array argument skx has wrong length\")\n _copyback_skn = False\n if skn is None:\n skn_ = None\n else:\n try:\n skn_ = memoryview(skn)\n except TypeError:\n try:\n _tmparr_skn = array.array(\"i\",skn)\n except TypeError:\n raise TypeError(\"Argument skn has wrong type\")\n else:\n skn_ = memoryview(_tmparr_skn)\n _copyback_skn = True\n else:\n if skn_.format != \"i\":\n skn_ = memoryview(array.array(\"i\",skn))\n _copyback_skn = True\n if skn_ is not None and len(skn_) != self.getnumcone():\n raise ValueError(\"Array argument skn has wrong length\")\n _copyback_xc = False\n if xc is None:\n xc_ = None\n else:\n try:\n xc_ = memoryview(xc)\n except TypeError:\n try:\n _tmparr_xc = array.array(\"d\",xc)\n except TypeError:\n raise TypeError(\"Argument xc has wrong type\")\n else:\n xc_ = memoryview(_tmparr_xc)\n _copyback_xc = True\n else:\n if xc_.format != \"d\":\n xc_ = memoryview(array.array(\"d\",xc))\n _copyback_xc = True\n if xc_ is not None and len(xc_) != self.getnumcon():\n raise ValueError(\"Array argument xc has wrong length\")\n _copyback_xx = False\n if xx is None:\n xx_ = None\n else:\n try:\n xx_ = memoryview(xx)\n except TypeError:\n try:\n _tmparr_xx = array.array(\"d\",xx)\n except TypeError:\n raise TypeError(\"Argument xx has wrong type\")\n else:\n xx_ = memoryview(_tmparr_xx)\n _copyback_xx = True\n else:\n if xx_.format != \"d\":\n xx_ = memoryview(array.array(\"d\",xx))\n _copyback_xx = True\n if xx_ is not None and len(xx_) != self.getnumvar():\n raise ValueError(\"Array argument xx has wrong length\")\n _copyback_y = False\n if y is None:\n y_ = None\n else:\n try:\n y_ = memoryview(y)\n except TypeError:\n try:\n _tmparr_y = array.array(\"d\",y)\n except TypeError:\n raise TypeError(\"Argument y has wrong type\")\n else:\n y_ = memoryview(_tmparr_y)\n _copyback_y = True\n else:\n if y_.format != \"d\":\n y_ = memoryview(array.array(\"d\",y))\n _copyback_y = True\n if y_ is not None and len(y_) != self.getnumcon():\n raise ValueError(\"Array argument y has wrong length\")\n _copyback_slc = False\n if slc is None:\n slc_ = None\n else:\n try:\n slc_ = memoryview(slc)\n except TypeError:\n try:\n _tmparr_slc = array.array(\"d\",slc)\n except TypeError:\n raise TypeError(\"Argument slc has wrong type\")\n else:\n slc_ = memoryview(_tmparr_slc)\n _copyback_slc = True\n else:\n if slc_.format != \"d\":\n slc_ = memoryview(array.array(\"d\",slc))\n _copyback_slc = True\n if slc_ is not None and len(slc_) != self.getnumcon():\n raise ValueError(\"Array argument slc has wrong length\")\n _copyback_suc = False\n if suc is None:\n suc_ = None\n else:\n try:\n suc_ = memoryview(suc)\n except TypeError:\n try:\n _tmparr_suc = array.array(\"d\",suc)\n except TypeError:\n raise TypeError(\"Argument suc has wrong type\")\n else:\n suc_ = memoryview(_tmparr_suc)\n _copyback_suc = True\n else:\n if suc_.format != \"d\":\n suc_ = memoryview(array.array(\"d\",suc))\n _copyback_suc = True\n if suc_ is not None and len(suc_) != self.getnumcon():\n raise ValueError(\"Array argument suc has wrong length\")\n _copyback_slx = False\n if slx is None:\n slx_ = None\n else:\n try:\n slx_ = memoryview(slx)\n except TypeError:\n try:\n _tmparr_slx = array.array(\"d\",slx)\n except TypeError:\n raise TypeError(\"Argument slx has wrong type\")\n else:\n slx_ = memoryview(_tmparr_slx)\n _copyback_slx = True\n else:\n if slx_.format != \"d\":\n slx_ = memoryview(array.array(\"d\",slx))\n _copyback_slx = True\n if slx_ is not None and len(slx_) != self.getnumvar():\n raise ValueError(\"Array argument slx has wrong length\")\n _copyback_sux = False\n if sux is None:\n sux_ = None\n else:\n try:\n sux_ = memoryview(sux)\n except TypeError:\n try:\n _tmparr_sux = array.array(\"d\",sux)\n except TypeError:\n raise TypeError(\"Argument sux has wrong type\")\n else:\n sux_ = memoryview(_tmparr_sux)\n _copyback_sux = True\n else:\n if sux_.format != \"d\":\n sux_ = memoryview(array.array(\"d\",sux))\n _copyback_sux = True\n if sux_ is not None and len(sux_) != self.getnumvar():\n raise ValueError(\"Array argument sux has wrong length\")\n _copyback_snx = False\n if snx is None:\n snx_ = None\n else:\n try:\n snx_ = memoryview(snx)\n except TypeError:\n try:\n _tmparr_snx = array.array(\"d\",snx)\n except TypeError:\n raise TypeError(\"Argument snx has wrong type\")\n else:\n snx_ = memoryview(_tmparr_snx)\n _copyback_snx = True\n else:\n if snx_.format != \"d\":\n snx_ = memoryview(array.array(\"d\",snx))\n _copyback_snx = True\n if snx_ is not None and len(snx_) != self.getnumvar():\n raise ValueError(\"Array argument snx has wrong length\")\n res,resargs = self.__obj.getsolution(whichsol_,skc_,skx_,skn_,xc_,xx_,y_,slc_,suc_,slx_,sux_,snx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _prosta_return_value,_solsta_return_value = resargs\n if _copyback_snx:\n snx[:] = _tmparr_snx\n if _copyback_sux:\n sux[:] = _tmparr_sux\n if _copyback_slx:\n slx[:] = _tmparr_slx\n if _copyback_suc:\n suc[:] = _tmparr_suc\n if _copyback_slc:\n slc[:] = _tmparr_slc\n if _copyback_y:\n y[:] = _tmparr_y\n if _copyback_xx:\n xx[:] = _tmparr_xx\n if _copyback_xc:\n xc[:] = _tmparr_xc\n if _copyback_skn:\n for __tmp_var_2 in range(len(skn_)): skn[__tmp_var_2] = stakey(_tmparr_skn[__tmp_var_2])\n if _copyback_skx:\n for __tmp_var_1 in range(len(skx_)): skx[__tmp_var_1] = stakey(_tmparr_skx[__tmp_var_1])\n if _copyback_skc:\n for __tmp_var_0 in range(len(skc_)): skc[__tmp_var_0] = stakey(_tmparr_skc[__tmp_var_0])\n _solsta_return_value = solsta(_solsta_return_value)\n _prosta_return_value = prosta(_prosta_return_value)\n return _prosta_return_value,_solsta_return_value", "def putslx(self,whichsol_,slx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if slx is None: raise TypeError(\"Invalid type for argument slx\")\n if slx is None:\n slx_ = None\n else:\n try:\n slx_ = memoryview(slx)\n except TypeError:\n try:\n _tmparr_slx = array.array(\"d\",slx)\n except TypeError:\n raise TypeError(\"Argument slx has wrong type\")\n else:\n slx_ = memoryview(_tmparr_slx)\n \n else:\n if slx_.format != \"d\":\n slx_ = memoryview(array.array(\"d\",slx))\n \n if slx_ is not None and len(slx_) != self.getnumvar():\n raise ValueError(\"Array argument slx has wrong length\")\n res = self.__obj.putslx(whichsol_,slx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putsnx(self,whichsol_,sux_):\n _sux_minlength = self.getnumvar()\n if self.getnumvar() > 0 and sux_ is not None and len(sux_) != self.getnumvar():\n raise ValueError(\"Array argument sux is not long enough: Is %d, expected %d\" % (len(sux_),self.getnumvar()))\n if sux_ is None:\n raise ValueError(\"Argument sux cannot be None\")\n if sux_ is None:\n raise ValueError(\"Argument sux may not be None\")\n if isinstance(sux_, numpy.ndarray) and sux_.dtype is numpy.dtype(numpy.float64) and sux_.flags.contiguous:\n _sux_copyarray = False\n _sux_tmp = ctypes.cast(sux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif sux_ is not None:\n _sux_copyarray = True\n _sux_np_tmp = numpy.zeros(len(sux_),numpy.dtype(numpy.float64))\n _sux_np_tmp[:] = sux_\n assert _sux_np_tmp.flags.contiguous\n _sux_tmp = ctypes.cast(_sux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _sux_copyarray = False\n _sux_tmp = None\n \n res = __library__.MSK_XX_putsnx(self.__nativep,whichsol_,_sux_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def updatesolutioninfo(self,whichsol_): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res = self.__obj.updatesolutioninfo(whichsol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putbarxj(self,whichsol_,j_,barxj_):\n _barxj_minlength = self.getlenbarvarj((j_))\n if self.getlenbarvarj((j_)) > 0 and barxj_ is not None and len(barxj_) != self.getlenbarvarj((j_)):\n raise ValueError(\"Array argument barxj is not long enough: Is %d, expected %d\" % (len(barxj_),self.getlenbarvarj((j_))))\n if barxj_ is None:\n raise ValueError(\"Argument barxj cannot be None\")\n if barxj_ is None:\n raise ValueError(\"Argument barxj may not be None\")\n if isinstance(barxj_, numpy.ndarray) and barxj_.dtype is numpy.dtype(numpy.float64) and barxj_.flags.contiguous:\n _barxj_copyarray = False\n _barxj_tmp = ctypes.cast(barxj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif barxj_ is not None:\n _barxj_copyarray = True\n _barxj_np_tmp = numpy.zeros(len(barxj_),numpy.dtype(numpy.float64))\n _barxj_np_tmp[:] = barxj_\n assert _barxj_np_tmp.flags.contiguous\n _barxj_tmp = ctypes.cast(_barxj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _barxj_copyarray = False\n _barxj_tmp = None\n \n res = __library__.MSK_XX_putbarxj(self.__nativep,whichsol_,j_,_barxj_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putvarbound(self,j_,bkx_,blx_,bux_):\n res = __library__.MSK_XX_putvarbound(self.__nativep,j_,bkx_,blx_,bux_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putslx(self,whichsol_,slx_):\n _slx_minlength = self.getnumvar()\n if self.getnumvar() > 0 and slx_ is not None and len(slx_) != self.getnumvar():\n raise ValueError(\"Array argument slx is not long enough: Is %d, expected %d\" % (len(slx_),self.getnumvar()))\n if slx_ is None:\n raise ValueError(\"Argument slx cannot be None\")\n if slx_ is None:\n raise ValueError(\"Argument slx may not be None\")\n if isinstance(slx_, numpy.ndarray) and slx_.dtype is numpy.dtype(numpy.float64) and slx_.flags.contiguous:\n _slx_copyarray = False\n _slx_tmp = ctypes.cast(slx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slx_ is not None:\n _slx_copyarray = True\n _slx_np_tmp = numpy.zeros(len(slx_),numpy.dtype(numpy.float64))\n _slx_np_tmp[:] = slx_\n assert _slx_np_tmp.flags.contiguous\n _slx_tmp = ctypes.cast(_slx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slx_copyarray = False\n _slx_tmp = None\n \n res = __library__.MSK_XX_putslx(self.__nativep,whichsol_,_slx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def getsolution(self,whichsol_,skc_,skx_,skn_,xc_,xx_,y_,slc_,suc_,slx_,sux_,snx_):\n prosta_ = ctypes.c_int32()\n solsta_ = ctypes.c_int32()\n _skc_minlength = self.getnumcon()\n if self.getnumcon() > 0 and skc_ is not None and len(skc_) != self.getnumcon():\n raise ValueError(\"Array argument skc is not long enough: Is %d, expected %d\" % (len(skc_),self.getnumcon()))\n if isinstance(skc_,numpy.ndarray) and not skc_.flags.writeable:\n raise ValueError(\"Argument skc must be writable\")\n if skc_ is not None:\n _skc_tmp = (ctypes.c_int32 * len(skc_))()\n else:\n _skc_tmp = None\n _skx_minlength = self.getnumvar()\n if self.getnumvar() > 0 and skx_ is not None and len(skx_) != self.getnumvar():\n raise ValueError(\"Array argument skx is not long enough: Is %d, expected %d\" % (len(skx_),self.getnumvar()))\n if isinstance(skx_,numpy.ndarray) and not skx_.flags.writeable:\n raise ValueError(\"Argument skx must be writable\")\n if skx_ is not None:\n _skx_tmp = (ctypes.c_int32 * len(skx_))()\n else:\n _skx_tmp = None\n _skn_minlength = self.getnumcone()\n if self.getnumcone() > 0 and skn_ is not None and len(skn_) != self.getnumcone():\n raise ValueError(\"Array argument skn is not long enough: Is %d, expected %d\" % (len(skn_),self.getnumcone()))\n if isinstance(skn_,numpy.ndarray) and not skn_.flags.writeable:\n raise ValueError(\"Argument skn must be writable\")\n if skn_ is not None:\n _skn_tmp = (ctypes.c_int32 * len(skn_))()\n else:\n _skn_tmp = None\n _xc_minlength = self.getnumcon()\n if self.getnumcon() > 0 and xc_ is not None and len(xc_) != self.getnumcon():\n raise ValueError(\"Array argument xc is not long enough: Is %d, expected %d\" % (len(xc_),self.getnumcon()))\n if isinstance(xc_,numpy.ndarray) and not xc_.flags.writeable:\n raise ValueError(\"Argument xc must be writable\")\n if isinstance(xc_, numpy.ndarray) and xc_.dtype is numpy.dtype(numpy.float64) and xc_.flags.contiguous:\n _xc_copyarray = False\n _xc_tmp = ctypes.cast(xc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif xc_ is not None:\n _xc_copyarray = True\n _xc_np_tmp = numpy.zeros(len(xc_),numpy.dtype(numpy.float64))\n _xc_np_tmp[:] = xc_\n assert _xc_np_tmp.flags.contiguous\n _xc_tmp = ctypes.cast(_xc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _xc_copyarray = False\n _xc_tmp = None\n \n _xx_minlength = self.getnumvar()\n if self.getnumvar() > 0 and xx_ is not None and len(xx_) != self.getnumvar():\n raise ValueError(\"Array argument xx is not long enough: Is %d, expected %d\" % (len(xx_),self.getnumvar()))\n if isinstance(xx_,numpy.ndarray) and not xx_.flags.writeable:\n raise ValueError(\"Argument xx must be writable\")\n if isinstance(xx_, numpy.ndarray) and xx_.dtype is numpy.dtype(numpy.float64) and xx_.flags.contiguous:\n _xx_copyarray = False\n _xx_tmp = ctypes.cast(xx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif xx_ is not None:\n _xx_copyarray = True\n _xx_np_tmp = numpy.zeros(len(xx_),numpy.dtype(numpy.float64))\n _xx_np_tmp[:] = xx_\n assert _xx_np_tmp.flags.contiguous\n _xx_tmp = ctypes.cast(_xx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _xx_copyarray = False\n _xx_tmp = None\n \n _y_minlength = self.getnumcon()\n if self.getnumcon() > 0 and y_ is not None and len(y_) != self.getnumcon():\n raise ValueError(\"Array argument y is not long enough: Is %d, expected %d\" % (len(y_),self.getnumcon()))\n if isinstance(y_,numpy.ndarray) and not y_.flags.writeable:\n raise ValueError(\"Argument y must be writable\")\n if isinstance(y_, numpy.ndarray) and y_.dtype is numpy.dtype(numpy.float64) and y_.flags.contiguous:\n _y_copyarray = False\n _y_tmp = ctypes.cast(y_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif y_ is not None:\n _y_copyarray = True\n _y_np_tmp = numpy.zeros(len(y_),numpy.dtype(numpy.float64))\n _y_np_tmp[:] = y_\n assert _y_np_tmp.flags.contiguous\n _y_tmp = ctypes.cast(_y_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _y_copyarray = False\n _y_tmp = None\n \n _slc_minlength = self.getnumcon()\n if self.getnumcon() > 0 and slc_ is not None and len(slc_) != self.getnumcon():\n raise ValueError(\"Array argument slc is not long enough: Is %d, expected %d\" % (len(slc_),self.getnumcon()))\n if isinstance(slc_,numpy.ndarray) and not slc_.flags.writeable:\n raise ValueError(\"Argument slc must be writable\")\n if isinstance(slc_, numpy.ndarray) and slc_.dtype is numpy.dtype(numpy.float64) and slc_.flags.contiguous:\n _slc_copyarray = False\n _slc_tmp = ctypes.cast(slc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slc_ is not None:\n _slc_copyarray = True\n _slc_np_tmp = numpy.zeros(len(slc_),numpy.dtype(numpy.float64))\n _slc_np_tmp[:] = slc_\n assert _slc_np_tmp.flags.contiguous\n _slc_tmp = ctypes.cast(_slc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slc_copyarray = False\n _slc_tmp = None\n \n _suc_minlength = self.getnumcon()\n if self.getnumcon() > 0 and suc_ is not None and len(suc_) != self.getnumcon():\n raise ValueError(\"Array argument suc is not long enough: Is %d, expected %d\" % (len(suc_),self.getnumcon()))\n if isinstance(suc_,numpy.ndarray) and not suc_.flags.writeable:\n raise ValueError(\"Argument suc must be writable\")\n if isinstance(suc_, numpy.ndarray) and suc_.dtype is numpy.dtype(numpy.float64) and suc_.flags.contiguous:\n _suc_copyarray = False\n _suc_tmp = ctypes.cast(suc_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif suc_ is not None:\n _suc_copyarray = True\n _suc_np_tmp = numpy.zeros(len(suc_),numpy.dtype(numpy.float64))\n _suc_np_tmp[:] = suc_\n assert _suc_np_tmp.flags.contiguous\n _suc_tmp = ctypes.cast(_suc_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _suc_copyarray = False\n _suc_tmp = None\n \n _slx_minlength = self.getnumvar()\n if self.getnumvar() > 0 and slx_ is not None and len(slx_) != self.getnumvar():\n raise ValueError(\"Array argument slx is not long enough: Is %d, expected %d\" % (len(slx_),self.getnumvar()))\n if isinstance(slx_,numpy.ndarray) and not slx_.flags.writeable:\n raise ValueError(\"Argument slx must be writable\")\n if isinstance(slx_, numpy.ndarray) and slx_.dtype is numpy.dtype(numpy.float64) and slx_.flags.contiguous:\n _slx_copyarray = False\n _slx_tmp = ctypes.cast(slx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif slx_ is not None:\n _slx_copyarray = True\n _slx_np_tmp = numpy.zeros(len(slx_),numpy.dtype(numpy.float64))\n _slx_np_tmp[:] = slx_\n assert _slx_np_tmp.flags.contiguous\n _slx_tmp = ctypes.cast(_slx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _slx_copyarray = False\n _slx_tmp = None\n \n _sux_minlength = self.getnumvar()\n if self.getnumvar() > 0 and sux_ is not None and len(sux_) != self.getnumvar():\n raise ValueError(\"Array argument sux is not long enough: Is %d, expected %d\" % (len(sux_),self.getnumvar()))\n if isinstance(sux_,numpy.ndarray) and not sux_.flags.writeable:\n raise ValueError(\"Argument sux must be writable\")\n if isinstance(sux_, numpy.ndarray) and sux_.dtype is numpy.dtype(numpy.float64) and sux_.flags.contiguous:\n _sux_copyarray = False\n _sux_tmp = ctypes.cast(sux_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif sux_ is not None:\n _sux_copyarray = True\n _sux_np_tmp = numpy.zeros(len(sux_),numpy.dtype(numpy.float64))\n _sux_np_tmp[:] = sux_\n assert _sux_np_tmp.flags.contiguous\n _sux_tmp = ctypes.cast(_sux_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _sux_copyarray = False\n _sux_tmp = None\n \n _snx_minlength = self.getnumvar()\n if self.getnumvar() > 0 and snx_ is not None and len(snx_) != self.getnumvar():\n raise ValueError(\"Array argument snx is not long enough: Is %d, expected %d\" % (len(snx_),self.getnumvar()))\n if isinstance(snx_,numpy.ndarray) and not snx_.flags.writeable:\n raise ValueError(\"Argument snx must be writable\")\n if isinstance(snx_, numpy.ndarray) and snx_.dtype is numpy.dtype(numpy.float64) and snx_.flags.contiguous:\n _snx_copyarray = False\n _snx_tmp = ctypes.cast(snx_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif snx_ is not None:\n _snx_copyarray = True\n _snx_np_tmp = numpy.zeros(len(snx_),numpy.dtype(numpy.float64))\n _snx_np_tmp[:] = snx_\n assert _snx_np_tmp.flags.contiguous\n _snx_tmp = ctypes.cast(_snx_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _snx_copyarray = False\n _snx_tmp = None\n \n res = __library__.MSK_XX_getsolution(self.__nativep,whichsol_,ctypes.byref(prosta_),ctypes.byref(solsta_),_skc_tmp,_skx_tmp,_skn_tmp,_xc_tmp,_xx_tmp,_y_tmp,_slc_tmp,_suc_tmp,_slx_tmp,_sux_tmp,_snx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _prosta_return_value = prosta(prosta_.value)\n _solsta_return_value = solsta(solsta_.value)\n if skc_ is not None: skc_[:] = [ stakey(v) for v in _skc_tmp[0:len(skc_)] ]\n if skx_ is not None: skx_[:] = [ stakey(v) for v in _skx_tmp[0:len(skx_)] ]\n if skn_ is not None: skn_[:] = [ stakey(v) for v in _skn_tmp[0:len(skn_)] ]\n if _xc_copyarray:\n xc_[:] = _xc_np_tmp\n if _xx_copyarray:\n xx_[:] = _xx_np_tmp\n if _y_copyarray:\n y_[:] = _y_np_tmp\n if _slc_copyarray:\n slc_[:] = _slc_np_tmp\n if _suc_copyarray:\n suc_[:] = _suc_np_tmp\n if _slx_copyarray:\n slx_[:] = _slx_np_tmp\n if _sux_copyarray:\n sux_[:] = _sux_np_tmp\n if _snx_copyarray:\n snx_[:] = _snx_np_tmp\n return (_prosta_return_value,_solsta_return_value)", "def putskx(self,whichsol_,skx_):\n _skx_minlength = self.getnumvar()\n if self.getnumvar() > 0 and skx_ is not None and len(skx_) != self.getnumvar():\n raise ValueError(\"Array argument skx is not long enough: Is %d, expected %d\" % (len(skx_),self.getnumvar()))\n if skx_ is None:\n raise ValueError(\"Argument skx cannot be None\")\n if skx_ is None:\n raise ValueError(\"Argument skx may not be None\")\n if skx_ is not None:\n _skx_tmp = (ctypes.c_int32 * len(skx_))(*skx_)\n else:\n _skx_tmp = None\n res = __library__.MSK_XX_putskx(self.__nativep,whichsol_,_skx_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putvartype(self,j_,vartype_): # 3\n if not isinstance(vartype_,variabletype): raise TypeError(\"Argument vartype has wrong type\")\n res = self.__obj.putvartype(j_,vartype_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putvartype(self,j_,vartype_):\n res = __library__.MSK_XX_putvartype(self.__nativep,j_,vartype_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def set_sparsity(self,use_sparse):\n \n if hasattr(self.problem,'sparse_jac'):\n self.use_sparse = use_sparse\n else:\n raise KINSOL_Exception(\"The problem must have implemented a method 'sparse_jac' for sparsity to by used.\")", "def writesolution(self,whichsol_,filename_): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res = self.__obj.writesolution(whichsol_,filename_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def _var_sol(self, var: Union[LpVariable, Var]) -> float:\n\n return value(var) if self.optimizer == 'pulp' else var.x", "def setup_solver(self):\n option = Options()\n if logger.getEffectiveLevel() == logging.DEBUG:\n # option.printLevel = PrintLevel.HIGH\n option.printLevel = PrintLevel.NONE\n else:\n option.printLevel = PrintLevel.NONE\n self.solver_minimizing = SQProblem(self.nV, self.nC)\n self.solver_minimizing.setOptions(option)\n self.solver_maximizing = SQProblem(self.nV, self.nC)\n self.solver_maximizing.setOptions(option)\n\n self.solver_minimizing_recent_index = -2\n self.solver_maximizing_recent_index = -2", "def _parse_var_initsol(self,varname) :\n\t\tinitsol = self.ss.constraint.initsol\n\t\tparams = getattr(initsol,varname)\n\t\tnvars = len(self.ss.variables) # num of variables\n\n\t\tif varname in ('alpha','beta') : \n\t\t\tself.initsol[varname] = np.ones(nvars)\n\t\t\tkeys = params.keys()\n\t\t\tself.initsol[varname][:] = params['defaultInitialValue']\n\t\t\tfor key in keys : \n\t\t\t\tif re.match(varname+'_\\d+',key)\t:\n\t\t\t\t\tidx = int(key.split('_')[1])\n\t\t\t\t\tself.initsol[varname][idx-1] = params[key]\n\t\telif varname in ('g','h') :\n\t\t\tself.initsol[varname] = np.ones([nvars,nvars])\n\t\t\tkeys = params.keys()\n\t\t\tself.initsol[varname][:] = params['defaultInitialValue']\n\t\t\tfor key in keys : \n\t\t\t\tif re.match(varname+'_\\d+_\\d+',key)\t:\n\t\t\t\t\tidr,idc = map(int,(key.split('_')[1:3]))\n\t\t\t\t\tself.initsol[varname][idr-1][idc-1] = params[key]\n\t\t\n\t\telse :\n\t\t\tlogging.error(\"Unrecognized varname %s quitting..\" \\\n\t\t\t%(varname))\n\t\t\tsys.exit(1)", "def solve(self):\n # check for jacobian and set it if present and to be used\n if self.use_sparse:\n if self._use_jac and hasattr(self.problem,'sparse_jac'):\n jac = self.problem.sparse_jac\n else:\n jac = None\n else:\n if self._use_jac and hasattr(self.problem,'jac'):\n jac = self.problem.jac\n else:\n jac = None\n \n # Initialize solver and solve \n \n solved = False\n local_min = False\n\n res = N.zeros(self.x0.__len__())\n while (not solved) and self.reg_count < 2:\n try:\n if self._use_fscale:\n self.solver.KINSOL_init(self.func,self.x0,self.dim,jac,self.constraints,self.use_sparse,self.verbosity,self.norm_of_res,self.reg_param,self.fscale)\n else:\n self.solver.KINSOL_init(self.func,self.x0,self.dim,jac,self.constraints,self.use_sparse,self.verbosity,self.norm_of_res,self.reg_param,None)\n start = time.clock()\n res = self.solver.KINSOL_solve(not self._use_ls)\n stop = time.clock()\n self.exec_time += (stop - start)\n solved = True\n except KINError as error:\n if error.value == 42:\n # Try the heuristic\n if hasattr(self.problem, 'get_heuristic_x0'):\n print \"----------------------------------------------------\"\n print \" Solver stuck with zero step-length.\"\n print \"----------------------------------------------------\"\n print \"The following variables have start value zero\"\n print \"and min set to zero causing the zero step-lenght.\"\n print \"These settings are either set by default or by user.\"\n print \"\"\n\n self.x0 = self.problem.get_heuristic_x0()\n self.reg_count += 1\n \n print \"\"\n print \"This setting (start and min to zero) can often\"\n print \"cause problem when initializing the system. \"\n print \"\"\n print \"To avoid this the above variables have\"\n print \"their start attributes reset to one.\"\n print \"\"\n print \"Trying to solve the system again...\"\n else:\n raise KINSOL_Exception(\"Regularization failed due to constraints, tried getting heuristic initial guess but failed.\")\n \n\n elif (error.value == 2):\n print \"---------------------------------------------------------\"\n print \"\"\n print \" !!! WARNING !!!\"\n print \"\"\n print \" KINSOL has returned a result but the algorithm has converged\"\n print \" to a local minima, the initial values are NOT consistant!\"\n print \"\"\n print \"---------------------------------------------------------\"\n solved = True\n local_min = True\n else:\n # Other error, send onward as exception\n self.problem.check_constraints(res)\n raise KINSOL_Exception(error.msg[error.value])\n \n if not solved:\n self.solver.Free_KINSOL()\n raise KINSOL_Exception(\"Algorithm exited solution loop without finding a solution, please contact Assimulo support.\")\n\n if self.check_with_model:\n self.problem.check_constraints(res)\n if not local_min:\n print \"Problem sent to KINSOL solved.\"\n \n return res", "def Solve(self,iter_val=0):\n\n ### Save Files before solve ###\n self.fprint(\"Saving Input Data\",special=\"header\")\n if \"mesh\" in self.params.output:\n self.problem.dom.Save(val=iter_val)\n if \"initial_guess\" in self.params.output:\n self.problem.bd.SaveInitialGuess(val=iter_val)\n if \"height\" in self.params.output and self.problem.dom.dim == 3:\n self.problem.bd.SaveHeight(val=iter_val)\n if \"turbine_force\" in self.params.output:\n self.problem.farm.SaveRotorDisks(val=iter_val)\n self.fprint(\"Finished\",special=\"footer\")\n\n ####################################################################\n ### This is the better way to define a nonlinear problem but it \n ### doesn't play nice with dolfin_adjoint\n # ### Define Jacobian ###\n # dU = TrialFunction(self.problem.fs.W)\n # J = derivative(self.problem.F, self.problem.up_next, dU)\n\n # ### Setup nonlinear solver ###\n # nonlinear_problem = NonlinearVariationalProblem(self.problem.F, self.problem.up_next, self.problem.bd.bcs, J)\n # nonlinear_solver = NonlinearVariationalSolver(nonlinear_problem)\n\n # ### Set some parameters ###\n # solver_parameters = nonlinear_solver.parameters\n # solver_parameters[\"nonlinear_solver\"] = \"snes\"\n # solver_parameters[\"snes_solver\"][\"linear_solver\"] = \"mumps\"\n # solver_parameters[\"snes_solver\"][\"maximum_iterations\"] = 50\n # solver_parameters[\"snes_solver\"][\"error_on_nonconvergence\"] = False\n # solver_parameters[\"snes_solver\"][\"line_search\"] = \"bt\" # Available: basic, bt, cp, l2, nleqerr\n\n ### Solve the problem ###\n # self.fprint(\"Solving\",special=\"header\")\n # start = time.time()\n # iters, converged = nonlinear_solver.solve()\n # stop = time.time()\n # self.fprint(\"Total Nonlinear Iterations: {:d}\".format(iters))\n # self.fprint(\"Converged Successfully: {0}\".format(converged))\n ####################################################################\n\n\n nonlinear_solver = self.params[\"solver\"].get(\"nonlinear_solver\", \"snes\")\n relaxation = self.params[\"solver\"].get(\"newton_relaxation\", 1.0)\n\n self.fprint(\"Solving with {0}\".format(nonlinear_solver))\n if nonlinear_solver == \"newton\":\n self.fprint(\"Relaxation parameter = {: 1.2f}\".format(relaxation))\n\n newton_options = {\"relaxation_parameter\": relaxation,\n \"maximum_iterations\": 40,\n \"linear_solver\": \"mumps\",\n \"absolute_tolerance\": 1e-6,\n \"relative_tolerance\": 1e-5}\n \n solver_parameters = {\"nonlinear_solver\": \"newton\",\n \"newton_solver\": newton_options}\n\n elif nonlinear_solver == \"snes\":\n # ### Add some helper functions to solver options ###\n solver_parameters = {\"nonlinear_solver\": \"snes\",\n \"snes_solver\": {\n \"linear_solver\": \"mumps\", \n \"maximum_iterations\": 40,\n \"error_on_nonconvergence\": True,\n \"line_search\": \"bt\",\n }}\n \n else:\n raise ValueError(\"Unknown nonlinear solver type: {0}\".format(nonlinear_solver))\n\n ### Start the Solve Process ###\n self.fprint(\"Solving\",special=\"header\")\n start = time.time()\n \n # ### Solve the Baseline Problem ###\n # solve(self.problem.F_sans_tf == 0, self.problem.up_next, self.problem.bd.bcs, solver_parameters=solver_parameters, **self.extra_kwarg)\n\n # ### Store the Baseline and Assign for the real solve ###\n # self.up_baseline = self.problem.up_next.copy(deepcopy=True)\n # self.problem.up_next.assign(self.up_baseline)\n\n ### Solve the real problem ###\n solve(self.problem.F == 0, self.problem.up_next, self.problem.bd.bcs, solver_parameters=solver_parameters)\n stop = time.time()\n self.fprint(\"Solve Complete: {:1.2f} s\".format(stop-start),special=\"footer\")\n # self.u_next,self.p_next = self.problem.up_next.split(True)\n self.u_next,self.p_next = split(self.problem.up_next)\n # self.nu_T = project(self.problem.nu_T,self.problem.fs.Q,solver_type='mumps',**self.extra_kwarg)\n self.nu_T = None\n\n\n ### Save solutions ###\n if \"solution\" in self.params.output:\n self.fprint(\"Saving Solution\",special=\"header\")\n self.Save(val=iter_val)\n self.fprint(\"Finished\",special=\"footer\")\n\n ### calculate the power for each turbine ###\n ###################################\n ### Fix how angle is transfered ###\n ###################################\n if self.optimizing or self.save_power:\n self.J += -self.CalculatePowerFunctional((iter_val-self.problem.dom.init_wind)) \n\n # self.fprint(\"Speed Percent of Inflow Speed\")\n # ps = []\n # for i in range(6):\n # HH = self.problem.farm.HH[0]\n # RD = self.problem.farm.RD[0]\n # x_val = (i+1)*RD\n # vel = self.problem.up_next([x_val,0,HH])\n # vel = vel[0:3]\n # nom = np.linalg.norm(vel)\n # perc = nom/self.problem.bd.HH_vel\n # ps.append(perc)\n # self.fprint(\"Speed Percent at (\"+repr(int(x_val))+\", 0, \"+repr(HH)+\"): \"+repr(perc))\n # print(ps)" ]

[ "0.6384697", "0.59763306", "0.5868142", "0.58261186", "0.58098394", "0.5764213", "0.5686661", "0.56294215", "0.55897075", "0.555756", "0.55094", "0.54587734", "0.5450689", "0.5421204", "0.5396719", "0.5390872", "0.53769356", "0.53164923", "0.52948296", "0.52874386", "0.52444196", "0.51348186", "0.513479", "0.51198614", "0.5118228", "0.5117997", "0.50903535", "0.50476485", "0.50258005", "0.5002769" ]

[ "def putsolutionyi(self,i_,whichsol_,y_): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res = self.__obj.putsolutionyi(i_,whichsol_,y_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def puty(self,whichsol_,y): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if y is None: raise TypeError(\"Invalid type for argument y\")\n if y is None:\n y_ = None\n else:\n try:\n y_ = memoryview(y)\n except TypeError:\n try:\n _tmparr_y = array.array(\"d\",y)\n except TypeError:\n raise TypeError(\"Argument y has wrong type\")\n else:\n y_ = memoryview(_tmparr_y)\n \n else:\n if y_.format != \"d\":\n y_ = memoryview(array.array(\"d\",y))\n \n if y_ is not None and len(y_) != self.getnumcon():\n raise ValueError(\"Array argument y has wrong length\")\n res = self.__obj.puty(whichsol_,y_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def puty(self,whichsol_,y_):\n _y_minlength = self.getnumcon()\n if self.getnumcon() > 0 and y_ is not None and len(y_) != self.getnumcon():\n raise ValueError(\"Array argument y is not long enough: Is %d, expected %d\" % (len(y_),self.getnumcon()))\n if y_ is None:\n raise ValueError(\"Argument y cannot be None\")\n if y_ is None:\n raise ValueError(\"Argument y may not be None\")\n if isinstance(y_, numpy.ndarray) and y_.dtype is numpy.dtype(numpy.float64) and y_.flags.contiguous:\n _y_copyarray = False\n _y_tmp = ctypes.cast(y_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif y_ is not None:\n _y_copyarray = True\n _y_np_tmp = numpy.zeros(len(y_),numpy.dtype(numpy.float64))\n _y_np_tmp[:] = y_\n assert _y_np_tmp.flags.contiguous\n _y_tmp = ctypes.cast(_y_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _y_copyarray = False\n _y_tmp = None\n \n res = __library__.MSK_XX_puty(self.__nativep,whichsol_,_y_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def gety(self,whichsol_,y): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if y is None: raise TypeError(\"Invalid type for argument y\")\n _copyback_y = False\n if y is None:\n y_ = None\n else:\n try:\n y_ = memoryview(y)\n except TypeError:\n try:\n _tmparr_y = array.array(\"d\",y)\n except TypeError:\n raise TypeError(\"Argument y has wrong type\")\n else:\n y_ = memoryview(_tmparr_y)\n _copyback_y = True\n else:\n if y_.format != \"d\":\n y_ = memoryview(array.array(\"d\",y))\n _copyback_y = True\n if y_ is not None and len(y_) != self.getnumcon():\n raise ValueError(\"Array argument y has wrong length\")\n res = self.__obj.gety(whichsol_,y_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_y:\n y[:] = _tmparr_y", "def gety(self,whichsol_,y_):\n _y_minlength = self.getnumcon()\n if self.getnumcon() > 0 and y_ is not None and len(y_) != self.getnumcon():\n raise ValueError(\"Array argument y is not long enough: Is %d, expected %d\" % (len(y_),self.getnumcon()))\n if isinstance(y_,numpy.ndarray) and not y_.flags.writeable:\n raise ValueError(\"Argument y must be writable\")\n if y_ is None:\n raise ValueError(\"Argument y may not be None\")\n if isinstance(y_, numpy.ndarray) and y_.dtype is numpy.dtype(numpy.float64) and y_.flags.contiguous:\n _y_copyarray = False\n _y_tmp = ctypes.cast(y_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif y_ is not None:\n _y_copyarray = True\n _y_np_tmp = numpy.zeros(len(y_),numpy.dtype(numpy.float64))\n _y_np_tmp[:] = y_\n assert _y_np_tmp.flags.contiguous\n _y_tmp = ctypes.cast(_y_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _y_copyarray = False\n _y_tmp = None\n \n res = __library__.MSK_XX_gety(self.__nativep,whichsol_,_y_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _y_copyarray:\n y_[:] = _y_np_tmp", "def putvarsolutionj(self,j_,whichsol_,sk_,x_,sl_,su_,sn_):\n res = __library__.MSK_XX_putvarsolutionj(self.__nativep,j_,whichsol_,sk_,x_,sl_,su_,sn_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def solve(self, x, y):\n\t\tx = np.concatenate((np.ones([x.shape[0], 1]), x), axis=1)\n\t\txtx = np.dot(x.T, x)\n\t\txty = np.dot(y, x)\n\t\tself.w = np.dot(np.linalg.inv(xtx), xty.T)", "def y_constraint(q, xy):\n y = ( self.L[0]*np.sin(q[0]) + self.L[1]*np.sin(q[0]+q[1]) + \n self.L[2]*np.sin(q[0]+q[1]+q[2]) + self.L[3]*np.sin(np.sum(q)) ) - xy[1]\n return y", "def updatesolutioninfo(self,whichsol_): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res = self.__obj.updatesolutioninfo(whichsol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def analytic_solution(self, x, y):\n return (1/np.sinh(2*np.pi))*np.sinh(2*np.pi*y)*np.sin(2*np.pi*x)", "def make_solver(y, x, ylim, xlim, invertible_fitter=None):\n cb_y = lambdify(x, y)\n cb_dydx = lambdify(x, y.diff(x))\n\n y0 = ylim[0]\n if invertible_fitter:\n pass\n # TODO:\n # fit parameterized invertible function\n # calculated the invese and use as guess\n fitexpr, params = invertible_fitter\n cb_fitexpr = lambdify(x, fitexpr)\n else:\n DxDy = (xlim[1]-xlim[0])/(ylim[1]-ylim[0])\n def inv_y(y, abstol=1e-13, itermax=30, conv=None):\n \"\"\"\n Returns x and error estimate thereof\n \"\"\"\n if invertible_fitter:\n pass\n else:\n x_ = y0+y*DxDy # guess (linear over xspan)\n dy = cb_y(x_)-y\n i=0\n dx=0.0 # could skip while-loop\n while abs(dy) > abstol and i < itermax:\n dx = -dy/cb_dydx(x_)\n x_ += dx\n dy = cb_y(x_)-y\n i += 1\n if conv != None: conv.append(dx)\n if i==itermax:\n raise RuntimeError(\"Did not converge\")\n return x_, abs(dx)\n\n return inv_y", "def putsolution(self,whichsol_,skc,skx,skn,xc,xx,y,slc,suc,slx,sux,snx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if skc is None:\n skc_ = None\n else:\n try:\n skc_ = memoryview(skc)\n except TypeError:\n try:\n _tmparr_skc = array.array(\"i\",skc)\n except TypeError:\n raise TypeError(\"Argument skc has wrong type\")\n else:\n skc_ = memoryview(_tmparr_skc)\n \n else:\n if skc_.format != \"i\":\n skc_ = memoryview(array.array(\"i\",skc))\n \n if skx is None:\n skx_ = None\n else:\n try:\n skx_ = memoryview(skx)\n except TypeError:\n try:\n _tmparr_skx = array.array(\"i\",skx)\n except TypeError:\n raise TypeError(\"Argument skx has wrong type\")\n else:\n skx_ = memoryview(_tmparr_skx)\n \n else:\n if skx_.format != \"i\":\n skx_ = memoryview(array.array(\"i\",skx))\n \n if skn is None:\n skn_ = None\n else:\n try:\n skn_ = memoryview(skn)\n except TypeError:\n try:\n _tmparr_skn = array.array(\"i\",skn)\n except TypeError:\n raise TypeError(\"Argument skn has wrong type\")\n else:\n skn_ = memoryview(_tmparr_skn)\n \n else:\n if skn_.format != \"i\":\n skn_ = memoryview(array.array(\"i\",skn))\n \n if xc is None:\n xc_ = None\n else:\n try:\n xc_ = memoryview(xc)\n except TypeError:\n try:\n _tmparr_xc = array.array(\"d\",xc)\n except TypeError:\n raise TypeError(\"Argument xc has wrong type\")\n else:\n xc_ = memoryview(_tmparr_xc)\n \n else:\n if xc_.format != \"d\":\n xc_ = memoryview(array.array(\"d\",xc))\n \n if xx is None:\n xx_ = None\n else:\n try:\n xx_ = memoryview(xx)\n except TypeError:\n try:\n _tmparr_xx = array.array(\"d\",xx)\n except TypeError:\n raise TypeError(\"Argument xx has wrong type\")\n else:\n xx_ = memoryview(_tmparr_xx)\n \n else:\n if xx_.format != \"d\":\n xx_ = memoryview(array.array(\"d\",xx))\n \n if y is None:\n y_ = None\n else:\n try:\n y_ = memoryview(y)\n except TypeError:\n try:\n _tmparr_y = array.array(\"d\",y)\n except TypeError:\n raise TypeError(\"Argument y has wrong type\")\n else:\n y_ = memoryview(_tmparr_y)\n \n else:\n if y_.format != \"d\":\n y_ = memoryview(array.array(\"d\",y))\n \n if slc is None:\n slc_ = None\n else:\n try:\n slc_ = memoryview(slc)\n except TypeError:\n try:\n _tmparr_slc = array.array(\"d\",slc)\n except TypeError:\n raise TypeError(\"Argument slc has wrong type\")\n else:\n slc_ = memoryview(_tmparr_slc)\n \n else:\n if slc_.format != \"d\":\n slc_ = memoryview(array.array(\"d\",slc))\n \n if suc is None:\n suc_ = None\n else:\n try:\n suc_ = memoryview(suc)\n except TypeError:\n try:\n _tmparr_suc = array.array(\"d\",suc)\n except TypeError:\n raise TypeError(\"Argument suc has wrong type\")\n else:\n suc_ = memoryview(_tmparr_suc)\n \n else:\n if suc_.format != \"d\":\n suc_ = memoryview(array.array(\"d\",suc))\n \n if slx is None:\n slx_ = None\n else:\n try:\n slx_ = memoryview(slx)\n except TypeError:\n try:\n _tmparr_slx = array.array(\"d\",slx)\n except TypeError:\n raise TypeError(\"Argument slx has wrong type\")\n else:\n slx_ = memoryview(_tmparr_slx)\n \n else:\n if slx_.format != \"d\":\n slx_ = memoryview(array.array(\"d\",slx))\n \n if sux is None:\n sux_ = None\n else:\n try:\n sux_ = memoryview(sux)\n except TypeError:\n try:\n _tmparr_sux = array.array(\"d\",sux)\n except TypeError:\n raise TypeError(\"Argument sux has wrong type\")\n else:\n sux_ = memoryview(_tmparr_sux)\n \n else:\n if sux_.format != \"d\":\n sux_ = memoryview(array.array(\"d\",sux))\n \n if snx is None:\n snx_ = None\n else:\n try:\n snx_ = memoryview(snx)\n except TypeError:\n try:\n _tmparr_snx = array.array(\"d\",snx)\n except TypeError:\n raise TypeError(\"Argument snx has wrong type\")\n else:\n snx_ = memoryview(_tmparr_snx)\n \n else:\n if snx_.format != \"d\":\n snx_ = memoryview(array.array(\"d\",snx))\n \n res = self.__obj.putsolution(whichsol_,skc_,skx_,skn_,xc_,xx_,y_,slc_,suc_,slx_,sux_,snx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putconsolutioni(self,i_,whichsol_,sk_,x_,sl_,su_):\n res = __library__.MSK_XX_putconsolutioni(self.__nativep,i_,whichsol_,sk_,x_,sl_,su_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def writesolution(self,whichsol_,filename_): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res = self.__obj.writesolution(whichsol_,filename_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def updatesolutioninfo(self,whichsol_):\n res = __library__.MSK_XX_updatesolutioninfo(self.__nativep,whichsol_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def yxal(self, i):\n return self.y[i]", "def DirectSolve(self, y):\n if y >= -self.R:\n a1 = self.apath[y+1]\n if y == -2:\n a2 = 0\n else:\n a2 = self.apath[y+2] \n def constraints(a):\n c0 = (1+self.r)*a + self.b - a1\n c1 = (1+self.r)*a1 + self.b - a2\n return self.uc(c0,0)-self.beta*self.uc(c1,0)*(1+self.r)\n a, n = fsolve(constraints, a1), 0\n c = (1+self.r)*a + self.b - a1\n else:\n a1 = self.apath[y+1]\n a2 = self.apath[y+2]\n if y == -(self.R+1):\n n1 = 0\n c1 = (1+self.r)*a1 + self.b - a2\n else:\n n1 = self.npath[y+1]\n c1 = (1+self.r)*a1 + (1-self.tau)*self.w*n1 - a2\n def constraints((a0,n0)):\n c0 = (1+self.r)*a0 + (1-self.tau)*self.w*n0 - a1\n return self.uc(c0,n0) - self.beta*self.uc(c1,n1)*(1+self.r),\\\n (1-self.tau)*self.w*self.uc(c0,n0) + self.un(c0,n0)\n a, n = fsolve(constraints,(a1,n1))\n c = (1+self.r)*a + (1-self.tau)*self.w*n - a1\n return a, n, c", "def y(self, y):\n if type(y) is not int:\n raise TypeError(\"y must be an integer\")\n if y < 0:\n raise ValueError(\"y must be >= 0\")\n self.__y = y", "def y(self, y):\n if type(y) is not int:\n raise TypeError(\"y must be an integer\")\n if y < 0:\n raise ValueError(\"y must be >= 0\")\n self.__y = y", "def solve(self, solver):\n solver.solve()", "def y(self, y):\n if type(y) is not int:\n raise TypeError(\"y must be an integer\")\n elif y < 0:\n raise ValueError(\"y must be >= 0\")\n else:\n self.__y = y", "def set_params(self, solver):\n params = yicespy.yices_new_param_record()\n yicespy.yices_default_params_for_context(solver.yices, params)\n for k,v in self.solver_options.items():\n rv = yicespy.yices_set_param(params, k, v)\n if rv != 0:\n raise PysmtValueError(\"Error setting the option '%s=%s'\" % (k,v))\n solver.yices_params = params", "def getdviolvar(self,whichsol_,sub,viol): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n num_ = None\n if num_ is None:\n num_ = len(sub)\n elif num_ != len(sub):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None: num_ = 0\n if sub is None: raise TypeError(\"Invalid type for argument sub\")\n if sub is None:\n sub_ = None\n else:\n try:\n sub_ = memoryview(sub)\n except TypeError:\n try:\n _tmparr_sub = array.array(\"i\",sub)\n except TypeError:\n raise TypeError(\"Argument sub has wrong type\")\n else:\n sub_ = memoryview(_tmparr_sub)\n \n else:\n if sub_.format != \"i\":\n sub_ = memoryview(array.array(\"i\",sub))\n \n if viol is None: raise TypeError(\"Invalid type for argument viol\")\n _copyback_viol = False\n if viol is None:\n viol_ = None\n else:\n try:\n viol_ = memoryview(viol)\n except TypeError:\n try:\n _tmparr_viol = array.array(\"d\",viol)\n except TypeError:\n raise TypeError(\"Argument viol has wrong type\")\n else:\n viol_ = memoryview(_tmparr_viol)\n _copyback_viol = True\n else:\n if viol_.format != \"d\":\n viol_ = memoryview(array.array(\"d\",viol))\n _copyback_viol = True\n if viol_ is not None and len(viol_) != (num_):\n raise ValueError(\"Array argument viol has wrong length\")\n res = self.__obj.getdviolvar(whichsol_,num_,sub_,viol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_viol:\n viol[:] = _tmparr_viol", "def putslx(self,whichsol_,slx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n if slx is None: raise TypeError(\"Invalid type for argument slx\")\n if slx is None:\n slx_ = None\n else:\n try:\n slx_ = memoryview(slx)\n except TypeError:\n try:\n _tmparr_slx = array.array(\"d\",slx)\n except TypeError:\n raise TypeError(\"Argument slx has wrong type\")\n else:\n slx_ = memoryview(_tmparr_slx)\n \n else:\n if slx_.format != \"d\":\n slx_ = memoryview(array.array(\"d\",slx))\n \n if slx_ is not None and len(slx_) != self.getnumvar():\n raise ValueError(\"Array argument slx has wrong length\")\n res = self.__obj.putslx(whichsol_,slx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def solve_nonlinear(self, params, unknowns, resids):\n\n x = params['x']\n a = self.a\n b = self.b\n c = self.c\n\n unknowns['y'] = a*x**2 + b*x + c", "def setBetaEqState(self, pointDict, useThisYeIfSolveFails=None):\n assert isinstance(pointDict, dict)\n assert 'ye' not in pointDict, \"You can't SPECIFY a Ye if you're \" \\\n \"setting neutrinoless beta equlibrium!\"\n self.validatePointDict(pointDict)\n assert len(pointDict) < 3, \"State overdetermined for more than 2 indVars!\"\n\n #defines 1D root solver to use in routine\n solveRoot = scipyOptimize.brentq # solveRootBisect\n\n\n #ASSUME 2 INDEPENENT VARIABLES ARE rho & temp\n logtemp = pointDict['logtemp']\n logrho = pointDict['logrho']\n\n tol = 1.e-6\n getYe = lambda x : multidimInterp((x, logtemp, logrho),\n [self.h5file['ye'][:],\n self.h5file['logtemp'],\n self.h5file['logrho']],\n self.h5file['munu'][...],\n linInterp, 2)\n #check for bracketing error in root solve for ye\n try:\n currentYe = solveRoot(getYe,\n self.h5file['ye'][0],\n self.h5file['ye'][-1], (), tol)\n except ValueError as err:\n print \"Error in scipy root solver solving for ye: \", str(err)\n if useThisYeIfSolveFails is None:\n currentYe = self.findYeOfMinAbsMunu((logtemp, logrho))\n print \"Recovering with findYeOfMinAbsMunu, answer: %s\" % currentYe\n else:\n currentYe = useThisYeIfSolveFails\n print \"Setting Ye to useThisYeIfSolveFails, answer: %s\" % currentYe\n\n newDict = pointDict.copy()\n newDict['ye'] = currentYe\n self.setState(newDict)\n return currentYe", "def solve_nonlinear(self, params, unknowns, resids):\n\n x = params['x']\n m = self.slope\n b = self.intercept\n\n unknowns['y'] = m*x + b", "def y(x,xi):\n return np.exp(-xi)-np.exp(-xi)*(x-xi)", "def getdviolbarvar(self,whichsol_,sub_,viol_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n _viol_minlength = (num_)\n if (num_) > 0 and viol_ is not None and len(viol_) != (num_):\n raise ValueError(\"Array argument viol is not long enough: Is %d, expected %d\" % (len(viol_),(num_)))\n if isinstance(viol_,numpy.ndarray) and not viol_.flags.writeable:\n raise ValueError(\"Argument viol must be writable\")\n if viol_ is None:\n raise ValueError(\"Argument viol may not be None\")\n if isinstance(viol_, numpy.ndarray) and viol_.dtype is numpy.dtype(numpy.float64) and viol_.flags.contiguous:\n _viol_copyarray = False\n _viol_tmp = ctypes.cast(viol_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif viol_ is not None:\n _viol_copyarray = True\n _viol_np_tmp = numpy.zeros(len(viol_),numpy.dtype(numpy.float64))\n _viol_np_tmp[:] = viol_\n assert _viol_np_tmp.flags.contiguous\n _viol_tmp = ctypes.cast(_viol_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _viol_copyarray = False\n _viol_tmp = None\n \n res = __library__.MSK_XX_getdviolbarvar(self.__nativep,whichsol_,num_,_sub_tmp,_viol_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _viol_copyarray:\n viol_[:] = _viol_np_tmp", "def y(self, y):\n if y is None:\n raise ValueError(\"Invalid value for `y`, must not be `None`\") # noqa: E501\n\n self._y = y" ]

[ "0.85774046", "0.70034355", "0.66922426", "0.66091764", "0.62563616", "0.6108404", "0.5998291", "0.56189865", "0.5569256", "0.549024", "0.5483207", "0.5337774", "0.52960414", "0.52665466", "0.5235813", "0.52331036", "0.52272004", "0.51770955", "0.51770955", "0.5159293", "0.5152492", "0.513468", "0.51193005", "0.51014566", "0.50550455", "0.5054728", "0.50455624", "0.50371575", "0.50365764", "0.5031399" ]

[ "def putstrparam(self,param_,parvalue_): # 3\n if not isinstance(param_,sparam): raise TypeError(\"Argument param has wrong type\")\n res = self.__obj.putstrparam(param_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putnastrparam(self,paramname_,parvalue_):\n if isinstance(paramname_,unicode):\n paramname_ = paramname_.encode(\"utf-8\",errors=\"replace\")\n if isinstance(parvalue_,unicode):\n parvalue_ = parvalue_.encode(\"utf-8\",errors=\"replace\")\n res = __library__.MSK_XX_putnastrparam(self.__nativep,paramname_,parvalue_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putnastrparam(self,paramname_,parvalue_): # 3\n res = self.__obj.putnastrparam(paramname_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putparam(self,parname_,parvalue_):\n if isinstance(parname_,unicode):\n parname_ = parname_.encode(\"utf-8\",errors=\"replace\")\n if isinstance(parvalue_,unicode):\n parvalue_ = parvalue_.encode(\"utf-8\",errors=\"replace\")\n res = __library__.MSK_XX_putparam(self.__nativep,parname_,parvalue_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def setString(self, name: unicode, value: unicode) -> None:\n ...", "def putparam(self,parname_,parvalue_): # 3\n res = self.__obj.putparam(parname_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def setParam(self,param,value):\n if param in self.params.keys():\n self.params[param] = value", "def set_param(self, name, value):\n name = str(name)\n value = str(value)\n cxnlib.CXNNetSetParam(self.handle,\n ctypes.c_char_p(name.encode('utf-8')),\n ctypes.c_char_p(value.encode('utf-8')))", "def _set_string_value_pair(self, parameter, value=None):\n if type(parameter) is str:\n if value==None:\n raise Exception(\"Error: No value given in set() function for population parameter. Exiting.\")\n self.parameters[parameter] = value\n return\n if type(parameter) is not dict:\n raise Exception(\"Error: invalid parameter type for set() function for population parameter. Exiting.\")\n # Add a dictionary-structured set of new parameters to the current set:\n self.parameters.update(parameter)", "def set_param(self, param, value):\n self._set_param_client(param, value)", "def getstrparam(self,param_):\n maxlen_ = (1 + self.getstrparamlen((param_)))\n len_ = ctypes.c_int32()\n parvalue_ = (ctypes.c_char * (maxlen_))()\n res = __library__.MSK_XX_getstrparam(self.__nativep,param_,maxlen_,ctypes.byref(len_),parvalue_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n len_ = len_.value\n _len_return_value = len_\n _parvalue_retval = parvalue_.value.decode(\"utf-8\",errors=\"replace\")\n return (_len_return_value,_parvalue_retval)", "def setParameter(self, name, value):", "def isstrparname(self,parname_):\n if isinstance(parname_,unicode):\n parname_ = parname_.encode(\"utf-8\",errors=\"replace\")\n param_ = ctypes.c_int32()\n res = __library__.MSK_XX_isstrparname(self.__nativep,parname_,ctypes.byref(param_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _param_return_value = sparam(param_.value)\n return (_param_return_value)", "def putnaintparam(self,paramname_,parvalue_):\n if isinstance(paramname_,unicode):\n paramname_ = paramname_.encode(\"utf-8\",errors=\"replace\")\n res = __library__.MSK_XX_putnaintparam(self.__nativep,paramname_,parvalue_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def getstrparam(self,param_): # 3\n if not isinstance(param_,sparam): raise TypeError(\"Argument param has wrong type\")\n maxlen_ = (1 + self.getstrparamlen((param_)))\n arr_parvalue = array.array(\"b\",[0]*((maxlen_)))\n memview_arr_parvalue = memoryview(arr_parvalue)\n res,resargs = self.__obj.getstrparam(param_,maxlen_,memview_arr_parvalue)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _len_return_value,retarg_parvalue = resargs\n retarg_parvalue = arr_parvalue.tobytes()[:-1].decode(\"utf-8\",errors=\"ignore\")\n return _len_return_value,retarg_parvalue", "def set_parameter(self, params, name, val):\n raise NotImplementedError()", "def isstrparname(self,parname_): # 3\n res,resargs = self.__obj.isstrparname(parname_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _param_return_value = resargs\n _param_return_value = sparam(_param_return_value)\n return _param_return_value", "def set_param(self, param_key, value):\n return self._params.set_param_value(param_key, value)", "def putnaintparam(self,paramname_,parvalue_): # 3\n res = self.__obj.putnaintparam(paramname_,parvalue_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def set_param(command):\n namespace = app.main(command)\n assert namespace.command == 'sp' or namespace.command == \"setparam\"\n assert namespace.name == \"test\"\n assert namespace.value == \"test\"", "def _put_ssm_param(self, parameter, parameter_name):\n self.ssm_client.put_parameter(\n Name=parameter_name,\n Type=\"String\",\n Value=json.dumps(parameter),\n Overwrite=True,\n Tier=\"Intelligent-Tiering\",\n )", "def set_param(self, label, val):\n assert type(label) is str, 'Parameter name \"%s\" is not string' % label\n assert type(val) is float or type(val) is int, 'Fixed parameter value is not numeric for %s' % label\n self.params[label] = val", "def param_name(self, value):\n self._param_name = value", "def set_param(self, name, value, *, distrib=None, ref=None):\n raise NotImplementedError", "def set_parameter(doc, o_path, param, value, mn_consts):\n if isinstance(value, str):\n doc.setParameter(o_path, param, value, mn_consts.infoStringParameter)\n elif isinstance(value, int):\n doc.setParameter(o_path, param, str(value), mn_consts.infoNumberParameter)\n elif isinstance(value, list):\n doc.setParameter(o_path, param, str(value), mn_consts.infoArrayParameter)", "def string_value(self, string_value):\n\n self._string_value = string_value", "def put_param(self, attr_name, val):\n self._params[attr_name] = val", "def set_param(self, param_value):\n with open(\"settings.txt\", \"r\") as f:\n filedata = f.read()\n settings = [_.split(\"=\") for _ in filedata.split(\"\\n\")]\n for setting in settings:\n if len(setting) < 2: # if blank line\n continue\n if setting[0] == self.param:\n\n setting[1] = param_value\n\n with open(\"settings.txt\", \"w\") as f:\n for setting in settings:\n if len(setting) < 2: # if blank line\n continue\n f.write(setting[0] + \"=\" + setting[1] + \"\\n\")", "def set_parameter_value(self, parameter, value):\n pass", "async def set_param(self, param: str, value: int) -> ArchonCommand:\n cmd = await self.send_command(f\"FASTLOADPARAM {param} {value}\")\n if not cmd.succeeded():\n raise ArchonError(\n f\"Failed setting parameter {param!r} ({cmd.status.name}).\"\n )\n return cmd" ]

[ "0.8903032", "0.79385155", "0.78946966", "0.6862305", "0.6671494", "0.6618401", "0.66161126", "0.65867805", "0.64877427", "0.64677244", "0.6323704", "0.6307091", "0.62468153", "0.6246462", "0.6167524", "0.616714", "0.61054486", "0.60639423", "0.59956676", "0.5957143", "0.59525156", "0.59516406", "0.5897451", "0.5867103", "0.5864356", "0.5851361", "0.5846874", "0.58267576", "0.58057153", "0.5766778" ]

[ "def putvartype(self,j_,vartype_): # 3\n if not isinstance(vartype_,variabletype): raise TypeError(\"Argument vartype has wrong type\")\n res = self.__obj.putvartype(j_,vartype_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def getvartype(self,j_): # 3\n res,resargs = self.__obj.getvartype(j_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _vartype_return_value = resargs\n _vartype_return_value = variabletype(_vartype_return_value)\n return _vartype_return_value", "def getvartype(self,j_):\n vartype_ = ctypes.c_int32()\n res = __library__.MSK_XX_getvartype(self.__nativep,j_,ctypes.byref(vartype_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _vartype_return_value = variabletype(vartype_.value)\n return (_vartype_return_value)", "def putvartypelist(self,subj,vartype): # 3\n num_ = None\n if num_ is None:\n num_ = len(subj)\n elif num_ != len(subj):\n raise IndexError(\"Inconsistent length of array subj\")\n if num_ is None:\n num_ = len(vartype)\n elif num_ != len(vartype):\n raise IndexError(\"Inconsistent length of array vartype\")\n if num_ is None: num_ = 0\n if subj is None: raise TypeError(\"Invalid type for argument subj\")\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n \n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n \n if vartype is None: raise TypeError(\"Invalid type for argument vartype\")\n if vartype is None:\n vartype_ = None\n else:\n try:\n vartype_ = memoryview(vartype)\n except TypeError:\n try:\n _tmparr_vartype = array.array(\"i\",vartype)\n except TypeError:\n raise TypeError(\"Argument vartype has wrong type\")\n else:\n vartype_ = memoryview(_tmparr_vartype)\n \n else:\n if vartype_.format != \"i\":\n vartype_ = memoryview(array.array(\"i\",vartype))\n \n res = self.__obj.putvartypelist(num_,subj_,vartype_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putvartypelist(self,subj_,vartype_):\n num_ = None\n if num_ is None:\n num_ = len(subj_)\n elif num_ != len(subj_):\n raise IndexError(\"Inconsistent length of array subj\")\n if num_ is None:\n num_ = len(vartype_)\n elif num_ != len(vartype_):\n raise IndexError(\"Inconsistent length of array vartype\")\n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n if vartype_ is None:\n raise ValueError(\"Argument vartype cannot be None\")\n if vartype_ is None:\n raise ValueError(\"Argument vartype may not be None\")\n if vartype_ is not None:\n _vartype_tmp = (ctypes.c_int32 * len(vartype_))(*vartype_)\n else:\n _vartype_tmp = None\n res = __library__.MSK_XX_putvartypelist(self.__nativep,num_,_subj_tmp,_vartype_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def put_var_param(self, var_type, num_vars):\n if var_type.upper() not in EX_VAR_TYPES:\n raise ExodusIIWriterError(\n \"var_type {0} not recognized\".format(var_type))\n ierr = exolib.py_expvp(self.exoid, var_type.lower(), num_vars)\n if ierr:\n raise ExodusIIWriterError(\"Error putting var params\")", "def set_lvar_type(self, *args):\n return _ida_hexrays.lvar_t_set_lvar_type(self, *args)", "def set_lvar_type(self, *args):\n return _ida_hexrays.vdui_t_set_lvar_type(self, *args)", "def set(self, key, value):\n if (\n key in self.variables and\n type(value).__name__ == self.variables[key]['type']\n ):\n self.variables[key]['value'] = value\n else:\n raise ValueError(\"Bad key or wrong variable type\")", "def _fset(self, value):\n # type: (...) -> None\n rtype = type_\n if isinstance(type_, TypeVar):\n type_map = dict(\n zip(self.__parameters__, self.__orig_class__.__args__)\n )\n rtype = type_map[type_]\n vars(self)[private_attr] = cast(rtype, value)", "def ui_set_lvar_type(self, *args):\n return _ida_hexrays.vdui_t_ui_set_lvar_type(self, *args)", "def _fset(self, value):\n # type: (...) -> None\n rtype = type_\n if isinstance(type_, TypeVar):\n type_map = dict(\n zip(self.__parameters__, self.__orig_class__.__args__)\n )\n rtype = type_map[type_]\n if not is_instance(value, rtype):\n raise TypeError(\n \"Cannot assign type of {} to attribute of type {}.\".format(\n _get_type_name(type(value)), _get_type_name(rtype)\n )\n )\n vars(self)[private_attr] = value", "def putvarname(self,j_,name_): # 3\n res = self.__obj.putvarname(j_,name_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def ptype(self, ptype):\n\n self._ptype = ptype", "def _tkvar_set(self,param_name,val):\n self.debug(\"_tkvar_set(%s,%s)\"%(param_name,val))\n val = self._object2string(param_name,val)\n tkvar = self._tkvars[param_name]\n tkvar._original_set(val) # trace not called because we're already in trace,\n # and tk disables trace activation during trace", "def set_variable(self, name, value):\n if self._scalamagic and (not name.startswith(\"_i\")):\n self.scala_interpreter.bind(name, value)\n else:\n self.log.debug('Not setting variable %s', name)", "def setType(self,newtype):\n\t\tself.type = newtype;", "def refine_type(self, new_type):\n if new_type is NodeType.UNKNOWN or new_type is self.var_type:\n return\n elif self.var_type is NodeType.UNKNOWN:\n self.var_type = new_type\n else:\n raise TigerTypeError, self._name", "def putvarname(self,j_,name_):\n if isinstance(name_,unicode):\n name_ = name_.encode(\"utf-8\",errors=\"replace\")\n res = __library__.MSK_XX_putvarname(self.__nativep,j_,name_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def store_type(self, ptype):\n attr = self.node.get_attr(Type)\n attr.store(ptype)", "def set_typ(self, refobj, typ):\n try:\n enum = JB_ReftrackNode.types.index(typ)\n except ValueError:\n raise ValueError(\"The given type %s could not be found in available types: %\" % (typ, JB_ReftrackNode.types))\n cmds.setAttr(\"%s.type\" % refobj, enum)", "def variable_type(self, variable): # pragma: no cover\n raise NotImplementedError('Implemented in child class')", "def putvarsolutionj(self,j_,whichsol_,sk_,x_,sl_,su_,sn_):\n res = __library__.MSK_XX_putvarsolutionj(self.__nativep,j_,whichsol_,sk_,x_,sl_,su_,sn_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def set_ivt_variable(self, var):\n self.set_input_variable(var)", "def updateVar(self, id, value, type_):\n if id in self.variables:\n symbol = self.variables[id]\n symbol = sym.Symbol(id, value, type_, symbol.row, symbol.column)\n self.variables[id] = symbol\n return True", "def putbarvarname(self,j_,name_): # 3\n res = self.__obj.putbarvarname(j_,name_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def value_type(self, value_type):\n\n self._value_type = value_type", "def settype(self, graphtype):\n\n if str(graphtype).find(\"GRAPH\") > -1:\n self.__type = \"GRAPHS\"\n elif str(graphtype).find(\"SCATTER\") > -1:\n self.__type = \"SCATTER\"\n else:\n # Unknown type of graph - raise an exception\n raise ValueError(\n \"Unknown graph type: \"\n + graphtype\n + \"\\n\"\n + \"Must be one of 'GRAPHS' or 'SCATTER'\"\n )\n self.__nonzero = True", "def _assign_type(self, type):\n if self.is_input:\n return 'data'\n else:\n return type", "def putvarbound(self,j_,bk_,bl_,bu_): # 3\n if not isinstance(bk_,boundkey): raise TypeError(\"Argument bk has wrong type\")\n res = self.__obj.putvarbound(j_,bk_,bl_,bu_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)" ]

[ "0.89949", "0.6965111", "0.6361787", "0.62369287", "0.6113074", "0.60967517", "0.6074677", "0.6043116", "0.60060585", "0.59845704", "0.59814596", "0.5941611", "0.59217054", "0.5831631", "0.58297473", "0.57571024", "0.5754986", "0.5676297", "0.5632185", "0.5622995", "0.5618078", "0.55962425", "0.5589023", "0.5586686", "0.55739367", "0.5452266", "0.5388818", "0.5386131", "0.5384163", "0.5355476" ]

[ "def putvartypelist(self,subj,vartype): # 3\n num_ = None\n if num_ is None:\n num_ = len(subj)\n elif num_ != len(subj):\n raise IndexError(\"Inconsistent length of array subj\")\n if num_ is None:\n num_ = len(vartype)\n elif num_ != len(vartype):\n raise IndexError(\"Inconsistent length of array vartype\")\n if num_ is None: num_ = 0\n if subj is None: raise TypeError(\"Invalid type for argument subj\")\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n \n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n \n if vartype is None: raise TypeError(\"Invalid type for argument vartype\")\n if vartype is None:\n vartype_ = None\n else:\n try:\n vartype_ = memoryview(vartype)\n except TypeError:\n try:\n _tmparr_vartype = array.array(\"i\",vartype)\n except TypeError:\n raise TypeError(\"Argument vartype has wrong type\")\n else:\n vartype_ = memoryview(_tmparr_vartype)\n \n else:\n if vartype_.format != \"i\":\n vartype_ = memoryview(array.array(\"i\",vartype))\n \n res = self.__obj.putvartypelist(num_,subj_,vartype_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def putvartype(self,j_,vartype_): # 3\n if not isinstance(vartype_,variabletype): raise TypeError(\"Argument vartype has wrong type\")\n res = self.__obj.putvartype(j_,vartype_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def getvartypelist(self,subj,vartype): # 3\n num_ = None\n if num_ is None:\n num_ = len(subj)\n elif num_ != len(subj):\n raise IndexError(\"Inconsistent length of array subj\")\n if num_ is None: num_ = 0\n if subj is None: raise TypeError(\"Invalid type for argument subj\")\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n \n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n \n _copyback_vartype = False\n if vartype is None:\n vartype_ = None\n else:\n try:\n vartype_ = memoryview(vartype)\n except TypeError:\n try:\n _tmparr_vartype = array.array(\"i\",vartype)\n except TypeError:\n raise TypeError(\"Argument vartype has wrong type\")\n else:\n vartype_ = memoryview(_tmparr_vartype)\n _copyback_vartype = True\n else:\n if vartype_.format != \"i\":\n vartype_ = memoryview(array.array(\"i\",vartype))\n _copyback_vartype = True\n if vartype_ is not None and len(vartype_) != (num_):\n raise ValueError(\"Array argument vartype has wrong length\")\n res = self.__obj.getvartypelist(num_,subj_,vartype_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_vartype:\n for __tmp_var_0 in range(len(vartype_)): vartype[__tmp_var_0] = variabletype(_tmparr_vartype[__tmp_var_0])", "def getvartypelist(self,subj_,vartype_):\n num_ = None\n if num_ is None:\n num_ = len(subj_)\n elif num_ != len(subj_):\n raise IndexError(\"Inconsistent length of array subj\")\n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n _vartype_minlength = (num_)\n if (num_) > 0 and vartype_ is not None and len(vartype_) != (num_):\n raise ValueError(\"Array argument vartype is not long enough: Is %d, expected %d\" % (len(vartype_),(num_)))\n if isinstance(vartype_,numpy.ndarray) and not vartype_.flags.writeable:\n raise ValueError(\"Argument vartype must be writable\")\n if vartype_ is not None:\n _vartype_tmp = (ctypes.c_int32 * len(vartype_))()\n else:\n _vartype_tmp = None\n res = __library__.MSK_XX_getvartypelist(self.__nativep,num_,_subj_tmp,_vartype_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if vartype_ is not None: vartype_[:] = [ variabletype(v) for v in _vartype_tmp[0:len(vartype_)] ]", "def putvartype(self,j_,vartype_):\n res = __library__.MSK_XX_putvartype(self.__nativep,j_,vartype_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def set_lvar_type(self, *args):\n return _ida_hexrays.lvar_t_set_lvar_type(self, *args)", "def set_lvar_type(self, *args):\n return _ida_hexrays.vdui_t_set_lvar_type(self, *args)", "def put_var_param(self, var_type, num_vars):\n if var_type.upper() not in EX_VAR_TYPES:\n raise ExodusIIWriterError(\n \"var_type {0} not recognized\".format(var_type))\n ierr = exolib.py_expvp(self.exoid, var_type.lower(), num_vars)\n if ierr:\n raise ExodusIIWriterError(\"Error putting var params\")", "def validVarConstructType(self,vartype):\r\n indArray = vartype.find('[]')\r\n if indArray>0:\r\n thisType = vartype[0:indArray]\r\n isArray = True\r\n else:\r\n thisType = vartype\r\n isArray = False\r\n \r\n if thisType in ('rng','range'):\r\n type = 'range'\r\n elif thisType in ('rate'):\r\n type = 'rate'\r\n elif thisType in ('amt','amount'):\r\n type = 'amount'\r\n elif thisType in ('minamt','minamount'):\r\n type = 'minamount'\r\n elif thisType in ('bool'):\r\n type = 'bool'\r\n else:\r\n print 'variable type must be range, rate, amount, minamount, bool (or abbreviated forms)'\r\n return False, ''\r\n \r\n return True, type, isArray", "def ui_set_lvar_type(self, *args):\n return _ida_hexrays.vdui_t_ui_set_lvar_type(self, *args)", "def append_var(self, name, values, st_type=None, compress=True):\n global get_missing\n \n if (any(isinstance(values, t) for t in (str,bytes,bytearray))\n or not isinstance(values, collections.Iterable)):\n if self._nobs <= 1:\n values = [values]\n else:\n raise TypeError(\"values to add must be in an iterable\")\n if not isinstance(name, str):\n raise TypeError(\"variable name must be str\")\n \n name = name.strip()\n if name == \"\":\n raise ValueError(\"variable name required\")\n \n if name in self._varlist:\n raise ValueError(\"variable name already exists\")\n elif not self._is_valid_varname(name):\n raise ValueError(name + \" is not a valid Stata name\")\n \n type_names = (\"byte\", \"int\", \"long\", \"float\", \"double\")\n \n init_st_type = st_type\n if st_type is None:\n st_type = 65530 if compress else 65527\n elif isinstance(st_type, str):\n m = re.match(r'^str([0-9]+|L)$', st_type)\n if m:\n if m.group(1) == \"L\":\n st_type = 32768\n else:\n st_type = int(m.group(1)) \n if st_type > 2045:\n if not self._quiet:\n print(\"string type > 2045; appending as strL\")\n st_type = 32768\n init_st_type = st_type\n elif st_type in type_names:\n st_type = 65530 - type_names.index(st_type)\n init_st_type = st_type\n else:\n raise TypeError(str(st_type) + \" is not a valid Stata type\")\n elif (st_type not in (65530, 65529, 65528, 65527, 65526, 32768) \n and not (isinstance(st_type, int) and 1 <= st_type <= 2045)):\n raise TypeError(str(st_type) + \" is not a valid Stata type\")\n \n # Given iterable could be generator. Ensure it is in static form.\n values = [v for v in values]\n nvals = len(values)\n \n varvals = self._varvals\n \n if nvals == 0:\n this_missing = '' if st_type <= 32768 else MISSING\n for row in varvals:\n row.append(this_missing)\n else:\n alt_missing = False\n \n ismissing = self.ismissing\n \n for val, i in zip(values, range(nvals)):\n if st_type == 32768:\n if any(isinstance(val, t) \n for t in (str, bytes, bytearray)):\n pass\n elif val is None or isinstance(val, MissingValue):\n values[i] = ''\n alt_missing = True\n elif (not isinstance(val, int) and \n not isinstance(val, float)):\n msg = (\"value in position {} has invalid \".format(i) +\n \"type {}\".format(val.__class__.__name__))\n raise TypeError(msg)\n elif (-1.7976931348623157e+308 > val or\n val > 8.988465674311579e+307):\n values[i] = ''\n alt_missing = True\n else:\n values[i] = str(val)\n elif st_type <= 2045:\n if isinstance(val, str):\n val_len = len(val)\n st_type = (32768 if val_len > 2045 \n else max(st_type, val_len))\n elif val is None or isinstance(val, MissingValue):\n values[i] = ''\n alt_missing = True\n elif isinstance(val, bytes) or isinstance(val, bytearray):\n st_type = 32768\n elif (not isinstance(val, int) and \n not isinstance(val, float)):\n msg = (\"value in position {} has invalid \".format(i) +\n \"type {}\".format(val.__class__.__name__))\n raise TypeError(msg)\n elif (-1.7976931348623157e+308 > val or\n val > 8.988465674311579e+307):\n values[i] = ''\n alt_missing = True\n else:\n val = str(val)\n val_len = len(val)\n values[i] = val\n st_type = (32768 if val_len > 2045 \n else max(st_type, val_len))\n else:\n if isinstance(val, str):\n max_len = len(val)\n for j in range(i):\n valj = values[j]\n if ismissing(valj): \n # If encountering a missing value here, \n # should be instance of MissingValue.\n # Could just check for that.\n values[j] = ''\n alt_missing = True\n else:\n new_val = str(valj)\n max_len = max(max_len, len(new_val))\n values[j] = new_val\n st_type = 32768 if max_len > 2045 else max_len\n elif isinstance(val, bytes) or isinstance(val, bytearray):\n for j in range(i):\n new_val = values[j]\n if ismissing(new_val): \n # all missing values already encountered \n # should be instances of MissingValue, \n # so could just check that\n values[j] = ''\n alt_missing = True\n else:\n values[j] = str(new_val)\n st_type = 32768\n elif val is None:\n values[i] = MISSING\n alt_missing = True\n elif isinstance(val, MissingValue):\n pass\n elif (not isinstance(val, float) and \n not isinstance(val, int)):\n msg = (\"value in position {} has invalid \".format(i) +\n \"type {}\".format(val.__class__.__name__))\n raise TypeError(msg)\n elif (-1.7976931348623157e+308 > val or\n val > 8.988465674311579e+307):\n values[i] = get_missing(val)\n alt_missing = True\n elif st_type >= 65528: # int types\n if (val != int(val) or -2147483647 > val \n or val > 2147483620): \n # val is not int or is outside of bounds of long\n st_type = 65526 # double\n elif st_type <= 65529 and not (-32767 <= val <= 32740):\n # st_type int, but val outside of bounds\n st_type = 65528 # long\n elif st_type == 65530 and not (-127 <= val <= 100): \n # st_type byte, but val outside of bounds\n st_type = 65529 # int\n else: # was float or double and will continue to be\n if (st_type == 65527 and \n (-1.7014117331926443e+38 > val or\n val > 1.7014117331926443e+38)): \n # st_type float, but outside of bounds\n st_type = 65526 # double\n # This should maybe just set value to missing?\n # Stata sets value to missing, \n # does not promote float to double.\n \n if nvals < self._nobs:\n this_missing = '' if st_type <= 32768 else MISSING\n values += [this_missing]*(self._nobs - nvals)\n elif nvals > self._nobs:\n self.set_obs(nvals)\n \n for row, new_val in zip(varvals, values):\n row.append(new_val)\n \n if not self._quiet:\n smcl = \"{err}\" if IN_STATA else \"\"\n if init_st_type is not None and init_st_type != st_type:\n st_type_name = self._get_type_name(st_type)\n msg = (\"warning: some values were incompatible with \" + \n \"specified type;\\n type changed to \" + st_type_name)\n print(smcl + msg)\n if alt_missing:\n print(smcl + \"warning: some missing values inserted\")\n \n \n self._typlist.append(st_type)\n self._varlist.append(name)\n self._srtlist.append(None)\n self._fmtlist.append(\n '%' + str(max(9,st_type) if st_type <= 2045 else 9) + 's'\n if st_type <= 32768 else self._default_fmts[st_type])\n self._lbllist.append('')\n self._vlblist.append('')\n \n self._nvar += 1\n self._changed = True", "def putclist(self,subj,val): # 3\n num_ = None\n if num_ is None:\n num_ = len(subj)\n elif num_ != len(subj):\n raise IndexError(\"Inconsistent length of array subj\")\n if num_ is None:\n num_ = len(val)\n elif num_ != len(val):\n raise IndexError(\"Inconsistent length of array val\")\n if num_ is None: num_ = 0\n if subj is None: raise TypeError(\"Invalid type for argument subj\")\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n \n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n \n if val is None: raise TypeError(\"Invalid type for argument val\")\n if val is None:\n val_ = None\n else:\n try:\n val_ = memoryview(val)\n except TypeError:\n try:\n _tmparr_val = array.array(\"d\",val)\n except TypeError:\n raise TypeError(\"Argument val has wrong type\")\n else:\n val_ = memoryview(_tmparr_val)\n \n else:\n if val_.format != \"d\":\n val_ = memoryview(array.array(\"d\",val))\n \n res = self.__obj.putclist(num_,subj_,val_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def MigrateV2VarType(vartype, log):\n return {\n VarType.Integer: qtiv2.variables.BaseType.integer,\n VarType.String: qtiv2.variables.BaseType.string,\n VarType.Decimal: qtiv2.variables.BaseType.float,\n VarType.Scientific: qtiv2.variables.BaseType.float,\n VarType.Boolean: qtiv2.variables.BaseType.boolean,\n VarType.Enumerated: qtiv2.variables.BaseType.identifier,\n VarType.Set: qtiv2.variables.BaseType.identifier\n }[vartype]", "def set_type(self, val):\n if not contain_in_list_equal(val, PARAM_TYPES):\n raise ArgumentError(\"[WARNING] `type`, should be \" + \", \".join(PARAM_TYPES))\n self._type = val\n pass", "def getvartype(self,j_): # 3\n res,resargs = self.__obj.getvartype(j_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _vartype_return_value = resargs\n _vartype_return_value = variabletype(_vartype_return_value)\n return _vartype_return_value", "def setTypes(self):\n\n integers = []\n floats = [\n 'S',\n 'Pinj',\n 'coreRadFrac',\n 'qBG',\n 'lqCN',\n 'lqCF',\n 'lqPN',\n 'lqPF',\n 'fracPN',\n 'fracPF',\n 'fracCN',\n 'fracCF',\n 'fracUI',\n 'fracUO',\n 'fracLI',\n 'fracLO',\n 'fG',\n ]\n\n\n for var in integers:\n if (getattr(self, var) is not None) and (~np.isnan(float(getattr(self, var)))):\n try:\n setattr(self, var, tools.makeInt(getattr(self, var)))\n except:\n print(\"Error with input file var \"+var+\". Perhaps you have invalid input values?\")\n log.info(\"Error with input file var \"+var+\". Perhaps you have invalid input values?\")\n for var in floats:\n if var is not None:\n if (getattr(self, var) is not None) and (~np.isnan(float(getattr(self, var)))):\n try:\n setattr(self, var, tools.makeFloat(getattr(self, var)))\n except:\n print(\"Error with input file var \"+var+\". Perhaps you have invalid input values?\")\n log.info(\"Error with input file var \"+var+\". Perhaps you have invalid input values?\")\n\n return", "def write_variables(var_or_list, values):\n session = ph.get_session()\n if isinstance(var_or_list, (tuple, list)):\n for var, value in zip(var_or_list, values):\n var.load(value, session)\n else:\n var_or_list.load(values, session)", "def append_var(self, name, values, st_type=None, compress=True):\n global get_missing\n \n if (isinstance(values, str) or \n not isinstance(values, collections.Iterable)):\n if self._nobs <= 1:\n values = [values]\n else:\n raise TypeError(\"values to add must be in an iterable\")\n if not isinstance(name, str):\n raise TypeError(\"variable name must be str\")\n \n name = name.strip()\n if name == \"\":\n raise ValueError(\"variable name required\")\n \n if name in self._varlist:\n raise ValueError(\"variable name already exists\")\n elif not self._is_valid_varname(name):\n raise ValueError(name + \" is not a valid Stata name\")\n \n type_names = (\"byte\", \"int\", \"long\", \"float\", \"double\")\n \n init_st_type = st_type\n if st_type is None:\n st_type = 251 if compress else 254\n elif isinstance(st_type, str):\n if re.match(r'^str[0-9]+$', st_type):\n st_type = int(st_type[3:])\n if st_type > 244:\n msg = \"given string type too large; shortening to 244\"\n print((\"{err}\" if IN_STATA else \"\") + msg)\n st_type = 244\n init_st_type = st_type\n elif st_type in type_names:\n st_type = 251 + type_names.index(st_type)\n init_st_type = st_type\n else:\n raise TypeError(str(st_type) + \" is not a valid Stata type\")\n elif (st_type not in (251, 252, 253, 254, 255) \n and not (isinstance(st_type, int) and 1 <= st_type <= 244)):\n raise TypeError(str(st_type) + \" is not a valid Stata type\")\n \n # Given iterable could be generator. Ensure it is in static form.\n values = [v for v in values]\n nvals = len(values)\n \n varvals = self._varvals\n \n if nvals == 0:\n this_missing = '' if st_type <= 244 else MISSING\n for row in varvals:\n row.append(this_missing)\n else:\n str_clipped = False\n alt_missing = False\n \n ismissing = self.ismissing\n \n for val, i in zip(values, range(nvals)):\n if st_type <= 244:\n if isinstance(val, str):\n val_len = len(val)\n if val_len > 244:\n values[i] = val[:244]\n val_len = 244\n str_clipped = True\n st_type = max(st_type, val_len)\n elif val is None or isinstance(val, MissingValue):\n values[i] = ''\n alt_missing = True\n elif not (isinstance(val, int) or isinstance(val, float)):\n msg = (\"value in position {} has invalid \".format(i) +\n \"type {}\".format(val.__class__.__name__))\n raise TypeError(msg)\n elif (-1.7976931348623157e+308 > val or\n val > 8.988465674311579e+307):\n values[i] = ''\n alt_missing = True\n else:\n val = str(val)\n val_len = len(val)\n if val_len > 244:\n val = val[:244]\n val_len = 244\n str_clipped = True\n values[i] = val\n st_type = max(st_type, val_len)\n else:\n if isinstance(val, str):\n val_len = len(val)\n if val_len > 244:\n values[i] = val[:244]\n val_len = 244\n str_clipped = True\n st_type = val_len\n for j in range(i):\n valj = values[j]\n if ismissing(valj): \n # If encountering a missing value here, \n # should be instance of MissingValue. \n # Could just check for that.\n values[j] = ''\n alt_missing = True\n else:\n new_val_j = str(values[j])\n val_len = len(new_val_j)\n if val_len > 244:\n new_val_j = new_val_j[:244]\n val_len = 244\n str_clipped = True\n values[j] = new_val_j\n st_type = max(st_type, val_len)\n elif val is None:\n values[i] = MISSING\n alt_missing = True\n elif isinstance(val, MissingValue):\n pass\n elif not (isinstance(val, float) or isinstance(val, int)):\n msg = (\"value in position {} has invalid \".format(i) +\n \"type {}\".format(val.__class__.__name__))\n raise TypeError(msg)\n elif (-1.7976931348623157e+308 > val or\n val > 8.988465674311579e+307):\n values[i] = get_missing(val)\n alt_missing = True\n elif st_type <= 253: # int types\n if (val != int(val) or \n not (-2147483647 <= val <= 2147483620)):\n # val is not int or is outside of bounds of long\n st_type = 255 # double\n elif st_type <= 252 and not (-32767 <= val <= 32740):\n # st_type int, but val is outside of bounds\n st_type = 253 # long\n elif st_type == 251 and not (-127 <= val <= 100):\n # st_type byte, but val is outside of bounds\n st_type = 252 # int\n else: # was float and will continue to be\n if st_type == 254 and (-1.7014117331926443e+38 > val or\n val > 1.7014117331926443e+38):\n # st_type float, but val is outisde of bounds\n st_type = 255 # double\n # This should maybe just set value to missing?\n # Stata sets value to missing, \n # does not promote float to double.\n \n if nvals < self._nobs:\n this_missing = '' if st_type <= 244 else MISSING\n values += [this_missing]*(self._nobs - nvals)\n elif nvals > self._nobs:\n self.set_obs(nvals)\n \n for row, new_val in zip(varvals, values):\n row.append(new_val)\n \n if not self._quiet:\n smcl = \"{err}\" if IN_STATA else \"\"\n if init_st_type is not None and init_st_type != st_type:\n st_type_name = self._get_type_name(st_type)\n msg = (smcl + \"warning: some values were incompatible with \" + \n \"specified type;\\n type changed to \" + st_type_name)\n print(msg)\n if str_clipped:\n print(smcl + \"warning: some strings were \" + \n \"shortened to 244 characters\")\n if alt_missing:\n print(smcl + \"warning: some missing values inserted\")\n \n \n self._typlist.append(st_type)\n self._varlist.append(name)\n self._srtlist.append(None)\n self._fmtlist.append('%' + str(max(9,st_type)) + 's' if st_type <= 244\n else self._default_fmts[st_type])\n self._lbllist.append('')\n self._vlblist.append('')\n \n self._nvar += 1\n self._changed = True", "def add_variables(self, n_variables, lb=None, ub=None, var_type=None):\n curr_n_vars = self.problem.variables.get_num()\n\n lb = convert_cplex_val(lb)\n ub = convert_cplex_val(ub)\n\n if var_type.lower() == \"real\" or var_type.lower() == \"continuous\":\n vtype = cplex.Cplex.variables.type.continuous\n\n elif var_type.lower() == \"int\" or var_type.lower() == \"integer\":\n vtype = cplex.Cplex.variables.type.integer\n\n elif var_type.lower() == \"binary\" or var_type.lower() == \"bool\" or var_type.lower() == \"boolean\":\n vtype = cplex.Cplex.variables.type.binary\n\n elif var_type.lower() == \"auto\" or var_type is None:\n vtype = cplex.Cplex.variables.type.binary\n\n else:\n raise Exception(\"Vartype '{}' unsupported.\".format(var_type))\n\n if lb is not None and ub is not None:\n self.problem.variables.add(\n lb=[ lb ] * n_variables,\n ub=[ ub ] * n_variables,\n types=[ vtype ] * n_variables)\n\n elif lb is not None:\n self.problem.variables.add(\n lb=[ lb ] * n_variables,\n types=[ vtype ] * n_variables)\n\n elif ub is not None:\n self.problem.variables.add(\n ub=[ ub ] * n_variables,\n types=[ vtype ] * n_variables)\n\n else:\n self.problem.variables.add(\n types=[ vtype ] * n_variables)\n\n # Return the 0-based indexes of the new variables\n new_var_idxs = xrange(curr_n_vars, curr_n_vars + n_variables)\n return new_var_idxs", "def put_var_names(self, var_type, num_vars, var_names):\n if var_type.upper() not in EX_VAR_TYPES:\n raise ExodusIIWriterError(\n \"var_type {0} not recognized\".format(var_type))\n # var names must all be of same length due to Fortran restrictions\n var_names = [\"{0:{1}s}\".format(x, MAX_STR_LENGTH)[:MAX_STR_LENGTH]\n for x in var_names]\n ierr = exolib.py_expvan(self.exoid, var_type.lower(), var_names)\n if ierr:\n raise ExodusIIWriterError(\"Error putting var names\")", "def dynamic_list_type(self, dynamic_list_type):\n\n self._dynamic_list_type = dynamic_list_type", "def import_types(self, typerule_list, variable_types = []):\n\n # For simplicity, variable types are treated exactly the same as type rules\n all_type_rules = variable_types + typerule_list\n\n # Sort all type rules by their input lengths into the _type_rules dict\n for type_rule in all_type_rules:\n self._type_rules[len(type_rule[0])].append(TypeRule(type_rule[0], type_rule[1]))\n\n # Add wildcard types as lowest priority for cleanup\n self._type_rules[1].append(TypeRule(['?'], '?'))\n self._type_rules[3].append(TypeRule(['(', '?', ')'], '?'))", "def __setitem__(self, name, val):\n\n if name in self.vars:\n l[name].setVal(val)\n else:\n l[name] = YPFVal(name, val)", "def __setitem__(self, item, value):\n self.vars[item] = value", "def is_valid(var, var_type, list_type=None):\n if not isinstance(var, var_type):\n raise AttributeError(f\"The given variable is not a {var_type}\")\n\n if var_type is list and list_type is not None:\n for element in var:\n _ = is_valid(element, list_type)\n\n return var", "def set_variable_values(self, vars_values):\n raise NotImplementedError()", "def set(self, key, value):\n if (\n key in self.variables and\n type(value).__name__ == self.variables[key]['type']\n ):\n self.variables[key]['value'] = value\n else:\n raise ValueError(\"Bad key or wrong variable type\")", "def regenerate_variables(self):\n\n # Let us not forget to remove fields that might be empty by now\n if hasattr(self, '_var_kinds'):\n for k in self._var_kinds:\n attrname = camel2underscores(k)\n try:\n delattr(self, attrname)\n except AttributeError:\n pass # The attribute may not have been set up yet\n\n _var_kinds = defaultdict(DictList)\n for k, v in self._var_dict.items():\n _var_kinds[v.__class__.__name__].append(v)\n\n for k in _var_kinds:\n attrname = camel2underscores(k)\n setattr(self, attrname, _var_kinds[k])\n\n self._var_kinds = _var_kinds", "def setTypeItem(self,data):\n currNode = self.head\n while currNode is not None:\n currNode.data.setType(data)\n currNode = currNode.next", "def set_vars_as_type(df, varNames, dtype):\n\n myVars = list(set(df.columns).intersection(set(varNames)))\n df[myVars] = df[myVars].astype(dtype)" ]

[ "0.8250177", "0.7224048", "0.707309", "0.6871811", "0.6064735", "0.58782834", "0.5704154", "0.56204504", "0.54422724", "0.5369249", "0.52549726", "0.5253745", "0.5215147", "0.51658744", "0.5073383", "0.50687295", "0.5062825", "0.5059147", "0.50579226", "0.50538254", "0.49503917", "0.49355346", "0.49239615", "0.487696", "0.48569608", "0.48520616", "0.4835246", "0.48299286", "0.48142484", "0.48083636" ]

[ "def readdataformat(self,filename_,format_,compress_): # 3\n if not isinstance(format_,dataformat): raise TypeError(\"Argument format has wrong type\")\n if not isinstance(compress_,compresstype): raise TypeError(\"Argument compress has wrong type\")\n res = self.__obj.readdataformat(filename_,format_,compress_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def parse(self, filename):\n try:\n if 't' in self.FILE_OPEN_MODE:\n kw = {'encoding': self.FILE_ENCODING, 'errors': 'ignore'}\n else:\n kw = {}\n with open(filename, self.FILE_OPEN_MODE, **kw) as infile:\n self._parse(infile)\n except IOError:\n raise FileFormatError()", "def read_file(self,filename):\n\n if (config.mode_format == \"simple\"): return self.read_file_simple(filename)\n if (config.mode_format == \"agsm\"): return self.read_file_agsm(filename)\n sys.exit(\"ERROR: unrecognised format \\\"\"+config.mode_format+\"\\\".\\n\" \\\n +\" Please choose another value for mode_format in AIMS_configure.py\")", "def __read(self, filename):\n f = open(filename)\n\n self.startDate = self.__parseDate(f.readline())\n (nRows, nCols) = [int(s) for s in f.readline().split() ]\n\n dataArray = self.__readData(f, nRows, nCols)\n self.__storeDataDict(dataArray)\n self.__appendMetaData(filename)\n self._appendDerivedQuantities()", "def readdata(self,filename_): # 3\n res = self.__obj.readdataautoformat(filename_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def read(file):\n\n blocks = ['bus', 'load', 'fshunt', 'gen', 'branch', 'transf', 'area',\n 'twotermdc', 'vscdc', 'impedcorr', 'mtdc', 'msline', 'zone',\n 'interarea', 'owner', 'facts', 'swshunt', 'gne', 'Q']\n nol = [1, 1, 1, 1, 1, 4, 1,\n 0, 0, 0, 0, 0, 1,\n 0, 1, 0, 0, 0, 0]\n rawd = re.compile('rawd\\d\\d')\n\n retval = True\n version = 0\n b = 0 # current block index\n raw = {}\n for item in blocks:\n raw[item] = []\n\n data = []\n mdata = [] # multi-line data\n mline = 0 # line counter for multi-line models\n\n # parse file into raw with to_number conversions\n fid = open(file, 'r')\n for num, line in enumerate(fid.readlines()):\n line = line.strip()\n if num == 0: # get basemva and frequency\n data = line.split('/')[0]\n data = data.split(',')\n\n mva = float(data[1])\n freq = float(data[5])\n version = int(data[2])\n\n if not version:\n version = int(rawd.search(line).group(0).strip('rawd'))\n if version < 32 or version > 33:\n logging.warning('RAW file version is not 32 or 33. Error may occur.')\n continue\n elif num == 1: # store the case info line\n logging.info(line)\n continue\n elif num == 2:\n continue\n elif num >= 3:\n if line[0:2] == '0 ' or line[0:3] == ' 0 ': # end of block\n b += 1\n continue\n elif line[0] is 'Q': # end of file\n break\n data = line.split(',')\n\n data = [to_number(item) for item in data]\n mdata.append(data)\n mline += 1\n if mline == nol[b]:\n if nol[b] == 1:\n mdata = mdata[0]\n raw[blocks[b]].append(mdata)\n mdata = []\n mline = 0\n fid.close()\n\n # add device elements params and add to PSAT formatted dictionary\n\n for data in raw['bus']:\n \"\"\"version 32:\n 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10\n ID, NAME, BasekV, Type, Area Zone Owner Va, Vm, latitude longitude\n \"\"\"\n idx = data[0]\n ty = data[3]\n angle = data[8]\n try:\n lat = data[9]\n except:\n # logging.warning('<No Coordinates in .raw file>')\n param = {'idx': idx,\n 'name': data[1],\n 'Vn': data[2],\n 'type': data[3],\n 'area': data[4],\n 'voltage': data[7],\n 'region': data[5],\n 'owner': data[6],\n 'angle': angle,\n }\n psatlist = [data[0], data[2], data[7], angle, data[4], data[5]]\n else:\n param = {'idx': idx,\n 'name': data[1],\n 'Vn': data[2],\n 'type': data[3],\n 'area': data[4],\n 'voltage': data[7],\n 'region': data[5],\n 'owner': data[6],\n 'angle': angle,\n 'latitude': data[9],\n 'longitude': data[10]\n }\n psatlist = [data[0], data[2], data[7], angle, data[4], data[5], data[9], data[10]]\n Settings.Bus.append(psatlist)\n Settings.BusNames.append(data[1])\n # Add BusSTORE Dictionary For Later Reference\n Settings.BusStore[idx] = param\n\n xcoord = [34.560040, 34.938385, 34.360040, 40.5152473, 40.3142473, 36.527401, 36.857401, 36.687401, 36.856401,\n 40.487041, 36.903901, 36.702901, 35.832561, 33.386047, 33.185047, 37.105571, 37.104154, 33.706718,\n 37.103549, 36.703539, 37.103559, 36.703549, 36.033561, 35.631561, 36.032561, 35.732561, 36.525401,\n 36.857401, 49.869314, 50.969314, 51.979314, 52.481674, 54.973192, 56.276212, 41.734596, 34.551015,\n 34.652015, 34.537507, 34.587507, 34.157904, 33.714453, 33.762453, 39.548160, 39.496160, 34.313143,\n 34.545782, 34.380686, 34.111686, 34.137762, 34.118650, 34.158650, 33.918650, 33.718650, 34.018650,\n 34.018650, 34.018650, 34.018650, 34.018650, 34.312456, 34.315456, 34.243600, 34.566258, 34.565258,\n 46.064672, 46.565672, 45.514571, 45.606833, 45.806833, 44.890000, 45.596416, 45.295416, 45.891161,\n 47.954899, 46.511440, 45.913936, 45.713936, 46.669335, 47.954899, 47.624154, 43.784730, 44.482350,\n 42.006860, 42.934919, 42.731919, 43.013135, 44.068350, 43.558350, 42.438350, 42.938350, 44.068350,\n 43.558350, 43.048350, 42.638350, 44.068350, 43.558350, 43.048350, 42.638350, 43.620189, 39.120428,\n 40.398031, 35.216200, 35.215200, 36.202099, 39.777745, 39.539598, 37.052929, 35.403217, 35.352217,\n 36.807243, 39.567450, 40.807689, 40.806689, 41.008689, 39.555494, 37.954721, 38.406721, 38.906721,\n 38.656721]\n ycoord = [-109.277313, -110.303798, -109.777313, -107.546455, -107.546455, -108.325669, -108.654569, -108.486669,\n -108.325669, -107.185575, -111.390408, -111.390408, -111.448566, -112.860397, -112.659397, -108.243555,\n -108.441191, -112.322033, -111.590816, -111.190816, -111.190816, -111.590806, -111.648566, -111.248566,\n -111.249566, -111.647566, -108.655669, -108.323669, -122.150895, -122.150895, -122.150895, -121.61684,\n -121.924221, -122.21370, -108.790427, -117.568105, -117.538105, -118.607375, -118.658375, -118.280282,\n -118.146319, -118.096319, -112.52797, -112.72797, -118.690631, -118.389938, -118.478496, -118.478496,\n -118.299917, -118.095428, -118.095428, -118.095428, -118.095428, -118.195428, -118.395428, -117.995428,\n -117.795428, -117.995428, -118.481217, -118.891217, -118.391667, -117.166428, -117.368428, -106.60906,\n -106.80906, -122.681289, -121.114785, -122.113785, -123.29000, -121.312202, -121.114202, -106.612578,\n -118.997945, -112.88531, -120.692286, -120.693974, -119.571501, -120.997945, -122.219492, -118.77463,\n -121.019484, -121.316546, -114.419206, -114.419206, -120.956476, -120.79484, -120.93484, -121.216546,\n -121.156546, -121.215484, -121.135484, -121.255484, -121.175484, -121.013484, -120.733484, -121.053484,\n -120.973484, -118.865882, -122.073631, -122.263453, -120.847567, -120.900567, -120.129849, -122.142965,\n -122.262993, -121.021929, -119.450452, -119.450452, -121.779037, -122.276225, -122.135718, -121.935718,\n -121.935718, -121.24000, -121.18379, -121.10879, -121.27379, -121.23979]\n\n #for idx, line in enumerate(Settings.Bus):\n # line.extend([xcoord[idx], ycoord[idx]])\n\n maxV = 1.1\n minV = 0.9\n maxQ = 1\n minQ = 0\n convimp = 0\n status = 1\n loss = 1\n\n for data in raw['load']:\n \"\"\"version 32:\n 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11\n Bus, Id, Status, Area, Zone, PL(MW), QL (MW), IP, IQ, YP, YQ, OWNER\n \"\"\"\n\n busidx = data[0]\n vn = Settings.BusStore[busidx]['Vn']\n voltage = Settings.BusStore[busidx]['voltage']\n param = {'bus': busidx,\n 'Vn': vn,\n 'Sn': mva,\n 'p': (data[5] + data[7] * voltage + data[9] * voltage ** 2) / mva,\n 'q': (data[6] + data[8] * voltage - data[10] * voltage ** 2) / mva,\n 'owner': data[11],\n 'type': Settings.BusStore[busidx]['type'],\n 'voltage': voltage\n }\n\n psatlist = [busidx, mva, vn, param['p'], param['q'], maxV, minV, convimp, status]\n Settings.PQ.append(psatlist)\n \"\"\"CONFIRM THAT OTHER BUSES HAVE 0 P and 0 Q which are not added\"\"\"\n\n for data in raw['fshunt']:\n \"\"\"\n 0, 1, 2, 3, 4\n Bus, name, Status, g (MW), b (Mvar)\n \"\"\"\n busidx = data[0]\n vn = Settings.BusStore[busidx]['Vn']\n param = {'bus': busidx,\n 'Vn': vn,\n 'status': data[2],\n 'Sn': mva,\n 'g': data[3] / mva,\n 'b': data[4] / mva,\n }\n\n psatlist = [busidx, mva, vn, freq, param['g'], param['b'], param['status']]\n Settings.Shunt.append(psatlist)\n\n gen_idx = 0\n type = 6\n\n for data in raw['gen']:\n \"\"\"\n 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,10,11, 12, 13, 14, 15, 16,17,18,19\n I,ID,PG,QG,QT,QB,VS,IREG,MBASE,ZR,ZX,RT,XT,GTAP,STAT,RMPCT,PT,PB,O1,F1\n \"\"\"\n busidx = data[0]\n vn = Settings.BusStore[busidx]['Vn']\n gen_mva = data[8]\n gen_idx += 1\n status = data[14]\n leak = 0\n param = {'Sn': gen_mva,\n 'Vn': vn,\n 'u': status,\n 'idx': gen_idx,\n 'bus': busidx,\n 'pg': status * data[2] / mva,\n 'qg': status * data[3] / mva,\n 'qmax': data[4] / mva,\n 'qmin': data[5] / mva,\n 'v0': data[6],\n 'ra': data[9], # ra armature resistance\n 'xs': data[10], # xs synchronous reactance\n 'pmax': data[16] / mva,\n 'pmin': data[17] / mva,\n }\n\n if Settings.BusStore[busidx]['type'] == 3: #Check Bus Type for Slack\n refangle = 0\n refBus = 1\n PGuess = 1\n swlist = [busidx, gen_mva, vn, param['v0'], refangle, param['qmax'], param['qmin'],\n maxV, minV, PGuess, loss, refBus, status]\n SW = swlist\n Settings.SW.append(swlist)\n Settings.SWStore[busidx] = param\n Settings.SynStore[busidx] = param\n continue\n\n if busidx not in Settings.BusStore.keys():\n \"\"\" Need data from .dyr file. Create initial list, then append data from .dyr\"\"\"\n else:\n # psatlist = [busidx, gen_mva, vn, freq, type, leak, param['ra'],param['xs']]\n # Syn.append(psatlist)\n Settings.SynStore[busidx] = param\n pvlist = [busidx, gen_mva, vn, param['pg'], Settings.BusStore[busidx]['voltage'],\n param['qmax'], param['qmin'], maxV, minV, loss, status]\n Settings.PV.append(pvlist)\n\n\n for data in raw['branch']:\n \"\"\"\n I,J,ID,R,X,B,RATEA,RATEB,RATEC,GI,BI,GJ,BJ,ST,LEN,O1,F1,...,O4,F4\n \"\"\"\n param = {'bus1': data[0],\n 'bus2': data[1],\n 'id' : data[2],\n 'r': data[3],\n 'x': data[4],\n 'b': data[5],\n 'rate_a': data[6],\n 'rate_b': data[7],\n 'rate_c': data[8],\n 'Vn': Settings.BusStore[data[0]]['Vn'],\n 'Vn2': Settings.BusStore[data[1]]['Vn'],\n 'length': data[14],\n 'Ilim': EMPTY,\n 'Plim': EMPTY,\n 'Slim': EMPTY,\n 'status': data[13]\n }\n\n psatlist = [param['bus1'], param['bus2'], param['rate_c'], param['Vn'], freq, EMPTY,\n param['length'], param['r'], param['x'], param['b'], param['Ilim'], param['Plim'], EMPTY, EMPTY,\n param['Slim'], param['status']]\n Settings.Lineij.append([data[0], data[1], data[2]])\n Settings.Lineji.append([data[1], data[0], data[2]])\n Settings.LineOrd[param['bus1']].append(psatlist)\n Settings.branches += 1\n Settings.linecount += 1\n Settings.LineBusMatij[param['bus2']].append(Settings.branches)\n Settings.LineBusMatji[param['bus1']].append(Settings.branches)\n\n for data in raw['transf']:\n \"\"\"\n I,J,K,CKT,CW,CZ,CM,MAG1,MAG2,NMETR,'NAME',STAT,O1,F1,...,O4,F4\n R1-2,X1-2,SBASE1-2\n WINDV1,NOMV1,ANG1,RATA1,RATB1,RATC1,COD1,CONT1,RMA1,RMI1,VMA1,VMI1,NTP1,TAB1,CR1,CX1\n WINDV2,NOMV2\n \"\"\"\n if len(data[1]) < 5:\n ty = 2\n else:\n ty = 3\n if ty == 3:\n continue\n # raise NotImplementedError('Three-winding transformer not implemented')\n\n tap = data[2][0]\n phi = data[2][2]\n\n if tap == 1 and phi == 0:\n trasf = False\n else:\n trasf = True\n param = {'trasf': trasf,\n 'bus1': data[0][0],\n 'bus2': data[0][1],\n 'u': data[0][11],\n 'b': data[0][8],\n 'r': data[1][0],\n 'x': data[1][1],\n 'tap': tap,\n 'phi': phi,\n 'rate_a': data[2][3],\n 'Vn': Settings.BusStore[busidx]['Vn'],\n 'Vn2': Settings.BusStore[busidx]['Vn'],\n # 'length': data[?][?], FIND CORRECT INDEX\n 'Ilim': EMPTY,\n 'Plim': EMPTY,\n 'Slim': EMPTY,\n }\n psatlist = [param['bus1'], param['bus2'], param['rate_a'], param['Vn'], freq, EMPTY,\n EMPTY, param['r'], param['x'], param['b'], param['Ilim'], param['Plim'], EMPTY, EMPTY,\n param['Slim'], param['u']]\n\n Settings.LineOrd[param['bus1']].append(psatlist)\n Settings.linecount += 1\n Settings.transformers += 1\n # ADD Line Data(All Branch Types) to Sys Param Dict after .dyr Transformer Data Added\n # Re-Order Line Data for correct sequence\n for key in Settings.LineOrd:\n for item in Settings.LineOrd[key]:\n Settings.Line.append(item)\n\n for data in raw['area']:\n Settings.Areas.append(data[4])\n\n for data in raw['zone']:\n Settings.Regions.append(data[1])\n\n return retval", "def _read_file(self):\n\n with open(self.file_name, 'rb') as f:\n new_test = struct.unpack('<l', f.read(8)[4:])[0]\n f.close()\n\n with open(self.file_name, 'rb') as f:\n old_test = struct.unpack('<h', f.read(6)[4:])[0]\n f.close()\n\n with open(self.file_name, 'rb') as f:\n other_test = struct.unpack('<l', f.read(20)[16:])[0]\n f.close()\n\n open_file = open(self.file_name, 'rb')\n\n if (other_test==202):\n raw = open_file.read(1236)[11:]\n self.model = '202'\n elif ((not new_test==102) and old_test==102):\n raw = open_file.read(1133)\n self.model = '102old'\n elif (new_test==102 and old_test==102):\n raw = open_file.read(1224)\n self.model = '102new'\n\n self.header = DpHeader(raw, self.model)\n\n self.data = DpData(open_file, \n self.model, \n self.header.interferogram_size, \n self.header.number_of_coadds, \n 2048*self.header.zero_fill,\n self.header.laser_wavelength_microns, \n self.header.dispersion_constant_xm,\n self.header.dispersion_constant_xb)\n\n open_file.close()", "def read(self, filename):\n pass", "def read(self, filename):\n pass", "def read(self, filename):\n raise NotImplementedError", "def read_from_file(self, filename: str) -> None:", "def read_file(filename,res_format=None,filename_format=None,verbose=False):\n\n # parse results filename for any supplementary run parameters\n info_from_filename = parse_filename(filename,filename_format)\n\n if res_format is None:\n if info_from_filename.get(\"code_name\") is not None:\n res_format = code_name_map[info_from_filename[\"code_name\"]]\n else:\n raise ValueError(\"unable to deduce res_format\")\n\n # parse results file contents for run parameters and data\n if (verbose):\n print(\" read_file: filename {}\".format(filename))\n with open(filename,'rt') as fin:\n try:\n results_list = data_format_parser[res_format](fin,verbose=verbose)\n except Exception as e:\n print(\"filename {} filename_format {} res_format {}\".format(filename, filename_format, res_format))\n raise e\n if (verbose):\n print(\" read_file: mesh points {:d}\".format(len(results_list)))\n\n # augment parameters with those obtained from filename\n #\n # Note: The parameter values obtained from the filename will\n # *override* any parameter values obtained by parsing the results\n # file. So beware that parameter values formatted for the\n # filename might have lower precision than those stored in the\n # results file.\n\n for results in results_list:\n results.params.update(info_from_filename)\n results.filename = os.path.basename(filename)\n\n return results_list", "def read (self, file):\n\t\tself.unpack (file.read (self.size()))", "def read(self, filename):\n with RavenFileReader(filename) as f:\n line = f.nexttag()\n while line:\n # Begin data type checks\n if self.cleantag(line) == 'Gauge':\n self.read_metgauge(line, f)\n elif self.cleantag(line) == 'ObservationData':\n self.read_obsgauge(line, f)\n # Next line\n line = f.nexttag()", "def read_raw(rawfile, shape, dtype=np.uint16, kind='middleton'):\n\n # -- alert\n print(\"READ_RAW: reading {0}...\".format(rawfile))\n\n\n # -- read file\n if kind=='middleton':\n return np.fromfile(open(rawfile),dtype) \\\n .reshape(shape[2],shape[0],shape[1])[:,:,::-1] \\\n .transpose(1,2,0) \\\n .astype(float)", "def read(self, filename): # real signature unknown; restored from __doc__\n pass", "def parseFile(self, filename):\n self.__filename = filename\n\n if os.path.isfile(filename) == False:\n self.LogError(\"Unable to open input file \" + str(filename))\n raise IOError\n\n self.__file = open(filename, 'r')\n\n while True:\n string = self.__file.readline()\n if string == \"\":\n break\n\n if string.upper().find(\"[SYSTEM]\") != -1:\n #print string.upper()\n self.__parseSystem()\n\n if string.upper().find(\"[GRASS]\") != -1:\n #print string.upper()\n self.__parseGrass()\n\n if string.upper().find(\"[COMPLEXDATA]\") != -1:\n #print string.upper()\n self.complexDataList.append(ComplexData(self.__file))\n\n if string.upper().find(\"[COMPLEXOUTPUT]\") != -1:\n #print string.upper()\n self.complexOutputList.append(ComplexOutput(self.__file))\n\n if string.upper().find(\"[LITERALDATA]\") != -1:\n #print string.upper()\n LD = LiteralData(self.__file)\n if LD.identifier == 'multi_output':\n self.LogWarning(\"multi_output: \" + LD.value.upper())\n if LD.value.upper() == 'TRUE':\n self.multiOutput = True\n else:\n self.literalDataList.append(LD)", "def read_file_object(self, file_obj, file_format='FASTA'):\n if ( file_format.upper() == 'FASTA' ):\n read_func = read_fasta \n #elif ( file_format.upper() == 'COMPACT' ):\n # read_func = read_compact\n #elif ( file_format.upper() == 'COMPACT3' ):\n # read_func = read_compact3\n else:\n raise NotImplementedError(\"Unknown file format (%s) is not supported\" % file_format)\n self.colcount = 0\n for name, seq in read_func(file_obj):\n cseq, l = self.get_alignment_seq_object(seq)\n self[name] = cseq\n self.colcount = max(l, self.colcount)", "def __init__(self, inFilename):\n\n self._prmtopVersion=None\n self._flags=[]\n self._raw_format={}\n self._raw_data={}\n self._has_nbfix_terms = False\n\n with open(inFilename, 'r') as fIn:\n for line in fIn:\n if line[0] == '%':\n if line.startswith('%VERSION'):\n tag, self._prmtopVersion = line.rstrip().split(None, 1)\n elif line.startswith('%FLAG'):\n tag, flag = line.rstrip().split(None, 1)\n self._flags.append(flag)\n self._raw_data[flag] = []\n elif line.startswith('%FORMAT'):\n format = line.rstrip()\n index0=format.index('(')\n index1=format.index(')')\n format = format[index0+1:index1]\n try:\n m = FORMAT_RE_PATTERN.search(format)\n self._raw_format[self._flags[-1]] = (format, m.group(1), m.group(2), int(m.group(3)), m.group(4))\n except:\n # We couldn't parse the format, so just treat the whole line as a single string.\n self._raw_format[self._flags[-1]] = (format, 1, 'a', 80, '')\n elif line.startswith('%COMMENT'):\n continue\n elif self._flags \\\n and 'TITLE'==self._flags[-1] \\\n and not self._raw_data['TITLE']:\n self._raw_data['TITLE'] = line.rstrip()\n else:\n flag=self._flags[-1]\n (format, numItems, itemType,\n iLength, itemPrecision) = self._getFormat(flag)\n line = line.rstrip()\n for index in range(0, len(line), iLength):\n item = line[index:index+iLength]\n if item:\n self._raw_data[flag].append(item.strip())\n # See if this is a CHAMBER-style topology file, which is not supported\n # for creating Systems\n self.chamber = 'CTITLE' in self._flags", "def _read_data(self):\n with self._open(self.filename, 'rb') as f:\n try:\n f.seek(self._offset_data, self._offset_whence)\n except IOError:\n print('Error: hedp.io.HamamatsuFile seeking outside of file limits.')\n print(' Failed to parse file.')\n print(\" Either the 'offset' or 'dtype' input arguments must be wrong!\")\n raise\n except:\n raise\n\n data_len = np.prod(self.shape)*np.dtype(self._dtype).itemsize\n data_str = f.read(data_len)\n if data_len != len(data_str):\n print(data_len, len(data_str))\n raise ValueError('File ended before all data was read. Probably wrong offset or dtype!')\n\n\n self.data = np.fromstring(data_str, dtype=self._dtype).reshape(self.shape[::-1])\n self.data = np.ndarray.astype(self.data, 'float32')\n\n #self.data = np.fromfile(f, dtype=self._dtype,\n # count=np.prod(self.shape)).reshape(self.shape[::-1])", "def read_data(self, f):\n\n f.seek(self.offset)\n # assume files are small enough to fit in memory\n data = f.read(self.compressed_size)\n if self.type == 0:\n return data\n elif self.type == 1:\n return gzip.decompress(data)\n elif self.type == 2:\n n, = struct.unpack('<L', data[:4])\n target = data[4:4+n].rstrip(b'\\0').decode('utf-8')\n logger.debug(f\"file redirection: {target}\")\n return None\n elif self.type == 3:\n return zstd_decompress(data)\n raise ValueError(f\"unsupported file type: {self.type}\")", "def readdata(filename):\n\tdt = np.dtype([('date','int'),('val','<f8')])\n\tdata = np.loadtxt(filename,dtype = dt,skiprows = 1)\n\treturn data", "def parse_data(fp):\n pass", "def readFromFile(filename):\n raise NotImplementedError", "def _read(self):\n # initializng data dictionary\n self.data={}\n\n f = FortranFile(self.filename)\n # Default omnivor binary header\n self.data['MK'] = f.readInts('i')\n self.data['itime'] = f.readInts('i')\n self.data['version'] = f.readString()\n self.data['file_id'] = f.readInts('i')\n self.data['sversion'] = f.readString()\n # Velocity field\n self.data['stype'] = f.readString()\n self.data['is_grid'] = f.readInts('i')\n nCPs = f.readInts('i')\n self.data['nCPs'] = nCPs\n if self.data['MK'] == 8:\n real_char='d'\n else:\n real_char='f'\n if self.data['is_grid']:\n #print('File is a velocity grid file')\n n1 = f.readInts('i')\n n2 = f.readInts('i')\n n3 = f.readInts('i')\n self.data['n1'] = n1\n self.data['n2'] = n2\n self.data['n3'] = n3\n self.data['is_straight'] = f.readInts('i')\n self.data['v1'] = f.readReals(real_char)\n self.data['v2'] = f.readReals(real_char)\n self.data['v3'] = f.readReals(real_char)\n\n CPs_raw = f.readReals(real_char)\n Utot_raw = f.readReals(real_char)\n CPs = np.reshape(CPs_raw,(3,nCPs),order = 'F')\n Utot = np.reshape(Utot_raw,(3,nCPs),order = 'F')\n\n acc=-1\n CPsTab = np.zeros((3, n1,n2,n3))\n UtotTab = np.zeros((3, n1,n2,n3))\n # Reshaping the nasty way (this is natural order). \n for i in range(0,n1):\n for j in range(0,n2):\n for k in range(0,n3):\n acc=acc+1\n CPsTab[0:3,i,j,k] = CPs[0:3,acc]\n UtotTab[0:3,i,j,k] = Utot[0:3,acc]\n\n self.data['CPs'] = CPs\n self.data['CPsTab'] = CPsTab\n self.data['Utot'] = Utot\n self.data['UtotTab'] = UtotTab", "def read(self, database ='project'):\n\t\tfile = open(self.file_name, \"r\")\n\n\t\ti = 1\n\t\tseptics = []\n\t\tfor line in file:\n\t\t\tif i > 2:\n\t\t\t\tval = line.split()\n\t\t\t\tself.check_cols(val, 13, 'septic')\n\n\t\t\t\tsep = {\n\t\t\t\t\t'name': val[0].lower(),\n\t\t\t\t\t'q_rate': val[1],\n\t\t\t\t\t'bod': val[2],\n\t\t\t\t\t'tss': val[3],\n\t\t\t\t\t'nh4_n': val[4],\n\t\t\t\t\t'no3_n': val[5],\n\t\t\t\t\t'no2_n': val[6],\n\t\t\t\t\t'org_n': val[7],\n\t\t\t\t\t'min_p': val[8],\n\t\t\t\t\t'org_p': val[9],\n\t\t\t\t\t'fcoli': val[10],\n\t\t\t\t\t'description': val[12] if val[12] != 'null' else None # 12 index because extra column\n\t\t\t\t}\n\t\t\t\tseptics.append(sep)\n\t\t\ti += 1\n\n\t\tif database == 'project':\n\t\t\tdb_lib.bulk_insert(project_base.db, project_parmdb.Septic_sep, septics)\n\t\telse:\n\t\t\tdb_lib.bulk_insert(datasets_base.db, datasets_parmdb.Septic_sep, septics)", "def read(self, filename):\n with RavenFileReader(filename) as f:\n line = f.nexttag()\n while line:\n # Begin data type checks\n if self.cleantag(line) == 'SubBasins':\n self.read_subbasins(f)\n elif self.cleantag(line) == 'HRUs':\n self.read_HRUs(f)\n # Next line\n line = f.nexttag()", "def parse_file(self, domain_filename,\n gzip=False,\n fixed=True, pdf=False, output_path=None, **kargs):\n valid_file_formats = set(['fasta_style', 'dfam'])\n assert format in valid_file_formats, '{0} not in {1} valid_file_formats'.format(format, valid_file_formats)\n\n if output_path:\n if not os.path.exists(output_path):\n os.mkdir(output_path)\n self.output_path = output_path\n\n self.fixed = fixed\n self.pdf = pdf\n\n self.data = []\n\n if format == 'fasta_style':\n self.data = self.__load_fasta_style(filename, gzip)\n elif format == 'dfam':\n self.data = self.__load_dfam_style(filename, gzip)", "def readdata(self,filename_):\n if isinstance(filename_,unicode):\n filename_ = filename_.encode(\"utf-8\",errors=\"replace\")\n res = __library__.MSK_XX_readdataautoformat(self.__nativep,filename_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def _read(self, in_file):\n in_file.read(18) # pad bytes\n self.numnod = int(in_file.read(12))\n in_file.read(37) # pad bytes\n self.format = int(in_file.read(1))\n in_file.read(1) # eol\n self.nodes = []\n\n for _ in range(self.numnod):\n node = FRDNode()\n self.nodes.append(node)\n if self.format < 2:\n in_file.read(1)\n node.key = int(in_file.read(2))\n node.number = int(in_file.read(5*(self.format+1)))\n node.pos = [float(in_file.read(12)) for j in range(3)]\n in_file.read(1) # eol\n else:\n node.number = struct.unpack('i', in_file.read(4))[0]\n if self.format == 2:\n node.pos = struct.unpack('fff', in_file.read(12))\n else:\n node.pos = struct.unpack('ddd', in_file.read(24))\n\n if self.format < 2:\n in_file.readline() # last record for ascii only" ]

[ "0.7553127", "0.59015036", "0.5858663", "0.5723628", "0.5667749", "0.56350636", "0.56292766", "0.55843794", "0.55843794", "0.55649745", "0.5564228", "0.5543848", "0.5543789", "0.5518771", "0.5512596", "0.54900783", "0.54300296", "0.5406005", "0.535901", "0.5344803", "0.5340776", "0.5312764", "0.5273111", "0.5266757", "0.5265474", "0.5256343", "0.52510536", "0.5204346", "0.51906544", "0.5180773" ]

[ "def readsolution(self,whichsol_,filename_): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res = self.__obj.readsolution(whichsol_,filename_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def readsol(self,filename):\n\t\tf = file(filename)\n\t\tfor i in range(3): f.readline()\n\t\tstatusString = f.readline()[18:30]\n\t\tcplexStatus = {\n\t\t\t\"OPTIMAL SOLN\":LpStatusOptimal,\n\t\t\t}\n\t\tif statusString not in cplexStatus:\n\t\t\traise ValueError, \"Unknow status returned by CPLEX: \"+statusString\n\t\tstatus = cplexStatus[statusString]\n\n\t\twhile 1:\n\t\t\tl = f.readline()\n\t\t\tif l[:10] == \" SECTION 2\": break\n\t\t\n\t\tfor i in range(3): f.readline()\n\t\tvalues = {}\n\t\twhile 1:\n\t\t\tl = f.readline()\n\t\t\tif l == \"\": break\n\t\t\tline = l[3:].split()\n\t\t\tif len(line):\n\t\t\t\tname = line[1]\n\t\t\t\tvalue = float(line[3])\n\t\t\t\tvalues[name] = value\n\n\t\treturn status, values", "def loadSol(fileName):\n\n ITman = interf.ITman(probName=prob.prob.probName,isInteractive=True)\n sol = ITman.loadSol(path=fileName)\n\n return sol", "def readsol(self,filename):\n\t\tf = file(filename)\n\t\tfor i in range(6): f.readline()\n\t\tl = f.readline().split()\n\n\t\trows = int(l[2])\n\t\tcols = int(l[5])\n\t\tfor i in range(3): f.readline()\n\t\tstatusString = f.readline().split()[0]\n\t\txpressStatus = {\n\t\t\t\"Optimal\":LpStatusOptimal,\n\t\t\t}\n\t\tif statusString not in xpressStatus:\n\t\t\traise ValueError, \"Unknow status returned by XPRESS: \"+statusString\n\t\tstatus = xpressStatus[statusString]\n\t\tvalues = {}\n\t\twhile 1:\n\t\t\tl = f.readline()\n\t\t\tif l == \"\": break\n\t\t\tline = l.split()\n\t\t\tif len(line) and line[0] == 'C':\n\t\t\t\tname = line[2]\n\t\t\t\tvalue = float(line[4])\n\t\t\t\tvalues[name] = value\n\t\treturn status, values", "def readSolution(solution):\n g = solution\n __data.g = g\n __data.nsp = g.n_species", "def readsol(self,filename):\n\t\tf = file(filename)\n\t\tf.readline()\n\t\trows = int(f.readline().split()[1])\n\t\tcols = int(f.readline().split()[1])\n\t\tf.readline()\n\t\tstatusString = f.readline()[12:-1]\n\t\tglpkStatus = {\n\t\t\t\"INTEGER OPTIMAL\":LpStatusOptimal,\n\t\t\t\"INTEGER NON-OPTIMAL\":LpStatusFeasible,\n\t\t\t\"OPTIMAL\":LpStatusOptimal,\n\t\t\t\"INFEASIBLE (FINAL)\":LpStatusInfeasible,\n\t\t\t\"INTEGER EMPTY\":LpStatusInfeasible,\n\t\t\t\"INTEGER UNDEFINED\":LpStatusUndefined,\n\t\t\t\"UNBOUNDED\":LpStatusUnbounded,\n\t\t\t\"UNDEFINED\":LpStatusUndefined\n\t\t\t}\n\t\tif statusString not in glpkStatus:\n\t\t\traise ValueError, \"Unknow status returned by GLPK: \"+statusString\n\t\tstatus = glpkStatus[statusString]\n\t\tisInteger = statusString in [\"INTEGER OPTIMAL\",\"INTEGER UNDEFINED\"]\n\t\tvalues = {}\n\t\tfor i in range(4): f.readline()\n\t\tfor i in range(rows):\n\t\t\tline = f.readline().split()\n\t\t\tif len(line) ==2: f.readline()\n\t\tfor i in range(3):\n\t\t\tf.readline()\n\t\tfor i in range(cols):\n\t\t\tline = f.readline().split()\n\t\t\tname = line[1]\n\t\t\tif len(line) ==2: line = [0,0]+f.readline().split()\n\t\t\tif isInteger:\n\t\t\t\tif line[2] == \"*\": value = int(line[3])\n\t\t\t\telse: value = float(line[2])\n\t\t\telse:\n\t\t\t\tvalue = float(line[3])\n\t\t\tvalues[name] = value\n\t\treturn status, values", "def read_solution_from_txt(instance: dict, solution_filename: str):\r\n\r\n print(\"Loading solution from \" + solution_filename + \"...\")\r\n # Load interventions\r\n interventions = instance[INTERVENTIONS_STR]\r\n # Read file line by line, and store starting time value (no checks yet, except format and duplicate)\r\n solution_file = open(solution_filename, \"r\")\r\n for line in solution_file:\r\n # Split line to retrive infos: Intervention name and decided starting date\r\n tmp = line.split(\" \")\r\n intervention_name = tmp[0]\r\n start_time_str = tmp[1].split(\"\\n\")[0]\r\n # Assert Intervention exists\r\n if not intervention_name in interventions:\r\n print(\r\n \"ERROR: Unexpected Intervention \"\r\n + intervention_name\r\n + \" in solution file \"\r\n + solution_filename\r\n + \".\"\r\n )\r\n continue\r\n # Assert starting date is an integer\r\n start_time: int\r\n try:\r\n start_time = int(start_time_str)\r\n except ValueError:\r\n print(\r\n \"ERROR: Unexpected starting time \"\r\n + start_time_str\r\n + \" for Intervention \"\r\n + intervention_name\r\n + \". Expect integer value.\"\r\n )\r\n continue\r\n # Assert no duplicate\r\n if START_STR in interventions[intervention_name]:\r\n print(\r\n \"ERROR: Duplicate entry for Intervention \"\r\n + intervention_name\r\n + \". Only first read value is being considered.\"\r\n )\r\n continue\r\n # Store starting time\r\n interventions[intervention_name][START_STR] = start_time\r\n solution_file.close()\r\n print(\"Done\")", "def readFromFile(filename):\n raise NotImplementedError", "def solve(ctx):\n my_solver(ctx.obj['filename'])", "def readsol(filename, attrfile):\n values = {}\n redcost = {}\n slacks = {}\n duals = {}\n with open(filename) as f:\n for lineno, _line in enumerate(f):\n # The first 6 lines are status information\n if lineno < 6:\n continue\n elif lineno == 6:\n # Line with status information\n _line = _line.split()\n rows = int(_line[2])\n cols = int(_line[5])\n elif lineno < 10:\n # Empty line, \"Solution Statistics\", objective direction\n pass\n elif lineno == 10:\n # Solution status\n pass\n else:\n # There is some more stuff and then follows the \"Rows\" and\n # \"Columns\" section. That other stuff does not match the\n # format of the rows/columns lines, so we can keep the\n # parser simple\n line = _line.split()\n if len(line) > 1:\n if line[0] == \"C\":\n # A column\n # (C, Number, Name, At, Value, Input Cost, Reduced Cost)\n name = line[2]\n values[name] = float(line[4])\n redcost[name] = float(line[6])\n elif len(line[0]) == 1 and line[0] in \"LGRE\":\n # A row\n # ([LGRE], Number, Name, At, Value, Slack, Dual, RHS)\n name = line[2]\n slacks[name] = float(line[5])\n duals[name] = float(line[6])\n # Read the attributes that we wrote explicitly\n attrs = dict()\n with open(attrfile) as f:\n for line in f:\n fields = line.strip().split(\"=\")\n if len(fields) == 2 and fields[0].lower() == fields[0]:\n value = fields[1].strip()\n try:\n value = int(fields[1].strip())\n except ValueError:\n try:\n value = float(fields[1].strip())\n except ValueError:\n pass\n attrs[fields[0].strip()] = value\n return values, redcost, slacks, duals, attrs", "def read_from_file(self, filename: str) -> None:", "def read(self, filename):\n pass", "def read(self, filename):\n pass", "def main() -> None:\n with open(f'{os.path.dirname(__file__)}/input.txt', 'r') as input_file:\n for solution in solve(input_file):\n print(solution)", "def main() -> None:\n with open(f'{os.path.dirname(__file__)}/input.txt', 'r') as input_file:\n for solution in solve(input_file):\n print(solution)", "def main() -> None:\n with open(f'{os.path.dirname(__file__)}/input.txt', 'r') as input_file:\n for solution in solve(input_file):\n print(solution)", "def readsol_CLP(self,filename, lp, vs, variablesNames, constraintsNames, objectiveName):\n\t\tvalues = {}\n\n\t\treverseVn = {}\n\t\tfor k,n in variablesNames.iteritems():\n\t\t\treverseVn[n] = k\n\n\t\tfor v in vs:\n\t\t\tvalues[v.name] = 0.0\n\n\t\tstatus = LpStatusOptimal # status is very approximate\n\t\tf = file(filename)\n\t\tfor l in f:\n\t\t\tif len(l)<=2: break\n\t\t\tif l[:2] == \"**\":\n\t\t\t\tstatus = LpStatusInfeasible\n\t\t\t\tl = l[2:]\n\t\t\tl = l.split()\n\t\t\tvn = l[1]\n\t\t\tif vn in reverseVn:\n\t\t\t\tvalues[reverseVn[vn]] = float(l[2])\n\t\treturn status, values", "def read(self, filename):\n raise NotImplementedError", "def read_inpfile(self, filename):\n return wntr.network.io.read_inpfile(filename, append=self)", "def compare_solutions(solution, filename):\n with open(filename) as f:\n content = ast.literal_eval(f.read()) # parse string repr of the output to a list\n return solution == content", "def readSoluFile(self, solufilename : str) -> dict:\n\n soludict = dict()\n with open(solufilename, \"r\") as solufile:\n for line in solufile:\n if line.strip() == \"\":\n continue\n\n spline = line.split()\n marker = spline[0]\n problemname = spline[1]\n\n infotuple = list(soludict.get(problemname, (None, None)))\n if marker == SolufileMarkers.OPT:\n infotuple[self.__primalidx__] = infotuple[self.__dualidx__] = float(spline[2])\n\n elif marker == SolufileMarkers.BEST:\n infotuple[self.__primalidx__] = float(spline[2])\n\n elif marker == SolufileMarkers.BESTDUAL:\n infotuple[self.__dualidx__] = float(spline[2])\n\n elif marker == SolufileMarkers.FEAS:\n infotuple[self.__primalidx__] = self.__feas__\n\n elif marker == SolufileMarkers.INF:\n infotuple[self.__primalidx__] = self.__infeas__\n\n\n soludict[problemname] = tuple(infotuple)\n return soludict", "def fetch_solution(self, run_id, backend=None, results_dir=None, file_name=None):\n run_dir = self._fetch_run_dir(run_id, backend, results_dir)\n if file_name is None:\n file_name = Config.default_log_name('solution', run_id=run_id) # get default file name\n sol_path = os.path.join(run_dir, file_name)\n if not os.path.isfile(sol_path):\n raise ResultsNotFoundError('Solution not found in \"{}\"!'.format(sol_path))\n solution = pd.read_csv(sol_path, index_col=0) # read the solution from the csv log file\n return solution", "def writesolution(self,whichsol_,filename_): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res = self.__obj.writesolution(whichsol_,filename_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def get_solution(problem):\n solutionsFile = os.path.join(os.path.dirname(__file__), 'solutions.txt')\n line = linecache.getline(solutionsFile, problem)\n\n try:\n answer = line.split('. ')[1].strip()\n except IndexError:\n answer = None\n\n if answer:\n return answer\n else:\n # Either entry is missing in solutions.txt or the line doesn't exist\n msg = 'Answer for problem %i not found in solutions.txt.' % problem\n click.secho(msg, fg='red')\n click.echo('If you have an answer, consider submitting a pull request '\n 'to EulerPy at https://github.com/iKevinY/EulerPy.')\n sys.exit(1)", "def solve_problem(filename):\n if len(es.conflict_graph.edges()) == 0: # Checking if a problem is loaded\n print(\"No problem to solve!\") # If it is loaded then len must be > 0\n return()\n\n exams2 = nx.coloring.greedy_color(\n es.conflict_graph, strategy=nx.coloring.strategy_largest_first)\n\n es.optimize_exams = dict(exams2)\n # es.optimize_exams2 = dict(exams2)\n es.best = dict(exams2)\n\n \"\"\" EXPORT SOLUTIONS FILE\n ---------------------------------------------------------------------------\n 1. We itterate through the period_exams dictionary and export to the file\n two columns. The first column contains the subject and the other one\n contains the period that was assigned into.\n ---------------------------------------------------------------------------\n \"\"\"\n\n with open(filename[0:-4]+'.sol', 'w') as f:\n for k, v in exams2.items():\n f.write('{}\\t{}\\n'.format(k, v))\n\n \"\"\"\n In the next itteration of the exams2 dictionary we switch dictionary\n keys and now the period becomes they key and the lessons assigned to it\n the values. It is being saved in the period_exams dictionary.\n \"\"\"\n period_exams = {}\n for k, v in exams2.items():\n if v not in period_exams:\n period_exams[v] = [k]\n else:\n period_exams[v].append(k)\n cost(period_exams)", "def solve(self, output=sys.stdout):\n try:\n with open(self._filename, 'r') as f:\n lines = f.readlines()\n num = int(lines[0])\n\n for i in xrange(num):\n idx = i + 1\n out = self._solve_revenge_pancakes(lines[idx].strip())\n output.write(\"Case #%d: %d\\n\" %(idx, out))\n except IOError:\n print \"Error opening file\"\n pass", "def read_file(self, file_name):\n f = file(file_name, \"r\")\n temp = f.read()\n f.close()", "def read_file(filename,res_format=None,filename_format=None,verbose=False):\n\n # parse results filename for any supplementary run parameters\n info_from_filename = parse_filename(filename,filename_format)\n\n if res_format is None:\n if info_from_filename.get(\"code_name\") is not None:\n res_format = code_name_map[info_from_filename[\"code_name\"]]\n else:\n raise ValueError(\"unable to deduce res_format\")\n\n # parse results file contents for run parameters and data\n if (verbose):\n print(\" read_file: filename {}\".format(filename))\n with open(filename,'rt') as fin:\n try:\n results_list = data_format_parser[res_format](fin,verbose=verbose)\n except Exception as e:\n print(\"filename {} filename_format {} res_format {}\".format(filename, filename_format, res_format))\n raise e\n if (verbose):\n print(\" read_file: mesh points {:d}\".format(len(results_list)))\n\n # augment parameters with those obtained from filename\n #\n # Note: The parameter values obtained from the filename will\n # *override* any parameter values obtained by parsing the results\n # file. So beware that parameter values formatted for the\n # filename might have lower precision than those stored in the\n # results file.\n\n for results in results_list:\n results.params.update(info_from_filename)\n results.filename = os.path.basename(filename)\n\n return results_list", "def readInConfigFile( self, fileName ):\n self.console.info( \"Read input file\" )", "def read_file(self, filename=None):\n print(f'reading file')\n\n if filename is None:\n filename = self.model_file\n\n with open(filename, 'r') as f:\n # count number of lines\n npts_file = sum([1 for line in f])\n\n # go back to start and read second line in file to get number of variables\n f.seek(0)\n f.readline()\n l = f.readline()\n nvars_file = int(l.split(' ')[-1])\n\n # subtract header rows\n npts_file -= (nvars_file + 2)\n\n print(f'{nvars_file} variables found in the initial model file')\n print(f'{npts_file} points found in the initial model file')\n\n var_idx_map = {}\n\n # read in the names of the variables\n for i in range(nvars_file):\n var_name_file = f.readline().strip()\n if var_name_file.lower() == 'n':\n var_name_file = 'neut'\n elif var_name_file == 'p':\n var_name_file = 'prot'\n\n # create map of file indices to model indices\n try:\n var_idx_map[self.idx[var_name_file]] = i+1\n except KeyError:\n pass\n\n base_r = np.zeros(npts_file)\n base_state = np.zeros((npts_file, self.nvar))\n\n # read in model data\n for i, line in enumerate(f):\n variables = [float(v) for v in line.split(' ')]\n\n base_r[i] = variables[2]\n\n for j in range(self.nvar):\n if j in var_idx_map:\n base_state[i, j] = variables[var_idx_map[j]]\n\n return npts_file, base_r, base_state" ]

[ "0.8768888", "0.67774564", "0.6366709", "0.6275069", "0.6227779", "0.62035143", "0.61865556", "0.60015136", "0.5968991", "0.5949018", "0.58523256", "0.5774951", "0.5774951", "0.57240754", "0.57240754", "0.57240754", "0.5700822", "0.5640482", "0.5634896", "0.5604267", "0.5585037", "0.5506879", "0.5490261", "0.54899424", "0.54880697", "0.5463452", "0.5448544", "0.54074085", "0.5351884", "0.5321469" ]

[ "def readsummary(self,whichstream_): # 3\n if not isinstance(whichstream_,streamtype): raise TypeError(\"Argument whichstream has wrong type\")\n res = self.__obj.readsummary(whichstream_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def summary(self, logfile=None):\n if self._filein == None:\n print(\"no filein set\")\n return None\n print(\"FILEIN: %s\" % self._filein)\n # for now\n print(self._session)", "def solutionsummary(self,whichstream_): # 3\n if not isinstance(whichstream_,streamtype): raise TypeError(\"Argument whichstream has wrong type\")\n res = self.__obj.solutionsummary(whichstream_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def write_stats(self, filestream):\n if not self.summary:\n self.summarize()\n\n print(self.scores, file=filestream)", "def get_summary_filename(self):\n fn = os.path.join(SUMMARY_PREFIX,SUMMARY_CURRENT)\n if (os.path.isfile(fn)):\n try:\n fd = open(fn,\"r\")\n fname = fd.read()\n except :\n cmd = \"rm -f %s\"%fn\n result,status = self.cli(cmd)\n return \"\"\n return fname\n return \"\"", "def optimizersummary(self,whichstream_): # 3\n if not isinstance(whichstream_,streamtype): raise TypeError(\"Argument whichstream has wrong type\")\n res = self.__obj.optimizersummary(whichstream_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def solutionsummary(self,whichstream_):\n res = __library__.MSK_XX_solutionsummary(self.__nativep,whichstream_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def _show_summary(self):\n print 'Summary:'\n print ' Reports downloaded successfully: %d' % self.counts\n print ' Reports not downloaded: %d\\n' % self.failed", "def log_summary(self, no_run_list):\n self.log_message('Entries not run' ,step='summary',status='start',name='config_file_reader')\n for name in no_run_list.keys():\n self.log_message('Did not run: '+name+', '+no_run_list[name],status='running')\n \n ret_total = 0\n for x in xrange(2):\n for ent in self.entries[x]:\n ret_total = ret_total + 0 if ent.return_val == None else ent.return_val\n self.log_message('Summary Complete, Run Time = ('+str(self.total_time)+')',status='complete')\n return ret_total", "def open(self, folder):\n Reference.open(self, folder)\n # Open the summary mirror\n if not self.path.startswith('/summary') and len(self.fields) == 2:\n self.summary = Array(self.outfile, '/summary' + self.path)", "def printSummary(self):\n pass", "def _printSummary(self):\n\t\t### COP OUT\n\t\tif self.params['background'] is True:\n\t\t\tself.stats['count'] += 1\n\t\t\treturn\n\n\t\t### THIS NEEDS TO BECOME MUCH MORE GENERAL, e.g. Peaks\n\t\ttdiff = time.time()-self.stats['startseries']\n\t\tif not self.params['continue'] or tdiff > 0.1:\n\t\t\tcount = self.stats['count']\n\t\t\t#if(count != self.stats['lastcount']):\n\t\t\tsys.stderr.write(\"\\n\\tSUMMARY: \"+self.functionname+\"\\n\")\n\t\t\tself._printLine()\n\t\t\tsys.stderr.write(\"\\tTIME: \\t\"+apDisplay.timeString(tdiff)+\"\\n\")\n\t\t\tself.stats['timesum'] = self.stats['timesum'] + tdiff\n\t\t\tself.stats['timesumsq'] = self.stats['timesumsq'] + (tdiff**2)\n\t\t\ttimesum = self.stats['timesum']\n\t\t\ttimesumsq = self.stats['timesumsq']\n\t\t\tif(count > 1):\n\t\t\t\ttimeavg = float(timesum)/float(count)\n\t\t\t\ttimestdev = math.sqrt(float(count*timesumsq - timesum**2) / float(count*(count-1)))\n\t\t\t\ttimeremain = (float(timeavg)+float(timestdev))*self.stats['seriesleft']\n\t\t\t\tsys.stderr.write(\"\\tAVG TIME: \\t\"+apDisplay.timeString(timeavg,timestdev)+\"\\n\")\n\t\t\t\t#print \"\\t(- TOTAL:\",apDisplay.timeString(timesum),\" -)\"\n\t\t\t\tif(self.stats['seriesleft'] > 0):\n\t\t\t\t\tsys.stderr.write(\"\\t(- REMAINING TIME: \"+apDisplay.timeString(timeremain)+\" for \"\n\t\t\t\t\t\t+str(self.stats['seriesleft'])+\" series -)\\n\")\n\t\t\t#print \"\\tMEM: \",(mem.active()-startmem)/1024,\"M (\",(mem.active()-startmem)/(1024*count),\"M)\"\n\t\t\tself.stats['count'] += 1\n\t\t\tself._printLine()", "def summarise(thislog):\n\n # Logfile name\n print(\"Summary for \" + thislog.filename() + \"\\n\")\n # Was it from CCP4i?\n if thislog.isccp4i():\n print(\"This is a CCP4i logfile\\n\")\n # Number of programs or pseudo-programs\n print(str(thislog.nfragments()) + \" logfile fragments\\n\")\n print(\"Fragments:\")\n for i in range(0, thislog.nfragments()):\n fragment = thislog.fragment(i)\n if fragment.isprogram():\n if fragment.has_attribute(\"name\"):\n print(\"\\tProgram: \" + str(fragment.name))\n else:\n print(\"\\tProgram: <no name>\")\n else:\n if fragment.isccp4i_info():\n print(\"\\tCCP4i info\")\n elif fragment.isfragment():\n print(\"\\tFragment\")\n if fragment.ntables():\n print(\"\\t\\t\" + str(fragment.ntables()) + \" tables\")\n if fragment.nkeytexts():\n print(\"\\t\\t\" + str(fragment.nkeytexts()) + \" keytexts\")\n\n print(\"\")\n # Summarise program logfile fragments\n if thislog.nprograms() > 0:\n print(str(thislog.nprograms()) + \" program logfiles\\n\")\n print(\"Programs:\")\n for i in range(0, thislog.nprograms()):\n prog = thislog.program(i)\n # Is it a CCP4 program?\n if prog.isccp4():\n # Print name, version (and CCP4 version)\n print(\n \"\\t\"\n + prog.name\n + \"\\tv\"\n + prog.version\n + \"\\t(CCP4 \"\n + prog.ccp4version\n + \")\"\n )\n else:\n # Print name and version\n if prog.has_attribute(\"name\") and prog.has_attribute(\"version\"):\n print(\"\\t\" + prog.name + \"\\t\" + prog.version)\n else:\n print(\"\\t<No name and/or version>\")\n if prog.termination():\n print(\"\\tTerminated with: \" + prog.termination_message)\n else:\n print(\"\\tNo termination message found\")\n # Keytexts\n if prog.nkeytexts():\n print(\"\\n\\t\\tKeytext messages:\")\n for j in range(0, prog.nkeytexts()):\n print(\n \"\\t\\t\"\n + str(prog.keytext(j).name())\n + ': \"'\n + str(prog.keytext(j).message())\n + '\"'\n )\n # Tables\n if prog.ntables():\n print(\"\\n\\t\\tTables:\")\n for table in prog.tables():\n print('\\t\\tTable: \"' + table.title() + '\"')\n print(\"\")\n else:\n print(\"No program logfiles found\")\n print(\"\")\n # Total set of CCP4i information messages in the file\n print(\"CCP4i messages in file:\")\n if thislog.nccp4i_info():\n for i in range(0, thislog.nccp4i_info()):\n print('\\tCCP4i info: \"' + thislog.ccp4i_info(i).message + '\"')\n else:\n print(\"\\tNo messages found\")\n print(\"\")\n # Total set of tables in the file\n print(\"Tables in file:\")\n if thislog.ntables():\n for table in thislog.tables():\n print('\\tTable: \"' + table.title() + '\" (' + str(table.nrows()) + \" rows)\")\n else:\n print(\"\\tNo tables found\")\n print(\"\")\n # Total set of keytexts in the file\n print(\"Keytext messages in file:\")\n if thislog.nkeytexts():\n for i in range(0, thislog.nkeytexts()):\n print(\n \"\\t\"\n + str(thislog.keytext(i).name())\n + ': \"'\n + thislog.keytext(i).message()\n + '\"'\n )\n else:\n print(\"\\tNo keytext messages found\")\n print(\"\")", "def print_file_stats(self):\n\n # current epoch time, file number, filename, filesize, trans secs, status\n print(f\"TRANS_STATS_FILE: {time.time()} {self.batchvals['numfiles']} {self.filevals['filename']} {self.filevals['numbytes']} {self.filevals['end_time'] - self.filevals['start_time']} {self.filevals['status']}\")", "def summaryText(self):\n\n print('\\nReport Summary:\\n')\n for author in self.lowQuality.keys():\n if len(self.lowQuality[author]) > 0:\n print('Author: ' + author)\n print('---------------------')\n # do some sorting for readability\n files = []\n file2rating = {}\n for fileRating in self.lowQuality[author]:\n files.append(fileRating[1])\n file2rating[fileRating[1]] = fileRating[0]\n files.sort()\n for fileRating in files:\n print(file2rating[fileRating] + ' :: ' + fileRating)\n print('\\n\\n')", "def summary(self, i):\n return self.__summaries[i]", "def printdata(self,whichstream_,firsti_,lasti_,firstj_,lastj_,firstk_,lastk_,c_,qo_,a_,qc_,bc_,bx_,vartype_,cones_): # 3\n if not isinstance(whichstream_,streamtype): raise TypeError(\"Argument whichstream has wrong type\")\n res = self.__obj.printdata(whichstream_,firsti_,lasti_,firstj_,lastj_,firstk_,lastk_,c_,qo_,a_,qc_,bc_,bx_,vartype_,cones_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def makeSummary(self, i):\n try:\n summary = self._sess.run(self._merged_summaries)\n self._writer.add_summary(summary=summary, global_step=i)\n except:\n print('FileWriter is either closed or does not exist.')\n print('Use createFileWriter member function to create a new FileWriter.')", "def _parse_summary(self):\r\n if self._is_at_section():\r\n return\r\n\r\n summary = self._doc.read_to_next_empty_line()\r\n summary_str = \" \".join([s.strip() for s in summary]).strip()\r\n if re.compile('^([\\w., ]+=)?\\s*[\\w\\.]+\\(.*\\)$').match(summary_str):\r\n self['Signature'] = summary_str\r\n if not self._is_at_section():\r\n self['Summary'] = self._doc.read_to_next_empty_line()\r\n else:\r\n self['Summary'] = summary\r\n\r\n if not self._is_at_section():\r\n self['Extended Summary'] = self._read_to_next_section()", "def _parse_summary(self):\r\n if self._is_at_section():\r\n return\r\n\r\n summary = self._doc.read_to_next_empty_line()\r\n summary_str = \" \".join([s.strip() for s in summary]).strip()\r\n if re.compile('^([\\w., ]+=)?\\s*[\\w\\.]+\\(.*\\)$').match(summary_str):\r\n self['Signature'] = summary_str\r\n if not self._is_at_section():\r\n self['Summary'] = self._doc.read_to_next_empty_line()\r\n else:\r\n self['Summary'] = summary\r\n\r\n if not self._is_at_section():\r\n self['Extended Summary'] = self._read_to_next_section()", "def fopenhelp(self):", "def inspect(self, stream):\n self.inspect_quick(stream)\n pos = stream.tell()\n try:\n self._header_value_.read(stream, self)\n finally:\n stream.seek(pos)", "def summary(self) -> str:\n pass", "def onesolutionsummary(self,whichstream_,whichsol_): # 3\n if not isinstance(whichstream_,streamtype): raise TypeError(\"Argument whichstream has wrong type\")\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res = self.__obj.onesolutionsummary(whichstream_,whichsol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def summary(self) -> str:\n return pulumi.get(self, \"summary\")", "def getSummary(self):\n return self.base.get(\"summary\", [])", "def PrintFinalSummaryMessage(self, stream=sys.stderr):\n string_to_print = ('Operation completed over %s objects' %\n DecimalShort(self.num_objects))\n if self.total_size:\n string_to_print += ('/%s' %\n HumanReadableWithDecimalPlaces(self.total_size))\n remaining_width = self.console_width - len(string_to_print)\n if not self.quiet_mode:\n stream.write(('\\n' + string_to_print + '.' +\n (max(remaining_width, 0) * ' ') + '\\n'))", "def optimizersummary(self,whichstream_):\n res = __library__.MSK_XX_optimizersummary(self.__nativep,whichstream_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def _print_summary(case, summary):\n for dof, data in summary.items():\n b4b = data[\"Bit for Bit\"]\n conf = data[\"Configurations\"]\n stdout = data[\"Std. Out Files\"]\n print(\" \" + case + \" \" + str(dof))\n print(\" --------------------\")\n print(\" Bit for bit matches : \" + str(b4b[0]) + \" of \" + str(b4b[1]))\n print(\" Configuration matches : \" + str(conf[0]) + \" of \" + str(conf[1]))\n print(\" Std. Out files parsed : \" + str(stdout))\n print(\"\")", "def summary(self, summary):\n\n self._summary = summary" ]

[ "0.7899001", "0.6519644", "0.65138835", "0.60788524", "0.59421", "0.59183806", "0.5882844", "0.5876368", "0.57434404", "0.5713873", "0.5700075", "0.557035", "0.5532855", "0.55146515", "0.5475546", "0.53840125", "0.5378493", "0.53440875", "0.5329391", "0.5329391", "0.53114873", "0.5292089", "0.5278237", "0.52688694", "0.52586037", "0.5258042", "0.52580327", "0.5253789", "0.5253707", "0.5235601" ]

[ "def resizetask(self,maxnumcon_,maxnumvar_,maxnumcone_,maxnumanz_,maxnumqnz_): # 3\n res = self.__obj.resizetask(maxnumcon_,maxnumvar_,maxnumcone_,maxnumanz_,maxnumqnz_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def prepare_pr_optimal_model_condor_job(self, pool_type, pool_address, number_of_jobs, subtask_index, data_files, rank='0', extraArgs=''):\n ############\n copasi_file = 'auto_copasi_%d.$(Process).cps' % subtask_index\n output_file = ''\n \n \n \n if pool_type == 'ec2':\n binary_dir = '/usr/local/bin'\n transfer_executable = 'NO'\n else:\n binary_dir, binary = os.path.split(settings.COPASI_LOCAL_BINARY)\n transfer_executable = 'YES'\n \n input_files_string = ', '\n for data_file in data_files:\n input_files_string += (data_file + ', ')\n input_files_string = input_files_string.rstrip(', ')\n\n condor_job_string = Template(condor_spec.raw_condor_job_string).substitute(copasiFile=copasi_file, \n otherFiles=input_files_string,\n rank=rank,\n binary_dir = binary_dir,\n transfer_executable = transfer_executable,\n pool_type = pool_type,\n pool_address = pool_address,\n subtask=str(subtask_index),\n n = number_of_jobs,\n outputFile = output_file,\n extraArgs='',\n )\n \n condor_job_filename = 'auto_condor_%d.job'%subtask_index\n condor_job_full_filename = os.path.join(self.path, condor_job_filename)\n condor_file = open(condor_job_full_filename, 'w')\n condor_file.write(condor_job_string)\n condor_file.close()\n\n return condor_job_filename", "def maximize(self, budget, optimizer):\n\n\t\tpass", "def maximize(self):\n raise NotImplementedError", "def resnet152(scale=1, **kwargs):\n model = ResNet(Bottleneck, [3, 8, 36, 3], scale=scale, **kwargs)\n return model", "def resnet152(**kwargs):\n model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)\n return model", "def prepare_so_task(self, subtask_index=1):\n #First clear the task list, to ensure that no tasks are set to run\n self._clear_tasks()\n \n #Next, go to the sensitivities task and set the appropriate variables\n sensTask = self._getTask('sensitivities')\n problem = sensTask.find(xmlns + 'Problem')\n #And open the listofvariables\n for pG in problem:\n if (pG.attrib['name'] == 'ListOfVariables'):\n listOfVariables = pG\n assert listOfVariables != None\n \n #Reset the listOfVariables, and add the appropriate objects\n listOfVariables.clear()\n listOfVariables.set('name', 'ListOfVariables')\n\n #Add a new child element: <ParameterGroup name='Variables'>\n variables = etree.SubElement(listOfVariables, xmlns + 'ParameterGroup')\n variables.set('name', 'Variables')\n\n #Add two new children to variables:\n #<Parameter name='SingleObject')\n singleObject = etree.SubElement(variables, xmlns + 'Parameter')\n singleObject.set('name', 'SingleObject')\n singleObject.set('type', 'cn')\n #<Parameter name='ObjectListType'>\n objectListType = etree.SubElement(variables, xmlns + 'Parameter')\n objectListType.set('name', 'ObjectListType')\n objectListType.set('type', 'unsignedInteger')\n objectListType.set('value', '1')\n \n ############\n \n #Next, load the optimization task\n optTask = self._getTask('optimization')\n #And set it scheduled to run, and to update the model\n optTask.attrib['scheduled'] = 'true'\n optTask.attrib['updateModel'] = 'true'\n \n #Find the objective function we wish to change\n problemParameters = optTask.find(xmlns + 'Problem')\n for parameter in problemParameters:\n if (parameter.attrib['name'] == 'ObjectiveExpression'):\n objectiveFunction = parameter\n \n if (parameter.attrib['name'] == 'Maximize'):\n maximizeParameter = parameter\n \n #Set the subtask to sensitivities\n #TODO: At some point allow for other subtasks\n if (parameter.attrib['name'] == 'Subtask'):\n parameter.attrib['value'] = 'CN=Root,Vector=TaskList[Sensitivities]'\n\n assert objectiveFunction != None\n assert maximizeParameter != None\n\n #Set the appropriate objective function for the optimization task:\n objectiveFunction.text = '<CN=Root,Vector=TaskList[Sensitivities],Problem=Sensitivities,Array=Scaled sensitivities array[.]>'\n \n ############\n #Create a new report for the optimization task\n report_key = 'condor_copasi_sensitivity_optimization_report'\n self._create_report('SO', report_key, report_key)\n \n #And set the new report for the optimization task\n report = optTask.find(xmlns + 'Report')\n \n #If no report has yet been set, report == None. Therefore, create new report\n if report == None:\n report = etree.Element(xmlns + 'Report')\n optTask.insert(0,report)\n \n report.set('reference', report_key)\n report.set('append', '1')\n \n \n #############\n #get the list of strings to optimize\n #self.get_optimization_parameters(friendly=False) returns a tuple containing the parameter name as the first element\n optimizationStrings = []\n for parameter in self.get_optimization_parameters(friendly=False):\n optimizationStrings.append(parameter[0])\n \n #Build the new xml files and save them\n i = 0\n file_list = []\n for optString in optimizationStrings:\n maximizeParameter.attrib['value'] = '1'\n output = 'output_%d.%d.txt' % (subtask_index, i)\n report.attrib['target'] = output\n \n #Update the sensitivities object\n singleObject.set('value',optString)\n \n target = os.path.join(self.path, 'auto_copasi_%d.%d.cps' %(subtask_index, i))\n \n self.write(target)\n file_list.append(target)\n \n maximizeParameter.attrib['value'] = '0'\n output = 'output_%d.%d.txt' % (subtask_index, i + 1)\n report.attrib['target'] = output\n \n target = os.path.join(self.path, 'auto_copasi_%d.%d.cps' % (subtask_index, i+1))\n self.write(target)\n file_list.append(target)\n i = i + 2\n \n return file_list", "def main(\n rbsize: int = Argument(..., help='Size of the reduced basis.'),\n\n cache_region: Choices('none memory disk persistent') = Option(\n 'none',\n help='Name of cache region to use for caching solution snapshots.'\n ),\n error_estimator: bool = Option(True, help='Use error estimator for basis generation.'),\n gamma: float = Option(0.2, help='Weight factor for age penalty term in refinement indicators.'),\n grid: int = Option(100, help='Use grid with 2*NI*NI elements.'),\n ipython_engines: int = Option(\n 0,\n help='If positive, the number of IPython cluster engines to use for parallel greedy search. '\n 'If zero, no parallelization is performed.'\n ),\n ipython_profile: str = Option(None, help='IPython profile to use for parallelization.'),\n list_vector_array: bool = Option(\n False,\n help='Solve using ListVectorArray[NumpyVector] instead of NumpyVectorArray.'\n ),\n pickle: str = Option(\n None,\n help='Pickle reduced discretization, as well as reductor and high-dimensional model to files with this prefix.'\n ),\n plot_err: bool = Option(False, help='Plot error.'),\n plot_solutions: bool = Option(False, help='Plot some example solutions.'),\n plot_error_sequence: bool = Option(False, help='Plot reduction error vs. basis size.'),\n product: Choices('euclidean h1') = Option(\n 'h1',\n help='Product w.r.t. which to orthonormalize and calculate Riesz representatives.'\n ),\n reductor: Choices('traditional residual_basis') = Option(\n 'residual_basis',\n help='Reductor (error estimator) to choose (traditional, residual_basis).'\n ),\n rho: float = Option(1.1, help='Maximum allowed ratio between error on validation set and on training set.'),\n test: int = Option(10, help='Use COUNT snapshots for stochastic error estimation.'),\n theta: float = Option(0., help='Ratio of elements to refine.'),\n validation_mus: int = Option(0, help='Size of validation set.'),\n visualize_refinement: bool = Option(True, help='Visualize the training set refinement indicators.'),\n):\n problem = thermal_block_problem(num_blocks=(2, 2))\n functionals = [ExpressionParameterFunctional('diffusion[0]', {'diffusion': 2}),\n ExpressionParameterFunctional('diffusion[1]**2', {'diffusion': 2}),\n ExpressionParameterFunctional('diffusion[0]', {'diffusion': 2}),\n ExpressionParameterFunctional('diffusion[1]', {'diffusion': 2})]\n problem = problem.with_(\n diffusion=problem.diffusion.with_(coefficients=functionals),\n )\n\n print('Discretize ...')\n fom, _ = discretize_stationary_cg(problem, diameter=1. / grid)\n\n if list_vector_array:\n from pymor.discretizers.builtin.list import convert_to_numpy_list_vector_array\n fom = convert_to_numpy_list_vector_array(fom)\n\n if cache_region != 'none':\n # building a cache_id is only needed for persistent CacheRegions\n cache_id = f\"pymordemos.thermalblock_adaptive {grid}\"\n fom.enable_caching(cache_region.value, cache_id)\n\n if plot_solutions:\n print('Showing some solutions')\n Us = ()\n legend = ()\n for mu in problem.parameter_space.sample_randomly(2):\n print(f\"Solving for diffusion = \\n{mu['diffusion']} ... \")\n sys.stdout.flush()\n Us = Us + (fom.solve(mu),)\n legend = legend + (str(mu['diffusion']),)\n fom.visualize(Us, legend=legend, title='Detailed Solutions for different parameters', block=True)\n\n print('RB generation ...')\n\n product_op = fom.h1_0_semi_product if product == 'h1' else None\n coercivity_estimator = ExpressionParameterFunctional('min([diffusion[0], diffusion[1]**2])',\n fom.parameters)\n reductors = {'residual_basis': CoerciveRBReductor(fom, product=product_op,\n coercivity_estimator=coercivity_estimator),\n 'traditional': SimpleCoerciveRBReductor(fom, product=product_op,\n coercivity_estimator=coercivity_estimator)}\n reductor = reductors[reductor]\n\n pool = new_parallel_pool(ipython_num_engines=ipython_engines, ipython_profile=ipython_profile)\n greedy_data = rb_adaptive_greedy(\n fom, reductor, problem.parameter_space,\n validation_mus=validation_mus,\n rho=rho,\n gamma=gamma,\n theta=theta,\n use_error_estimator=error_estimator,\n error_norm=fom.h1_0_semi_norm,\n max_extensions=rbsize,\n visualize=visualize_refinement\n )\n\n rom = greedy_data['rom']\n\n if pickle:\n print(f\"\\nWriting reduced model to file {pickle}_reduced ...\")\n with open(pickle + '_reduced', 'wb') as f:\n dump((rom, problem.parameter_space), f)\n print(f\"Writing detailed model and reductor to file {pickle}_detailed ...\")\n with open(pickle + '_detailed', 'wb') as f:\n dump((fom, reductor), f)\n\n print('\\nSearching for maximum error on random snapshots ...')\n\n results = reduction_error_analysis(rom,\n fom=fom,\n reductor=reductor,\n error_estimator=True,\n error_norms=(fom.h1_0_semi_norm,),\n condition=True,\n test_mus=problem.parameter_space.sample_randomly(test),\n basis_sizes=25 if plot_error_sequence else 1,\n pool=pool)\n\n real_rb_size = rom.solution_space.dim\n\n print('''\n*** RESULTS ***\n\nProblem:\n number of blocks: 2x2\n h: sqrt(2)/{grid}\n\nGreedy basis generation:\n error estimator enabled: {error_estimator}\n product: {product}\n prescribed basis size: {rbsize}\n actual basis size: {real_rb_size}\n elapsed time: {greedy_data[time]}\n'''.format(**locals()))\n print(results['summary'])\n\n sys.stdout.flush()\n\n if plot_error_sequence:\n plot_reduction_error_analysis(results)\n if plot_err:\n mumax = results['max_error_mus'][0, -1]\n U = fom.solve(mumax)\n URB = reductor.reconstruct(rom.solve(mumax))\n fom.visualize((U, URB, U - URB), legend=('Detailed Solution', 'Reduced Solution', 'Error'),\n title='Maximum Error Solution', separate_colorbars=True, block=True)", "def sg_resnet_152(x, opt):\n opt += tf.sg_opt(num_class=1000, conv_only=False, squeeze=True)\n\n # convolution layers ( residual net v2 arch )\n conv = (x\n .sg_conv(dim=64, size=7, stride=2)\n .sg_pool(size=3, stride=2, pad='SAME')\n .sg_resnet_layer(dim=64, num=3, stride=1)\n .sg_resnet_layer(dim=128, num=8, stride=2)\n .sg_resnet_layer(dim=256, num=36, stride=2)\n .sg_resnet_layer(dim=512, num=3, stride=2)\n .sg_bypass(act='relu', bn=True)\n .sg_pool(size=7, stride=1, avg=True)) # global average pool\n\n # fully convolution layers\n fc = (conv\n .sg_conv(dim=opt.num_class, size=1, act='linear', bn=False))\n\n if opt.conv_only:\n return conv\n else:\n if opt.squeeze:\n return fc.sg_squeeze(dim=(1, 2))\n else:\n return fc", "def main(Args):\n norm = [1.9844158727667542, 413.83759806375525,\n 51.2789974336363, 1038.4760551905683]\n input_pull = False\n input_model_mapping = False\n max_number = 2\n count = 40000\n catalog_name = os.path.join(DATA_PATH, 'OneDegSq.fits')\n # Define parameters for mrcnn model with btk here\n resid_model = btk_utils.Resid_btk_model(\n Args.model_name, Args.model_path, MODEL_DIR, training=True,\n images_per_gpu=4, validation_for_training=True)\n # Load parameters for dataset and load model\n resid_model.config.WEIGHT_DECAY = 0.001\n resid_model.config.STEPS_PER_EPOCH = 1000\n resid_model.config.VALIDATION_STEPS = 20\n sampling_function = None\n layers = 'all'\n if Args.model_name == 'model1':\n resid_model.config.BACKBONE = 'resnet41'\n elif Args.model_name == 'model2':\n resid_model.config.SKIP_P2_RPN = True\n resid_model.config.BACKBONE_STRIDES = [8, 16, 32, 64]\n resid_model.config.RPN_ANCHOR_SCALES = (8, 16, 32, 64)\n elif Args.model_name == 'model3':\n resid_model.config.BACKBONE = 'resnet41'\n resid_model.config.SKIP_P2_RPN = True\n resid_model.config.BACKBONE_STRIDES = [8, 16, 32, 64]\n resid_model.config.RPN_ANCHOR_SCALES = (8, 16, 32, 64)\n elif Args.model_name == 'model4':\n resid_model.config.TRAIN_BN = None\n resid_model.config.BACKBONE = 'resnet41'\n resid_model.config.SKIP_P2_RPN = True\n resid_model.config.BACKBONE_STRIDES = [8, 16, 32, 64]\n resid_model.config.RPN_ANCHOR_SCALES = (8, 16, 32, 64)\n elif Args.model_name == 'model5':\n resid_model.config.TRAIN_BN = None\n resid_model.config.BACKBONE = 'resnet35'\n resid_model.config.SKIP_P2_RPN = True\n resid_model.config.BACKBONE_STRIDES = [8, 16, 32, 64]\n resid_model.config.RPN_ANCHOR_SCALES = (8, 16, 32, 64)\n elif Args.model_name == 'model4_large':\n resid_model.config.TRAIN_BN = False\n resid_model.config.BACKBONE = 'resnet41'\n resid_model.config.SKIP_P2_RPN = True\n resid_model.config.BACKBONE_STRIDES = [8, 16, 32, 64]\n resid_model.config.RPN_ANCHOR_SCALES = (8, 16, 32, 64)\n sampling_function = btk_utils.resid_general_sampling_function_large\n layers = '4+' # '3+'\n elif Args.model_name == 'model6':\n resid_model.config.TRAIN_BN = False\n resid_model.config.BACKBONE = 'resnet41'\n resid_model.config.SKIP_P2_RPN = True\n resid_model.config.BACKBONE_STRIDES = [8, 16, 32, 64]\n resid_model.config.RPN_ANCHOR_SCALES = (8, 16, 32, 64)\n sampling_function = btk_utils.resid_general_sampling_function_large\n layers = 'all'\n norm = [0, 1, 51.2789974336363, 1038.4760551905683]\n input_pull = True\n elif Args.model_name == 'model7':\n resid_model.config.TRAIN_BN = False\n resid_model.config.BACKBONE = 'resnet41'\n resid_model.config.SKIP_P2_RPN = True\n resid_model.config.BACKBONE_STRIDES = [8, 16, 32, 64]\n resid_model.config.RPN_ANCHOR_SCALES = (8, 16, 32, 64)\n sampling_function = btk_utils.resid_general_sampling_function_large\n layers = 'all'\n norm = [0, 1, 0, 1]\n input_pull = True\n input_model_mapping = True\n elif Args.model_name == 'model8': # stretch = 0.1, Q = 3\n resid_model.config.TRAIN_BN = None\n resid_model.config.BACKBONE = 'resnet41'\n resid_model.config.SKIP_P2_RPN = True\n resid_model.config.BACKBONE_STRIDES = [8, 16, 32, 64]\n resid_model.config.RPN_ANCHOR_SCALES = (8, 16, 32, 64)\n sampling_function = btk_utils.resid_general_sampling_function_large\n layers = 'all'\n norm = [0, 1, 0, 1]\n input_pull = True\n input_model_mapping = True\n elif Args.model_name == 'model9': # stretch = 2000, Q = 0.5\n resid_model.config.TRAIN_BN = None\n resid_model.config.BACKBONE = 'resnet41'\n resid_model.config.SKIP_P2_RPN = True\n resid_model.config.BACKBONE_STRIDES = [8, 16, 32, 64]\n resid_model.config.RPN_ANCHOR_SCALES = (8, 16, 32, 64)\n sampling_function = btk_utils.resid_general_sampling_function_large\n layers = 'all'\n norm = [0, 1, 0, 1]\n input_pull = True\n input_model_mapping = True\n elif Args.model_name == 'model10': # stretch = 2000, Q = 0.5\n resid_model.config.TRAIN_BN = None\n resid_model.config.BACKBONE = 'resnet41'\n resid_model.config.SKIP_P2_RPN = True\n resid_model.config.BACKBONE_STRIDES = [8, 16, 32, 64]\n resid_model.config.RPN_ANCHOR_SCALES = (8, 16, 32, 64)\n sampling_function = btk_utils.resid_general_sampling_function_large\n layers = 'all'\n norm = [0., 1., 0, 1.] # [0, 1, 0, 1]\n input_pull = True\n input_model_mapping = True\n elif Args.model_name == 'model10_again': # stretch = 2000, Q = 0.5\n resid_model.config.TRAIN_BN = None\n resid_model.config.BACKBONE = 'resnet41'\n resid_model.config.SKIP_P2_RPN = True\n resid_model.config.BACKBONE_STRIDES = [8, 16, 32, 64]\n resid_model.config.RPN_ANCHOR_SCALES = (8, 16, 32, 64)\n sampling_function = btk_utils.resid_general_sampling_function_large\n layers = 'all'\n norm = [0., 1.45, 0, 1.] # [0, 1, 0, 1]\n input_pull = True\n input_model_mapping = True\n elif Args.model_name == 'model10_again2': # stretch = 2000, Q = 0.5\n resid_model.config.TRAIN_BN = None\n resid_model.config.BACKBONE = 'resnet41'\n resid_model.config.SKIP_P2_RPN = True\n resid_model.config.BACKBONE_STRIDES = [8, 16, 32, 64]\n resid_model.config.RPN_ANCHOR_SCALES = (8, 16, 32, 64)\n sampling_function = btk_utils.resid_general_sampling_function_large\n layers = 'all'\n norm = [0., 1.45, 0, 1.] # [0, 1, 0, 1]\n input_pull = True\n input_model_mapping = True\n elif Args.model_name == 'model10_again3': # stretch = 2000, Q = 0.5\n resid_model.config.TRAIN_BN = False\n resid_model.config.BACKBONE = 'resnet41'\n resid_model.config.SKIP_P2_RPN = True\n resid_model.config.BACKBONE_STRIDES = [8, 16, 32, 64]\n resid_model.config.RPN_ANCHOR_SCALES = (8, 16, 32, 64)\n sampling_function = btk_utils.resid_general_sampling_function_large\n layers = 'all'\n norm = [0., 1.45, 0, 1.] # [0, 1, 0, 1]\n input_pull = True\n input_model_mapping = True\n elif Args.model_name == 'model10_2': # stretch = 2000, Q = 0.5\n resid_model.config.TRAIN_BN = False\n resid_model.config.BACKBONE = 'resnet41'\n resid_model.config.SKIP_P2_RPN = True\n resid_model.config.BACKBONE_STRIDES = [8, 16, 32, 64]\n resid_model.config.RPN_ANCHOR_SCALES = (8, 16, 32, 64)\n sampling_function = btk_utils.resid_general_sampling_function_large\n layers = 'all'\n norm = [0., 1.45, 0., 1.] # [0, 1, 0, 1]\n input_pull = True\n input_model_mapping = True\n elif Args.model_name == 'model11': # stretch = 2000, Q = 0.5\n resid_model.config.TRAIN_BN = False\n resid_model.config.BACKBONE = 'resnet41'\n resid_model.config.SKIP_P2_RPN = True\n resid_model.config.BACKBONE_STRIDES = [8, 16, 32, 64]\n resid_model.config.RPN_ANCHOR_SCALES = (8, 16, 32, 64)\n sampling_function = btk_utils.resid_general_sampling_function_large\n layers = 'all'\n norm = [0., 1., 0., 1.] # [0, 1, 0, 1]\n input_pull = True\n input_model_mapping = True\n elif Args.model_name == 'model11_2': # stretch = 2000, Q = 0.5\n resid_model.config.TRAIN_BN = False\n resid_model.config.BACKBONE = 'resnet41'\n resid_model.config.SKIP_P2_RPN = True\n resid_model.config.BACKBONE_STRIDES = [8, 16, 32, 64]\n resid_model.config.RPN_ANCHOR_SCALES = (8, 16, 32, 64)\n sampling_function = btk_utils.resid_general_sampling_function_large\n layers = 'all'\n norm = [0., 1., 0., 1.] # [0, 1, 0, 1]\n input_pull = True\n input_model_mapping = True\n elif Args.model_name == 'model12': # stretch = 2000, Q = 0.5\n resid_model.config.TRAIN_BN = False\n resid_model.config.BACKBONE = 'resnet41'\n resid_model.config.SKIP_P2_RPN = True\n resid_model.config.BACKBONE_STRIDES = [8, 16, 32, 64]\n resid_model.config.RPN_ANCHOR_SCALES = (8, 16, 32, 64)\n sampling_function = btk_utils.resid_general_sampling_function_large\n layers = 'all'\n norm = [0., 1.45, 0, 1.] # [0, 1, 0, 1]\n input_pull = True\n input_model_mapping = True\n max_number = 6\n elif Args.model_name == 'model12_again': # stretch = 2000, Q = 0.5 # larger learning rate\n resid_model.config.TRAIN_BN = False\n resid_model.config.BACKBONE = 'resnet41'\n resid_model.config.SKIP_P2_RPN = True\n resid_model.config.BACKBONE_STRIDES = [8, 16, 32, 64]\n resid_model.config.RPN_ANCHOR_SCALES = (8, 16, 32, 64)\n sampling_function = btk_utils.resid_general_sampling_function_large\n layers = 'all'\n norm = [0., 1.45, 0, 1.] # [0, 1, 0, 1]\n input_pull = True\n input_model_mapping = True\n max_number = 10 # changed from 6 to 10 for run 4\n elif Args.model_name == 'model12_again2': # stretch = 2000, Q = 0.5 # larger learning rate val set reduced to 10\n resid_model.config.TRAIN_BN = False\n resid_model.config.BACKBONE = 'resnet41'\n resid_model.config.SKIP_P2_RPN = True\n resid_model.config.BACKBONE_STRIDES = [8, 16, 32, 64]\n resid_model.config.RPN_ANCHOR_SCALES = (8, 16, 32, 64)\n sampling_function = btk_utils.resid_general_sampling_function_large\n layers = 'all'\n norm = [0., 1.45, 0, 1.] # [0, 1, 0, 1]\n input_pull = True\n input_model_mapping = True\n max_number = 6\n resid_model.config.VALIDATION_STEPS = 10\n else:\n raise AttributeError(\"model not found\", Args.model_name)\n print(\"Train in model:\", Args.model_name)\n resid_model.config.display()\n resid_model.make_resid_model(catalog_name, count=count,\n max_number=max_number, augmentation=True,\n norm_val=norm, input_pull=input_pull,\n sampling_function=sampling_function,\n input_model_mapping=input_model_mapping)\n learning_rate = resid_model.config.LEARNING_RATE/10.\n np.random.seed(Args.epochs)\n history = resid_model.model.train(resid_model.dataset,\n resid_model.dataset_val,\n learning_rate=learning_rate,\n epochs=Args.epochs,\n layers=layers)\n name = Args.model_name + '_run2'\n with open(name + \".dill\", 'wb') as handle:\n dill.dump(history.history, handle)\n learning_rate = resid_model.config.LEARNING_RATE/10.\n np.random.seed(Args.epochs + 10)\n history = resid_model.model.train(resid_model.dataset,\n resid_model.dataset_val,\n learning_rate=learning_rate,\n epochs=Args.epochs+10,\n layers=layers)\n name = Args.model_name + '_run3'\n with open(name + \".dill\", 'wb') as handle:\n dill.dump(history.history, handle)", "def build_resnet152(self):\n use_batch_norm = self.use_batch_norm\n\n imgs = tf.placeholder(tf.float32, [self.batch_size]+self.img_shape)\n is_train = tf.placeholder(tf.bool)\n\n conv1_feats = convolution(imgs, 7, 7, 64, 2, 2, 'conv1')\n conv1_feats = batch_norm(conv1_feats, 'bn_conv1', is_train, use_batch_norm)\n conv1_feats = nonlinear(conv1_feats, 'relu')\n pool1_feats = max_pool(conv1_feats, 3, 3, 2, 2, 'pool1')\n\n res2a_feats = self.basic_block(pool1_feats, 'res2a', 'bn2a', is_train, use_batch_norm, 64, 1)\n res2b_feats = self.basic_block2(res2a_feats, 'res2b', 'bn2b', is_train, use_batch_norm, 64)\n res2c_feats = self.basic_block2(res2b_feats, 'res2c', 'bn2c', is_train, use_batch_norm, 64)\n \n res3a_feats = self.basic_block(res2c_feats, 'res3a', 'bn3a', is_train, use_batch_norm, 128) \n temp = res3a_feats\n for i in range(1, 8):\n temp = self.basic_block2(temp, 'res3b'+str(i), 'bn3b'+str(i), is_train, use_batch_norm, 128)\n res3b7_feats = temp\n \n res4a_feats = self.basic_block(res3b7_feats, 'res4a', 'bn4a', is_train, use_batch_norm, 256)\n temp = res4a_feats\n for i in range(1, 36):\n temp = self.basic_block2(temp, 'res4b'+str(i), 'bn4b'+str(i), is_train, use_batch_norm, 256)\n res4b35_feats = temp\n\n res5a_feats = self.basic_block(res4b35_feats, 'res5a', 'bn5a', is_train, use_batch_norm, 512)\n res5b_feats = self.basic_block2(res5a_feats, 'res5b', 'bn5b', is_train, use_batch_norm, 512)\n res5c_feats = self.basic_block2(res5b_feats, 'res5c', 'bn5c', is_train, use_batch_norm, 512)\n\n res5c_feats_flat = tf.reshape(res5c_feats, [self.batch_size, 49, 2048])\n self.conv_feats = res5c_feats_flat\n self.conv_feat_shape = [49, 2048]\n self.num_ctx = 49 \n self.dim_ctx = 2048\n\n self.imgs = imgs\n self.is_train = is_train", "def set_resize_parameters(\n self,\n degrad=6,\n labels=None,\n resize_mm=None,\n resize_voxel_number=None,\n\n ):\n # from . import show_segmentation\n\n logger.debug(\"set_resize_parameters(\\ndegrad={}, \\nlabels={}\\nresize_mm={}\\nresize_voxel_number={}\".format(\n degrad, labels, resize_mm, resize_voxel_number\n ))\n degrad = int(degrad)\n\n # import ipdb; ipdb.set_trace()\n # return voxelsize_mm, degrad\n self.degrad = degrad\n self.labels = labels\n segmentation = self._select_labels(self.segmentation, labels)\n\n if resize_voxel_number is not None:\n nvoxels = np.sum(segmentation > 0)\n volume = nvoxels * np.prod(self.voxelsize_mm)\n voxel_volume = volume / float(resize_voxel_number)\n resize_mm = voxel_volume ** (1.0 / 3.0)\n else:\n resize_mm = np.mean(self.voxelsize_mm)\n # self.working_voxelsize_mm = voxelsize_mm\n # self.working_segmentation = segmentation\n if np.sum(np.abs(self.resize_mm_1d - resize_mm)) != 0:\n # resize parameter changed\n self.resized_segmentation = None\n self.resized_binar_segmentation = None\n\n self.resize_mm_1d = resize_mm", "def fit(self):\n self._minuit_problem.migrad() # run optimizer\n self._status = 0 if self._minuit_problem.migrad_ok() else 1", "def resnext152(**kwargs):\n model = ResNeXt(Bottleneck, [3, 8, 36, 3], **kwargs)\n return model", "def resize(self):\n e = self.e\n if abs(self.dnp) * ( self.np-self.np_req) > 0:\n e = self.er\n self.dsize = numpy.clip((self.np_req/self.np)**(1./e), 1/self.r, self.r)\n self.size *= self.dsize", "def asyncoptimize(self,server_,port_): # 3\n arr_token = array.array(\"b\",[0]*(33))\n memview_arr_token = memoryview(arr_token)\n res,resargs = self.__obj.asyncoptimize(server_,port_,memview_arr_token)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n retarg_token = resargs\n retarg_token = arr_token.tobytes()[:-1].decode(\"utf-8\",errors=\"ignore\")\n return retarg_token", "def optimize_parameters(self):\n pass", "def optimize_parameters(self):\n pass", "def optimize_parameters(self):\n pass", "def constraints(imgType=SNR, minAnts=3, calMaxTime=100.0, calMaxRms=100.0, \n maxTsys=2E5, maxRmsPath=1E4, maxTau=1E5, maxDecor=1, \n srcRms=1E-5, subarray=DEFAULT) :\n \n if subarray == BOTH:\n raise Exception, \"Can't do constraints on BOTH subarrays\"\n multiSubarray('setConstraints', subarray, imgType, minAnts, calMaxTime,\n calMaxRms, maxTsys,maxRmsPath, maxTau, maxDecor, srcRms )", "def run_optimisation(model_path, tank1_outflow, tank2_outflow, tank3_outflow,\n h1_final, h2_final, h3_final, max_control, sim_control,\n h10=20.0, h20=20.0, h30=20.0, alpha1=0.5, alpha2=0.5,\n alpha3=0.5, ipopt_tolerance=1e-3,\n t_start=0, t_final=50.0, elements_number=50):\n # 2. Compute initial guess trajectories by means of simulation\n # Compile the optimization initialization model\n init_sim_fmu = compile_fmu(\"TanksPkg.ThreeTanks\", model_path)\n # Load the model\n simulation_model = load_fmu(init_sim_fmu)\n set_model_parameters(simulation_model,\n {'u': sim_control, \"h10\": h10, \"h20\": h20, \"h30\": h30,\n \"C1\": tank1_outflow, \"C2\": tank2_outflow,\n \"C3\": tank3_outflow, \"alpha1\": alpha1,\n \"alpha2\": alpha2, \"alpha3\": alpha3})\n init_result = simulation_model.simulate(start_time=t_start,\n final_time=t_final)\n # 3. Solve the optimal control problem\n # Compile and load optimization problem\n optimisation_model = \"TanksPkg.three_tanks_time_optimal\"\n op = transfer_optimization_problem(optimisation_model, model_path)\n # Set parameters\n set_model_parameters(op, {\"h10\": h10, \"h20\": h20, \"h30\": h30,\n 'h1_final': h1_final, 'h2_final': h2_final,\n 'h3_final': h3_final, \"C1\": tank1_outflow,\n \"C2\": tank2_outflow, \"C3\": tank3_outflow,\n \"alpha1\": alpha1, \"alpha2\": alpha2,\n \"alpha3\": alpha3, 'u_max': max_control})\n\n # Set options\n opt_options = op.optimize_options()\n opt_options['n_e'] = elements_number\n opt_options['variable_scaling'] = False\n opt_options['init_traj'] = init_result\n opt_options['IPOPT_options']['tol'] = ipopt_tolerance\n opt_options['verbosity'] = 1\n # Solve the optimal control problem\n res = op.optimize(options=opt_options)\n opt_result = {\"h1\": res['h1'], \"h2\": res['h2'], \"h3\": res['h3'],\n \"u\": res['u'], \"time\": res['time']}\n return opt_result", "def maximize(self):\n self.abstract_obj.maximize()", "def putmaxnumcon(self,maxnumcon_): # 3\n res = self.__obj.putmaxnumcon(maxnumcon_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def task_process(args):\n if args.mode == 'change model':\n for i in range(config.center_len):\n h, w = config.center_list[i][0], config.center_list[i][1]\n os.system('rm -rf ctpn_change_{}x{}.onnx'.format(h, w))\n for i in range(config.center_len):\n h, w = config.center_list[i][0], config.center_list[i][1]\n os.system('{} change_model.py --input_path={}/ctpn_{}x{}.onnx --output_path={}/ctpn_change_{}x{}.onnx' \\\n .format(args.interpreter, args.src_dir, h, w,args.res_dir, h, w)) \n if args.mode == 'preprocess':\n for i in range(config.center_len):\n os.system('mkdir -p {}_{}x{}'.format(args.res_dir, config.center_list[i][0], config.center_list[i][1]))\n os.system('{} ctpn_preprocess.py --src_dir={} --save_path={}' \\\n .format(args.interpreter, args.src_dir, args.res_dir))\n if args.mode == 'ais_infer':\n fps_all = 0\n os.system('mkdir -p {}/inf_output'.format(args.res_dir))\n for i in range(config.center_len):\n h, w = config.center_list[i][0], config.center_list[i][1]\n\n os.system('{} --model={} --input={}_{}x{} --dymHW {},{} --device {} --batchsize={} --output={}/inf_output' \\\n .format(args.interpreter, args.om_path, args.src_dir ,h , w, h, w,args.device, args.batch_size, args.res_dir))\n\n sumary_path = glob.glob('{}/inf_output/*ary.json'.format(args.res_dir))[0]\n with open(sumary_path, 'r') as f:\n output = json.load(f)\n throughput = output['throughput'] \n fps_all = fps_all + throughput * config.center_count[i]\n os.system('rm -f {}'.format(sumary_path))\n os.system('mv {}/inf_output/*/*.bin {}'.format(args.res_dir, args.res_dir))\n os.system('rm {}/inf_output -rf'.format(args.res_dir))\n fps_all = fps_all / config.imgs_len\n print(\"====performance data====\")\n print('CTPN bs{} models fps:{}'.format(args.batch_size, fps_all))", "def resize(self, *args):\n return _ida_hexrays.qvector_ccase_t_resize(self, *args)", "def minimize(self):\n pass", "def create_inference_tasks(task_queue, image_layer_path, convnet_path, \n mask_layer_path, output_layer_path, output_block_start, output_block_size, \n grid_size, patch_size, patch_overlap, cropping_margin_size,\n output_key='output', num_output_channels=3, \n image_mip=1, output_mip=1, mask_mip=3):\n for z in tqdm(range(grid_size[0]), desc='z loop'):\n for y in range(grid_size[1]):\n for x in range(grid_size[2]):\n output_offset = tuple(s+x*b for (s, x, b) in \n zip(output_block_start, (z, y, x), \n output_block_size))\n task = InferenceTask(\n image_layer_path=image_layer_path,\n convnet_path=convnet_path,\n mask_layer_path=mask_layer_path,\n output_layer_path=output_layer_path,\n output_offset=output_offset,\n output_shape=output_block_size,\n patch_size=patch_size, \n patch_overlap=patch_overlap,\n cropping_margin_size=cropping_margin_size,\n output_key=output_key,\n num_output_channels=num_output_channels,\n image_mip=image_mip,\n output_mip=output_mip,\n mask_mip=mask_mip\n )\n task_queue.insert(task)\n task_queue.wait('Uploading InferenceTasks')\n\n vol = CloudVolume(output_layer_path, mip=output_mip)\n vol.provenance.processing.append({\n 'method': {\n 'task': 'InferenceTask',\n 'image_layer_path': image_layer_path,\n 'convnet_path': convnet_path,\n 'mask_layer_path': mask_layer_path,\n 'output_layer_path': output_layer_path,\n 'output_offset': output_offset,\n 'output_shape': output_block_size,\n 'patch_size': patch_size,\n 'patch_overlap': patch_overlap,\n 'cropping_margin_size': cropping_margin_size,\n 'output_key': output_key,\n 'num_output_channels': num_output_channels,\n 'image_mip': image_mip,\n 'output_mip': output_mip,\n 'mask_mip': mask_mip,\n },\n 'by': OPERATOR_CONTACT,\n 'date': strftime('%Y-%m-%d %H:%M %Z'),\n })\n vol.commit_provenance()", "def get_optimizer(args, net):\n if args.backbone_lr > 0.0:\n base_params = []\n resnet_params = []\n resnet_name = []\n resnet_name.append('layer0')\n resnet_name.append('layer1')\n #resnet_name.append('layer2')\n #resnet_name.append('layer3')\n #resnet_name.append('layer4')\n len_resnet = len(resnet_name)\n else:\n param_groups = net.parameters()\n\n if args.backbone_lr > 0.0:\n for name, param in net.named_parameters():\n is_resnet = False\n for i in range(len_resnet):\n if resnet_name[i] in name:\n resnet_params.append(param)\n # param.requires_grad=False\n print(\"resnet_name\", name)\n is_resnet = True\n break\n if not is_resnet:\n base_params.append(param)\n\n if args.sgd:\n if args.backbone_lr > 0.0:\n optimizer = optim.SGD([\n {'params': base_params},\n {'params': resnet_params, 'lr':args.backbone_lr}\n ],\n lr=args.lr,\n weight_decay=5e-4, #args.weight_decay,\n momentum=args.momentum,\n nesterov=False)\n else:\n optimizer = optim.SGD(param_groups,\n lr=args.lr,\n weight_decay=5e-4, #args.weight_decay,\n momentum=args.momentum,\n nesterov=False)\n else:\n raise ValueError('Not a valid optimizer')\n\n if args.lr_schedule == 'scl-poly':\n if cfg.REDUCE_BORDER_ITER == -1:\n raise ValueError('ERROR Cannot Do Scale Poly')\n\n rescale_thresh = cfg.REDUCE_BORDER_ITER\n scale_value = args.rescale\n lambda1 = lambda iteration: \\\n math.pow(1 - iteration / args.max_iter,\n args.poly_exp) if iteration < rescale_thresh else scale_value * math.pow(\n 1 - (iteration - rescale_thresh) / (args.max_iter - rescale_thresh),\n args.repoly)\n scheduler = optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda1)\n elif args.lr_schedule == 'poly':\n lambda1 = lambda iteration: math.pow(1 - iteration / args.max_iter, args.poly_exp)\n scheduler = optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda1)\n else:\n raise ValueError('unknown lr schedule {}'.format(args.lr_schedule))\n\n return optimizer, scheduler", "def putmaxnumcon(self,maxnumcon_):\n res = __library__.MSK_XX_putmaxnumcon(self.__nativep,maxnumcon_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def save_cma_optimization_results(self, es):\n # code extra verbose to understand what is going on\n generation = es.result.iterations\n evals = es.result.evaluations # number of evals at start of each gen\n xfavorite = es.result.xfavorite # center of distribution, best est\n stds = es.result.stds # stds of distribution, stds of xfavorite\n fbest = es.result.fbest # best ever measured\n xbest = es.result.xbest # coordinates of best ever measured\n evals_best = es.result.evals_best # index of best measurement\n\n if not self.minimize_optimization:\n fbest = -fbest\n\n results_array = np.concatenate([[generation, evals],\n xfavorite, stds,\n [fbest], xbest, [evals_best]])\n if (not 'optimization_result'\n in self.data_object[EXPERIMENTAL_DATA_GROUP_NAME].keys()):\n opt_res_grp = self.data_object[EXPERIMENTAL_DATA_GROUP_NAME]\n self.opt_res_dset = opt_res_grp.create_dataset(\n 'optimization_result', (0, len(results_array)),\n maxshape=(None, len(results_array)),\n dtype='float64')\n\n # FIXME: Jan 2018, add the names of the parameters to column names\n self.opt_res_dset.attrs['column_names'] = h5d.encode_to_utf8(\n 'generation, ' + 'evaluations, ' +\n 'xfavorite, ' * len(xfavorite) +\n 'stds, '*len(stds) +\n 'fbest, ' + 'xbest, '*len(xbest) +\n 'best evaluation,')\n\n old_shape = self.opt_res_dset.shape\n new_shape = (old_shape[0]+1, old_shape[1])\n self.opt_res_dset.resize(new_shape)\n self.opt_res_dset[-1, :] = results_array" ]

[ "0.8304745", "0.52103585", "0.51943374", "0.50631404", "0.50303227", "0.50238323", "0.49848595", "0.4903367", "0.4879516", "0.48169866", "0.4804174", "0.47891757", "0.47816435", "0.47773397", "0.47563007", "0.4723178", "0.47225302", "0.47225302", "0.47225302", "0.47050464", "0.47023097", "0.46823868", "0.46738628", "0.46641922", "0.46459436", "0.46328843", "0.46305603", "0.46279505", "0.46080375", "0.46048748" ]

[ "def checkmem(self,file_,line_): # 3\n res = self.__obj.checkmemtask(file_,line_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def _checkAvailableMemory():\n #execute free -m to get output in MB\n logging.debug(\"checking total memory\")\n cmd = [\n basedefs.EXEC_FREE, \"-m\"\n ]\n output, rc = utils.execCmd(cmdList=cmd, failOnError=True, msg=output_messages.ERR_EXP_FREE_MEM)\n\n #itterate over output and look for the line: \"Mem: 1 something\"\n #and extract 1 from it (1 is an example to the free memory)\n availableMemory = 0\n for line in output.split(\"\\n\"):\n result = re.match(\"Mem:\\s+(\\d+)\\s+.+\", line)\n if result:\n logging.debug(\"Found a match, amount of memory: %s\" % result.group(1))\n availableMemory = result.group(1)\n\n #compare found memory to restrictions\n availableMemory = int(availableMemory)\n #multiplying CONST_MIN_MEMORY by 0.95 to have tolerance of 5%\n if availableMemory < (basedefs.CONST_MIN_MEMORY_MB * 0.95):\n logging.error(\"Availble memory (%s) is lower then the minimum requirments (%s)\" % (availableMemory, basedefs.CONST_MIN_MEMORY_MB))\n raise Exception(output_messages.ERR_EXP_NOT_EMOUGH_MEMORY)\n\n if availableMemory < basedefs.CONST_WARN_MEMORY_MB:\n logging.warn(\"There is less then %s available memory \" % basedefs.CONST_WARN_MEMORY_MB)\n controller.MESSAGES.append(output_messages.WARN_LOW_MEMORY)", "def check_mem(self, values):\n try:\n virt_mem = psutil.virtual_memory()\n values[keys.KEY_VIRTUAL_MEM_TOTAL] = virt_mem.total\n values[keys.KEY_VIRTUAL_MEM_PERCENT] = virt_mem.percent\n except:\n logging.error(\"Error collecting memory stats.\")", "def _checkMemLeak(self):\n\t\t### Memory leak code:\n\t\t#self.stats['memlist'].append(mem.mySize()/1024)\n\t\tself.stats['memlist'].append(mem.active())\n\t\tmemfree = mem.free()\n\t\tminavailmem = 64*1024; # 64 MB, size of one image\n\t\tif(memfree < minavailmem):\n\t\t\tapDisplay.printError(\"Memory is low (\"+str(int(memfree/1024))+\"MB): there is probably a memory leak\")\n\n\t\tif(self.stats['count'] > 15):\n\t\t\tmemlist = self.stats['memlist'][-15:]\n\t\t\tn = len(memlist)\n\t\t\t\n\t\t\tgain = (memlist[n-1] - memlist[0])/1024.0\n\t\t\tsumx = n*(n-1.0)/2.0\n\t\t\tsumxsq = n*(n-1.0)*(2.0*n-1.0)/6.0\n\t\t\tsumy = 0.0; sumxy = 0.0; sumysq = 0.0\n\t\t\tfor i in range(n):\n\t\t\t\tvalue = float(memlist[i])/1024.0\n\t\t\t\tsumxy += float(i)*value\n\t\t\t\tsumy += value\n\t\t\t\tsumysq += value**2\n\t\t\t###\n\t\t\tstdx = math.sqrt(n*sumxsq - sumx**2)\n\t\t\tstdy = math.sqrt(n*sumysq - sumy**2)\n\t\t\trho = float(n*sumxy - sumx*sumy)/float(stdx*stdy+1e-6)\n\t\t\tslope = float(n*sumxy - sumx*sumy)/float(n*sumxsq - sumx*sumx)\n\t\t\tmemleak = rho*slope\n\t\t\t###\n\t\t\tif(self.stats['memleak'] > 3 and slope > 20 and memleak > 512 and gain > 2048):\n\t\t\t\tapDisplay.printError(\"Memory leak of \"+str(round(memleak,2))+\"MB\")\n\t\t\telif(memleak > 32):\n\t\t\t\tself.stats['memleak'] += 1\n\t\t\t\tapDisplay.printWarning(\"substantial memory leak \"+str(round(memleak,2))+\"MB\")\n\t\t\t\tprint \"(\",str(n),round(slope,5),round(rho,5),round(gain,2),\")\"", "def hasmem(state, mem):\n if mem <= state[HEAD][MEM]:\n return True\n else:\n state[HEAD][STATUS] = OOM\n return False", "def precheck(self):\n if self.__memory_size is None:\n self.logger.exception(\"[Memory] Please set memory size.\")\n raise ArgsNotCorrect(\"Please set memory size.\")", "def __check_memory_limit(self, efile_path):\n try:\n log.debug('Checking %s for exceeded memory message from SLURM', efile_path)\n with open(efile_path) as f:\n if os.path.getsize(efile_path) > 2048:\n f.seek(-2048, os.SEEK_END)\n f.readline()\n for line in f.readlines():\n stripped_line = line.strip()\n if stripped_line == SLURM_MEMORY_LIMIT_EXCEEDED_MSG:\n return OUT_OF_MEMORY_MSG\n elif any(_ in stripped_line for _ in SLURM_MEMORY_LIMIT_EXCEEDED_PARTIAL_WARNINGS):\n return PROBABLY_OUT_OF_MEMORY_MSG\n except Exception:\n log.exception('Error reading end of %s:', efile_path)\n\n return False", "def test_mem_available():\n result = _run_metric('mem_available')\n assert result.exit_code == 0", "def check(self, num, line):\n\t\tif self.re.match(line):\n\t\t\treturn self.error", "def check_if_sufficient_memory():\n percent_memory = psutil.virtual_memory().percent\n if percent_memory > 75:\n raise ValueError('Please use a device with more CPU ram or a smaller dataset')", "def phase_check(self, num, line):\n\t\tpass", "def has_memory(self, user_id, memory_date):\n raise NotImplementedError()", "def auditmemallocfail(self) :\n\t\ttry :\n\t\t\treturn self._auditmemallocfail\n\t\texcept Exception as e:\n\t\t\traise e", "def verifyAvailableSpace(sitemover, totalFileSize, path, error):\n\n ec = 0\n pilotErrorDiag = \"\"\n\n # skip for now: add the 5 GB + 2 GB limits for output and log files to the total input file size\n _neededSpace = totalFileSize\n tolog(\"Needed space: %d B\" % (_neededSpace))\n # get the locally available space\n _availableSpace = getLocalSpace(path)\n tolog(\"Locally available space: %d B\" % (_availableSpace))\n\n # should the file size verification be done? (not if \"mv\" is used)\n doVerification = sitemover.doFileVerifications()\n \n # are we wihin the limit?\n if (_neededSpace > _availableSpace) and doVerification:\n pilotErrorDiag = \"Not enough local space for staging input files and run the job (need %d B, but only have %d B)\" %\\\n (_neededSpace, _availableSpace)\n tolog(\"!!FAILED!!2999!! %s\" % (pilotErrorDiag))\n ec = error.ERR_NOLOCALSPACE\n\n return ec, pilotErrorDiag", "def check_mem_usage():\n mem = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss\n return mem", "def allocatememory(self):\n pass", "def test_is_memleak(self):\n subprocess.call(\n [\"g++\", \"-g\", \"test/with_leak.cc\", \"-o\", \"test/leaky.out\"])\n self.assertTrue(uut.is_memleak(\"test/leaky.out\"))\n subprocess.call(\n [\"g++\", \"-g\", \"test/without_leak.cc\", \"-o\",\n \"test/not_leaky.out\"])\n self.assertFalse(uut.is_memleak(\"test/not_leaky.out\"))", "def checkMemDetail(self):\n mem = self.getMemDetail()\n err_msg = []\n task_result = device_status = 0\n\n if not mem:\n err_msg.append('Get Memory detail info failed')\n task_result = device_status = 1\n else:\n # 以后可扩展告警条件\n pass\n return mem, err_msg, task_result, device_status", "def check_Lines(self):\n\n pass", "def size_check(block_contents, file_name_current): \r\n print \"Performing size check on \"+file_name_current\r\n #print block_contents\r\n size_value = block_contents[\"size\"]\r\n indirect_value = block_contents[\"indirect\"]\r\n location_pointer = block_contents[\"location\"]\r\n\r\n if not(location_pointer in global_used_list):\r\n global_used_list.append(location_pointer)# add to used list \r\n \r\n file_location_checking = \"fusedata.\" + str(location_pointer)\r\n #print \"file_location_checking = \"+ file_location_checking\r\n\r\n file_access = open(file_location_checking, \"r\")\r\n length_array_location = 1\r\n\r\n try:\r\n block_contents = json.load(file_access)\r\n type_contents = type(block_contents)\r\n\r\n except ValueError:\r\n print \"\\\"\"+file_access+\"\\\" not in json format (catch 4)\" \r\n type_contents = None\r\n \r\n if(type_contents == list):\r\n length_array_location = len(block_contents) \r\n \r\n if (size_value < BLOCK_SIZE):\r\n if(indirect_value == 0):\r\n if(size_value > 0):\r\n print(\"Size test passed\")\r\n else:\r\n print(\"Size <= 0\")\r\n else:\r\n print \"Size does not make sense. Not changing the size.\" \r\n \r\n if(size_value < (BLOCK_SIZE * length_array_location)):\r\n if(type_contents == list):\r\n \r\n if(indirect_value == 1):\r\n print \"Size Test passed (2)\"\r\n else:\r\n print\"Wrong use of indirect. Changing indirect to 1\"\r\n block_contents[\"indirect\"] = 1\r\n print \"Changes made.\" \r\n \r\n else:\r\n if(indirect_value != 0):\r\n print\"Wrong use of indirect. Changing indirect to 0\"\r\n block_contents[\"indirect\"] = 0\r\n print \"Changes made.\"", "def check_disk_usage(disk):\n du= shutil.disk_usage(disk)\n free =du.free/du.total * 100\n return free > 30", "def check_memory(self, lambda_memory):\n if (lambda_memory < 128) or (lambda_memory > 1536):\n raise Exception('Incorrect memory size specified')\n else:\n res = lambda_memory % 64\n if (res == 0):\n return lambda_memory\n else:\n return lambda_memory - res + 64", "def IsAllocated(self):\n return self._fsntfs_file_entry.is_allocated()", "def validmemory(state, area, addr):\n if not validarea(state, area) or addr >= len(state[MEMORY+area]):\n state[HEAD][STATUS] = OOB\n return False\n else:\n return True", "def testMemory1(self):\n mtt.makeTempDirParent()\n valgrind = mtt.which('valgrind')\n if valgrind is None:\n return\n shuffledTargets = list(g_targetBlocks)\n for i in xrange(0, 20):\n tmpDir = os.path.abspath(mtt.makeTempDir('memory1'))\n random.shuffle(g_nonTargetBlocks)\n random.shuffle(shuffledTargets)\n shuffledBlocks = list(shuffledTargets)\n lower = 0\n for j in xrange(0, len(g_nonTargetBlocks)):\n # randomly insert the non target blocks, but keep a record\n # of their relative order.\n index = random.randint(lower, len(shuffledBlocks))\n shuffledBlocks.insert(index, g_nonTargetBlocks[j])\n lower = index + 1\n testMaf = mtt.testFile(os.path.abspath(os.path.join(tmpDir, 'test.maf')),\n ''.join(shuffledBlocks), g_headers)\n parent = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\n cmd = mtt.genericValgrind(tmpDir)\n cmd.append(os.path.abspath(os.path.join(parent, 'test', 'mafSorter')))\n cmd += ['--maf', os.path.abspath(os.path.join(tmpDir, 'test.maf')), \n '--seq', 'hg18.chr7']\n outpipes = [os.path.abspath(os.path.join(tmpDir, 'sorted.maf'))]\n mtt.recordCommands([cmd], tmpDir, outPipes=outpipes)\n mtt.runCommandsS([cmd], tmpDir, outPipes=outpipes)\n self.assertTrue(mtt.noMemoryErrors(os.path.join(tmpDir, 'valgrind.xml')))\n mtt.removeDir(tmpDir)", "def check_available_memory(self,unit='B'):\n free = psutil.virtual_memory().available\n\n if unit == 'MB':\n\n return free/10**6\n\n elif unit == 'GB':\n\n return free/10**9\n\n else:\n\n return free", "def do_allocate(self, line):\n cmd_args = io.parse_cmd_args(line, io.allocate_cmd_pattern)\n if cmd_args:\n success = self.manager.allocate(**cmd_args)\n if success:\n self.console_print(\"Noice! Allocation complete!\", settings.INFO_FORMAT)\n else:\n self.console_print(\"Awww...something went wrong while allocating.\", settings.ERROR_FORMAT)\n else:\n self.console_print(settings.COMMMAND_ARGS_ERROR_MSG, settings.ERROR_FORMAT)", "def allocated(self):\n if self.filename()==\"$OrphanFiles\": return False\n return isone(self.tag(\"alloc\")) or isone(self.tag(\"ALLOC\"))", "def test_free_space_rejects_file_arguments():\n result = _run_metric('free_space', '/etc/hosts')\n # 2 is the exit code for a UsageError, which includes bad parameters.\n assert result.exit_code == 2\n # Is this too fragile?\n assert 'Invalid value' in result.output", "def oswmem_free_memory(self,min=0): \n result = self.df[self.df['free mmemory'] > min].all \n return result" ]

[ "0.8956478", "0.6172901", "0.61476266", "0.5966271", "0.5839869", "0.5747777", "0.5662355", "0.558952", "0.55579066", "0.5434767", "0.5403096", "0.54001987", "0.5385859", "0.53383994", "0.5325104", "0.5304694", "0.52878374", "0.52868325", "0.52741265", "0.5272602", "0.5210603", "0.52054536", "0.52054113", "0.51781195", "0.51709807", "0.51580465", "0.5155097", "0.5152815", "0.5143162", "0.5099357" ]

[ "def solutiondef(self,whichsol_): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res,resargs = self.__obj.solutiondef(whichsol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _isdef_return_value = resargs\n return _isdef_return_value", "def has_solution(self) -> bool:\n pass", "def has_solution(self) -> bool:\n if self in [self.SATISFIED, self.ALL_SOLUTIONS, self.OPTIMAL_SOLUTION]:\n return True\n return False", "def did_solve(self) -> bool:\n return self._solution.info.status == \"solved\"", "def is_solved(self):\n raise NotImplementedError()", "def test_is_solved_when_puzzle_is_solved(self):\n self.assertTrue(self.sudoku.is_solved())", "def is_solution(self):\n # Only need to check the length because the configuration expansion assesses the feasibility.\n return len(self._path) == self._N", "def test_is_solved_when_puzzle_is_not_solved(self):\n sudoku = sudolver.Sudoku()\n self.assertFalse(sudoku.is_solved())", "def did_solve(self):\n return self._solution[\"status\"] == \"optimal\"", "def checkSolution(self):\n movesToEndblock = self.gridSize - self.changeable[0] - 2\n if self.checkMove(0,movesToEndblock) == 0:\n return 0\n return 1", "def did_solve(self) -> bool:\n pass", "def is_solved(self):\n if not self._find_empty():\n return True\n else:\n return False", "def is_solvable(self):\n self_copy = deepcopy(self)\n return self_copy.solve()", "def is_legal_solution(self, solution):\r\n if self.sorting_order is ScoresSortingOrder.ASCENDING:\r\n return self.fit_score(solution) == 0\r\n else:\r\n return self.fit_score(solution) == sum(x for x in range(1, 12))", "def ok(self, solution):\n if self.constraints is not None:\n for constraint in self.constraints:\n if not constraint(solution):\n return False\n return True", "def test_is_solved(self):\n p = hw.TilePuzzle([[1, 2], [3, 0]])\n self.assertTrue(p.is_solved())\n p = hw.TilePuzzle([[0, 1], [3, 2]])\n self.assertFalse(p.is_solved())", "def find_solution(self):\r\n for solution in self.solutions:\r\n if self.fitting_function.is_legal_solution(solution):\r\n return solution\r\n return None", "def check_if_solvable(self):\n\n self.solvable=True #status of sudoku\n for i in range(0, 9):\n for j in range(0, 9):\n if self.a[i][j]==0:\n continue\n if self.check(i, j)[self.a[i][j]]==0:\n self.solvable=False\n return False", "def is_solved(self):\n # Iterate through each square of the puzzle\n for row in range(self.sl):\n for col in range(self.sl):\n val = self.puzzle[row][col]\n\n # If any square value is blank (0), not solved, return False\n if val == 0:\n return False\n\n # Trick to keep DRY code: replace each value temporarily with a\n # 0, and use valid_square method with original value to determine\n # if every square is valid\n self.puzzle[row][col] = 0\n valid = self.valid_square(row, col, val)\n self.puzzle[row][col] = val\n \n # If not a valid value for square, return False\n if not valid:\n return False\n return True", "def readsolution(self,whichsol_,filename_): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res = self.__obj.readsolution(whichsol_,filename_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def sketch_of_solution(self,sol=None):\n raise NotImplementedError", "def solveOneStep(self):\n ### Student code goes here\n return True", "def is_solved(self):\n self.solved = self.current_pos == self.finish_pos\n return self.solved", "def noSol(self):\n noSol = False \n\n cost_min_bilet = 100000\n\n for a in self.info.autobuze:\n if a.price < cost_min_bilet:\n cost_min_bilet = a.price\n\n for o in self.info.oameni:\n if o.money < cost_min_bilet and o.remaining_dest != []: \n noSol = True\n break\n \n set_destinatii = set()\n\n for o in self.info.oameni:\n if o.current_loc in set_destinatii:\n noSol = True\n break\n else:\n set_destinatii.add(o.current_loc)\n\n return noSol", "def solve(self):\n pass", "def solve(self):\n pass", "def analyzesolution(self,whichstream_,whichsol_):\n res = __library__.MSK_XX_analyzesolution(self.__nativep,whichstream_,whichsol_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def solve(self, solver):\n solver.solve()", "def solve_step(self,puzzle_grid,x,y):\n self.puzzleGrid = puzzle_grid\n if(self.foundStep == False):\n self.targetCell = self.puzzleGrid.grid[x][y]\n if(self.targetCell.isSolved == False):\n self.calculate_possibilities()\n if len(self.targetCell.possibilities) == 1: #README method 1\n self.targetCell.solve()\n return True\n else:\n return self.check_neighbours() #README method 2", "def solve(self):\n ..." ]

[ "0.82378125", "0.7520855", "0.7130622", "0.6716045", "0.65864074", "0.6542276", "0.65399134", "0.65024155", "0.64941233", "0.6442446", "0.64409184", "0.64374423", "0.6399567", "0.6366495", "0.6350611", "0.6329383", "0.6290776", "0.62602293", "0.61625254", "0.61530423", "0.61501133", "0.61419344", "0.6089668", "0.60655683", "0.6056831", "0.6056831", "0.6030579", "0.6023574", "0.60124105", "0.5984638" ]

[ "def deletesolution(self,whichsol_): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res = self.__obj.deletesolution(whichsol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def clear(self):\n del self[:]\n self.checked_sol = None\n self.checked_obj = None\n self.feas_count = 0\n self.infeas_count = 0\n self.best_feas_value = None\n self.worst_feas_value = None\n self.least_infeas_value = INF\n self.most_infeas_value = 0.0\n self.best_value = Infeasible(INF)", "def reset(self):\n self.solver = None", "def free(self):\n self.linit = False\n self.nx = 0\n self.nz = 0\n self.nsrc = 0\n self.nrec = 0\n self.fteik2d.fteik_solver2d_free()\n return", "def __del__(self):\n del self.traj\n del self.dcf\n del self._tmp_fft_array\n del self.cl_kerneltable\n del self.cl_deapo\n del self._check\n del self.queue\n del self.ctx\n del self.prg\n del self.fft", "def __del__(self):\n del self.traj\n del self.dcf\n del self._tmp_fft_array\n del self.cl_kerneltable\n del self.cl_deapo\n del self._check\n del self.queue\n del self.ctx\n del self.prg\n del self.fft", "def _removeclause(self, solver):\n if not self.__learnt:\n return\n solver._watches[self._lits[0]._varsymbol].remove(self)\n if len(self._lits) > 1:\n solver._watches[self._lits[1]._varsymbol].remove(self)\n solver._learntclause.remove(self)", "def __del__(self):\n if self.fft_dim is not None:\n del self._tmp_fft_array\n del self.fft\n del self.mask\n del self.queue\n del self.ctx\n del self.prg", "def __del__(self):\n if self.fft_dim is not None:\n del self._tmp_fft_array\n del self.fft\n del self.mask\n del self.queue\n del self.ctx\n del self.prg", "def __del__(self) -> None:\n self.map.solid_id.discard(self.id)", "def _reset(lp):\n if hasattr(lp, \"solverModel\"):\n delattr(lp, \"solverModel\")\n for v in lp.variables():\n if hasattr(v, \"_xprs\"):\n delattr(v, \"_xprs\")\n for c in lp.constraints.values():\n if hasattr(c, \"_xprs\"):\n delattr(c, \"_xprs\")", "def delete_grid(self):\n\n\t\tself.a_grid = None\t\t# Deletes the object from memory", "def del_cells(self):\t\r\n del self._cells", "def __del__(self):\n del self.board_\n del self.children_edges_\n self.board_ = None\n del self.parent_edge_\n # print(\"destruct node\")", "def end(self):\n self.solver = None\n # Other resources not released because can be called after the end\n #self.model = None\n #self.params = None\n #self.context = None", "def __delitem__(self, choice):\n if choice not in self._weights:\n return\n del self._weights[choice]\n self._generate_p()", "def __del__(self):\n\n # Base class destructor is called ?? needed\n sim.Simulation.__del__(self)\n\n if self.verbose:\n print \"Cleaning derived simulation object LAMMPS1\"\n\n del self.pairCoeffDct\n del self.bondCoeffDct", "def __del__(self):\n\n ipset.ipmap_free(self.map)", "def clear(self):\n self._multivol.deallocate(self.id)", "def clear(self):\n self.globalDefines = {}\n self.axiom = self.setAxiomFromString(\"\")\n self.clearProductions()\n self.niterations = 1\n self.resultPString = None", "def _clear_cache(self):\n super(ShootingSolver, self)._clear_cache()\n self.__numeric_jacobian = None\n self.__numeric_system = None\n self.__ivp = None", "def destroy(self):\n for inst in self.module.global_insts[:]:\n if (inst.op_name in spirv.DECORATION_INSTRUCTIONS or\n inst.op_name in spirv.DEBUG_INSTRUCTIONS):\n if self.result_id in inst.operands:\n inst.destroy()\n if self.basic_block is None:\n if self not in self.module.global_insts:\n raise IRError('Instruction is not in basic block or module')\n self.module.global_insts.remove(self)\n return\n self.basic_block.insts.remove(self)\n if self.result_id is not None:\n del self.module.id_to_inst[self.result_id]\n self.basic_block = None\n self.op_name = None\n self.result_id = None\n self.type_id = None\n self.operands = None", "def __del__(self):\n self.Clear()", "def _del(self) -> None:\n self.variables.pop(prop_name, None)", "def __del__(self):\n # Only an integer is passed to the call\n self.ph.remove(self.ID)\n # No new references were created, nothing retained", "def __del__ ( self ) :\n \n if self.name and self.name in self.__pdf_names :\n self.__pdf_names.remove ( self.name ) \n while self.__local_names :\n a = self.__local_names.pop ()\n if a in self.__var_names :\n self.__var_names.remove ( a )", "def undelete_formula(self, unique_id):\n tf = util.return_element_from_list(int(unique_id), self.formulas_memory)\n node = util.return_element_from_list(tf.node.node_id, self.nodes)\n self.undelete_formula_helper(unique_id)\n node.insert_formula(tf)", "def __del__(self):\n\n ipset.ipset_free(self.set)", "def delete(self):\n del self.shx.atoms[self.index]", "def __del__(self):\n\t\tself._pc.gid_clear()" ]

[ "0.7279343", "0.6581316", "0.61854655", "0.61191547", "0.60800874", "0.60800874", "0.5985228", "0.5961063", "0.59411716", "0.5815005", "0.5813991", "0.5778785", "0.57717663", "0.5751076", "0.5720295", "0.5719897", "0.56984323", "0.5693372", "0.5637449", "0.56347036", "0.5630771", "0.5622891", "0.5621075", "0.5618331", "0.56166804", "0.5613579", "0.5604132", "0.5597423", "0.55720896", "0.55667186" ]

[ "def onesolutionsummary(self,whichstream_,whichsol_): # 3\n if not isinstance(whichstream_,streamtype): raise TypeError(\"Argument whichstream has wrong type\")\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res = self.__obj.onesolutionsummary(whichstream_,whichsol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def solutionsummary(self,whichstream_):\n res = __library__.MSK_XX_solutionsummary(self.__nativep,whichstream_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def solutionsummary(self,whichstream_): # 3\n if not isinstance(whichstream_,streamtype): raise TypeError(\"Argument whichstream has wrong type\")\n res = self.__obj.solutionsummary(whichstream_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def analyzesolution(self,whichstream_,whichsol_): # 3\n if not isinstance(whichstream_,streamtype): raise TypeError(\"Argument whichstream has wrong type\")\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res = self.__obj.analyzesolution(whichstream_,whichsol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def analyzesolution(self,whichstream_,whichsol_):\n res = __library__.MSK_XX_analyzesolution(self.__nativep,whichstream_,whichsol_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def print_solution():\n pass", "def show_summary(self, lang):\n return self.summary % self.vars", "def show_summary(self, out = None, debug = False):\n if (out is None) : out = sys.stdout\n results = self.matching_candidates\n if (len(results) > 0):\n self.atom_props.show_properties(identity = \"HOH\", out = out)\n if (self.nuc_phosphate_site):\n print(\" appears to be nucleotide coordination site\", file=out)\n if (self.no_final):\n print(\" Found potential ion%s outside of specified set:\" % \\\n (\"s\" if len(results) > 1 else \"\"), file=out)\n if (self.final_choice is not None):\n # We have one result that we are reasonably certain of\n elem_params, score = results[0]\n if elem_params.element not in mmtbx.ions.HALIDES:\n self.atom_props.show_ion_results(\n identity = str(self.final_choice),\n out = out,\n valence_used = self.valence_used,\n confirmed = True)\n else:\n print(\" Probable anion:\", str(elem_params), file=out)\n print(\"\", file=out)\n elif (len(results) > 1):\n # We have a couple possible identities for the atom\n below_cutoff = [ elem_params for elem_params, score in results\n if score < self.ambiguous_valence_cutoff]\n if len(below_cutoff) == 1:\n elem_params = below_cutoff[0]\n print(\" ambigous results, best valence from %s\" % \\\n str(elem_params), file=out)\n self.atom_props.show_ion_results(\n identity = str(elem_params),\n out = out,\n valence_used = True)\n print(\"\", file=out)\n else:\n ions = [str(i[0]) for i in sorted(results, key = lambda x: x[1])]\n print(\" ambiguous results, could be %s\" % \", \".join(ions), file=out)\n for elem_params, score in results :\n self.atom_props.show_ion_results(identity = str(elem_params),\n out = out)\n print(\"\", file=out)\n else:\n if (self.atom_type != WATER) or (self.nuc_phosphate_site):\n self.atom_props.show_properties(identity = \"HOH\", out = out)\n if (self.nuc_phosphate_site):\n print(\" appears to be nucleotide coordination site\", file=out)\n # try anions now\n if (self.looks_like_halide):\n print(\" Probable cation: %s\" % str(self.final_choice), file=out)\n print(\"\", file=out)\n else:\n # atom is definitely not water, but no reasonable candidates found\n # print out why all the metals we tried failed\n if (debug) and (len(self.filtered_candidates) > 0):\n print(\" insufficient data to identify atom\", file=out)\n possible = True\n for params in self.filtered_candidates:\n if (self.atom_props.has_compatible_ligands(str(params))):\n if possible:\n print(\" possible candidates:\", file=out)\n possible = False\n self.atom_props.show_ion_results(identity = str(params),\n out = out)\n else :\n print(\" incompatible ligands for %s\" % str(params), file=out)\n #print >> out, \" rejected as unsuitable:\"\n #for params in self.rejected_candidates:\n # if (self.atom_props.has_compatible_ligands(str(params))):\n # self.atom_props.show_ion_results(identity = str(params),\n # out = out)\n # else :\n # print >> out, \" incompatible ligands for %s\" % str(params)\n print(\"\", file=out)", "def show_solution(self,show):\r\n self.showSolution = show", "def optimizersummary(self,whichstream_): # 3\n if not isinstance(whichstream_,streamtype): raise TypeError(\"Argument whichstream has wrong type\")\n res = self.__obj.optimizersummary(whichstream_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def printSummary(self):\n pass", "def print_solution(self, solution_path):\n print(\"---SOLUTION---: \")\n for node in solution_path:\n node.state.plot_cube(\n \"SOLUTION: Node [\" + str(node.id) + \"] at depth \" + str(node.node_depth)\n )\n if node.last_action != None:\n print(\"Next action: \", node.last_action)\n print(\"[\" + str(node.id) + \"] \" + str(node.state.create_md5()))\n\n print(\"\\n TOTAL COST: \", solution_path[len(solution_path) - 1].cost)", "def get_summary(self, filename=None, tosay=False):\n prec = '{:.3g}'\n if self.dimensions == 1:\n parameter_string = str('parameter.')\n else:\n parameter_string = str('parameters.')\n introduction = str('Your problem has been defined by '+str(self.dimensions)+' '+parameter_string)\n added = str('Their distributions are given as follows:')\n for i in range(0, self.dimensions):\n added_new = ('\\nParameter '+str(i+1)+' '+str(self.parameters[i].get_description()))\n if i == 0:\n if self.variable is not None:\n title = str('This polynomial concerns the output variable '+str(self.variable) + '.\\n')\n added = title + introduction + added_new\n else:\n added = introduction + added_new\n else:\n added = added + added_new\n if self.statistics_object is not None:\n mean_value, var_value = self.get_mean_and_variance()\n X = self.get_points()\n y_eval = self.get_polyfit(X)\n y_valid = self._model_evaluations\n a,b,r,_,_ = st.linregress(y_eval.flatten(),y_valid.flatten())\n r2 = r**2\n statistics = '\\n \\nA summary of computed output statistics is given below:\\nThe mean is estimated to be '+ prec.format(mean_value) +\\\n ' while the variance is ' + prec.format(var_value) +'.\\nFor the data avaliable, the polynomial approximation had a r square value of '+prec.format(r2)+'.'\n if self.dimensions > 1:\n sobol_indices_array = np.argsort(self.get_total_sobol_indices())\n final_value = sobol_indices_array[-1] + 1\n statistics_extra = str('\\nAdditionally, the most important parameter--based on the total Sobol indices--was found to be parameter '+str(final_value)+'.')\n statistics = statistics + statistics_extra\n added = added + statistics\n if(tosay is True):\n added = added.replace('e-','e minus')\n added = added.replace('minus0','minus')\n if filename is None:\n filename = 'effective-quadratures-output.txt'\n output_file = open(filename, 'w')\n output_file.write(added)\n output_file.close()", "def summary(self, verbosity=0, file=None):\n\n if type(file) == type(\"\"):\n f=open(file, \"w\")\n else: f= sys.stdout\n\n f.write(_(\"The number of vertices is %d. \") % self.number_of_vertices)\n f.write(_(\"The largest %s is %d.\\n\") % (self.degree_type, self.max_deg))\n f.write(\"\\nDegree distribution:\\n\")\n f.write(_(\" 0:%7.4f%%\\n\") % \\\n (self.n_0/self.number_of_vertices*100))\n\n column=1\n for degree, probability in self.dd:\n f.write(\" %5d:%7.4f%%\" % (degree, probability*100))\n if column == 5:\n f.write(\"\\n\")\n column=1\n else: column += 1\n f.write(\"\\n\")", "def readsummary(self,whichstream_):\n res = __library__.MSK_XX_readsummary(self.__nativep,whichstream_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def printSolution(self):\n print \"----- Solution -----\"\n for feature in self.features:\n print \"Name = \" + feature.name + \" Value = \" + str(feature.value)", "def readsummary(self,whichstream_): # 3\n if not isinstance(whichstream_,streamtype): raise TypeError(\"Argument whichstream has wrong type\")\n res = self.__obj.readsummary(whichstream_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def print_solution(solution_list) -> 'Human Readable Solution':\n\tsize = len(solution_list[0][0])\n\ttry:\n\t\tprint('Starting Node'.center(20, ' '))\n\t\tprint(''.center(20, '-'))\n\t\tfor node in solution_list:\n\t\t\t\tfor i in range(size):\n\t\t\t\t\tprint(str(node[i]).center(20, ' '))\n\t\t\t\tprint(''.center(20, '-'))\n\t\tprint('Goal Node'.center(20, ' '))\n\texcept Exception as error_msg:\n\t\tprint(\"No solution found!\")", "def print_summary(self):\n #exec(\"print(storyline.{}_clause+', '+storyline.{}_clause.lower()+', '+storyline.{}_clause.lower())\".format(\"A\", \"B\", \"C\"))\n #exec(\"print(self.{}_clause+', '+self.{}_clause.lower()+', '+self.{}_clause.lower())\".format(\"A\", \"B\", \"C\"))\n lwr = \".lower()\"\n exec(\"print(\"+str(3*(\"self.{}_clause{}+',', \")).format(\"A\",\"\",\"B\",lwr,\"C\",lwr)+\"'\\b\\b')\")", "def summary_str(self):\n if not self.results:\n return self.summary.empty() or ''\n elif self.state == Ok:\n return self.summary.ok(self.results) or ''\n return self.summary.problem(self.results) or ''", "def solve(self):\n print(\"Problem %s Answer: %s\" % (self.number, self.solution()))", "def _print_summary(case, summary):\n for dof, data in summary.items():\n b4b = data[\"Bit for Bit\"]\n conf = data[\"Configurations\"]\n stdout = data[\"Std. Out Files\"]\n print(\" \" + case + \" \" + str(dof))\n print(\" --------------------\")\n print(\" Bit for bit matches : \" + str(b4b[0]) + \" of \" + str(b4b[1]))\n print(\" Configuration matches : \" + str(conf[0]) + \" of \" + str(conf[1]))\n print(\" Std. Out files parsed : \" + str(stdout))\n print(\"\")", "def optimizersummary(self,whichstream_):\n res = __library__.MSK_XX_optimizersummary(self.__nativep,whichstream_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def print_result(solution, states_expanded, max_fringe):\n if solution is None: \n print(\"No solution found.\")\n else: \n print(\"Solution has {} actions.\".format(len(solution)))\n print(\"Total states expanded: {}.\".format(states_expanded))\n print(\"Max fringe size: {}.\".format(max_fringe))", "def print_summary(self):\n #outcomes = self.get_outcomes()\n #passes = 'Passes: %i' % sum(1 for outcome in outcomes if outcome == Result.PASS)\n #untested = 'Untested: %i' % sum(1 for outcome in outcomes if outcome == Result.UNTESTED)\n #errors = 'Errors: %i' % sum(1 for outcome in outcomes if outcome == Result.ERROR)\n #fails = 'Fails: %i' % sum(1 for outcome in outcomes if outcome == Result.FAIL)\n print('')\n print ('Passes: %i' % self.get_pass_count())\n print ('Fails: %i' % self.get_fail_count())\n print ('Errors: %i' % self.get_error_count())\n print ('Untested: %i' % self.get_untested_count())\n print ('Skipped: %i' % self.get_skipped_count())", "def summary(self):\n return \"{0:}: {1:} -> {2:}\".format(self.name, self.var, self.out)", "def print_solution(self, solution):\n if self._background is None:\n bg_weights = solution[0 : self.nprimaries]\n mod_weights = solution[self.nprimaries : self.nprimaries * 2]\n else:\n bg_weights = self._background\n mod_weights = solution\n\n print(f\"Background spectrum: {self.w2s(bg_weights)}\")\n print(f\"Modulation spectrum: {self.w2s(mod_weights)}\")", "def summary(self, summary: str):\n return self.swag({\n 'summary': summary\n })", "def PrintSolution(self):\n sol = \"\"\n charMap = {\n Magnets.EMPTY: '.',\n Magnets.PLUS: '+',\n Magnets.MINUS: '-',\n }\n for row in self.Solution():\n for space in row:\n sol = sol + charMap.get(space, '?')\n sol = sol + '\\n'\n return sol", "def _explain(self, solution):\n all_true = self.implied_true.union(self.answered_true).union(self.current_subgraph)\n\n # recalculate all data\n self.data_graph = self._initialise_data()\n\n # get the nodes that were not used\n unused = all_true.symmetric_difference(self.data_graph.nodes)\n\n # remove the unused nodes from graph\n self.data_graph.remove_nodes(unused)\n\n # print the remaining graph:\n print(\"Řešení bylo odvozeno od následujícího průchodu grafem: \")\n self.data_graph.graphviz_draw(\"Solution to:\", solution.name)\n self.data_graph.print_nice()" ]

[ "0.8853265", "0.8302041", "0.81137526", "0.6555255", "0.6380672", "0.6087827", "0.60587895", "0.60307497", "0.6008222", "0.5991637", "0.59595245", "0.5958175", "0.59552217", "0.5938021", "0.5918894", "0.58800006", "0.58726686", "0.58662325", "0.5847555", "0.5815037", "0.5740796", "0.57334065", "0.5724244", "0.56847745", "0.5674444", "0.56494474", "0.56285053", "0.5620074", "0.56053865", "0.5592794" ]

[ "def solutionsummary(self,whichstream_): # 3\n if not isinstance(whichstream_,streamtype): raise TypeError(\"Argument whichstream has wrong type\")\n res = self.__obj.solutionsummary(whichstream_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def onesolutionsummary(self,whichstream_,whichsol_): # 3\n if not isinstance(whichstream_,streamtype): raise TypeError(\"Argument whichstream has wrong type\")\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res = self.__obj.onesolutionsummary(whichstream_,whichsol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def onesolutionsummary(self,whichstream_,whichsol_):\n res = __library__.MSK_XX_onesolutionsummary(self.__nativep,whichstream_,whichsol_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def printSummary(self):\n pass", "def optimizersummary(self,whichstream_): # 3\n if not isinstance(whichstream_,streamtype): raise TypeError(\"Argument whichstream has wrong type\")\n res = self.__obj.optimizersummary(whichstream_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def readsummary(self,whichstream_): # 3\n if not isinstance(whichstream_,streamtype): raise TypeError(\"Argument whichstream has wrong type\")\n res = self.__obj.readsummary(whichstream_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def print_summary(self):\n #outcomes = self.get_outcomes()\n #passes = 'Passes: %i' % sum(1 for outcome in outcomes if outcome == Result.PASS)\n #untested = 'Untested: %i' % sum(1 for outcome in outcomes if outcome == Result.UNTESTED)\n #errors = 'Errors: %i' % sum(1 for outcome in outcomes if outcome == Result.ERROR)\n #fails = 'Fails: %i' % sum(1 for outcome in outcomes if outcome == Result.FAIL)\n print('')\n print ('Passes: %i' % self.get_pass_count())\n print ('Fails: %i' % self.get_fail_count())\n print ('Errors: %i' % self.get_error_count())\n print ('Untested: %i' % self.get_untested_count())\n print ('Skipped: %i' % self.get_skipped_count())", "def readsummary(self,whichstream_):\n res = __library__.MSK_XX_readsummary(self.__nativep,whichstream_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def summary(self) -> str:\n return pulumi.get(self, \"summary\")", "def show_summary(self, lang):\n return self.summary % self.vars", "def summary(self) -> str:\n pass", "def summary(self):\n if _have_ipython:\n IPython.display.display(IPython.display.HTML(self._repr_html_()))\n else:\n print(self)", "def print_summary(self):\n #exec(\"print(storyline.{}_clause+', '+storyline.{}_clause.lower()+', '+storyline.{}_clause.lower())\".format(\"A\", \"B\", \"C\"))\n #exec(\"print(self.{}_clause+', '+self.{}_clause.lower()+', '+self.{}_clause.lower())\".format(\"A\", \"B\", \"C\"))\n lwr = \".lower()\"\n exec(\"print(\"+str(3*(\"self.{}_clause{}+',', \")).format(\"A\",\"\",\"B\",lwr,\"C\",lwr)+\"'\\b\\b')\")", "def optimizersummary(self,whichstream_):\n res = __library__.MSK_XX_optimizersummary(self.__nativep,whichstream_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def summary(self, summary: str):\n return self.swag({\n 'summary': summary\n })", "def print_summary(self):\n self.model.summary()", "def summary(self, verbosity=0, file=None):\n\n if type(file) == type(\"\"):\n f=open(file, \"w\")\n else: f= sys.stdout\n\n f.write(_(\"The number of vertices is %d. \") % self.number_of_vertices)\n f.write(_(\"The largest %s is %d.\\n\") % (self.degree_type, self.max_deg))\n f.write(\"\\nDegree distribution:\\n\")\n f.write(_(\" 0:%7.4f%%\\n\") % \\\n (self.n_0/self.number_of_vertices*100))\n\n column=1\n for degree, probability in self.dd:\n f.write(\" %5d:%7.4f%%\" % (degree, probability*100))\n if column == 5:\n f.write(\"\\n\")\n column=1\n else: column += 1\n f.write(\"\\n\")", "def summary_str(self):\n if not self.results:\n return self.summary.empty() or ''\n elif self.state == Ok:\n return self.summary.ok(self.results) or ''\n return self.summary.problem(self.results) or ''", "def show_summary(self, out = None, debug = False):\n if (out is None) : out = sys.stdout\n results = self.matching_candidates\n if (len(results) > 0):\n self.atom_props.show_properties(identity = \"HOH\", out = out)\n if (self.nuc_phosphate_site):\n print(\" appears to be nucleotide coordination site\", file=out)\n if (self.no_final):\n print(\" Found potential ion%s outside of specified set:\" % \\\n (\"s\" if len(results) > 1 else \"\"), file=out)\n if (self.final_choice is not None):\n # We have one result that we are reasonably certain of\n elem_params, score = results[0]\n if elem_params.element not in mmtbx.ions.HALIDES:\n self.atom_props.show_ion_results(\n identity = str(self.final_choice),\n out = out,\n valence_used = self.valence_used,\n confirmed = True)\n else:\n print(\" Probable anion:\", str(elem_params), file=out)\n print(\"\", file=out)\n elif (len(results) > 1):\n # We have a couple possible identities for the atom\n below_cutoff = [ elem_params for elem_params, score in results\n if score < self.ambiguous_valence_cutoff]\n if len(below_cutoff) == 1:\n elem_params = below_cutoff[0]\n print(\" ambigous results, best valence from %s\" % \\\n str(elem_params), file=out)\n self.atom_props.show_ion_results(\n identity = str(elem_params),\n out = out,\n valence_used = True)\n print(\"\", file=out)\n else:\n ions = [str(i[0]) for i in sorted(results, key = lambda x: x[1])]\n print(\" ambiguous results, could be %s\" % \", \".join(ions), file=out)\n for elem_params, score in results :\n self.atom_props.show_ion_results(identity = str(elem_params),\n out = out)\n print(\"\", file=out)\n else:\n if (self.atom_type != WATER) or (self.nuc_phosphate_site):\n self.atom_props.show_properties(identity = \"HOH\", out = out)\n if (self.nuc_phosphate_site):\n print(\" appears to be nucleotide coordination site\", file=out)\n # try anions now\n if (self.looks_like_halide):\n print(\" Probable cation: %s\" % str(self.final_choice), file=out)\n print(\"\", file=out)\n else:\n # atom is definitely not water, but no reasonable candidates found\n # print out why all the metals we tried failed\n if (debug) and (len(self.filtered_candidates) > 0):\n print(\" insufficient data to identify atom\", file=out)\n possible = True\n for params in self.filtered_candidates:\n if (self.atom_props.has_compatible_ligands(str(params))):\n if possible:\n print(\" possible candidates:\", file=out)\n possible = False\n self.atom_props.show_ion_results(identity = str(params),\n out = out)\n else :\n print(\" incompatible ligands for %s\" % str(params), file=out)\n #print >> out, \" rejected as unsuitable:\"\n #for params in self.rejected_candidates:\n # if (self.atom_props.has_compatible_ligands(str(params))):\n # self.atom_props.show_ion_results(identity = str(params),\n # out = out)\n # else :\n # print >> out, \" incompatible ligands for %s\" % str(params)\n print(\"\", file=out)", "def _show_summary(self):\n print 'Summary:'\n print ' Reports downloaded successfully: %d' % self.counts\n print ' Reports not downloaded: %d\\n' % self.failed", "def summary(self, printed=True):\n raise NotImplementedError", "def _print_summary(case, summary):\n for dof, data in summary.items():\n b4b = data[\"Bit for Bit\"]\n conf = data[\"Configurations\"]\n stdout = data[\"Std. Out Files\"]\n print(\" \" + case + \" \" + str(dof))\n print(\" --------------------\")\n print(\" Bit for bit matches : \" + str(b4b[0]) + \" of \" + str(b4b[1]))\n print(\" Configuration matches : \" + str(conf[0]) + \" of \" + str(conf[1]))\n print(\" Std. Out files parsed : \" + str(stdout))\n print(\"\")", "def summary(self):\n return \"{0:}: {1:} -> {2:}\".format(self.name, self.var, self.out)", "def printSolution(self):\n print \"----- Solution -----\"\n for feature in self.features:\n print \"Name = \" + feature.name + \" Value = \" + str(feature.value)", "def summary_string(self) -> str:", "def summary(self):\n return self._summary", "def summary(self):\n return self._summary", "def summary(self):\n return self._summary", "def summary(self):\n return self._summary", "def summary(self):\n return self._summary" ]

[ "0.8490436", "0.7806848", "0.74153596", "0.6858063", "0.67407346", "0.65829104", "0.64990044", "0.6447631", "0.6409437", "0.63984436", "0.63822955", "0.6373469", "0.63680404", "0.6366793", "0.6324539", "0.63099676", "0.6302245", "0.6295538", "0.62811995", "0.625854", "0.62493235", "0.6243161", "0.62297", "0.62239426", "0.62150043", "0.6201176", "0.6201176", "0.6201176", "0.6201176", "0.6201176" ]

[ "def updatesolutioninfo(self,whichsol_): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res = self.__obj.updatesolutioninfo(whichsol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def getsolutioninfo(self,whichsol_): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res,resargs = self.__obj.getsolutioninfo(whichsol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _pobj_return_value,_pviolcon_return_value,_pviolvar_return_value,_pviolbarvar_return_value,_pviolcone_return_value,_pviolitg_return_value,_dobj_return_value,_dviolcon_return_value,_dviolvar_return_value,_dviolbarvar_return_value,_dviolcone_return_value = resargs\n return _pobj_return_value,_pviolcon_return_value,_pviolvar_return_value,_pviolbarvar_return_value,_pviolcone_return_value,_pviolitg_return_value,_dobj_return_value,_dviolcon_return_value,_dviolvar_return_value,_dviolbarvar_return_value,_dviolcone_return_value", "def notify_solution(self, sol):\n pass # pragma: no cover", "def update_current_sol_and_cost(self,sol=None):\n\n # Update current sol if argument given\n if sol is not None:\n self.current_sol = sol\n \n # Update residual and cost\n try:\n self.residual = self.sketch_reweighted - self.sketch_of_solution(self.current_sol)\n self.current_sol_cost = np.linalg.norm(self.residual)\n except AttributeError: # We are here if self.current_sol does not exist yet\n self.current_sol, self.residual = None, self.sketch_reweighted\n self.current_sol_cost = np.inf", "def update_info(self):\n self.execution_status_widget.update()\n self.execution_info_widget.update()\n self.cluster_widget.update() # update the cluster info even if it is not being displayed\n self.details.original_widget.update()", "def getsolutioninfo(self,whichsol_):\n pobj_ = ctypes.c_double()\n pviolcon_ = ctypes.c_double()\n pviolvar_ = ctypes.c_double()\n pviolbarvar_ = ctypes.c_double()\n pviolcone_ = ctypes.c_double()\n pviolitg_ = ctypes.c_double()\n dobj_ = ctypes.c_double()\n dviolcon_ = ctypes.c_double()\n dviolvar_ = ctypes.c_double()\n dviolbarvar_ = ctypes.c_double()\n dviolcone_ = ctypes.c_double()\n res = __library__.MSK_XX_getsolutioninfo(self.__nativep,whichsol_,ctypes.byref(pobj_),ctypes.byref(pviolcon_),ctypes.byref(pviolvar_),ctypes.byref(pviolbarvar_),ctypes.byref(pviolcone_),ctypes.byref(pviolitg_),ctypes.byref(dobj_),ctypes.byref(dviolcon_),ctypes.byref(dviolvar_),ctypes.byref(dviolbarvar_),ctypes.byref(dviolcone_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n pobj_ = pobj_.value\n _pobj_return_value = pobj_\n pviolcon_ = pviolcon_.value\n _pviolcon_return_value = pviolcon_\n pviolvar_ = pviolvar_.value\n _pviolvar_return_value = pviolvar_\n pviolbarvar_ = pviolbarvar_.value\n _pviolbarvar_return_value = pviolbarvar_\n pviolcone_ = pviolcone_.value\n _pviolcone_return_value = pviolcone_\n pviolitg_ = pviolitg_.value\n _pviolitg_return_value = pviolitg_\n dobj_ = dobj_.value\n _dobj_return_value = dobj_\n dviolcon_ = dviolcon_.value\n _dviolcon_return_value = dviolcon_\n dviolvar_ = dviolvar_.value\n _dviolvar_return_value = dviolvar_\n dviolbarvar_ = dviolbarvar_.value\n _dviolbarvar_return_value = dviolbarvar_\n dviolcone_ = dviolcone_.value\n _dviolcone_return_value = dviolcone_\n return (_pobj_return_value,_pviolcon_return_value,_pviolvar_return_value,_pviolbarvar_return_value,_pviolcone_return_value,_pviolitg_return_value,_dobj_return_value,_dviolcon_return_value,_dviolvar_return_value,_dviolbarvar_return_value,_dviolcone_return_value)", "def update_info(self):\n self.m_canvas.master.m_informations_displayer.set_operations(\n self.m_current_index\n )\n self.m_canvas.master.m_informations_displayer.set_time(\n self.m_history[self.m_current_index].m_passed_time\n )", "def update_goal_info(self):\n self._goal_info_cache = self._get_goal_info()", "def change_info(self):\n\t\ttry:\n\t\t\tnewName = self.ui.lista_act.currentItem().text()\n\t\t\tnewData = controller.search_data_act(newName)\n\t\t\tnewData = newData[0]\n\t\t\tnombre = newData[1]\n\t\t\tyear = newData[2]\n\t\t\tgenero = newData[3]\n\t\t\timg = newData[4]\n\t\texcept AttributeError as e:\n\t\t\tnombre = \"\"\n\t\t\tgenero = \"\"\n\t\t\tyear = \"\"\n\t\t\timg = \"\"\n\n\t\tself.ui.txt_nombre.setText(nombre)\n\t\tself.ui.txt_year.setText(year)\n\t\tself.ui.txt_genero.setText(genero)\n\t\tself.ui.img.setPixmap(QtGui.QPixmap(img))", "def notify_solution(self, sol):\n self._solutions.append(sol)", "def OnInfoEdit(self,event):\r\n selections = self.list.GetSelections()\r\n if not selections: return bell()\r\n item = self.items[selections[0]]\r\n if self.gInfoBox.IsModified():\r\n self.data.setInfo(item,self.gInfoBox.GetValue())", "def updateQuestionsSolved(self):\r\n self.questionsCompletedLabel.setText(\"Questions completed: {}\".format(save.getProblemsSolved()))", "def update(self, solution):\n self.heuristic_path = [i for i in self.initial_path if i in solution]\n self.heuristic_cost = self.pathCost(self.heuristic_path)", "def updateFileInfo(self, data, pid):\n self.db.updateLinkInfo(data)\n self.evm.dispatchEvent(\"packageUpdated\", pid)", "def update_skill_info_box(self, skill_string):\r\n skill = self.__skills[skill_string]\r\n\r\n # When a skill is at lvl 0, information about it shown as if it\r\n # was level 1. Used below.\r\n if skill.skill_level == 0:\r\n display_skill_level = \"1\"\r\n else:\r\n display_skill_level = str(skill.skill_level)\r\n\r\n self.__skill_info_name.configure(\r\n text=skill.name + \" Lvl. \" + display_skill_level)\r\n\r\n # If there is no prerequisite skill.\r\n if skill.prereq_skill_name != \"-\":\r\n self.__skill_info_prerequisite.configure(\r\n text=(skill.prereq_skill_name + \" Lvl.\" +\r\n str(skill.prereq_skill_lvl)))\r\n # If there is a prequisite skill.\r\n else:\r\n self.__skill_info_prerequisite.configure(text=\"None\")\r\n\r\n self.__skill_info_points_to_up.configure(\r\n text=str(skill.points_to_up))\r\n\r\n if display_skill_level == \"1\":\r\n self.__skill_info_level_requirements.configure(\r\n text=skill.lvl_req[0])\r\n else:\r\n self.__skill_info_level_requirements.configure(\r\n text=skill.lvl_req[skill.skill_level-1])\r\n\r\n if skill.attack[0] == \"-\" or display_skill_level == \"1\":\r\n self.__skill_info_attack.configure(text=skill.attack[0])\r\n else:\r\n self.__skill_info_attack.configure(\r\n text=skill.attack[skill.skill_level - 1])\r\n\r\n # If nothing changes in the description with levels.\r\n if len(skill.description) == 1:\r\n self.__skill_info_description.configure(\r\n text=skill.description[0])\r\n # If some value changes in the description with levels.\r\n elif display_skill_level == \"1\":\r\n # 1 value changes\r\n if len(skill.description) == 6:\r\n self.__skill_info_description.configure(\r\n text=skill.description[0].format(\r\n skill.description[1]))\r\n # 2 values change\r\n if len(skill.description) == 11:\r\n self.__skill_info_description.configure(\r\n text=skill.description[0].format(\r\n skill.description[1], skill.description[6]))\r\n else:\r\n # 1 value changes\r\n if len(skill.description) == 6:\r\n self.__skill_info_description.configure(\r\n text=skill.description[0].format(\r\n skill.description[skill.skill_level]))\r\n # 2 values change\r\n if len(skill.description) == 11:\r\n self.__skill_info_description.configure(\r\n text=skill.description[0].format(\r\n skill.description[skill.skill_level],\r\n skill.description[skill.skill_level + 5]))", "def exchange_solution(self):\n for ss in self.solvers:\n ss.register_solution()\n\n if self.has_amr:\n self.tioga.data_update_amr()\n else:\n raise NotImplementedError(\"Invalid overset exchange\")\n\n for ss in self.solvers:\n ss.update_solution()", "def update_info(self, dbcurs, fields):\n if self.objtype is not None and len(self.objtype) != 0:\n fields.append(\"objtype=\" + dbcurs.connection.escape(self.objtype))\n if self.usable:\n fields.append(\"usable=1\")\n else:\n fields.append(\"usable=0\")\n fields.append(\"apsize={:.4g}\".format(self.apsize))\n fields.append(\"irapsize={:.4g}\".format(self.irapsize))\n if self.apstd is not None:\n fields.append(\"apstd={:.4e}\".format(self.apstd))\n if self.irapstd is not None:\n fields.append(\"irapstd={:.4e}\".format(self.irapstd))\n if self.basedon is not None:\n fields.append(\"basedon={:d}\".format(self.basedon))\n if self.irbasedon is not None:\n fields.append(\"irbasedon={:d}\".format(self.irbasedon))\n fields.append(\"variability={:.4f}\".format(self.variability))", "def updateAllGUIValues(self):\n if self.myGalaxy.shipSelected == self:\n d = {'shipISP':self.currentISP,\n 'shipStrength':self.strength,\n 'shipAccel':self.accel,\n 'shipRotation':self.rotation,\n 'shipPower':self.currentPower,\n 'shipBattery':self.currentBattery,\n 'maxAssault':self.maxAssault}\n for position in self.positions:\n myQuad = self.quads[position]\n d[position+'Shields'] = myQuad.currentSP\n d[position+'Armor'] = myQuad.currentAP\n d[position+'Comps'] = myQuad.currentComps\n self.myGalaxy.shipInfo.updateAttributes(d)", "def update_gui(self):\n for where, updates in self.gui_updates.items():\n self.window[where].update(**updates)\n self.gui_updates = {}", "async def update_info_data(_: datetime | None = None) -> None:\n\n try:\n (\n hass.data[DATA_INFO],\n hass.data[DATA_HOST_INFO],\n hass.data[DATA_STORE],\n hass.data[DATA_CORE_INFO],\n hass.data[DATA_SUPERVISOR_INFO],\n hass.data[DATA_OS_INFO],\n ) = await asyncio.gather(\n hassio.get_info(),\n hassio.get_host_info(),\n hassio.get_store(),\n hassio.get_core_info(),\n hassio.get_supervisor_info(),\n hassio.get_os_info(),\n )\n\n except HassioAPIError as err:\n _LOGGER.warning(\"Can't read Supervisor data: %s\", err)\n\n async_call_later(\n hass,\n HASSIO_UPDATE_INTERVAL,\n HassJob(update_info_data, cancel_on_shutdown=True),\n )", "def update(self):\r\n\r\n self.cmbAssembly.handler_block(self._lst_handler_id[0])\r\n self.cmbAssembly.set_active(self._model.assembly_id)\r\n self.cmbAssembly.handler_unblock(self._lst_handler_id[0])\r\n\r\n self.cmbDistribution.handler_block(self._lst_handler_id[1])\r\n self.cmbDistribution.set_active(self._model.distribution_id)\r\n self.cmbDistribution.handler_unblock(self._lst_handler_id[1])\r\n\r\n self.cmbConfType.handler_block(self._lst_handler_id[2])\r\n self.cmbConfType.set_active(self._model.confidence_type)\r\n self.cmbConfType.handler_unblock(self._lst_handler_id[2])\r\n\r\n self.cmbConfMethod.handler_block(self._lst_handler_id[3])\r\n self.cmbConfMethod.set_active(self._model.confidence_method)\r\n self.cmbConfMethod.handler_unblock(self._lst_handler_id[3])\r\n\r\n self.cmbFitMethod.handler_block(self._lst_handler_id[4])\r\n self.cmbFitMethod.set_active(self._model.fit_method)\r\n self.cmbFitMethod.handler_unblock(self._lst_handler_id[4])\r\n\r\n self.txtDescription.handler_block(self._lst_handler_id[5])\r\n self.txtDescription.set_text(self._model.description)\r\n self.txtDescription.handler_unblock(self._lst_handler_id[5])\r\n\r\n self.txtConfidence.handler_block(self._lst_handler_id[6])\r\n if self._model.confidence < 1.0:\r\n Configurationidence = self._model.confidence * 100.0\r\n else:\r\n Configurationidence = self._model.confidence\r\n self.txtConfidence.set_text(str(Configurationidence))\r\n self.txtConfidence.handler_unblock(self._lst_handler_id[6])\r\n\r\n self.txtStartTime.handler_block(self._lst_handler_id[7])\r\n self.txtStartTime.set_text(str(self._model.start_time))\r\n self.txtStartTime.handler_unblock(self._lst_handler_id[7])\r\n\r\n self.txtEndTime.handler_block(self._lst_handler_id[8])\r\n self.txtEndTime.set_text(str(self._model.rel_time))\r\n self.txtEndTime.handler_unblock(self._lst_handler_id[8])\r\n\r\n self.txtRelPoints.handler_block(self._lst_handler_id[9])\r\n self.txtRelPoints.set_text(str(self._model.n_rel_points))\r\n self.txtRelPoints.handler_unblock(self._lst_handler_id[9])\r\n\r\n self.txtStartDate.handler_block(self._lst_handler_id[10])\r\n _start_date = Utilities.ordinal_to_date(self._model.start_date)\r\n self.txtStartDate.set_text(str(_start_date))\r\n self.txtStartDate.handler_unblock(self._lst_handler_id[10])\r\n\r\n self.txtEndDate.handler_block(self._lst_handler_id[11])\r\n _end_date = Utilities.ordinal_to_date(self._model.end_date)\r\n self.txtEndDate.set_text(str(_end_date))\r\n self.txtEndDate.handler_unblock(self._lst_handler_id[11])\r\n\r\n return False", "def update(self):\n\n SolidSolver.update(self)\n\n self.__nextStep()", "def setupInfo(self):\n\n\t\tself.menu_window.setName(self.name)\n\t\tself.score_window.user_name_current.configure(text = self.name)\n\t\tself.score_window.user_score_current.configure(text = self.highscore)\n\n\n\t\tfield = \"id\"\n\t\thigh_scores = []\n\t\thigh_names = []\n\t\tID = True\n\t\tfor i in self.high_scores:\n\t\t\tfor j in i:\n\t\t\t\ttry:\n\t\t\t\t\tif field == \"id\":\n\t\t\t\t\t\tid_ = int(j)\n\t\t\t\t\t\tfield = \"name\"\n\t\t\t\t\telif field == \"name\":\n\t\t\t\t\t\thigh_names.append(str(j))\n\t\t\t\t\t\tfield = \"score\"\n\t\t\t\t\telif field == \"score\":\n\t\t\t\t\t\thigh_scores.append(int(j))\n\t\t\t\t\t\tfield = \"id\"\n\t\t\t\texcept:\n\t\t\t\t\tID = False\n\t\t\t\t\tprint \"Error setupInfo (gamemenu)\"\n\t\t\t\t\thigh_names.append(j)\n\t\tfor k in range(0, len(high_names)):\n\t\t\tself.score_window.user_name[k].configure(text = \"#\"+str(k+1)+\" \" +high_names[k])\n\t\t\tself.score_window.user_score[k].configure(text = high_scores[k])", "def update_info(self):\n # Return if it is locked\n if self.lock:\n return\n # Hide again if it was shown due to an error message\n if self.was_hidden:\n self.was_hidden = False\n self.toggle()\n # Left side\n try:\n # Directory if library is focused\n if self.vimiv.library.treeview.is_focus():\n self.left_label.set_text(os.getcwd())\n # Position, name and thumbnail size in thumb mode\n elif self.vimiv.thumbnail.toggled:\n pos = self.vimiv.get_pos()\n name = os.path.basename(self.vimiv.paths[pos])\n message = \"{0}/{1} {2} {3}\". \\\n format(pos + 1, len(self.vimiv.paths),\n name, self.vimiv.thumbnail.size)\n self.left_label.set_text(message)\n # Image info in image mode\n else:\n name = os.path.basename(self.vimiv.paths[self.vimiv.index])\n message = \"{0}/{1} {2} [{3:.0f}%]\". \\\n format(self.vimiv.index + 1, len(self.vimiv.paths), name,\n self.vimiv.image.zoom_percent * 100)\n self.left_label.set_text(message)\n except:\n self.left_label.set_text(\"No open images\")\n # Center\n if not (self.vimiv.thumbnail.toggled or\n self.vimiv.library.treeview.is_focus()) and self.vimiv.paths:\n mark = \"[*]\" if self.vimiv.paths[self.vimiv.index] \\\n in self.vimiv.mark.marked else \"\"\n else:\n mark = \"\"\n if self.vimiv.slideshow.running:\n slideshow = \"[slideshow - {0:.1f}s]\".format(\n self.vimiv.slideshow.delay)\n else:\n slideshow = \"\"\n message = \"{0} {1}\".format(mark, slideshow)\n self.center_label.set_text(message)\n # Right side\n mode = self.get_mode()\n message = \"{0:15} {1:4}\".format(mode, self.vimiv.keyhandler.num_str)\n self.right_label.set_markup(message)\n # Window title\n try:\n name = os.path.basename(self.vimiv.paths[self.vimiv.index])\n self.vimiv.set_title(\"vimiv - \" + name)\n except:\n self.vimiv.set_title(\"vimiv\")\n # Size of statusbar for resizing image\n self.size = self.vimiv.statusbar.bar.get_allocated_height()", "def update_information_box_text(self):\n # Get the minefield options from the model.\n options = self.controller.get_minefield_options()\n\n # Default values.\n message = \"Unrecognized difficulty.\"\n length = 10\n height = 10\n density = 10\n option = Option(length, height, density)\n\n # Change default values based on button hovering.\n if self.selected is self.buttons[0]:\n message = \"Small field and easy mine density.\"\n option = options[\"easy\"]\n length = option.l\n height = option.h\n density = option.d\n elif self.selected is self.buttons[1]:\n message = \"Increased field area and mine density.\"\n option = options[\"medium\"]\n length = option.l\n height = option.h\n density = option.d\n elif self.selected is self.buttons[2]:\n message = \"Challenging field and mine density.\"\n option = options[\"hard\"]\n length = option.l\n height = option.h\n density = option.d\n elif (self.selected is self.buttons[3] or\n self.selected.get_type() is UIType.NumberField):\n message = \"Customized settings.\"\n option = options[\"custom\"]\n length = option.l\n height = option.h\n density = option.d\n\n # Set values.\n self.info_message_textbox.set_text(message)\n self.numberfields[0].set_value(length)\n self.numberfields[1].set_value(height)\n mines = self.controller.calculate_mines(option)\n plural = \"\" if mines == 1 else \"s\"\n num_mines_msg = \"% ({} mine{})\".format(mines, plural)\n self.numberfields[2].set_value(density)\n self.numberfields[2].set_postfix(num_mines_msg)", "def add_solution(self, solution):\n if self.check_solution(solution):\n self._solution = solution\n self.solution_status = 'OK'\n else:\n self._solution = None\n self.solution_status = 'X'", "def update(self, info):\n self.is_active = info.p.active\n self.rev_info = info.rev_info", "def update_info(self, ego_pos, ego_spd):\n self.current_transform = ego_pos\n self.current_speed = ego_spd\n if self.dynamic:\n self.dynamic_pid()", "def update_data():\n pass", "def putsolutionyi(self,i_,whichsol_,y_): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res = self.__obj.putsolutionyi(i_,whichsol_,y_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)" ]

[ "0.8501529", "0.64527994", "0.61899984", "0.6184468", "0.61392975", "0.61199677", "0.58779514", "0.58288413", "0.5672516", "0.55942315", "0.5495648", "0.5467258", "0.54266113", "0.54105836", "0.53628093", "0.5335758", "0.53230315", "0.5302198", "0.5299515", "0.52725923", "0.5265733", "0.5257701", "0.52566874", "0.5234265", "0.52223647", "0.51857615", "0.5184954", "0.51469606", "0.5136978", "0.5115384" ]

[ "def optimizersummary(self,whichstream_): # 3\n if not isinstance(whichstream_,streamtype): raise TypeError(\"Argument whichstream has wrong type\")\n res = self.__obj.optimizersummary(whichstream_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def print_stats(self):\n if self.n_iter % 5 != 0:\n return\n\n s_iter = \"%7i - \" % self.n_iter\n s_stat = ' || '.join([\n '{}: {:7.4f}'.format(k, np.mean(v)) for k, v in self.stats.items()\n if type(v) is list and len(v) > 0\n ])\n for k in self.stats.keys():\n if type(self.stats[k]) is list:\n del self.stats[k][:]\n\n # transformer learning rate\n # learning rates\n s_lr = \" - \"\n for k, v in self.optimizers.items():\n s_lr = s_lr + (\" - %s LR: \" % k) + \" / \".join(\n \"{:.4e}\".format(group['lr']) for group in v.param_groups)\n\n # processing speed\n new_time = time.time()\n diff = new_time - self.last_time\n s_speed = \"{:7.2f} sent/s - {:8.2f} words/s - \".format(\n self.stats['processed_s'] * 1.0 / diff,\n self.stats['processed_w'] * 1.0 / diff\n )\n self.stats['processed_s'] = 0\n self.stats['processed_w'] = 0\n self.last_time = new_time\n\n # log speed + stats + learning rate\n logger.info(s_iter + s_speed + s_stat + s_lr)", "def printSummary(self):\n pass", "def solutionsummary(self,whichstream_):\n res = __library__.MSK_XX_solutionsummary(self.__nativep,whichstream_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def solutionsummary(self,whichstream_): # 3\n if not isinstance(whichstream_,streamtype): raise TypeError(\"Argument whichstream has wrong type\")\n res = self.__obj.solutionsummary(whichstream_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def summarize(self):\n info(\"Running \" + self.title + \" generator\")", "def summary(self) -> str:\n pass", "def summary(self) -> None:\n print(\"Model manager summary:\")\n print(\"Preprocessor:\")\n print(self.preprocessor)\n print(\"Model summary:\")\n self.model.summary()\n print(\"Postprocessor:\")\n print(self.postprocessor)", "def print_summary(self):\n self.network.print_summary()", "def do_overview(self):\n summaries = []\n for name, cmd in self.base.commands.iteritems():\n summaries.append(' %-14s %s\\n' % (name, cmd.get_summary()))\n summaries.sort()\n sys.stdout.write('Usage: %s COMMAND ARGUMENTS...\\n\\n' \\\n 'Available commands:\\n' % (self.base.scriptname, ))\n for line in summaries:\n sys.stdout.write(line)", "def summary(\n self, parameters_to_show=4, show_parameters=True, show_nsamples=True\n ):\n string = \"\"\n if self.path_to_results_file is not None:\n string += \"file: {}\\n\".format(self.path_to_results_file)\n string += \"cls: {}.{}\\n\".format(\n self.__class__.__module__, self.__class__.__name__\n )\n if show_nsamples:\n string += \"nsamples: {}\\n\".format(len(self.samples))\n if show_parameters:\n string += \"parameters: {}\".format(\n self._parameter_summary(\n self.parameters, parameters_to_show=parameters_to_show\n )\n )\n return string", "def summary(self):\n from statsmodels.iolib.summary import Summary\n from statsmodels.iolib.table import SimpleTable\n model = self.model\n title = model.__class__.__name__ + ' Model Results'\n\n dep_variable = 'endog'\n if isinstance(self.model.endog, pd.DataFrame):\n dep_variable = self.model.endog.columns[0]\n elif isinstance(self.model.endog, pd.Series):\n dep_variable = self.model.endog.name\n seasonal_periods = None if self.model.seasonal is None else self.model.seasonal_periods\n lookup = {'add': 'Additive', 'additive': 'Additive',\n 'mul': 'Multiplicative', 'multiplicative': 'Multiplicative', None: 'None'}\n transform = self.params['use_boxcox']\n box_cox_transform = True if transform else False\n box_cox_coeff = transform if isinstance(transform, str) else self.params['lamda']\n if isinstance(box_cox_coeff, float):\n box_cox_coeff = '{:>10.5f}'.format(box_cox_coeff)\n top_left = [('Dep. Variable:', [dep_variable]),\n ('Model:', [model.__class__.__name__]),\n ('Optimized:', [str(np.any(self.optimized))]),\n ('Trend:', [lookup[self.model.trend]]),\n ('Seasonal:', [lookup[self.model.seasonal]]),\n ('Seasonal Periods:', [str(seasonal_periods)]),\n ('Box-Cox:', [str(box_cox_transform)]),\n ('Box-Cox Coeff.:', [str(box_cox_coeff)])]\n\n top_right = [\n ('No. Observations:', [str(len(self.model.endog))]),\n ('SSE', ['{:5.3f}'.format(self.sse)]),\n ('AIC', ['{:5.3f}'.format(self.aic)]),\n ('BIC', ['{:5.3f}'.format(self.bic)]),\n ('AICC', ['{:5.3f}'.format(self.aicc)]),\n ('Date:', None),\n ('Time:', None)]\n\n smry = Summary()\n smry.add_table_2cols(self, gleft=top_left, gright=top_right,\n title=title)\n formatted = self.params_formatted # type: pd.DataFrame\n\n def _fmt(x):\n abs_x = np.abs(x)\n scale = 1\n if abs_x != 0:\n scale = int(np.log10(abs_x))\n if scale > 4 or scale < -3:\n return '{:>20.5g}'.format(x)\n dec = min(7 - scale, 7)\n fmt = '{{:>20.{0}f}}'.format(dec)\n return fmt.format(x)\n\n tab = []\n for _, vals in formatted.iterrows():\n tab.append([_fmt(vals.iloc[1]),\n '{0:>20}'.format(vals.iloc[0]),\n '{0:>20}'.format(str(bool(vals.iloc[2])))])\n params_table = SimpleTable(tab, headers=['coeff', 'code', 'optimized'],\n title=\"\",\n stubs=list(formatted.index))\n\n smry.tables.append(params_table)\n\n return smry", "def summary(self, verbose=False):\n for i, layer in enumerate(self._layers):\n print('%d: %s' % (i, str(layer)))\n if verbose:\n print('weights:', layer.get_weights())\n if layer._use_bias:\n print('bias:', layer._bias)\n print()", "def print_summary(self):\n self.model.summary()", "def print_summary(self, **kwargs):\r\n compile_time = sum([ps.compile_time for ps\r\n in self.profile_stats.values()])\r\n\r\n fct_call = dict([(fn, ps.fct_callcount)\r\n for (fn, ps) in self.profile_stats.items()])\r\n\r\n fct_call_time = dict([(fn, ps.fct_call_time)\r\n for (fn, ps) in self.profile_stats.items()])\r\n\r\n apply_time = {}\r\n for fn, ps in self.profile_stats.items():\r\n for (i, node) in enumerate(fn.maker.fgraph.toposort()):\r\n apply_time[(i, node)] = ps.apply_time[node]\r\n for (i, n), t in apply_time.items():\r\n if t == 0:\r\n print i, n\r\n\r\n apply_cimpl = {}\r\n for fn, ps in self.profile_stats.items():\r\n apply_cimpl.update(ps.apply_cimpl)\r\n\r\n message = self.message\r\n\r\n variable_shape = {}\r\n for fn, ps in self.profile_stats.items():\r\n variable_shape.update(ps.variable_shape)\r\n\r\n other_time = dict(\r\n linker_time=sum(\r\n [ps.linker_time for ps in self.profile_stats.values()]),\r\n optimizer_time=sum(\r\n [ps.optimizer_time for ps in self.profile_stats.values()]))\r\n\r\n self.print_summary_(\"print_summary\",\r\n compile_time, fct_call_time, fct_call,\r\n apply_time, apply_cimpl, message, variable_shape,\r\n self.local_time, other_time,\r\n **kwargs)", "def summarise(self):\n self.summary = az.summary(self.trace, var_names=[\"~chol\"], round_to=2)\n print(self.summary)\n return self.summary", "def summary(self):\n print(self.model.summary())", "def summary_string(self) -> str:", "def summary(self):\r\n print(self.model.summary())", "def summary(self):\n if _have_ipython:\n IPython.display.display(IPython.display.HTML(self._repr_html_()))\n else:\n print(self)", "def summarize(self):\n # go recursively in the model architecture\n summary_str = self.recursive_summarize(self, 0, self.name)\n\n # Sum the model parameters.\n num_total_params = sum([np.prod(p.size()) for p in self.parameters()])\n mod_trainable_params = filter(lambda p: p.requires_grad, self.parameters())\n num_trainable_params = sum([np.prod(p.size()) for p in mod_trainable_params])\n\n summary_str += 'Total Trainable Params: {}\\n'.format(num_trainable_params)\n summary_str += 'Total Non-trainable Params: {}\\n'.format(num_total_params-num_trainable_params) \n summary_str += '='*80 + '\\n'\n\n return summary_str", "def print_summary(self, **kwargs):\n compile_time = sum([ps.compile_time for ps\n in self.profile_stats.values()])\n\n fct_call = dict([(fn, ps.fct_callcount)\n for (fn, ps) in iteritems(self.profile_stats)])\n\n fct_call_time = dict([(fn, ps.fct_call_time)\n for (fn, ps) in iteritems(self.profile_stats)])\n\n apply_time = {}\n for fn, ps in iteritems(self.profile_stats):\n for (i, node) in enumerate(fn.maker.fgraph.toposort()):\n apply_time[(i, node)] = ps.apply_time[node]\n for (i, n), t in iteritems(apply_time):\n if t == 0:\n print(i, n)\n\n apply_cimpl = {}\n for ps in itervalues(self.profile_stats):\n apply_cimpl.update(ps.apply_cimpl)\n\n message = self.message\n\n variable_shape = {}\n for ps in itervalues(self.profile_stats):\n variable_shape.update(ps.variable_shape)\n\n other_time = dict(\n linker_time=sum(\n [ps.linker_time for ps in self.profile_stats.values()]),\n optimizer_time=sum(\n [ps.optimizer_time for ps in self.profile_stats.values()]))\n\n self.print_summary_(\"print_summary\",\n compile_time, fct_call_time, fct_call,\n apply_time, apply_cimpl, message, variable_shape,\n self.local_time, other_time,\n **kwargs)", "def _printSummary(self):\n\t\t### COP OUT\n\t\tif self.params['background'] is True:\n\t\t\tself.stats['count'] += 1\n\t\t\treturn\n\n\t\t### THIS NEEDS TO BECOME MUCH MORE GENERAL, e.g. Peaks\n\t\ttdiff = time.time()-self.stats['startseries']\n\t\tif not self.params['continue'] or tdiff > 0.1:\n\t\t\tcount = self.stats['count']\n\t\t\t#if(count != self.stats['lastcount']):\n\t\t\tsys.stderr.write(\"\\n\\tSUMMARY: \"+self.functionname+\"\\n\")\n\t\t\tself._printLine()\n\t\t\tsys.stderr.write(\"\\tTIME: \\t\"+apDisplay.timeString(tdiff)+\"\\n\")\n\t\t\tself.stats['timesum'] = self.stats['timesum'] + tdiff\n\t\t\tself.stats['timesumsq'] = self.stats['timesumsq'] + (tdiff**2)\n\t\t\ttimesum = self.stats['timesum']\n\t\t\ttimesumsq = self.stats['timesumsq']\n\t\t\tif(count > 1):\n\t\t\t\ttimeavg = float(timesum)/float(count)\n\t\t\t\ttimestdev = math.sqrt(float(count*timesumsq - timesum**2) / float(count*(count-1)))\n\t\t\t\ttimeremain = (float(timeavg)+float(timestdev))*self.stats['seriesleft']\n\t\t\t\tsys.stderr.write(\"\\tAVG TIME: \\t\"+apDisplay.timeString(timeavg,timestdev)+\"\\n\")\n\t\t\t\t#print \"\\t(- TOTAL:\",apDisplay.timeString(timesum),\" -)\"\n\t\t\t\tif(self.stats['seriesleft'] > 0):\n\t\t\t\t\tsys.stderr.write(\"\\t(- REMAINING TIME: \"+apDisplay.timeString(timeremain)+\" for \"\n\t\t\t\t\t\t+str(self.stats['seriesleft'])+\" series -)\\n\")\n\t\t\t#print \"\\tMEM: \",(mem.active()-startmem)/1024,\"M (\",(mem.active()-startmem)/(1024*count),\"M)\"\n\t\t\tself.stats['count'] += 1\n\t\t\tself._printLine()", "def onesolutionsummary(self,whichstream_,whichsol_): # 3\n if not isinstance(whichstream_,streamtype): raise TypeError(\"Argument whichstream has wrong type\")\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res = self.__obj.onesolutionsummary(whichstream_,whichsol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def print_summary(self):\n #exec(\"print(storyline.{}_clause+', '+storyline.{}_clause.lower()+', '+storyline.{}_clause.lower())\".format(\"A\", \"B\", \"C\"))\n #exec(\"print(self.{}_clause+', '+self.{}_clause.lower()+', '+self.{}_clause.lower())\".format(\"A\", \"B\", \"C\"))\n lwr = \".lower()\"\n exec(\"print(\"+str(3*(\"self.{}_clause{}+',', \")).format(\"A\",\"\",\"B\",lwr,\"C\",lwr)+\"'\\b\\b')\")", "def execute_summary(self, step):\n with self.summary_writer.as_default():\n tf.summary.scalar('bias', self.core.fmlayer.b, step=step)\n tf.summary.scalar('regularization_penalty', self.regularization, step=step)\n tf.summary.scalar('loss', self.reduced_loss, step=step)\n tf.summary.scalar('target', self.target, step=step)", "def summary(self):\n return \"{0:}: {1:} -> {2:}\".format(self.name, self.var, self.out)", "def summary(self):\n\n print(\n \"\\nModel trained with dataset %s that has maxlen=%d and charset=%s for %d epochs.\"\n % (self.dataset_name, self.maxlen, self.charset, self.epochs)\n )\n\n print(\n \"noise_std: %.6f, lstm_dim: %d, dec_layers: %d, td_dense_dim: %d, batch_size: %d, codelayer_dim: %d, lr: %.6f.\"\n % (\n self.noise_std,\n self.lstm_dim,\n self.dec_layers,\n self.td_dense_dim,\n self.batch_size,\n self.codelayer_dim,\n self.lr,\n )\n )", "def summary(self):\n raise NotImplementedError", "def summarize_plan(plan: Generator):\n read_cache: list[str] = []\n daq_keys = ['events', 'record', 'use_l3t', 'duration']\n daq_cfg = {k: None for k in daq_keys}\n for msg in plan:\n cmd = msg.command\n if cmd == 'open_run':\n print('{:=^80}'.format(' Open Run '))\n elif cmd == 'close_run':\n print('{:=^80}'.format(' Close Run '))\n elif cmd == 'configure':\n if msg.obj.name == 'daq':\n daq_cfg = {k: msg.kwargs[k] for k in daq_keys}\n print(\n f'Configure DAQ -> ('\n f'events={daq_cfg[\"events\"]}, '\n f'record={daq_cfg[\"record\"]}, '\n f'use_l3t={daq_cfg[\"use_l3t\"]}, '\n f'duration={daq_cfg[\"duration\"]})'\n )\n elif cmd == 'set':\n print('{motor.name} -> {args[0]}'.format(motor=msg.obj,\n args=msg.args))\n elif cmd == 'create':\n read_cache = []\n elif cmd == 'read':\n read_cache.append(msg.obj.name)\n if msg.obj.name == 'daq':\n print(f' Run DAQ for {daq_cfg[\"events\"]} events, '\n f'(record={daq_cfg[\"record\"]})')\n elif cmd == 'save':\n print(f' Read {read_cache}')" ]

[ "0.83530533", "0.6444", "0.6087136", "0.60660774", "0.6059675", "0.6035606", "0.5917163", "0.5904062", "0.58675843", "0.58628947", "0.5861443", "0.58124137", "0.5800309", "0.57916075", "0.57707924", "0.5766543", "0.5747041", "0.5743333", "0.5730653", "0.5721178", "0.572087", "0.5712007", "0.56858075", "0.5667728", "0.5666033", "0.5654308", "0.5651632", "0.56493765", "0.5647251", "0.5630726" ]

[ "def strtoconetype(self,str_): # 3\n res,resargs = self.__obj.strtoconetype(str_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _conetype_return_value = resargs\n _conetype_return_value = conetype(_conetype_return_value)\n return _conetype_return_value", "def _grab_type(self):\r\n c = self._char\r\n if not c in \"0123456789+TgI\" + \"ih\":\r\n c = repr(c)\r\n self._error(f\"unknown type {c}\")\r\n self._get_char()\r\n return c", "def chtype(var):\n return str(type(var)).split('\\'')[1]", "def Type(self, String):\r\n\r\n if (String == \"byte\") or (String == \"sbyte\"):\r\n return 1\r\n elif (String == \"word\") or (String == \"sword\"):\r\n return 2\r\n else:\r\n return 4", "def getType(self):\n if (self.type == 's'):\n #suit type\n type = \"suit\"\n elif (self.type == 'b'):\n #boss type\n type = \"boss\"\n else:\n notify.error(\"Invalid DNA type: \", self.type)\n\n return type", "def py_to_clips_type(ptype):\n\n ctype = None\n if ptype == int:\n ctype = \"INTEGER\"\n elif ptype == float:\n ctype = \"FLOAT\"\n elif ptype == str:\n ctype = \"STRING\"\n elif ptype == bool:\n ctype = \"BOOLEAN\"\n return ctype", "def _encode_type(att_type):\n if att_type is str:\n return 'S'\n elif att_type is float:\n return 'N'\n elif att_type is bytes:\n return 'B'\n else:\n raise TypeError", "def get_type_from_str(type_str: str) -> str:\n query = [x\n for x in PRIMITIVE_TYPES\n if type_str.lower() in PRIMITIVE_TYPES[x]]\n return query[0] if len(query) > 0 else 'None'", "def _convert(string, type, message):\n try:\n return type(string)\n except ValueError as e:\n print(e)\n raise CharmmPSFError('Could not convert %s' % message)", "def get_type_from_str(type_str):\n try:\n # Assume the current language to be C/C++ and make a try.\n return gdb.parse_and_eval(\"(%s *)0\" % type_str).type.target()\n except RuntimeError:\n # If assumption of current language to be C/C++ was wrong, then\n # lookup the type using the API.\n try:\n return gdb.lookup_type(type_str)\n except RuntimeError:\n return None", "def _type(string, has_invisible=True, numparse=True):\n\n if has_invisible and isinstance(string, (str, bytes)):\n string = _strip_ansi(string)\n\n if string is None:\n return type(None)\n elif hasattr(string, \"isoformat\"): # datetime.datetime, date, and time\n return str\n elif _isbool(string):\n return bool\n elif _isint(string) and numparse:\n return int\n elif _isnumber(string) and numparse:\n return float\n elif isinstance(string, bytes):\n return bytes\n else:\n return str", "def data_type_str(self):\n return data_ref_type_str(self.data_type)", "def getTypeCode(self):\n return _libsbml.FbcOr_getTypeCode(self)", "def get_type(value):\n\n # Evaluated string statement for type()\n var_type = str(eval(\"type({})\".format(value)))\n\n # Remove unwanted portions of string\n var_type = var_type.replace(\"<class '\", \"\").split(\"'>\", 1)[0]\n\n # Return processed string\n return var_type", "def gen_type_string(self, node):\n return self._gen_table[node.node_type()](self, node)", "def checkDataType(self,str):\n accepted_vals = [\"HEXA\",\"NEHU\",\"NEDS\",\"NEDU\",\"NDHU\",\"NDDU\"]\n assert str in accepted_vals, \"Error: Data Type not accepted: \" + str\n if (str == 'HEXA') | (str[2] == 'H'):\n self.base = 16\n if str[3] == 'S':\n self.signed = True", "def _get_type_name(self, st_type):\n if st_type <= 2045: return 'str' + str(st_type)\n return self._type_names[st_type]", "def get_type_string(data):\r\n data_type = type(data)\r\n\r\n if data_type in (int, long):\r\n return 'integer'\r\n elif data_type == float:\r\n return 'float'\r\n elif data_type == bool:\r\n return 'boolean'\r\n elif data_type in (list, tuple):\r\n return 'list'\r\n elif data_type == dict:\r\n return 'hash'\r\n elif data is None:\r\n return 'null'\r\n elif isinstance(data, basestring):\r\n return 'string'", "def treetype(self):\n\t\treturn self._treetype", "def clips_to_py_type(ctype):\n\n ptype = None\n if ctype == \"INTEGER\":\n ptype = int\n elif ctype == \"FLOAT\":\n ptype = float\n elif ctype == \"STRING\":\n ptype = str\n elif ctype == \"BOOLEAN\":\n ptype = bool\n return ptype", "def getTypeCode(self):\n return _libsbml.Compartment_getTypeCode(self)", "def typeString(self):\n return Parameter.string_dict[self._field.type]", "def getTypeCode(self):\n return _libsbml.Rule_getTypeCode(self)", "def type_others():\n return \"<string>\"", "def getTypeCode(self):\n return _libsbml.Input_getTypeCode(self)", "def check_type(chain):\n atoms = chain.get_atoms()\n type_chain = \"\"\n list_c = []\n for element in atoms:\n list_c.append(element.get_name())\n if \"CA\" in list_c:\n type_chain = \"protein\"\n else:\n type_chain = \"nucleic_acid\"\n return type_chain", "def deptype(self) -> str:", "def getTypeCode(self):\n return _libsbml.CompartmentType_getTypeCode(self)", "def data_ref_type_str(dref_enum):\n if dref_enum == 0x9000:\n return \"unknown\"\n elif dref_enum == 0x9001:\n return \"integer\"\n elif dref_enum == 0x9002:\n return \"fp\"\n elif dref_enum == 0x9003:\n return \"integer(store)\"\n else:\n return \"INVALID\"", "def from_str(type_string):\n\t\tglobal type_enum\n\t\tif type_string == \"V\":\n\t\t\treturn MoviesType.V\n\t\telif type_string == \"VG\":\n\t\t\treturn MoviesType.VG\n\t\telif type_string == \"TV\":\n\t\t\treturn MoviesType.TV\n\t\telse:\n\t\t\treturn MoviesType.M" ]

[ "0.88242376", "0.6349188", "0.6107515", "0.6085521", "0.60675335", "0.6044869", "0.5956564", "0.5874813", "0.5784901", "0.57551634", "0.5743284", "0.5739826", "0.56517434", "0.5638696", "0.5600427", "0.55771667", "0.5558863", "0.5557807", "0.55551755", "0.5549347", "0.553369", "0.55192786", "0.5499835", "0.5481037", "0.547954", "0.5479043", "0.5471491", "0.5469218", "0.5462197", "0.54615694" ]

[ "def strtosk(self,str_): # 3\n res,resargs = self.__obj.strtosk(str_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _sk_return_value = resargs\n return _sk_return_value", "def _GetKeyString(self):", "def _GetKeyString(self):", "def _GetKeyString(self):\n return self.__key_string", "def _GetKeyString(self):\n return self.__key_string", "def __GetKeyString(self):\n return self._GetKeyString()", "def __GetKeyString(self):\n return self._GetKeyString()", "def test_key_str(self):\n key = Key({\"warning\": False, \"inCar\": True})\n\n string = str(key)\n assert isinstance(string, str)\n assert string == \"{'warning': False, 'in_car': True}\"", "def string_to_keypair(self, data): \n return keypair_lst", "def load_key():", "def getSit(self, key):\n if hasattr(key, \"encode\"):\n key = key.encode(\"utf-8\") # convert str to bytes\n return self.getVal(self.sits, key)", "def deserialize_key(key: bytes) -> str:\n return key.decode()", "def strkey(item):\n return '%s:%s:%s' % (item['group_id'], item['artifact_id'], item['version'])", "def _get_pe_key(self, pathstr):\n path = _path.Path.from_str(pathstr)\n return path.elems()[-1].key", "def get_spk_from_utt(utt):\n\treturn utt.split('-')[0]", "def MakeKey(self, string, string_1, string_2):\n ...", "def key(self):\n return self._key.decode('utf-8')", "def sesid(self, ld8):\n return self.ses_lookup.get(ld8, '')", "def _course_key_from_string(self, string):\r\n return self.course_locations[string].course_key", "def _get_raw_key(self, key_id):", "def key(key):\n return key", "def _parse_key(self): # type: () -> Key\n if self._current in \"\\\"'\":\n return self._parse_quoted_key()\n else:\n return self._parse_bare_key()", "def pskToString(psk: bytes):\n if len(psk) == 0:\n return \"unencrypted\"\n elif len(psk) == 1:\n b = psk[0]\n if b == 0:\n return \"unencrypted\"\n elif b == 1:\n return \"default\"\n else:\n return f\"simple{b - 1}\"\n else:\n return \"secret\"", "def get_key(command):\n return command.split(\" \")[1]", "def findSPKID(bsp):\n import spiceypy as spice\n\n bsp = [bsp]\n spice.furnsh(bsp)\n\n i = 0\n kind = \"spk\"\n fillen = 256\n typlen = 33\n srclen = 256\n keys = [\"Target SPK ID :\", \"ASTEROID_SPK_ID =\"]\n n = len(keys[0])\n\n name, kind, source, loc = spice.kdata(i, kind, fillen, typlen, srclen)\n flag = False\n spk = \"\"\n while not flag:\n try:\n m, header, flag = spice.dafec(loc, 1)\n row = header[0]\n if row[:n] in keys:\n spk = row[n:].strip()\n break\n except:\n break\n return spk", "def read_key_str(op, key, maxlen=None, fmt=None, allow_blank=False):\n if key not in op:\n return None\n assert isinstance(op[key], str), 'key `%s` was not str' % key\n assert allow_blank or op[key], 'key `%s` was blank' % key\n assert op[key] == op[key].strip(), 'invalid padding: %s' % key\n assert not maxlen or len(op[key]) <= maxlen, 'exceeds max len: %s' % key\n\n if fmt == 'hex':\n assert re.match(r'^#[0-9a-f]{6}$', op[key]), 'invalid HEX: %s' % key\n elif fmt == 'lang':\n assert op[key] in LANGS, 'invalid lang: %s' % key\n else:\n assert fmt is None, 'invalid fmt: %s' % fmt\n\n return op[key]", "def get_key(self) -> str:\n return f'{self.address}_{self.port}'", "def _get_key_pair_from_sk(sk: ecdsa.SigningKey) -> typing.Tuple[bytes, bytes]:\n return sk.to_string(), \\\n sk.verifying_key.to_string(\"compressed\")", "def prepare_key(self, key):\n return smart_str(key)", "def as_key(key):\n return key.lstrip('/').rstrip('/')" ]

[ "0.7463206", "0.63048255", "0.63048255", "0.6093281", "0.60203123", "0.5681855", "0.56371945", "0.55297583", "0.5436478", "0.5360586", "0.53496593", "0.53464997", "0.5341332", "0.53328174", "0.5318203", "0.52947754", "0.52796894", "0.52793884", "0.5278508", "0.5275866", "0.5263462", "0.5257995", "0.5255738", "0.5209546", "0.5208136", "0.5207505", "0.5203238", "0.5197837", "0.5132131", "0.51200026" ]

[ "def writetask(self,filename_): # 3\n res = self.__obj.writetask(filename_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def serialize(self, to_file=None):\n if to_file is not None:\n raise ValueError(\n \"TaskInfo does not support serialization to a custom filename\")\n\n to_file = self.filename\n gitrepo.write_task(to_file, self.pretty(self.dict()))", "def tasks_dump(self, task_id, fileformat, filename, **kwargs):\n url = self.api.tasks_id(task_id)\n response = self.session.get(url)\n response.raise_for_status()\n response_json = response.json()\n\n url = self.api.tasks_id_annotations_filename(task_id,\n response_json['name'],\n fileformat)\n while True:\n response = self.session.get(url)\n response.raise_for_status()\n log.info('STATUS {}'.format(response.status_code))\n if response.status_code == 201:\n break\n\n response = self.session.get(url + '&action=download')\n response.raise_for_status()\n\n with open(filename, 'wb') as fp:\n fp.write(response.content)", "def write(self, filename):\n pass", "def write(self, filename):\n pass", "def save_tasks(self, task_file):\n\n\t\tutil.save(self.tasklist.tasks, task_file)", "def save_task(self, task):\n if type(task) != Task:\n raise TypeError(\"Object type is not Task\")\n\n with open(self.path_to_task_file, 'a') as output:\n json.dump(task.__dict__, output)\n output.write('\\n')", "def write_to_file(self, filename: str) -> None:", "def write (self, file):\n\t\tfile.write (self.pack ())", "def write(self, fname):\n pass", "def dump(self, filename):\n\n pickle.dump(self, open(filename, \"w\"))", "def filewrite(self, filename):\n io.write(self, filename)", "def dump(self, value, filename):\n\n assert isinstance(filename, str)\n joblib.dump(value=value, filename=filename)", "def serialize(self, to_file=None):\n assert to_file is not None # FIXME\n task_list = [task.id for task in self.queue]\n with open(to_file, \"w\") as outfile:\n outfile.write(self.pretty(task_list))", "def save_taskgraph(self, filename):\n\n if not TaskGraph.__SETUP_YAML_ONCE:\n TaskGraph.setup_yaml()\n\n # we want -id to be first in the resulting yaml file.\n tlist_od = self.export_task_speclist()\n with open(filename, 'w') as fh:\n ruamel.yaml.dump(tlist_od, fh, default_flow_style=False)", "def write(self, filename, data):\n raise NotImplementedError", "def writeDtbFile(self, filename):\n filename = os.path.realpath(filename)\n try:\n with open(filename, \"wb\") as f:\n f.write(self.to_dtb())\n return filename\n except IOError:\n raise RuntimeError(\"Failed to open DTB output file\")", "def dump(self, filename, mode='w', rebox=False):\n from os import path\n filepath = path.abspath(path.expanduser(filename))\n if mode == 'w':\n open(filepath, 'w').close() \n for t, ts in self:\n ts.dump(filename, rebox=rebox)", "def writeto(self, fileout):\n \n dump_pkl(self.data, fileout)", "def save(self, filename: str):\n dump(self, filename)", "def write_to_disk(self):\n text_file = open(self.file_path, \"w\")\n text_file.write(str(self))\n text_file.close()\n # dump to pickle\n pickle.dump(self.blockchain, open(self.pickle_path, \"wb\"))", "def writedata(self,filename_):\n if isinstance(filename_,unicode):\n filename_ = filename_.encode(\"utf-8\",errors=\"replace\")\n res = __library__.MSK_XX_writedata(self.__nativep,filename_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def save(self, filename='test'):\n file = open(filename+'.txt','w')\n pickle.dump(self, file)\n file.close()", "def write(self, filename): # real signature unknown; restored from __doc__\n pass", "def writedata(self,filename_): # 3\n res = self.__obj.writedata(filename_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def dump(self, filepath, ):\n return joblib.dump(self, filepath)", "def add(self, task):\n self._count += 1\n path = os.path.join(self._root, \"%d_%s\" % (self._count, task.guid))\n j.sal.fs.writeFile(path, self._serialize_task(task))", "def _save(self):\n raise NotImplementedError(\"Don't know how to save the task\")", "def write_file(self):\n print 'Writing '+self.name+' binary...'\n if self.vals is not None:\n if len(self.vals) == self.size:\n stream = self.pack_mem()\n with open(self.name+'.bin','wb') as f:\n f.write(stream)\n print 'File written: '+self.name+'.bin'\n else:\n print 'Error: input array for '+self.name+'is not the right '+\\\n 'size (should be '+str(self.size)+'). Skipping.'\n else:\n print 'No array provided, skipping.'", "def to_file(self, filename=None):\n name = None\n if filename is not None:\n name = filename\n elif self.name:\n name = self.name\n\n if name:\n #f = open(self.name, 'w')\n f = codecs.open(name, 'w', encoding='utf-8')\n self.seek(0)\n f.write(self.read())\n f.close()\n else:\n print \"No log_name for this log\"" ]

[ "0.76278293", "0.66411906", "0.6338404", "0.6297968", "0.6297968", "0.6217573", "0.6178854", "0.60562927", "0.6003934", "0.59263074", "0.5922769", "0.5882908", "0.5819537", "0.58133584", "0.58092064", "0.5795702", "0.57952464", "0.57715166", "0.5770719", "0.575975", "0.5749596", "0.5747197", "0.57324684", "0.5728321", "0.5717504", "0.56838673", "0.5673843", "0.56611586", "0.5653681", "0.5647819" ]

[ "def readtask(self,filename_): # 3\n res = self.__obj.readtask(filename_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def load_tasks(self, task_file):\n\n\t\tself.tasklist.tasks = util.load(task_file)\n\t\tTask.last_id = len(self.tasklist.tasks)", "def load(self, filepath=file):\n try:\n with open(filepath, \"r\", encoding=\"utf-8\") as task_file:\n tasks_json = json.load(task_file)\n self.tasks = [Task(task[\"name\"], task[\"priority\"], task[\"steps\"]) for task in tasks_json]\n self.sort()\n except FileNotFoundError:\n pass", "def _load(self, data):\n raise NotImplementedError(\"Don't know how to load the task\")", "def read_from_file(self, filename: str) -> None:", "def read_filename(self, filename):\r\n self.text_lines = task3.read_text_file(filename)", "def _file_update(self, filename):\n values = TaskInfo._parse_file(filename)\n self._load_dict(values)", "def readFromFile(filename):\n raise NotImplementedError", "def load(self, filename):\n raise NotImplementedError", "def load_tasks(self):\n\n def _load_tasks(filename):\n filename = os.path.join(self.config['data']['location'], filename)\n filename = os.path.expanduser(filename)\n with open(filename, 'r') as f:\n lines = f.readlines()\n\n return list(map(taskw.utils.decode_task, lines))\n\n return dict(\n (db, _load_tasks('%s.data' % db))\n for db in ['completed', 'pending']\n )", "def load(cls, from_file):\n json_str = gitrepo.read_task(from_file)\n task_dict = json.loads(json_str)\n return cls(**task_dict)", "def read(self, filename):\n pass", "def read(self, filename):\n pass", "def read(self, filename):\n raise NotImplementedError", "def load(cls, filename):\n \n raise NotImplementedError(\"not implemented!\")", "def load(self):\r\n self.read(self.filename)", "def _read_data(self, \n data_path : str, \n bert : bool=False, \n mode : str=\"raw\", \n task : str=\"A\"\n ):\n print(f\"\\n[dataset]: Loading data from '{data_path}'...\")\n print(f\"[dataset]: performing task '{task}' preprocessing ...\")\n if task == \"A\":\n tokenizer = self._tokenize_line if mode == \"tokenize\" else self.bert_tokenizer\n return _read_data_taskA(data_path, tokenizer, bert, mode, tagger=self._tag_tokens, test=self.test)\n\n elif task == \"B\":\n return _read_data_taskB(data_path, test=False)\n \n elif task == \"C\":\n return _read_data_taskC(data_path, test=False)\n\n elif task == \"D\":\n return _read_data_taskD(data_path, test=False)", "def load(self, filename):\n f = open(filename, 'rb')\n try:\n data = pickle.load(f)\n activities, schedules, resources, resourceAsignaments = data\n except (pickle.UnpicklingError, AttributeError, EOFError, ImportError, IndexError, ValueError, KeyError):\n raise InvalidFileFormatException('Unpickle failed')\n\n # Check activities, schedules, resources, resourceAsignaments have the right data structure\n for row in activities:\n if len(row) != 9:\n raise InvalidFileFormatException('Incorrect data on file')\n \n f.close()\n return data", "def load_data(self, task):\n params = self.params\n data = {splt: {} for splt in ['train', 'valid', 'test']}\n dpath = os.path.join(params.data_path, 'eval', task)\n\n self.n_sent = 1 if task in ['SST-2', 'CoLA'] else 2\n\n for splt in ['train', 'valid', 'test']:\n\n # load data and dictionary\n data1 = load_binarized(os.path.join(dpath, '%s.s1.pth' % splt), params)\n data2 = load_binarized(os.path.join(dpath, '%s.s2.pth' % splt), params) if self.n_sent == 2 else None\n data['dico'] = data.get('dico', data1['dico'])\n\n # set dictionary parameters\n set_dico_parameters(params, data, data1['dico'])\n if self.n_sent == 2:\n set_dico_parameters(params, data, data2['dico'])\n\n # create dataset\n if self.n_sent == 1:\n data[splt]['x'] = Dataset(data1['sentences'], data1['positions'], params)\n else:\n data[splt]['x'] = ParallelDataset(\n data1['sentences'], data1['positions'],\n data2['sentences'], data2['positions'],\n params\n )\n\n # load labels\n if splt != 'test' or task in ['MRPC']:\n # read labels from file\n with open(os.path.join(dpath, '%s.label' % splt), 'r') as f:\n lines = [l.rstrip() for l in f]\n # STS-B task\n if task == 'STS-B':\n assert all(0 <= float(x) <= 5 for x in lines)\n y = [float(l) for l in lines]\n # QQP\n elif task == 'QQP':\n UNK_LABEL = 0\n lab2id = {x: i for i, x in enumerate(sorted(set(lines) - set([''])))}\n y = [lab2id.get(x, UNK_LABEL) for x in lines]\n # other tasks\n else:\n lab2id = {x: i for i, x in enumerate(sorted(set(lines)))}\n y = [lab2id[x] for x in lines]\n data[splt]['y'] = torch.LongTensor(y)\n assert len(data[splt]['x']) == len(data[splt]['y'])\n\n # compute weights for weighted training\n if task != 'STS-B' and params.weighted_training:\n weights = torch.FloatTensor([\n 1.0 / (data['train']['y'] == i).sum().item()\n for i in range(len(lab2id))\n ]).npu()\n self.weights = weights / weights.sum()\n else:\n self.weights = None\n\n return data", "def load_data_loader_from_file(cls, filename):\n print(\"Loading data loader from file: {}\".format(filename))\n\n with open(filename, \"rb\") as file:\n return pickle.load(file)", "def load (self, filename) :\n\t\tserialFile = open (filename, \"rb\")\n\t\tself.production_rules = pickle.load (serialFile)\n\t\tself.unitrelation = pickle.load (serialFile)\n\t\tself.labels = pickle.load (serialFile)\n\t\tself.keeper = pickle.load (serialFile)\n\t\tself.strnodes = pickle.load(serialFile)\n\t\tself.tokens = pickle.load (serialFile)\n\t\tserialFile.close()", "def __read(self, filename):\n f = open(filename)\n\n self.startDate = self.__parseDate(f.readline())\n (nRows, nCols) = [int(s) for s in f.readline().split() ]\n\n dataArray = self.__readData(f, nRows, nCols)\n self.__storeDataDict(dataArray)\n self.__appendMetaData(filename)\n self._appendDerivedQuantities()", "def _load(self, filename):\n with open(filename) as fp:\n reader = csv.DictReader(fp)\n self.events = list(reader)", "def load(self, filename):\n\n assert isinstance(filename, str) \n return joblib.load(filename=filename)", "def load_data_from_filename(self, filename):\n try:\n if self.verbose:\n print 'Getting data from ' + filename\n\n self.data_dict = {}\n\n with open(filename, 'rt') as f:\n for line in f:\n data_match = re.match(r'^(\\d+)[\\,|\\t|\\s|\\|](\\d+)$', line)\n if data_match:\n node = int(data_match.group(1))\n part = int(data_match.group(2))\n\n if part in self.data_dict:\n self.data_dict[part].append(node)\n else:\n self.data_dict[part] = [node]\n\n except Exception, e:\n print 'Unexpected error:', str(e)\n print 'Problems loading data from file.'\n exit()", "def load(self, filename):\n pass", "def loadFromFile(self,filename):\n path = os.path.dirname(__file__)+\"/\"+filename\n if os.path.exists(path) and os.path.isfile(path):\n self.load(yaml.load(open(path, 'r')))", "def prepare_taskfile(taskfile):\n path = os.path.dirname(taskfile)\n taskmodulename = os.path.splitext(os.path.basename(taskfile))[0]\n logging.info(\"Loading task file %s from %s\", taskmodulename, path)\n fp, pathname, description = imp.find_module(taskmodulename, [path])\n try:\n return imp.load_module(taskmodulename, fp, pathname, description)\n finally:\n if fp: \n fp.close()", "async def parse_files(file):\n data = yaml.full_load(file)\n try:\n new_data = {\n \"task_name\": data[\"metadata\"][\"name\"],\n \"task_type\": data[\"kind\"],\n \"scheduled_at\": data[\"spec\"].get(\"schedule\"),\n }\n\n except KeyError as e:\n raise KeyError(f\"Invalid yaml file uploded \\n {e}\")\n model = TaskModel(**new_data)\n return model", "def __init__(self, name, dir='.'):\n try:\n full_name = os.path.join(dir, name + '.task1')\n self.load(full_name)\n except Exception as e:\n print('Failed to load \"{}\"'.format(full_name))\n print(e)" ]

[ "0.80296904", "0.72496146", "0.7088076", "0.7083461", "0.69893193", "0.6900385", "0.68146265", "0.67244583", "0.6720494", "0.6645653", "0.6595403", "0.65645224", "0.65645224", "0.6510227", "0.6467875", "0.6425703", "0.6416887", "0.64050037", "0.63069814", "0.62808853", "0.62398785", "0.6207762", "0.6188492", "0.61844516", "0.6153029", "0.6081789", "0.6065231", "0.6045857", "0.6034418", "0.60299027" ]

[ "def readptfstring(self,data_):\n if isinstance(data_,unicode):\n data_ = data_.encode(\"utf-8\",errors=\"replace\")\n res = __library__.MSK_XX_readptfstring(self.__nativep,data_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def _load(self, data):\n raise NotImplementedError(\"Don't know how to load the task\")", "def load(cls,data, recovery_mode = False):\n opid = _read_delimited_field(data)\n operation_type = _read_delimited_field(data)\n modlogger.debug( \"loading: %s,%s\"%(opid,operation_type))\n return _operation_type_map[operation_type].load(opid,data, recovery_mode = recovery_mode)", "def parse_string(self, data):\n pass", "def load_from_string_list(self, data):\n self.data = data\n self.loaded = True", "def readlpstring(self,data_):\n if isinstance(data_,unicode):\n data_ = data_.encode(\"utf-8\",errors=\"replace\")\n res = __library__.MSK_XX_readlpstring(self.__nativep,data_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def parse_data(fp):\n pass", "def _read_data(self, \n data_path : str, \n bert : bool=False, \n mode : str=\"raw\", \n task : str=\"A\"\n ):\n print(f\"\\n[dataset]: Loading data from '{data_path}'...\")\n print(f\"[dataset]: performing task '{task}' preprocessing ...\")\n if task == \"A\":\n tokenizer = self._tokenize_line if mode == \"tokenize\" else self.bert_tokenizer\n return _read_data_taskA(data_path, tokenizer, bert, mode, tagger=self._tag_tokens, test=self.test)\n\n elif task == \"B\":\n return _read_data_taskB(data_path, test=False)\n \n elif task == \"C\":\n return _read_data_taskC(data_path, test=False)\n\n elif task == \"D\":\n return _read_data_taskD(data_path, test=False)", "def load_data(self, task):\n params = self.params\n data = {splt: {} for splt in ['train', 'valid', 'test']}\n dpath = os.path.join(params.data_path, 'eval', task)\n\n self.n_sent = 1 if task in ['SST-2', 'CoLA'] else 2\n\n for splt in ['train', 'valid', 'test']:\n\n # load data and dictionary\n data1 = load_binarized(os.path.join(dpath, '%s.s1.pth' % splt), params)\n data2 = load_binarized(os.path.join(dpath, '%s.s2.pth' % splt), params) if self.n_sent == 2 else None\n data['dico'] = data.get('dico', data1['dico'])\n\n # set dictionary parameters\n set_dico_parameters(params, data, data1['dico'])\n if self.n_sent == 2:\n set_dico_parameters(params, data, data2['dico'])\n\n # create dataset\n if self.n_sent == 1:\n data[splt]['x'] = Dataset(data1['sentences'], data1['positions'], params)\n else:\n data[splt]['x'] = ParallelDataset(\n data1['sentences'], data1['positions'],\n data2['sentences'], data2['positions'],\n params\n )\n\n # load labels\n if splt != 'test' or task in ['MRPC']:\n # read labels from file\n with open(os.path.join(dpath, '%s.label' % splt), 'r') as f:\n lines = [l.rstrip() for l in f]\n # STS-B task\n if task == 'STS-B':\n assert all(0 <= float(x) <= 5 for x in lines)\n y = [float(l) for l in lines]\n # QQP\n elif task == 'QQP':\n UNK_LABEL = 0\n lab2id = {x: i for i, x in enumerate(sorted(set(lines) - set([''])))}\n y = [lab2id.get(x, UNK_LABEL) for x in lines]\n # other tasks\n else:\n lab2id = {x: i for i, x in enumerate(sorted(set(lines)))}\n y = [lab2id[x] for x in lines]\n data[splt]['y'] = torch.LongTensor(y)\n assert len(data[splt]['x']) == len(data[splt]['y'])\n\n # compute weights for weighted training\n if task != 'STS-B' and params.weighted_training:\n weights = torch.FloatTensor([\n 1.0 / (data['train']['y'] == i).sum().item()\n for i in range(len(lab2id))\n ]).npu()\n self.weights = weights / weights.sum()\n else:\n self.weights = None\n\n return data", "def _load_data_from_str(self, data_as_str):\n try:\n data = json.loads(data_as_str)\n except json.JSONDecodeError:\n data = data_utils.data_generator(data_as_str.splitlines())\n data = data_utils.read_json(\n data_generator=data,\n selected_columns=self.selected_keys,\n read_in_string=False\n )\n return data", "def readstring(self, fstring):\n return self.parse(fstring)", "def load(datastream):", "def load(self, input):", "def load(self, input):\n pass", "def loads(self, data):\n return loads(data)", "def load_data(self) -> None:", "def parse_string(self, data):\n from pyexpat import ExpatError\n\n from openff.toolkit.utils.exceptions import SMIRNOFFParseError\n\n # Parse XML file\n try:\n smirnoff_data = xmltodict.parse(data, attr_prefix=\"\")\n return smirnoff_data\n except ExpatError as e:\n raise SMIRNOFFParseError(str(e))", "def load_data_str(rel_path):\r\n full_path = path(__file__).abspath().dirname() / \"data\" / rel_path # pylint: disable=E1120\r\n with open(full_path) as data_file:\r\n return data_file.read()", "def parse(self, fstring):\n pass", "def __init__(self, data, task):\n\n self.data = data\n self.task = task\n self.header = self.task.input_data_header\n self.file_path = self.task.file_storage\n self.file_name = self.task.file_name\n\n self.successful_run = False\n\n # Remove the header from the data set\n # if it is included in the data set\n if self.header is None:\n self.header = data[0]\n del self.data[0]", "def parse(data:str) -> object:\n\n return ast.parse(data)", "def loads(self, string):\n # to make sure that all the operations have a strong exception guarantee we are going to have here a try except\n # Exception which will catch any exception\n try:\n if '.txt' in string:\n return self.from_file(string)\n else:\n return self.from_string(string)\n except Exception as e:\n log.error(\"An error has appeared: %s\" % e)\n raise Exception(e)", "def load_data(self, data):\n self.data = data\n self.validate()", "def parse_string(self, in_str):\n match = MAIN_REGEX.search(in_str)\n if not match:\n err_str = \"Unable to parse string: %s\" % in_str\n raise ValueError(err_str)\n self.parse_completed(match.group(1))\n self.parse_priority(match.group(2))\n if match.group(3) and match.group(4):\n self.parse_completion_date(match.group(3))\n self.parse_creation_date(match.group(4))\n else:\n self.parse_creation_date(match.group(3))\n self.parse_description(match.group(5))", "def __charData(self, data):\n if self.taskID:\n # We only take character data fields into account for jobvalue\n # elements with a taskid attribute. taskID indicates this and gets\n # set in __startElem if defined for a job element.\n # Adding array job with <jobid>_<taskid> to the dictionary\n addHostToJob(self.host_dict, self.current_host, self.jobID + \"_\" + data)\n # Reset task ID.\n self.taskID = False", "def parse(cls, data):\n raise NotImplementedError", "def load_data(self, data):\n self._load_raw_data = data", "def parse_dataset(self, data):\n pass", "def load_data(data_name) -> str:\n with open(config_path / data_name, \"r\") as f:\n data = f.readline().strip().split()[0]\n\n return data", "def load_data(data_name) -> str:\n with open(config_path / data_name, \"r\") as f:\n data = f.readline().strip().split()[0]\n\n return data" ]

[ "0.6794951", "0.64501524", "0.6257614", "0.62188506", "0.5981521", "0.59138155", "0.58334607", "0.57561445", "0.55966187", "0.553957", "0.5505495", "0.53398323", "0.5314852", "0.52946866", "0.5283617", "0.525373", "0.52212423", "0.52142024", "0.5210204", "0.5205793", "0.52055144", "0.51744556", "0.5172062", "0.5170463", "0.515659", "0.51373607", "0.51350415", "0.51345336", "0.51338863", "0.51338863" ]

[ "def readptfstring(self,data_):\n if isinstance(data_,unicode):\n data_ = data_.encode(\"utf-8\",errors=\"replace\")\n res = __library__.MSK_XX_readptfstring(self.__nativep,data_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def _load(self, data):\n raise NotImplementedError(\"Don't know how to load the task\")", "def load_from_string_list(self, data):\n self.data = data\n self.loaded = True", "def loaddata(self,paramsortaskid,strkeyseq):\n if type(strkeyseq)==str: strkeyseq=[strkeyseq]\n ti=self.batchobj.alwaysreturntaskid1(paramsortaskid)\n k=list(strkeyseq)+[ti]\n return self.data.loaddata(k,data)", "def load_data(self, task):\n params = self.params\n data = {splt: {} for splt in ['train', 'valid', 'test']}\n dpath = os.path.join(params.data_path, 'eval', task)\n\n self.n_sent = 1 if task in ['SST-2', 'CoLA'] else 2\n\n for splt in ['train', 'valid', 'test']:\n\n # load data and dictionary\n data1 = load_binarized(os.path.join(dpath, '%s.s1.pth' % splt), params)\n data2 = load_binarized(os.path.join(dpath, '%s.s2.pth' % splt), params) if self.n_sent == 2 else None\n data['dico'] = data.get('dico', data1['dico'])\n\n # set dictionary parameters\n set_dico_parameters(params, data, data1['dico'])\n if self.n_sent == 2:\n set_dico_parameters(params, data, data2['dico'])\n\n # create dataset\n if self.n_sent == 1:\n data[splt]['x'] = Dataset(data1['sentences'], data1['positions'], params)\n else:\n data[splt]['x'] = ParallelDataset(\n data1['sentences'], data1['positions'],\n data2['sentences'], data2['positions'],\n params\n )\n\n # load labels\n if splt != 'test' or task in ['MRPC']:\n # read labels from file\n with open(os.path.join(dpath, '%s.label' % splt), 'r') as f:\n lines = [l.rstrip() for l in f]\n # STS-B task\n if task == 'STS-B':\n assert all(0 <= float(x) <= 5 for x in lines)\n y = [float(l) for l in lines]\n # QQP\n elif task == 'QQP':\n UNK_LABEL = 0\n lab2id = {x: i for i, x in enumerate(sorted(set(lines) - set([''])))}\n y = [lab2id.get(x, UNK_LABEL) for x in lines]\n # other tasks\n else:\n lab2id = {x: i for i, x in enumerate(sorted(set(lines)))}\n y = [lab2id[x] for x in lines]\n data[splt]['y'] = torch.LongTensor(y)\n assert len(data[splt]['x']) == len(data[splt]['y'])\n\n # compute weights for weighted training\n if task != 'STS-B' and params.weighted_training:\n weights = torch.FloatTensor([\n 1.0 / (data['train']['y'] == i).sum().item()\n for i in range(len(lab2id))\n ]).npu()\n self.weights = weights / weights.sum()\n else:\n self.weights = None\n\n return data", "def parse_string(self, data):\n pass", "def load_pn(self):\n self.pn = self.read_var(self.pnvar)\n self.pn = self.pn.astype('unicode')\n new_arr = []\n for p in range(np.shape(self.pn)[0]):\n new_arr.append(''.join(self.pn[p]))\n self.pn = new_arr\n self.pn = np.array(self.pn)\n self.test_shape(self.pnvar, self.pn.shape, 1)", "def _read_data(self, \n data_path : str, \n bert : bool=False, \n mode : str=\"raw\", \n task : str=\"A\"\n ):\n print(f\"\\n[dataset]: Loading data from '{data_path}'...\")\n print(f\"[dataset]: performing task '{task}' preprocessing ...\")\n if task == \"A\":\n tokenizer = self._tokenize_line if mode == \"tokenize\" else self.bert_tokenizer\n return _read_data_taskA(data_path, tokenizer, bert, mode, tagger=self._tag_tokens, test=self.test)\n\n elif task == \"B\":\n return _read_data_taskB(data_path, test=False)\n \n elif task == \"C\":\n return _read_data_taskC(data_path, test=False)\n\n elif task == \"D\":\n return _read_data_taskD(data_path, test=False)", "def readopfstring(self,data_):\n if isinstance(data_,unicode):\n data_ = data_.encode(\"utf-8\",errors=\"replace\")\n res = __library__.MSK_XX_readopfstring(self.__nativep,data_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def load(cls,data, recovery_mode = False):\n opid = _read_delimited_field(data)\n operation_type = _read_delimited_field(data)\n modlogger.debug( \"loading: %s,%s\"%(opid,operation_type))\n return _operation_type_map[operation_type].load(opid,data, recovery_mode = recovery_mode)", "def _load_data_from_str(self, data_as_str):\n try:\n data = json.loads(data_as_str)\n except json.JSONDecodeError:\n data = data_utils.data_generator(data_as_str.splitlines())\n data = data_utils.read_json(\n data_generator=data,\n selected_columns=self.selected_keys,\n read_in_string=False\n )\n return data", "def load(self, p):\n return", "def load_data(self) -> None:", "def parse_local_data_line(L):\n data = L.split()\n ncols = len(data)\n if ncols not in [6, 7]:\n print(\"local_data line {} does not have 6 or 7 fields, skipping\".format(L))\n return\n label, record = parse_line_label_cols(L)\n\n ldstring = \"\" if (ncols == 6) else data[4]\n ld, ldx = local_data_from_string(ldstring)\n record['local_data'] = ld\n record.update(ldx) # fields 'bad_primes', 'n_bad_primes', 'semistable', 'potential_good_reduction', 'tamagawa_product'\n\n # The non_min_p column is a list of strings\n # e.g. ['(g)', '(g1,g2)'] while the string in\n # the file will contain [(g),(g1,g2)].\n\n # Currently the list has 0 or 1 entries but we do not want to rely\n # on this.\n\n nmp = data[-2]\n #print(nmp)\n record['non_min_p'] = [] if nmp == '[]' else [\"(\"+P+\")\" for P in nmp[2:-2].split(\"),(\")]\n #print(record['non_min_p'])\n record['minD'] = data[-1]\n\n return label, record", "def load_data(self, data):\n self.data = data\n self.validate()", "def load_sequence_labelling_dataset(step, do_lower_case,data_type,data_subtype):\n assert step in ['train', 'test']\n path = os.path.join(DATA_PATH, 'sequence_labelling', f'{step}.txt')\n i = 0\n examples = []\n with open(path, 'r', encoding='utf-8') as data_file:\n lines = data_file.readlines()\n token_sequence = []\n label_sequence = []\n for line in tqdm(lines, desc=f'reading `{os.path.basename(path)}`...'):\n # example:\n # My O\n # name O\n # is O\n # Hicham B-PER\n # . O\n splitline = line.strip().split()\n if splitline:\n token, label = splitline\n token_sequence.append(token)\n label_sequence.append(label)\n else:\n examples.append(\n SequenceLabellingExample(\n id=i,\n token_sequence=token_sequence,\n label_sequence=label_sequence,\n )\n )\n i += 1\n token_sequence = []\n label_sequence = []\n\n # Don't forget to add the last example\n if token_sequence:\n examples.append(\n SequenceLabellingExample(\n id=i,\n token_sequence=token_sequence,\n label_sequence=label_sequence,\n )\n )\n\n retokenize(\n examples,\n tokenization_function=BasicTokenizer(do_lower_case=do_lower_case).tokenize)\n logging.info('Number of `%s` examples: %d', step, len(examples))\n return examples", "def loadLogicFromBinary(tapeString):\n\tpass", "def load_label(self, pr):\n return", "def loads(self, string):\n # to make sure that all the operations have a strong exception guarantee we are going to have here a try except\n # Exception which will catch any exception\n try:\n if '.txt' in string:\n return self.from_file(string)\n else:\n return self.from_string(string)\n except Exception as e:\n log.error(\"An error has appeared: %s\" % e)\n raise Exception(e)", "def load_data(self, data):\n self._load_raw_data = data", "def parse_data(self, data):\n\t\tname, value = self.parse_from_dref(data)\n\t\tpacket = TrollPacket.from_name(name, value)\n\t\tself.update_listeners(packet)", "def load(self, input):\n pass", "def load_str(self, string, filename=None):\n self.ffi_polar.load(string, filename)\n\n # check inline queries\n while True:\n query = self.ffi_polar.next_inline_query()\n if query is None: # Load is done\n break\n else:\n try:\n next(Query(query, host=self.host.copy()).run())\n except StopIteration:\n source = query.source()\n raise InlineQueryFailedError(source.get())", "def load_data_str(rel_path):\r\n full_path = path(__file__).abspath().dirname() / \"data\" / rel_path # pylint: disable=E1120\r\n with open(full_path) as data_file:\r\n return data_file.read()", "def load(self, input):", "def load_data():\n with open('../data/dataset.txt', 'r') as data_file:\n return data_file.read().split('\\n')", "def load_pkl_data(path):\n with open(path, 'rb') as fi:\n data = pickle.load(fi)\n return data", "def load(self):\n self.load_outputs()\n ## warning, ns lookups here\n self.pool = PLPoller(self, rawfile=self._rawfile, user=self.user, \n period=self.period, threadlimit=self.threadlimit,\n sshlimit=self.sshlimit, plslice=self.slice,\n initialdelay=self.initialdelay)", "def loads(self, data):\n return loads(data)", "def load_data(path_dataset):\n data = read_txt(path_dataset)[1:]\n return preprocess_data(data)" ]

[ "0.6374487", "0.63396025", "0.6261898", "0.5823688", "0.5761187", "0.572786", "0.5603073", "0.55118525", "0.548002", "0.5409333", "0.53713614", "0.53710246", "0.5349839", "0.53278965", "0.53266686", "0.5312053", "0.5271302", "0.52626383", "0.52433527", "0.52351403", "0.5206785", "0.52015114", "0.5196958", "0.5158784", "0.51522696", "0.5133443", "0.5120746", "0.51037264", "0.50923806", "0.50872874" ]

[ "def _load(self, data):\n raise NotImplementedError(\"Don't know how to load the task\")", "def load_from_string_list(self, data):\n self.data = data\n self.loaded = True", "def readstring(self, fstring):\n return self.parse(fstring)", "def _read_data(self, \n data_path : str, \n bert : bool=False, \n mode : str=\"raw\", \n task : str=\"A\"\n ):\n print(f\"\\n[dataset]: Loading data from '{data_path}'...\")\n print(f\"[dataset]: performing task '{task}' preprocessing ...\")\n if task == \"A\":\n tokenizer = self._tokenize_line if mode == \"tokenize\" else self.bert_tokenizer\n return _read_data_taskA(data_path, tokenizer, bert, mode, tagger=self._tag_tokens, test=self.test)\n\n elif task == \"B\":\n return _read_data_taskB(data_path, test=False)\n \n elif task == \"C\":\n return _read_data_taskC(data_path, test=False)\n\n elif task == \"D\":\n return _read_data_taskD(data_path, test=False)", "def parse_string(self, data):\n pass", "def load_data(self, task):\n params = self.params\n data = {splt: {} for splt in ['train', 'valid', 'test']}\n dpath = os.path.join(params.data_path, 'eval', task)\n\n self.n_sent = 1 if task in ['SST-2', 'CoLA'] else 2\n\n for splt in ['train', 'valid', 'test']:\n\n # load data and dictionary\n data1 = load_binarized(os.path.join(dpath, '%s.s1.pth' % splt), params)\n data2 = load_binarized(os.path.join(dpath, '%s.s2.pth' % splt), params) if self.n_sent == 2 else None\n data['dico'] = data.get('dico', data1['dico'])\n\n # set dictionary parameters\n set_dico_parameters(params, data, data1['dico'])\n if self.n_sent == 2:\n set_dico_parameters(params, data, data2['dico'])\n\n # create dataset\n if self.n_sent == 1:\n data[splt]['x'] = Dataset(data1['sentences'], data1['positions'], params)\n else:\n data[splt]['x'] = ParallelDataset(\n data1['sentences'], data1['positions'],\n data2['sentences'], data2['positions'],\n params\n )\n\n # load labels\n if splt != 'test' or task in ['MRPC']:\n # read labels from file\n with open(os.path.join(dpath, '%s.label' % splt), 'r') as f:\n lines = [l.rstrip() for l in f]\n # STS-B task\n if task == 'STS-B':\n assert all(0 <= float(x) <= 5 for x in lines)\n y = [float(l) for l in lines]\n # QQP\n elif task == 'QQP':\n UNK_LABEL = 0\n lab2id = {x: i for i, x in enumerate(sorted(set(lines) - set([''])))}\n y = [lab2id.get(x, UNK_LABEL) for x in lines]\n # other tasks\n else:\n lab2id = {x: i for i, x in enumerate(sorted(set(lines)))}\n y = [lab2id[x] for x in lines]\n data[splt]['y'] = torch.LongTensor(y)\n assert len(data[splt]['x']) == len(data[splt]['y'])\n\n # compute weights for weighted training\n if task != 'STS-B' and params.weighted_training:\n weights = torch.FloatTensor([\n 1.0 / (data['train']['y'] == i).sum().item()\n for i in range(len(lab2id))\n ]).npu()\n self.weights = weights / weights.sum()\n else:\n self.weights = None\n\n return data", "def loads(self, string):\n # to make sure that all the operations have a strong exception guarantee we are going to have here a try except\n # Exception which will catch any exception\n try:\n if '.txt' in string:\n return self.from_file(string)\n else:\n return self.from_string(string)\n except Exception as e:\n log.error(\"An error has appeared: %s\" % e)\n raise Exception(e)", "def ptb_raw_data(data_path=None):\n\n\t# train_path = os.path.join(data_path, \"ptb.train.txt\")\n\t# valid_path = os.path.join(data_path, \"ptb.valid.txt\")\n\t# test_path = os.path.join(data_path, \"ptb.test.txt\")\n\n\tdata = np.load(data_path)\n\t# data = np.load(data_path).item()\n\t# f = open(data_path)\n\t# data = f.readlines()\n\tword_to_id, id_to_word, wordList = build_vocab_(data)\n\t# word_to_id = _build_vocab(train_path)\n\ttrain_data = _file_to_word_ids(wordList[int(len(wordList)*0.3):int(len(wordList)*1.0)], word_to_id)\n\tvalid_data = _file_to_word_ids(wordList[int(len(wordList)*0.2):int(len(wordList)*0.3)], word_to_id)\n\ttest_data = _file_to_word_ids(wordList[int(len(wordList)*0):int(len(wordList)*0.2)], word_to_id)\n\tvocabulary = len(word_to_id)\n\treturn train_data, valid_data, test_data, vocabulary", "def parse_data(fp):\n pass", "def ptb_raw_data(data_path, simple):\n\n train_path = os.path.join(data_path, \"ptb.train.txt\")\n valid_path = os.path.join(data_path, \"ptb.valid.txt\")\n test_path = os.path.join(data_path, \"ptb.test.txt\")\n\n word_to_id, probs = _build_vocab(train_path)\n train_data = _file_to_word_ids(train_path, word_to_id, simple)\n valid_data = _file_to_word_ids(valid_path, word_to_id, simple)\n test_data = _file_to_word_ids(test_path, word_to_id, simple)\n return train_data, valid_data, test_data, probs", "def _load_data_from_str(self, data_as_str):\n try:\n data = json.loads(data_as_str)\n except json.JSONDecodeError:\n data = data_utils.data_generator(data_as_str.splitlines())\n data = data_utils.read_json(\n data_generator=data,\n selected_columns=self.selected_keys,\n read_in_string=False\n )\n return data", "def ptb_raw_data(data_path=None):\n\n train_path = os.path.join(data_path, \"ptb.train.txt\")\n valid_path = os.path.join(data_path, \"ptb.valid.txt\")\n test_path = os.path.join(data_path, \"ptb.test.txt\")\n\n word_to_id, unigrams = _build_vocab(train_path)\n train_data = _file_to_word_ids(train_path, word_to_id)\n valid_data = _file_to_word_ids(valid_path, word_to_id)\n test_data = _file_to_word_ids(test_path, word_to_id)\n vocabulary = len(word_to_id)\n return train_data, valid_data, test_data, vocabulary, unigrams", "def ptb_raw_data(data_path=None):\n\n train_path = os.path.join(data_path, \"ptb.train.txt\")\n valid_path = os.path.join(data_path, \"ptb.valid.txt\")\n test_path = os.path.join(data_path, \"ptb.test.txt\")\n\n word_to_id = _build_vocab(train_path)\n train_data = _file_to_word_ids(train_path, word_to_id)\n valid_data = _file_to_word_ids(valid_path, word_to_id)\n test_data = _file_to_word_ids(test_path, word_to_id)\n vocabulary = len(word_to_id)\n return train_data, valid_data, test_data, vocabulary", "def readlpstring(self,data_):\n if isinstance(data_,unicode):\n data_ = data_.encode(\"utf-8\",errors=\"replace\")\n res = __library__.MSK_XX_readlpstring(self.__nativep,data_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def readopfstring(self,data_):\n if isinstance(data_,unicode):\n data_ = data_.encode(\"utf-8\",errors=\"replace\")\n res = __library__.MSK_XX_readopfstring(self.__nativep,data_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def load_data(path_dataset):\n data = read_txt(path_dataset)[1:]\n return preprocess_data(data)", "def parse(self, fstring):\n pass", "def ptb_raw_data(data_path=None, prefix=\"ptb\"):\n\n train_path = os.path.join(data_path, prefix + \".train.txt\")\n valid_path = os.path.join(data_path, prefix + \".valid.txt\")\n test_path = os.path.join(data_path, prefix + \".test.txt\")\n train_w = _read_words(train_path)\n valid_w = _read_words(valid_path)\n test_w = _read_words(test_path)\n word_to_id, id_2_word = _build_vocab(train_w)\n train_data = _file_to_word_ids(train_w, word_to_id)\n valid_data = _file_to_word_ids(valid_w, word_to_id)\n test_data = _file_to_word_ids(test_w, word_to_id)\n return train_data, valid_data, test_data, word_to_id, id_2_word", "def ptb_raw_data(data_path=None, prefix=\"ptb\"):\n\n train_path = os.path.join(data_path, prefix + \".train.txt\")\n valid_path = os.path.join(data_path, prefix + \".valid.txt\")\n test_path = os.path.join(data_path, prefix + \".test.txt\")\n\n word_to_id, id_2_word = _build_vocab(train_path)\n train_data = _file_to_word_ids(train_path, word_to_id)\n valid_data = _file_to_word_ids(valid_path, word_to_id)\n test_data = _file_to_word_ids(test_path, word_to_id)\n return train_data, valid_data, test_data, word_to_id, id_2_word", "def ptb_raw_data(data_path=None, min_sentence_length=1, max_sentence_length=100):\n\n train_path = os.path.join(data_path, \"ptb.train.txt\")\n valid_path = os.path.join(data_path, \"ptb.valid.txt\")\n test_path = os.path.join(data_path, \"ptb.test.txt\")\n\n word_to_id = _build_vocab(train_path)\n train_data = _file_to_word_ids(train_path, word_to_id, min_sentence_length, max_sentence_length)\n valid_data = _file_to_word_ids(valid_path, word_to_id, min_sentence_length, max_sentence_length)\n test_data = _file_to_word_ids(test_path, word_to_id, min_sentence_length, max_sentence_length)\n vocabulary = len(word_to_id)\n return train_data, valid_data, test_data, vocabulary, word_to_id", "def readTsp(self, String0):\n Name = re.match(r\"NAME : (.*)\", String0)[1]\n COMMENT = re.search(r\"COMMENT : (.*)\", String0)[1]\n TYPE = re.search(r\"TYPE : (.*)\", String0)[1]\n DIMENSION = re.search(r\"DIMENSION : (.*)\", String0)[1]\n EDGE_WEIGHT_TYPE = re.search(r\"EDGE_WEIGHT_TYPE : (.*)\", String0)[1]\n NODE_COORD_SECTION = []\n split = String0.split(\"\\n\")\n for s0 in split:\n if (s0 and s0[0] <= '9' and s0[0] >= '0'):\n one = s0.split(\" \")\n One = []\n One.append(float(one[0]))\n One.append(float(one[1]))\n One.append(float(one[2]))\n if (One != []):\n NODE_COORD_SECTION.append(One)\n return Name, COMMENT, TYPE, DIMENSION, EDGE_WEIGHT_TYPE, NODE_COORD_SECTION", "def convert_txt_to_data():\n pass", "def load_data():\n with open('../data/dataset.txt', 'r') as data_file:\n return data_file.read().split('\\n')", "def get_task_data(self, task):\n raw = pickle.loads(task)\n if len(raw) == 7:\n task_id, klass_str, _, _, _, _, _ = raw\n elif len(raw) == 6:\n task_id, klass_str, _, _, _, _ = raw\n return task_id, klass_str", "def load_data(self, training_data):\n \"\"\"training data format [(instance, label),(instance, label),...]\"\"\"\n self.training_data = training_data", "def load_ptb_dataset(name='ptb', path='raw_data'):\n path = os.path.join(path, name)\n logging.info(\"Load or Download Penn TreeBank (PTB) dataset > {}\".format(path))\n\n # Maybe dowload and uncompress tar, or load exsisting files\n maybe_download_and_extract(PTB_FILENAME, path, PTB_URL, extract=True)\n\n data_path = os.path.join(path, 'simple-examples', 'data')\n train_path = os.path.join(data_path, \"ptb.train.txt\")\n valid_path = os.path.join(data_path, \"ptb.valid.txt\")\n test_path = os.path.join(data_path, \"ptb.test.txt\")\n\n word_to_id = nlp.build_vocab(nlp.read_words(train_path))\n\n train_data = nlp.words_to_word_ids(nlp.read_words(train_path), word_to_id)\n valid_data = nlp.words_to_word_ids(nlp.read_words(valid_path), word_to_id)\n test_data = nlp.words_to_word_ids(nlp.read_words(test_path), word_to_id)\n vocab_size = len(word_to_id)\n\n # logging.info(nlp.read_words(train_path)) # ... 'according', 'to', 'mr.', '<unk>', '<eos>']\n # logging.info(train_data) # ... 214, 5, 23, 1, 2]\n # logging.info(word_to_id) # ... 'beyond': 1295, 'anti-nuclear': 9599, 'trouble': 1520, '<eos>': 2 ... }\n # logging.info(vocabulary) # 10000\n # exit()\n return train_data, valid_data, test_data, vocab_size", "def _process_data_file(self):\n \n with open(self.data_file, 'r') as f:\n self.description = f.readline().strip()\n data = np.loadtxt(self.data_file, skiprows=1)\n\n return data", "def preprocess(self, data):\n\n input_data_str = data[0].get(\"data\")\n if input_data_str is None:\n input_data_str = data[0].get(\"body\")\n\n input_data = input_data_str.decode(\"utf-8\")\n input_tensor = torch.Tensor(ast.literal_eval(input_data))\n return input_tensor", "def _read_data(self, txtfile):\n data_string = open(txtfile,'r').read()\n return data_string", "def load_data(data_config):\n return tfds.load(data_config.path, with_info=data_config.load_with_info)" ]

[ "0.624653", "0.58884597", "0.5842658", "0.58061516", "0.57342064", "0.5731174", "0.5722274", "0.5629815", "0.5598952", "0.557254", "0.55650526", "0.5564843", "0.5533171", "0.54985625", "0.53941905", "0.53820044", "0.5362768", "0.5348556", "0.5286359", "0.5258125", "0.52522415", "0.5231253", "0.51859653", "0.5166963", "0.51578295", "0.5154556", "0.51504016", "0.51468414", "0.51278067", "0.51239353" ]

[ "def writeparamfile(self,filename_): # 3\n res = self.__obj.writeparamfile(filename_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def write_parameter_sets(self, filename = 'inputparameterfile', *args,\n **kwargs):\n try:\n np.savetxt(filename, self.parset2run, *args, **kwargs)\n print('file saved in directory %s' % os.getcwd())\n except PystanSequenceError:\n print('Parameter sets to run model with not yet setup.')", "def __openParameterfile(self, filename):\n #TODO: change from pure text file to xml?\n try:\n import CompuCellSetup\n self.__fileHandle, self.__fullFileName = CompuCellSetup.openFileInSimulationOutputDirectory(filename, \"a\")\n except IOError:\n print \"Could not open file \", filename, \\\n \" for writing. Check if you have necessary permissions.\"", "def save(self, filename, ftype='HDF5'):\n from . import Param\n from ...util.misc import param_to_array\n def gather_params(self, plist):\n if isinstance(self,Param):\n plist.append(self)\n plist = []\n self.traverse(gather_params, plist)\n names = self.parameter_names(adjust_for_printing=True)\n if ftype=='HDF5':\n try:\n import h5py\n f = h5py.File(filename,'w')\n for p,n in zip(plist,names):\n n = n.replace('.','_')\n p = param_to_array(p)\n d = f.create_dataset(n,p.shape,dtype=p.dtype)\n d[:] = p\n if hasattr(self, 'param_array'):\n d = f.create_dataset('param_array',self.param_array.shape, dtype=self.param_array.dtype)\n d[:] = self.param_array\n f.close()\n except:\n raise 'Fails to write the parameters into a HDF5 file!'", "def write_to(self, filename):\n paramstring = (\"# Correlation Length lc \\n{0} \\n\"\n \"# icoordchange \\n{1} \\n\"\n \"# ispec \\n{2} \\n\"\n \"# ireg \\n{3} \\n\"\n \"# xori \\n{4} \\n\"\n \"# yori \\n{5} \\n\"\n \"# dx \\n{6} \\n\"\n \"# dy \\n{7} \\n\"\n \"# nx \\n{8} \\n\"\n \"# ny \\n{9} \\n\"\n \"# valex \\n{10} \\n\"\n \"# snr \\n{11} \\n\"\n \"# varbak \\n{12}\").format(self.cl, self.icoordchange, self.ispec,\n self.ireg, self.xori, self.yori, self.dx, self.dy,\n self.nx, self.ny, self.valex, self.snr, self.varbak,\n )\n\n with open(filename, 'w') as f:\n f.write(paramstring)\n logger.info(\"Written parameters into file {0}\".format(filename))", "def _write_params(self, force=False):\n\t\tif force or not os.path.exists(self._get_params_filepath()):\n\t\t\tf = open(self._get_params_filepath(), 'w')\n\t\t\tf.write(\"\\n\".join(self.params))\n\t\t\tf.write(\"\\n\")\n\t\t\tf.close()\n\t\t\tlogger.debug(\"Wrote %s\" % (self._get_params_filepath()))\n\t\telse:\n\t\t\tlogger.debug(\"The params file already exists, I don't overwrite it.\")", "def writeParamToFile(self, file, sect):\r\n f = configparser.ConfigParser()\r\n f.add_section(sect)\r\n\r\n for (key, value) in self.m_param.items():\r\n f.set(sect, key, value)\r\n # print(key + ':' + value)\r\n f.write(open(file, 'w'))", "def write_model_params(self, file_name, params):\n params_to_save = {}\n for key, value in params.items():\n params_to_save[key.name] = value\n filename = os.getcwd() + file_name + \".txt\"\n print(filename)\n os.makedirs(os.path.dirname(filename), exist_ok=True)\n with open(filename, mode='w+', newline='') as params_file:\n #params_writer = csv.writer(params_file, delimiter=',', quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n params_file.write(json.dumps(params_to_save))", "def save(self, filename):\n with open(filename, 'w') as f:\n pickle.dump(self.pca.get_params(deep=True), f)", "def writeParameters(paramFile,paramWindDirection,paramWindSpeed,paramWaterHeight,paramRainFall):\n try:\n with open(paramFile,\"w\") as outfile:\n toWrite = {\"parameters\":{\"windDirection\":paramWindDirection,\"windSpeed\":paramWindSpeed,\"waterHeight\":\n paramWaterHeight,\"rainFall\":paramRainFall}}\n json.dump(toWrite,outfile)\n return \"Write successful\"\n except IOError:\n return \"Unable to write\"", "def save_pkl(self, filename):\n param_dict = {}\n param_dict['learningrate'] = self.learningrate\n param_dict['verbose'] = self.verbose\n param_dict['loadsize'] = self.loadsize\n param_dict['batchsize'] = self.batchsize\n param_dict['momentum'] = self.momentum\n param_dict['epochcount'] = self.epochcount\n param_dict['momentum_batchcounter'] = self.momentum_batchcounter\n param_dict['incs'] = dict(\n [(p.name, self._incs[p].get_value()) for p in self._params])\n if self.rmsprop is not None:\n param_dict['avg_grad_sqrs'] = dict(\n [(p.name, self._avg_grad_sqrs[p].get_value()) for p in self._params])\n pickle.dump(param_dict, open(filename, 'wb'))", "def parameters(ifilename, ofilename, parametername, parameter):\n ifile = open(ifilename, 'r')\n lines = ifile.readlines()\n ifile.close()\n ofile = open(ofilename, 'w')\n for line in lines:\n if line.find(parametername) != -1:\n ofile.write('\\t' + parametername + '=' + str(parameter) + ',\\n')\n else:\n ofile.write(line)\n ofile.close()", "def export_parameters(self, file_name):\n ssm = self.session.client('ssm')\n parameters = ssm_utils.get_parameters_by_path(\n ssm,\n Path=self.confetti_path\n )\n\n if parameters:\n with open(file_name, 'w') as out_file:\n out_file.write(json.dumps(parameters))", "def save(self, filename):\n self.graph.save(filename)\n with open(filename + \".json\", \"w\") as f:\n f.write(json.dumps(self.params))", "def save_parameters(self):\n paramfile = os.path.join(self._datadir, self.id.lower() + '.cfg')\n \n params_var = {}\n params_var['eta'] = self.system_param['eta']\n params_var['cov'] = self.system_param['cov']\n \n with open(paramfile, 'w') as paramjson:\n json.dump(params_var, paramjson)", "def write(filename, parameters):\n with open(filename, \"w\") as f:\n json.dump(parameters, f, indent=4)", "def write(self, filename):\n pass", "def write(self, filename):\n pass", "def saveToFile(self,filename):\n path = os.path.dirname(__file__)+\"/\"+filename\n stream = open(path,\"w\")\n yaml.dump(self.parameters(),stream)", "def on_save_parameters(self):\n obj_points = self.get_object_points()\n cam_pos = self.get_camera_position()\n distortion = self.get_distortion_coeeficients()\n\n d = {\n 'object positions': obj_points,\n 'camera positions': cam_pos,\n 'distortion coefficients': distortion\n }\n\n jsn = json.dumps(d)\n h = hashlib.sha1(jsn.encode('utf-8')).hexdigest()\n fn = f'{h}.json'\n\n with open(fn, 'w') as f:\n f.write(jsn)\n\n self.statusBar().showMessage(f'Parameters have been save to {fn}.')\n self.param_file = fn", "def write_param(self):\n param_file = f\"{self.name}.snapparam\"\n coeff_file = f\"{self.name}.snapcoeff\"\n model = self.model\n describer = self.model.describer\n profile = describer.element_profile\n ne = len(self.elements)\n nbc = len(describer.subscripts)\n if describer.quadratic:\n nbc += int((1 + nbc) * nbc / 2)\n\n coeff_lines = []\n coeff_lines.append(f\"{ne} {nbc + 1}\")\n for element, coeff in zip(self.elements, np.split(model.model.coef_, ne)):\n coeff_lines.append(f\"{element} {profile[element]['r']} {profile[element]['w']}\")\n coeff_lines.extend([str(c) for c in coeff])\n with open(coeff_file, \"w\") as f:\n f.write(\"\\n\".join(coeff_lines))\n\n param_lines = []\n keys = [\"rcutfac\", \"twojmax\"]\n param_lines.extend([f\"{k} {getattr(describer, k)}\" for k in keys])\n param_lines.extend([\"rfac0 0.99363\", \"rmin0 0\"])\n param_lines.append(f\"quadraticflag {int(describer.quadratic)}\")\n param_lines.append(\"bzeroflag 0\")\n with open(param_file, \"w\") as f:\n f.write(\"\\n\".join(param_lines))\n\n pair_style = self.pair_style\n pair_coeff = self.pair_coeff.format(\n elements=\" \".join(self.elements), coeff_file=coeff_file, param_file=param_file\n )\n return [pair_style, pair_coeff]", "def write_parameters(data, run_dir, is_parallel):\n pkio.write_text(\n run_dir.join(template_common.PARAMETERS_PYTHON_FILE),\n _generate_parameters_file(\n data,\n run_dir,\n is_parallel,\n ),\n )", "def WriteFile(self, filename) :\n\n # open file for writing:\n f = open(filename, 'w')\n\n ## loop over key/value pairs:\n #for k,v in self.iteritems():\n # # add line; at least the specified number of characters \n # # is used for the key:\n # f.write( '%-20s:%s\\n' % (k,v) )\n ##endfor\n\n # write processed input:\n f.writelines(self.outfile)\n \n # close file:\n f.close()", "def write_to(self, filename):\n with open(filename, 'w') as f:\n for xx, yy, zz, ww in zip(self.x, self.y, self.field, self.weight):\n f.write(\"%s %s %s %s\\n\" % (xx, yy, zz, ww))\n logger.info(\"Written data into file {0}\".format(filename))", "def _log_params(self):\n params_path = os.path.join(self._log_dir, self._name + \"params.json\")\n logger.info(\"Writing params to {}\".format(params_path))\n\n params = [(str(k),str(v)) for k,v in self.__dict__.items()]\n\n with open(params_path, 'w') as params_file:\n json.dump(dict(params), params_file, indent=4)", "def create_multiseq_parameters(filename, folder):\n default_filename = folder + PATHDELIM + 'resources'+ PATHDELIM + \"multiseq_params.txt\"\n try:\n filep = open(default_filename, 'r')\n except:\n eprintf(\"ERROR: cannot open the default parameter file \" + sQuote(default_filename) ) \n exit_process(\"ERROR: cannot open the default parameter file \" + sQuote(default_filename)) \n\n lines = filep.readlines()\n with open(filename, 'w') as newfile:\n for line in lines:\n fprintf(newfile, \"%s\", line);\n \n filep.close()\n #result['filename'] = filename\n return True", "def write(self, filename): # real signature unknown; restored from __doc__\n pass", "def write_initparams(params, outdir, padding_var=7, paramsfn='parameters', skiplat=False, skipglat=False):\n paramfile = outdir + paramsfn + '.txt'\n with open(paramfile, 'w') as myfile:\n myfile.write('# Parameters\\n')\n\n dio.ensure_dir(outdir)\n for key in params:\n if key == 'reg1' or key == 'reg2' or key == 'reg3':\n np.savetxt(outdir + key + '.txt', params[key], fmt='%d', delimiter=',', header=key + ' particle IDs')\n if key == 'xyv0':\n np.savetxt(outdir + 'xyv0.txt', params['xyv0'], delimiter=',',\n header='xy0 (initial positions) v0 (initial velocities)')\n elif key == 'xy':\n if not skiplat:\n np.savetxt(outdir + 'xy.txt', params['xy'], delimiter=',',\n header='xy0 (undeformed lattice positions from mesh)')\n elif key == 'KL':\n if not skiplat:\n np.savetxt(outdir + 'KL.txt', params['KL'], fmt='%i', delimiter=',',\n header='KL (Bond Connectivity List)')\n elif key == 'NL':\n if not skiplat:\n np.savetxt(outdir + 'NL.txt', params['NL'], fmt='%i', delimiter=',', header='NL (Neighbor List)')\n elif key == 'BND':\n np.savetxt(outdir + 'BND.txt', params['BND'], fmt='%i', header='BND (Boundary List)')\n elif key == 'OmK':\n if not skipglat:\n np.savetxt(outdir + 'OmK.txt', params['OmK'], fmt='%f', delimiter=',',\n header='OmK (spring frequency array, for Nash limit: (-1)^(c+b)kl^2/Iw')\n elif key == 'OmG':\n if not skipglat:\n np.savetxt(outdir + 'Omg.txt', params['OmG'], fmt='%f', delimiter=',',\n header='Omg (gravitational frequency array, for Nash limit: (-1)^(c+1)mgl/Iw')\n elif key == 'LVUC':\n if not skiplat:\n np.savetxt(outdir + 'LVUC.txt', params['LVUC'], fmt='%i', delimiter=',',\n header='Lattice Vector and Unit cell vector coordinates')\n else:\n with open(paramfile, 'a') as myfile:\n # print 'Writing param ', str(key)\n # print ' with value ', str(params[key])\n # print ' This param is of type ', type(params[key])\n\n if isinstance(params[key], str):\n myfile.write('{{0: <{}}}'.format(padding_var).format(key) + \\\n '= ' + params[key] + '\\n')\n elif isinstance(params[key], np.ndarray):\n # print params[key].dtype\n if key == 'BIND':\n print 'BIND = ', str(params[key]).replace('\\n', '')\n\n myfile.write('{{0: <{}}}'.format(padding_var).format(key) + \\\n '= ' + \", \".join(np.array_str(params[key]).split()).replace('[,', '[') + '\\n')\n # if params[key].dtype == 'float64':\n # myfile.write('{{0: <{}}}'.format(padding_var).format(key)+\\\n # '= '+ np.array_str(params[key]).replace('\\n','').replace(' ',',') +'\\n')\n # elif params[key].dtype == 'int32':\n # myfile.write('{{0: <{}}}'.format(padding_var).format(key)+\\\n # '= '+ str(params[key]).replace('\\n','').replace(' ',',') +'\\n')\n # else:\n # myfile.write('{{0: <{}}}'.format(padding_var).format(key)+\\\n # '= '+ str(params[key]).replace('\\n','').replace(' ',',') +'\\n')\n elif isinstance(params[key], list):\n myfile.write('{{0: <{}}}'.format(padding_var).format(key) + \\\n '= ' + str(params[key]) + '\\n')\n else:\n # print key, ' = ', params[key]\n myfile.write('{{0: <{}}}'.format(padding_var).format(key) + \\\n '= ' + '{0:.12e}'.format(params[key]) + '\\n')\n\n # elif key == 'LV':\n # np.savetxt(outdir+'LV.txt',params['LV'], fmt='%18e',delimiter=',', header='Lattice Vector coordinates')\n # elif key == 'UC':\n # np.savetxt(outdir+'UC.txt',params['UC'], fmt='%18e',delimiter=',', header='Unit cell vector coordinates')\n #\n # elif key == 'h':\n # with open(outdir+'h.txt', \"w\") as hfile:\n # hfile.write(\"# h (time step) \\n{0:5e}\".format(h) )\n # elif key == 'beta':\n # with open(outdir+'beta.txt', \"w\") as betafile:\n # betafile.write(\"# beta (damping coeff) \\n{0:5e}\".format(beta) )", "def save_params(params):\n with open('params.p', 'wb') as out_file:\n pickle.dump(params, out_file)", "def write_to_file(self, filename: str) -> None:" ]

[ "0.8479623", "0.7223541", "0.71134436", "0.704342", "0.69193083", "0.68712884", "0.6840946", "0.6750531", "0.6718471", "0.6663219", "0.66562045", "0.6610654", "0.65982085", "0.65181977", "0.647503", "0.6464667", "0.6421562", "0.6421562", "0.64120954", "0.6394938", "0.6388683", "0.637984", "0.63760895", "0.6374416", "0.6373787", "0.63291204", "0.62886137", "0.6285356", "0.6261053", "0.62370676" ]

[ "def ineqconstr(x, problem):\n x, t_final = matrify(x, problem)\n c = []\n\n # inter vehicles\n c += [veh_coll_avoid(x[:, :2, v1], x[:, :2, v2], problem)\n for v1 in range(problem['Nv']) for v2 in range(v1 + 1, problem['Nv'])]\n\n # obstacles\n c += [obs.avoid(x[:, :2, veh]) for obs in problem['obstacles'] for veh in range(problem['Nv'])]\n return np.concatenate(c) if c else np.array([])", "def analyzesolution(self,whichstream_,whichsol_):\n res = __library__.MSK_XX_analyzesolution(self.__nativep,whichstream_,whichsol_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def getpviolvar(self,whichsol_,sub_,viol_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n _viol_minlength = (num_)\n if (num_) > 0 and viol_ is not None and len(viol_) != (num_):\n raise ValueError(\"Array argument viol is not long enough: Is %d, expected %d\" % (len(viol_),(num_)))\n if isinstance(viol_,numpy.ndarray) and not viol_.flags.writeable:\n raise ValueError(\"Argument viol must be writable\")\n if viol_ is None:\n raise ValueError(\"Argument viol may not be None\")\n if isinstance(viol_, numpy.ndarray) and viol_.dtype is numpy.dtype(numpy.float64) and viol_.flags.contiguous:\n _viol_copyarray = False\n _viol_tmp = ctypes.cast(viol_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif viol_ is not None:\n _viol_copyarray = True\n _viol_np_tmp = numpy.zeros(len(viol_),numpy.dtype(numpy.float64))\n _viol_np_tmp[:] = viol_\n assert _viol_np_tmp.flags.contiguous\n _viol_tmp = ctypes.cast(_viol_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _viol_copyarray = False\n _viol_tmp = None\n \n res = __library__.MSK_XX_getpviolvar(self.__nativep,whichsol_,num_,_sub_tmp,_viol_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _viol_copyarray:\n viol_[:] = _viol_np_tmp", "def g_solving_subproblem_of_ALR(self,vehicle_id):\r\n global_LB = -10000\r\n global_UB = 10000\r\n iteration_for_RSP = 20\r\n optimal_solution_for_RSP = None\r\n self.multiplier_v = 0.5\r\n\r\n # solve the expected shortest path problem\r\n self.g_dynamic_programming_algorithm(vehicle_id, 3)\r\n\r\n # obtain the variance\r\n y_ =self.g_ending_state_vector[vehicle_id].VSStateVector[0].Primal_Label_cost_variance\r\n\r\n for k in range(iteration_for_RSP):\r\n # print(k)\r\n LB = 0\r\n # step 2: solve decomposed dual problems\r\n # Part I: subproblem of x\r\n self.g_dynamic_programming_algorithm(vehicle_id, 1)\r\n LB += self.g_ending_state_vector[vehicle_id].VSStateVector[0].Label_cost_for_searching\r\n\r\n # Part II: subproblem of y\r\n obj_of_y_ = self.reliability * (y_) ** 0.5 - self.multiplier_v * y_\r\n if obj_of_y_ > 0:\r\n y = 0\r\n LB += 0\r\n else:\r\n y = y_\r\n LB += obj_of_y_\r\n\r\n # generate an upper bound\r\n variance = self.g_ending_state_vector[vehicle_id].VSStateVector[0].Primal_Label_cost_variance\r\n Label_cost_for_lagrangian_mean = self.g_ending_state_vector[vehicle_id].VSStateVector[0].Label_cost_for_searching_mean\r\n UB = Label_cost_for_lagrangian_mean + self.reliability * (variance) ** 0.5\r\n\r\n # print(\"UB:{}\".format(UB))\r\n # print(\"LB:{}\".format(LB))\r\n\r\n # UB and LB update\r\n if LB > global_LB:\r\n global_LB = LB\r\n\r\n if UB < global_UB:\r\n global_UB = UB\r\n optimal_solution_for_RSP = self.g_ending_state_vector[vehicle_id].VSStateVector[0]\r\n\r\n # step 3: update multipliers\r\n if variance- y != 0:\r\n self.multiplier_v+= (global_UB - LB) / (variance-y)\r\n # if self.multiplier_v<0:\r\n # self.multiplier_v=1\r\n # print(self.multiplier_v)\r\n\r\n # step 4: termination condition test\r\n if global_UB != 0:\r\n gap = abs((global_UB - global_LB) / global_UB)\r\n # print(gap)\r\n if gap < 0.02:\r\n print(\"iteration{}\".format(k + 1))\r\n print(self.multiplier_v)\r\n print(global_LB, global_UB)\r\n return optimal_solution_for_RSP, global_LB\r\n else:\r\n if global_UB - global_LB == 0:\r\n print(\"iteration{}\".format(k + 1))\r\n print(self.multiplier_v)\r\n print(global_LB, global_UB)\r\n return optimal_solution_for_RSP, global_LB\r\n\r\n if k == iteration_for_RSP - 1:\r\n print(\"iteration{}\".format(k + 1))\r\n print(self.multiplier_v)\r\n print(global_LB, global_UB)\r\n return optimal_solution_for_RSP, global_LB", "def g_solving_subproblem_of_LR(self,vehicle_id):\r\n global_LB=-10000\r\n global_UB=10000\r\n iteration_for_RSP=20\r\n optimal_solution_for_RSP=None\r\n optimal_value_y=0\r\n self.multiplier_v=0.5\r\n\r\n #solve the expected shortest path problem\r\n self.g_dynamic_programming_algorithm(vehicle_id, 4)\r\n #obtain the variance\r\n y_=self.g_ending_state_vector[vehicle_id].VSStateVector[0].Primal_Label_cost_variance\r\n\r\n for k in range(iteration_for_RSP):\r\n # print(k)\r\n LB=0\r\n # step 2: solve decomposed dual problems\r\n # Part I: subproblem of x\r\n self.g_dynamic_programming_algorithm(vehicle_id, 2)\r\n LB+=self.g_ending_state_vector[vehicle_id].VSStateVector[0].Label_cost_for_lagrangian\r\n\r\n # Part II: subproblem of y\r\n obj_of_y_ = self.reliability * (y_) ** 0.5 - self.multiplier_v * y_\r\n if obj_of_y_ > 0:\r\n y = 0\r\n LB += 0\r\n else:\r\n y = y_\r\n LB += obj_of_y_\r\n # generate an upper bound\r\n variance = self.g_ending_state_vector[vehicle_id].VSStateVector[0].Primal_Label_cost_variance\r\n Label_cost_for_lagrangian_mean=self.g_ending_state_vector[vehicle_id].VSStateVector[0].Label_cost_for_lagrangian_mean\r\n UB=Label_cost_for_lagrangian_mean+self.reliability*(variance)**0.5\r\n\r\n # print(\"UB:{}\".format(UB))\r\n # print(\"LB:{}\".format(LB))\r\n\r\n # UB and LB update\r\n if LB > global_LB:\r\n global_LB = LB\r\n optimal_solution_for_RSP = self.g_ending_state_vector[vehicle_id].VSStateVector\r\n optimal_value_y = y\r\n\r\n if UB < global_UB:\r\n global_UB = UB\r\n\r\n\r\n # step 3: update multipliers\r\n if variance-y!= 0:\r\n self.multiplier_v+= (global_UB - LB) / (variance-y)\r\n # if self.multiplier_v<0:\r\n # self.multiplier_v=1\r\n # print(self.multiplier_v)\r\n\r\n # step 4: termination condition test\r\n if global_UB != 0:\r\n gap = abs((global_UB-global_LB) / global_UB)\r\n # print(gap)\r\n if gap < 0.02:\r\n print(\"iteration{}\".format(k + 1))\r\n print(self.multiplier_v)\r\n print(global_LB, global_UB)\r\n return optimal_solution_for_RSP, optimal_value_y,global_LB,global_UB\r\n else:\r\n if global_UB - global_LB == 0:\r\n print(\"iteration{}\".format(k + 1))\r\n print(self.multiplier_v)\r\n print(global_LB, global_UB)\r\n return optimal_solution_for_RSP,optimal_value_y,global_LB,global_UB\r\n\r\n if k == iteration_for_RSP - 1:\r\n print(\"iteration{}\".format(k + 1))\r\n print(self.multiplier_v)\r\n print(global_LB, global_UB)\r\n return optimal_solution_for_RSP,optimal_value_y,global_LB,global_UB", "def solve(self):\n # a stack of queries (aka subproblems to be solved)\n stack = []\n initial_query = (len(self.items), self.knapsack_size)\n stack.append(initial_query)\n # Run as long as there are subproblems that need to be solved.\n # - this might not pass through all possible subproblems; in fact, \n # we're counting on it\n # - it will only pass through the subproblems that the initial \n # problem needs solved\n while len(stack) > 0:\n (end, ksize) = stack[-1]\n # this is the subproblem where we have only items self.items[:end]\n # and the knapsack size is ksize\n if self.items[end - 1].size > ksize:\n # item end-1 does not fit\n try:\n # retrieve subproblem result from the cache\n self._cache[(end, ksize)] = self._cache[(end - 1, ksize)]\n except KeyError:\n # subproblem hasn't been solved yet, put it on the stack\n stack.append((end - 1, ksize))\n continue\n else:\n # item end-1 fits; we get two subproblems:\n # - one if we don't include item end-1 in the knapsack\n # - one if we do include it\n sub1 = (end - 1, ksize)\n sub2 = (end - 1, ksize - self.items[end - 1].size)\n try:\n # retrieve 1st subproblem's result from the cache and \n # compute max value if we don't include item end-1\n val1 = self._cache[sub1]\n except KeyError:\n # subproblem hasn't been solved yet, put it on the stack\n stack.append(sub1)\n continue\n try:\n # retrieve 2nd subproblem's result from the cache and\n # compute max value if we do include item end-1\n val2 = self.items[end - 1].value + self._cache[sub2]\n except KeyError:\n # subproblem hasn't been solved yet, put it on the stack\n stack.append(sub2)\n continue\n # is it better to include item end-1 or not?\n self._cache[(end, ksize)] = max(val1, val2)\n # done with this subproblem\n stack.pop()\n return self._cache[(initial_query)]", "def getpviolvar(self,whichsol_,sub,viol): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n num_ = None\n if num_ is None:\n num_ = len(sub)\n elif num_ != len(sub):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None: num_ = 0\n if sub is None: raise TypeError(\"Invalid type for argument sub\")\n if sub is None:\n sub_ = None\n else:\n try:\n sub_ = memoryview(sub)\n except TypeError:\n try:\n _tmparr_sub = array.array(\"i\",sub)\n except TypeError:\n raise TypeError(\"Argument sub has wrong type\")\n else:\n sub_ = memoryview(_tmparr_sub)\n \n else:\n if sub_.format != \"i\":\n sub_ = memoryview(array.array(\"i\",sub))\n \n if viol is None: raise TypeError(\"Invalid type for argument viol\")\n _copyback_viol = False\n if viol is None:\n viol_ = None\n else:\n try:\n viol_ = memoryview(viol)\n except TypeError:\n try:\n _tmparr_viol = array.array(\"d\",viol)\n except TypeError:\n raise TypeError(\"Argument viol has wrong type\")\n else:\n viol_ = memoryview(_tmparr_viol)\n _copyback_viol = True\n else:\n if viol_.format != \"d\":\n viol_ = memoryview(array.array(\"d\",viol))\n _copyback_viol = True\n if viol_ is not None and len(viol_) != (num_):\n raise ValueError(\"Array argument viol has wrong length\")\n res = self.__obj.getpviolvar(whichsol_,num_,sub_,viol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_viol:\n viol[:] = _tmparr_viol", "def obstacle(psi,f_rhs,tol,f_dist,h0,pts,tri,*args,**kwargs):\n announce = kwargs.get('announce',False)\n if announce:\n print (\" obstacle: asking poisson() for linear system and unconstrained soln ...\")\n # use poisson to get unconstrained stiffness, load\n uhpoisson, inside, AA, bb = poisson(f_rhs,f_dist,h0,pts,tri,announce=True,getsys=True)\n omega = 1.75 # found by trial and error\n maxiter = 500\n Npts = np.shape(pts)[0] # = number of nodes\n geps = 0.001 * h0\n ii = (f_dist(pts, *args) < -geps) # boolean array for interior nodes\n N = ii.sum() # = number of interior nodes\n UU = np.triu(AA,1)\n LL = np.tril(AA,-1)\n dd = np.diag(AA).copy()\n if any(dd == 0.0):\n print ('ERROR: stiffness matrix has zero on diagonal')\n return None\n # first guess is max(uhpoisson,psi)\n ps = np.maximum(psi(pts[ii]),np.zeros(N)) # FIXME: does not work well if f < 0?\n uold = np.maximum(uhpoisson[ii],ps)\n unew = uold.copy()\n omcomp = 1.0 - omega\n ierr = np.array([])\n # iterate: constrained point over-relaxation\n for l in range(maxiter+1):\n Ux = np.dot(UU,uold)\n for j in range(N): # iterate over interior vertices\n # Gauss-Seidel idea:\n if j == 0:\n utmp = (bb[j] - Ux[j]) / dd[j]\n else:\n utmp = (bb[j] - np.dot(LL[j,:j],unew[:j]) - Ux[j]) / dd[j]\n # over-relax and project up to psi if needed\n unew[j] = np.maximum(omcomp * uold[j] + omega * utmp, ps[j])\n er = max(abs(unew-uold))\n ierr = np.append(ierr,er)\n uold = unew.copy()\n if er < tol:\n break\n if l == maxiter:\n print ('WARNING: max number of iterations reached')\n # construct solution by filling interior values and boundary values\n uh = uhpoisson.copy()\n uh[ii] = unew\n return uh, ii, ierr", "def solve(self):\n # check for jacobian and set it if present and to be used\n if self.use_sparse:\n if self._use_jac and hasattr(self.problem,'sparse_jac'):\n jac = self.problem.sparse_jac\n else:\n jac = None\n else:\n if self._use_jac and hasattr(self.problem,'jac'):\n jac = self.problem.jac\n else:\n jac = None\n \n # Initialize solver and solve \n \n solved = False\n local_min = False\n\n res = N.zeros(self.x0.__len__())\n while (not solved) and self.reg_count < 2:\n try:\n if self._use_fscale:\n self.solver.KINSOL_init(self.func,self.x0,self.dim,jac,self.constraints,self.use_sparse,self.verbosity,self.norm_of_res,self.reg_param,self.fscale)\n else:\n self.solver.KINSOL_init(self.func,self.x0,self.dim,jac,self.constraints,self.use_sparse,self.verbosity,self.norm_of_res,self.reg_param,None)\n start = time.clock()\n res = self.solver.KINSOL_solve(not self._use_ls)\n stop = time.clock()\n self.exec_time += (stop - start)\n solved = True\n except KINError as error:\n if error.value == 42:\n # Try the heuristic\n if hasattr(self.problem, 'get_heuristic_x0'):\n print \"----------------------------------------------------\"\n print \" Solver stuck with zero step-length.\"\n print \"----------------------------------------------------\"\n print \"The following variables have start value zero\"\n print \"and min set to zero causing the zero step-lenght.\"\n print \"These settings are either set by default or by user.\"\n print \"\"\n\n self.x0 = self.problem.get_heuristic_x0()\n self.reg_count += 1\n \n print \"\"\n print \"This setting (start and min to zero) can often\"\n print \"cause problem when initializing the system. \"\n print \"\"\n print \"To avoid this the above variables have\"\n print \"their start attributes reset to one.\"\n print \"\"\n print \"Trying to solve the system again...\"\n else:\n raise KINSOL_Exception(\"Regularization failed due to constraints, tried getting heuristic initial guess but failed.\")\n \n\n elif (error.value == 2):\n print \"---------------------------------------------------------\"\n print \"\"\n print \" !!! WARNING !!!\"\n print \"\"\n print \" KINSOL has returned a result but the algorithm has converged\"\n print \" to a local minima, the initial values are NOT consistant!\"\n print \"\"\n print \"---------------------------------------------------------\"\n solved = True\n local_min = True\n else:\n # Other error, send onward as exception\n self.problem.check_constraints(res)\n raise KINSOL_Exception(error.msg[error.value])\n \n if not solved:\n self.solver.Free_KINSOL()\n raise KINSOL_Exception(\"Algorithm exited solution loop without finding a solution, please contact Assimulo support.\")\n\n if self.check_with_model:\n self.problem.check_constraints(res)\n if not local_min:\n print \"Problem sent to KINSOL solved.\"\n \n return res", "def _get_solution(self, x_0, sol, k_fb, k_fb_perf_0, sol_verbose=False,\n crashed=False, feas_tol=1e-6, q_0=None, k_fb_0=None):\n\n success = True\n feasible = True\n if crashed:\n feasible = False\n\n if self.verbosity > 1:\n print(\"Optimization crashed, infeasible soluion!\")\n else:\n g_res = np.array(sol[\"g\"]).squeeze()\n\n # This is not sufficient, since casadi gives out wrong feasibility values\n if np.any(np.array(self.lbg) - feas_tol > g_res) or np.any(\n np.array(self.ubg) + feas_tol < g_res):\n feasible = False\n\n x_opt = sol[\"x\"]\n self.has_openloop = True\n\n if self.opt_x0:\n x_0 = x_opt[:self.n_s]\n x_opt = x_opt[self.n_s:, :]\n\n # get indices of the respective variables\n n_u_0 = self.n_u\n n_u_perf = 0\n if self.n_perf > 1:\n n_u_perf = (self.n_perf - self.r) * self.n_u\n n_k_ff = (self.n_safe - 1) * self.n_u\n\n c = 0\n idx_u_0 = np.arange(n_u_0)\n c += n_u_0\n idx_u_perf = np.arange(c, c + n_u_perf)\n c += n_u_perf\n idx_k_ff = np.arange(c, c + n_k_ff)\n c += n_k_ff\n\n u_apply = np.array(cas_reshape(x_opt[idx_u_0], (1, self.n_u)))\n k_ff_perf = np.array(\n cas_reshape(x_opt[idx_u_perf], (self.n_perf - self.r, self.n_u)))\n\n k_ff_safe = np.array(\n cas_reshape(x_opt[idx_k_ff], (self.n_safe - 1, self.n_u)))\n k_ff_safe_all = np.vstack((u_apply, k_ff_safe))\n\n k_fb_safe_output = array_of_vec_to_array_of_mat(np.copy(k_fb), self.n_u,\n self.n_s)\n\n p_safe, q_safe, gp_sigma_pred_safe_all = self.get_safety_trajectory_openloop(x_0, u_apply,\n np.copy(k_fb),\n k_ff_safe, q_0, k_fb_0)\n\n p_safe = np.array(p_safe)\n q_safe = np.array(q_safe)\n\n if self.verbosity > 1:\n print(\"=== Safe Trajectory: ===\")\n print(\"Centers:\")\n print(p_safe)\n print(\"Shape matrices:\")\n print(q_safe)\n print(\"Safety controls:\")\n print(u_apply)\n print(k_ff_safe)\n\n k_fb_perf_traj_eval = np.empty((0, self.n_s * self.n_u))\n k_ff_perf_traj_eval = np.empty((0, self.n_u))\n if self.n_safe > 1:\n k_fb_perf_traj_eval = np.vstack(\n (k_fb_perf_traj_eval, k_fb[:self.r - 1, :]))\n k_ff_perf_traj_eval = np.vstack(\n (k_ff_perf_traj_eval, k_ff_safe[:self.r - 1, :]))\n if self.n_perf > self.r:\n k_fb_perf_traj_eval = np.vstack((k_fb_perf_traj_eval,\n np.matlib.repmat(k_fb_perf_0,\n self.n_perf - self.r,\n 1)))\n k_ff_perf_traj_eval = np.vstack((k_ff_perf_traj_eval, k_ff_perf))\n\n if self.n_perf > 1:\n mu_perf, sigma_perf = self._f_multistep_perf_eval(x_0.squeeze(),\n u_apply,\n k_fb_perf_traj_eval,\n k_ff_perf_traj_eval)\n\n if self.verbosity > 1:\n print(\"=== Performance Trajectory: ===\")\n print(\"Mu perf:\")\n print(mu_perf)\n print(\"Peformance controls:\")\n print(k_ff_perf_traj_eval)\n\n feasible, _ = self.eval_safety_constraints(p_safe, q_safe)\n\n if self.rhc and feasible:\n self.k_ff_safe = k_ff_safe\n self.k_ff_perf = k_ff_perf\n self.p_safe = p_safe\n self.k_fb_safe_all = np.copy(k_fb)\n self.u_apply = u_apply\n self.k_fb_perf_0 = k_fb_perf_0\n\n if feasible:\n self.n_fail = 0\n\n if not feasible:\n self.n_fail += 1\n q_all = None\n k_fb_safe_output = None\n k_ff_all = None\n p_safe = None\n q_safe = None\n g_res = None\n\n if self.n_fail >= self.n_safe:\n # Too many infeasible solutions -> switch to safe controller\n if self.verbosity > 1:\n print(\n \"Infeasible solution. Too many infeasible solutions, switching to safe controller\")\n u_apply = self.safe_policy(x_0)\n k_ff_safe_all = u_apply\n else:\n # can apply previous solution\n if self.verbosity > 1:\n print((\n \"Infeasible solution. Switching to previous solution, n_fail = {}, n_safe = {}\".format(\n self.n_fail, self.n_safe)))\n if sol_verbose:\n u_apply, k_fb_safe_output, k_ff_safe_all, p_safe = self.get_old_solution(\n x_0, get_ctrl_traj=True)\n else:\n u_apply = self.get_old_solution(x_0)\n k_ff_safe_all = u_apply\n\n if sol_verbose:\n return x_0, u_apply, feasible, success, k_fb_safe_output, k_ff_safe_all, p_safe, q_safe, sol, gp_sigma_pred_safe_all\n\n return x_0, u_apply, success", "def easy_solve_room(self):\n have_res=False\n res=None\n if self.opt_ct==self.gsq_ct:\n if not self.sqs:\n #no sqs, but all reqs; just fill in the reqs!\n have_res=True\n sgs=[]\n for gsq in self.gatesqs:\n if self.isopt(gsq):\n sg=(gsq,'skip',(),(), gsq)\n sgs.append(sg)\n res=all_permutations(sgs)\n elif len(self.gatesqs)==0:\n if self.sqs:\n res=[]\n have_res=True\n else:\n res=[]\n have_res=True\n print 'should never get here!'\n #if not, then break down the subrooms and return their res.\n elif self.opt_ct==0:\n if self.req_ct%2!=0:\n res=[]\n have_res=True\n #dont create internal alleys!\n else:\n for sq in self.sqs:\n neighbors=getopendvs(self.rows,sq)\n if len(neighbors)<2:\n res=[]\n have_res=True\n #~ print 'bad room! has internal alleys at !',sq\n #~ print self\n return have_res,res", "def getpviolbarvar(self,whichsol_,sub_,viol_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n _viol_minlength = (num_)\n if (num_) > 0 and viol_ is not None and len(viol_) != (num_):\n raise ValueError(\"Array argument viol is not long enough: Is %d, expected %d\" % (len(viol_),(num_)))\n if isinstance(viol_,numpy.ndarray) and not viol_.flags.writeable:\n raise ValueError(\"Argument viol must be writable\")\n if viol_ is None:\n raise ValueError(\"Argument viol may not be None\")\n if isinstance(viol_, numpy.ndarray) and viol_.dtype is numpy.dtype(numpy.float64) and viol_.flags.contiguous:\n _viol_copyarray = False\n _viol_tmp = ctypes.cast(viol_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif viol_ is not None:\n _viol_copyarray = True\n _viol_np_tmp = numpy.zeros(len(viol_),numpy.dtype(numpy.float64))\n _viol_np_tmp[:] = viol_\n assert _viol_np_tmp.flags.contiguous\n _viol_tmp = ctypes.cast(_viol_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _viol_copyarray = False\n _viol_tmp = None\n \n res = __library__.MSK_XX_getpviolbarvar(self.__nativep,whichsol_,num_,_sub_tmp,_viol_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _viol_copyarray:\n viol_[:] = _viol_np_tmp", "def find_solution(self):\r\n for solution in self.solutions:\r\n if self.fitting_function.is_legal_solution(solution):\r\n return solution\r\n return None", "def getpviolcon(self,whichsol_,sub,viol): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n num_ = None\n if num_ is None:\n num_ = len(sub)\n elif num_ != len(sub):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None: num_ = 0\n if sub is None: raise TypeError(\"Invalid type for argument sub\")\n if sub is None:\n sub_ = None\n else:\n try:\n sub_ = memoryview(sub)\n except TypeError:\n try:\n _tmparr_sub = array.array(\"i\",sub)\n except TypeError:\n raise TypeError(\"Argument sub has wrong type\")\n else:\n sub_ = memoryview(_tmparr_sub)\n \n else:\n if sub_.format != \"i\":\n sub_ = memoryview(array.array(\"i\",sub))\n \n if viol is None: raise TypeError(\"Invalid type for argument viol\")\n _copyback_viol = False\n if viol is None:\n viol_ = None\n else:\n try:\n viol_ = memoryview(viol)\n except TypeError:\n try:\n _tmparr_viol = array.array(\"d\",viol)\n except TypeError:\n raise TypeError(\"Argument viol has wrong type\")\n else:\n viol_ = memoryview(_tmparr_viol)\n _copyback_viol = True\n else:\n if viol_.format != \"d\":\n viol_ = memoryview(array.array(\"d\",viol))\n _copyback_viol = True\n if viol_ is not None and len(viol_) != (num_):\n raise ValueError(\"Array argument viol has wrong length\")\n res = self.__obj.getpviolcon(whichsol_,num_,sub_,viol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_viol:\n viol[:] = _tmparr_viol", "def solutiondef(self,whichsol_):\n isdef_ = ctypes.c_int32()\n res = __library__.MSK_XX_solutiondef(self.__nativep,whichsol_,ctypes.byref(isdef_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n isdef_ = isdef_.value\n _isdef_return_value = isdef_\n return (_isdef_return_value)", "def getsolutioni(self,accmode_,i_,whichsol_): # 3\n if not isinstance(accmode_,accmode): raise TypeError(\"Argument accmode has wrong type\")\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res,resargs = self.__obj.getsolutioni(accmode_,i_,whichsol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _sk_return_value,_x_return_value,_sl_return_value,_su_return_value,_sn_return_value = resargs\n _sk_return_value = stakey(_sk_return_value)\n return _sk_return_value,_x_return_value,_sl_return_value,_su_return_value,_sn_return_value", "def sketch_of_solution(self,sol=None):\n raise NotImplementedError", "def solutiondef(self,whichsol_): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res,resargs = self.__obj.solutiondef(whichsol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _isdef_return_value = resargs\n return _isdef_return_value", "def getdviolvar(self,whichsol_,sub_,viol_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n _viol_minlength = (num_)\n if (num_) > 0 and viol_ is not None and len(viol_) != (num_):\n raise ValueError(\"Array argument viol is not long enough: Is %d, expected %d\" % (len(viol_),(num_)))\n if isinstance(viol_,numpy.ndarray) and not viol_.flags.writeable:\n raise ValueError(\"Argument viol must be writable\")\n if viol_ is None:\n raise ValueError(\"Argument viol may not be None\")\n if isinstance(viol_, numpy.ndarray) and viol_.dtype is numpy.dtype(numpy.float64) and viol_.flags.contiguous:\n _viol_copyarray = False\n _viol_tmp = ctypes.cast(viol_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif viol_ is not None:\n _viol_copyarray = True\n _viol_np_tmp = numpy.zeros(len(viol_),numpy.dtype(numpy.float64))\n _viol_np_tmp[:] = viol_\n assert _viol_np_tmp.flags.contiguous\n _viol_tmp = ctypes.cast(_viol_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _viol_copyarray = False\n _viol_tmp = None\n \n res = __library__.MSK_XX_getdviolvar(self.__nativep,whichsol_,num_,_sub_tmp,_viol_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _viol_copyarray:\n viol_[:] = _viol_np_tmp", "def get_sol(self):", "def getsolution(self,whichsol_,skc,skx,skn,xc,xx,y,slc,suc,slx,sux,snx): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n _copyback_skc = False\n if skc is None:\n skc_ = None\n else:\n try:\n skc_ = memoryview(skc)\n except TypeError:\n try:\n _tmparr_skc = array.array(\"i\",skc)\n except TypeError:\n raise TypeError(\"Argument skc has wrong type\")\n else:\n skc_ = memoryview(_tmparr_skc)\n _copyback_skc = True\n else:\n if skc_.format != \"i\":\n skc_ = memoryview(array.array(\"i\",skc))\n _copyback_skc = True\n if skc_ is not None and len(skc_) != self.getnumcon():\n raise ValueError(\"Array argument skc has wrong length\")\n _copyback_skx = False\n if skx is None:\n skx_ = None\n else:\n try:\n skx_ = memoryview(skx)\n except TypeError:\n try:\n _tmparr_skx = array.array(\"i\",skx)\n except TypeError:\n raise TypeError(\"Argument skx has wrong type\")\n else:\n skx_ = memoryview(_tmparr_skx)\n _copyback_skx = True\n else:\n if skx_.format != \"i\":\n skx_ = memoryview(array.array(\"i\",skx))\n _copyback_skx = True\n if skx_ is not None and len(skx_) != self.getnumvar():\n raise ValueError(\"Array argument skx has wrong length\")\n _copyback_skn = False\n if skn is None:\n skn_ = None\n else:\n try:\n skn_ = memoryview(skn)\n except TypeError:\n try:\n _tmparr_skn = array.array(\"i\",skn)\n except TypeError:\n raise TypeError(\"Argument skn has wrong type\")\n else:\n skn_ = memoryview(_tmparr_skn)\n _copyback_skn = True\n else:\n if skn_.format != \"i\":\n skn_ = memoryview(array.array(\"i\",skn))\n _copyback_skn = True\n if skn_ is not None and len(skn_) != self.getnumcone():\n raise ValueError(\"Array argument skn has wrong length\")\n _copyback_xc = False\n if xc is None:\n xc_ = None\n else:\n try:\n xc_ = memoryview(xc)\n except TypeError:\n try:\n _tmparr_xc = array.array(\"d\",xc)\n except TypeError:\n raise TypeError(\"Argument xc has wrong type\")\n else:\n xc_ = memoryview(_tmparr_xc)\n _copyback_xc = True\n else:\n if xc_.format != \"d\":\n xc_ = memoryview(array.array(\"d\",xc))\n _copyback_xc = True\n if xc_ is not None and len(xc_) != self.getnumcon():\n raise ValueError(\"Array argument xc has wrong length\")\n _copyback_xx = False\n if xx is None:\n xx_ = None\n else:\n try:\n xx_ = memoryview(xx)\n except TypeError:\n try:\n _tmparr_xx = array.array(\"d\",xx)\n except TypeError:\n raise TypeError(\"Argument xx has wrong type\")\n else:\n xx_ = memoryview(_tmparr_xx)\n _copyback_xx = True\n else:\n if xx_.format != \"d\":\n xx_ = memoryview(array.array(\"d\",xx))\n _copyback_xx = True\n if xx_ is not None and len(xx_) != self.getnumvar():\n raise ValueError(\"Array argument xx has wrong length\")\n _copyback_y = False\n if y is None:\n y_ = None\n else:\n try:\n y_ = memoryview(y)\n except TypeError:\n try:\n _tmparr_y = array.array(\"d\",y)\n except TypeError:\n raise TypeError(\"Argument y has wrong type\")\n else:\n y_ = memoryview(_tmparr_y)\n _copyback_y = True\n else:\n if y_.format != \"d\":\n y_ = memoryview(array.array(\"d\",y))\n _copyback_y = True\n if y_ is not None and len(y_) != self.getnumcon():\n raise ValueError(\"Array argument y has wrong length\")\n _copyback_slc = False\n if slc is None:\n slc_ = None\n else:\n try:\n slc_ = memoryview(slc)\n except TypeError:\n try:\n _tmparr_slc = array.array(\"d\",slc)\n except TypeError:\n raise TypeError(\"Argument slc has wrong type\")\n else:\n slc_ = memoryview(_tmparr_slc)\n _copyback_slc = True\n else:\n if slc_.format != \"d\":\n slc_ = memoryview(array.array(\"d\",slc))\n _copyback_slc = True\n if slc_ is not None and len(slc_) != self.getnumcon():\n raise ValueError(\"Array argument slc has wrong length\")\n _copyback_suc = False\n if suc is None:\n suc_ = None\n else:\n try:\n suc_ = memoryview(suc)\n except TypeError:\n try:\n _tmparr_suc = array.array(\"d\",suc)\n except TypeError:\n raise TypeError(\"Argument suc has wrong type\")\n else:\n suc_ = memoryview(_tmparr_suc)\n _copyback_suc = True\n else:\n if suc_.format != \"d\":\n suc_ = memoryview(array.array(\"d\",suc))\n _copyback_suc = True\n if suc_ is not None and len(suc_) != self.getnumcon():\n raise ValueError(\"Array argument suc has wrong length\")\n _copyback_slx = False\n if slx is None:\n slx_ = None\n else:\n try:\n slx_ = memoryview(slx)\n except TypeError:\n try:\n _tmparr_slx = array.array(\"d\",slx)\n except TypeError:\n raise TypeError(\"Argument slx has wrong type\")\n else:\n slx_ = memoryview(_tmparr_slx)\n _copyback_slx = True\n else:\n if slx_.format != \"d\":\n slx_ = memoryview(array.array(\"d\",slx))\n _copyback_slx = True\n if slx_ is not None and len(slx_) != self.getnumvar():\n raise ValueError(\"Array argument slx has wrong length\")\n _copyback_sux = False\n if sux is None:\n sux_ = None\n else:\n try:\n sux_ = memoryview(sux)\n except TypeError:\n try:\n _tmparr_sux = array.array(\"d\",sux)\n except TypeError:\n raise TypeError(\"Argument sux has wrong type\")\n else:\n sux_ = memoryview(_tmparr_sux)\n _copyback_sux = True\n else:\n if sux_.format != \"d\":\n sux_ = memoryview(array.array(\"d\",sux))\n _copyback_sux = True\n if sux_ is not None and len(sux_) != self.getnumvar():\n raise ValueError(\"Array argument sux has wrong length\")\n _copyback_snx = False\n if snx is None:\n snx_ = None\n else:\n try:\n snx_ = memoryview(snx)\n except TypeError:\n try:\n _tmparr_snx = array.array(\"d\",snx)\n except TypeError:\n raise TypeError(\"Argument snx has wrong type\")\n else:\n snx_ = memoryview(_tmparr_snx)\n _copyback_snx = True\n else:\n if snx_.format != \"d\":\n snx_ = memoryview(array.array(\"d\",snx))\n _copyback_snx = True\n if snx_ is not None and len(snx_) != self.getnumvar():\n raise ValueError(\"Array argument snx has wrong length\")\n res,resargs = self.__obj.getsolution(whichsol_,skc_,skx_,skn_,xc_,xx_,y_,slc_,suc_,slx_,sux_,snx_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _prosta_return_value,_solsta_return_value = resargs\n if _copyback_snx:\n snx[:] = _tmparr_snx\n if _copyback_sux:\n sux[:] = _tmparr_sux\n if _copyback_slx:\n slx[:] = _tmparr_slx\n if _copyback_suc:\n suc[:] = _tmparr_suc\n if _copyback_slc:\n slc[:] = _tmparr_slc\n if _copyback_y:\n y[:] = _tmparr_y\n if _copyback_xx:\n xx[:] = _tmparr_xx\n if _copyback_xc:\n xc[:] = _tmparr_xc\n if _copyback_skn:\n for __tmp_var_2 in range(len(skn_)): skn[__tmp_var_2] = stakey(_tmparr_skn[__tmp_var_2])\n if _copyback_skx:\n for __tmp_var_1 in range(len(skx_)): skx[__tmp_var_1] = stakey(_tmparr_skx[__tmp_var_1])\n if _copyback_skc:\n for __tmp_var_0 in range(len(skc_)): skc[__tmp_var_0] = stakey(_tmparr_skc[__tmp_var_0])\n _solsta_return_value = solsta(_solsta_return_value)\n _prosta_return_value = prosta(_prosta_return_value)\n return _prosta_return_value,_solsta_return_value", "def getpviolcon(self,whichsol_,sub_,viol_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n _viol_minlength = (num_)\n if (num_) > 0 and viol_ is not None and len(viol_) != (num_):\n raise ValueError(\"Array argument viol is not long enough: Is %d, expected %d\" % (len(viol_),(num_)))\n if isinstance(viol_,numpy.ndarray) and not viol_.flags.writeable:\n raise ValueError(\"Argument viol must be writable\")\n if viol_ is None:\n raise ValueError(\"Argument viol may not be None\")\n if isinstance(viol_, numpy.ndarray) and viol_.dtype is numpy.dtype(numpy.float64) and viol_.flags.contiguous:\n _viol_copyarray = False\n _viol_tmp = ctypes.cast(viol_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif viol_ is not None:\n _viol_copyarray = True\n _viol_np_tmp = numpy.zeros(len(viol_),numpy.dtype(numpy.float64))\n _viol_np_tmp[:] = viol_\n assert _viol_np_tmp.flags.contiguous\n _viol_tmp = ctypes.cast(_viol_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _viol_copyarray = False\n _viol_tmp = None\n \n res = __library__.MSK_XX_getpviolcon(self.__nativep,whichsol_,num_,_sub_tmp,_viol_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _viol_copyarray:\n viol_[:] = _viol_np_tmp", "def isFeasible(self, A):\n\t\treturn False", "def getdviolvar(self,whichsol_,sub,viol): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n num_ = None\n if num_ is None:\n num_ = len(sub)\n elif num_ != len(sub):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None: num_ = 0\n if sub is None: raise TypeError(\"Invalid type for argument sub\")\n if sub is None:\n sub_ = None\n else:\n try:\n sub_ = memoryview(sub)\n except TypeError:\n try:\n _tmparr_sub = array.array(\"i\",sub)\n except TypeError:\n raise TypeError(\"Argument sub has wrong type\")\n else:\n sub_ = memoryview(_tmparr_sub)\n \n else:\n if sub_.format != \"i\":\n sub_ = memoryview(array.array(\"i\",sub))\n \n if viol is None: raise TypeError(\"Invalid type for argument viol\")\n _copyback_viol = False\n if viol is None:\n viol_ = None\n else:\n try:\n viol_ = memoryview(viol)\n except TypeError:\n try:\n _tmparr_viol = array.array(\"d\",viol)\n except TypeError:\n raise TypeError(\"Argument viol has wrong type\")\n else:\n viol_ = memoryview(_tmparr_viol)\n _copyback_viol = True\n else:\n if viol_.format != \"d\":\n viol_ = memoryview(array.array(\"d\",viol))\n _copyback_viol = True\n if viol_ is not None and len(viol_) != (num_):\n raise ValueError(\"Array argument viol has wrong length\")\n res = self.__obj.getdviolvar(whichsol_,num_,sub_,viol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_viol:\n viol[:] = _tmparr_viol", "def getpviolcones(self,whichsol_,sub,viol): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n num_ = None\n if num_ is None:\n num_ = len(sub)\n elif num_ != len(sub):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None: num_ = 0\n if sub is None: raise TypeError(\"Invalid type for argument sub\")\n if sub is None:\n sub_ = None\n else:\n try:\n sub_ = memoryview(sub)\n except TypeError:\n try:\n _tmparr_sub = array.array(\"i\",sub)\n except TypeError:\n raise TypeError(\"Argument sub has wrong type\")\n else:\n sub_ = memoryview(_tmparr_sub)\n \n else:\n if sub_.format != \"i\":\n sub_ = memoryview(array.array(\"i\",sub))\n \n if viol is None: raise TypeError(\"Invalid type for argument viol\")\n _copyback_viol = False\n if viol is None:\n viol_ = None\n else:\n try:\n viol_ = memoryview(viol)\n except TypeError:\n try:\n _tmparr_viol = array.array(\"d\",viol)\n except TypeError:\n raise TypeError(\"Argument viol has wrong type\")\n else:\n viol_ = memoryview(_tmparr_viol)\n _copyback_viol = True\n else:\n if viol_.format != \"d\":\n viol_ = memoryview(array.array(\"d\",viol))\n _copyback_viol = True\n if viol_ is not None and len(viol_) != (num_):\n raise ValueError(\"Array argument viol has wrong length\")\n res = self.__obj.getpviolcones(whichsol_,num_,sub_,viol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_viol:\n viol[:] = _tmparr_viol", "def getdviolcon(self,whichsol_,sub,viol): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n num_ = None\n if num_ is None:\n num_ = len(sub)\n elif num_ != len(sub):\n raise IndexError(\"Inconsistent length of array sub\")\n if num_ is None: num_ = 0\n if sub is None: raise TypeError(\"Invalid type for argument sub\")\n if sub is None:\n sub_ = None\n else:\n try:\n sub_ = memoryview(sub)\n except TypeError:\n try:\n _tmparr_sub = array.array(\"i\",sub)\n except TypeError:\n raise TypeError(\"Argument sub has wrong type\")\n else:\n sub_ = memoryview(_tmparr_sub)\n \n else:\n if sub_.format != \"i\":\n sub_ = memoryview(array.array(\"i\",sub))\n \n if viol is None: raise TypeError(\"Invalid type for argument viol\")\n _copyback_viol = False\n if viol is None:\n viol_ = None\n else:\n try:\n viol_ = memoryview(viol)\n except TypeError:\n try:\n _tmparr_viol = array.array(\"d\",viol)\n except TypeError:\n raise TypeError(\"Argument viol has wrong type\")\n else:\n viol_ = memoryview(_tmparr_viol)\n _copyback_viol = True\n else:\n if viol_.format != \"d\":\n viol_ = memoryview(array.array(\"d\",viol))\n _copyback_viol = True\n if viol_ is not None and len(viol_) != (num_):\n raise ValueError(\"Array argument viol has wrong length\")\n res = self.__obj.getdviolcon(whichsol_,num_,sub_,viol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_viol:\n viol[:] = _tmparr_viol", "def solve(self):\n self.m.optimize()\n if self.m.status == GRB.OPTIMAL:\n self.solution = self.sol_as_mat()\n return self.solution", "def solve(self):\n # Use a trivial tour (1-2-3-...-N-1) to set the global upper bound.\n tour = list(range(self._N))\n upper_bound = sum([self._G[i][(i + 1) % self._N] for i in range(self._N)])\n trace = []\n\n # Start from a configuration with a single vertex.\n frontier = [BranchAndBoundConfiguration(self._G, self._N, [0], LOWER_BOUND_METHOD)]\n\n # Set the start time.\n start_time = time.time()\n\n # Branch and bound until the frontier set is empty or the time has expired.\n while frontier and (time.time() - start_time) < self._cutoff_time:\n # Fetch the most promising configuration.\n config = heappop(frontier)\n\n # Expand configuration by appending a vertex to the path.\n for v in range(self._N):\n try:\n expanded_config = config.expand(v)\n except ValueError:\n # Expanded configuration is not valid.\n continue\n if expanded_config.is_solution():\n # Update the global upper bound, if needed.\n this_solution = expanded_config.get_cycle_cost()\n if this_solution < upper_bound:\n # Log it.\n trace.append((time.time() - start_time, this_solution))\n # Update the best solution.\n upper_bound = this_solution\n tour = list(expanded_config.get_path())\n elif expanded_config.get_lower_bound() < upper_bound:\n # Add to the frontier set.\n heappush(frontier, expanded_config)\n return (upper_bound, [self._index_to_id[v] for v in tour], trace)", "def solution(self,pos):\n\t\tif pos == 0:\n\t\t\treturn self.solve(pos)\n\n\t\tif not self.mem.has_key(pos-1):\n\t\t\tself.mem[pos-1] = self.solution(pos-1)\n\t\tif not self.mem.has_key(pos):\n\t\t\tself.mem[pos] = self.mem[pos-1] + self.solve(pos)\n\t\tif pos == len(self.objective)-1 and not self.eob():\n\t\t\t\"\"\"Ya obtuvimos la solucion y aun hay elementos en la base que deben ser\n\t\t\tdescartados\"\"\"\n\t\t\tself.mem[pos] += self.terminar()\n\t\treturn self.mem[pos]", "def getpviolcones(self,whichsol_,sub_,viol_):\n num_ = None\n if num_ is None:\n num_ = len(sub_)\n elif num_ != len(sub_):\n raise IndexError(\"Inconsistent length of array sub\")\n if sub_ is None:\n raise ValueError(\"Argument sub cannot be None\")\n if sub_ is None:\n raise ValueError(\"Argument sub may not be None\")\n if isinstance(sub_, numpy.ndarray) and sub_.dtype is numpy.dtype(numpy.int32) and sub_.flags.contiguous:\n _sub_copyarray = False\n _sub_tmp = ctypes.cast(sub_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif sub_ is not None:\n _sub_copyarray = True\n _sub_np_tmp = numpy.zeros(len(sub_),numpy.dtype(numpy.int32))\n _sub_np_tmp[:] = sub_\n assert _sub_np_tmp.flags.contiguous\n _sub_tmp = ctypes.cast(_sub_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _sub_copyarray = False\n _sub_tmp = None\n \n _viol_minlength = (num_)\n if (num_) > 0 and viol_ is not None and len(viol_) != (num_):\n raise ValueError(\"Array argument viol is not long enough: Is %d, expected %d\" % (len(viol_),(num_)))\n if isinstance(viol_,numpy.ndarray) and not viol_.flags.writeable:\n raise ValueError(\"Argument viol must be writable\")\n if viol_ is None:\n raise ValueError(\"Argument viol may not be None\")\n if isinstance(viol_, numpy.ndarray) and viol_.dtype is numpy.dtype(numpy.float64) and viol_.flags.contiguous:\n _viol_copyarray = False\n _viol_tmp = ctypes.cast(viol_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif viol_ is not None:\n _viol_copyarray = True\n _viol_np_tmp = numpy.zeros(len(viol_),numpy.dtype(numpy.float64))\n _viol_np_tmp[:] = viol_\n assert _viol_np_tmp.flags.contiguous\n _viol_tmp = ctypes.cast(_viol_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _viol_copyarray = False\n _viol_tmp = None\n \n res = __library__.MSK_XX_getpviolcones(self.__nativep,whichsol_,num_,_sub_tmp,_viol_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _viol_copyarray:\n viol_[:] = _viol_np_tmp" ]

[ "0.5406454", "0.5405638", "0.54013044", "0.54003686", "0.53582644", "0.53561485", "0.534527", "0.53344864", "0.53250533", "0.53217924", "0.53196096", "0.53157514", "0.5308851", "0.5305068", "0.5287506", "0.5270815", "0.5264659", "0.52540594", "0.52518165", "0.524636", "0.52275515", "0.5219632", "0.5200734", "0.5175888", "0.51738435", "0.51636016", "0.51542944", "0.5147555", "0.51317894", "0.51268226" ]

[ "def writesolution(self,whichsol_,filename_): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res = self.__obj.writesolution(whichsol_,filename_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def write_puzzle(to_file: str, solution: str):\n with open(to_file, \"w\") as file:\n file.write(solution)", "def write_solution(self, file):\n phaseflow.helpers.print_once(\"Writing solution to \" + str(file.path))\n \n pressure, velocity, temperature = self.solution.leaf_node().split()\n \n pressure.rename(\"p\", \"pressure\")\n \n velocity.rename(\"u\", \"velocity\")\n \n temperature.rename(\"T\", \"temperature\")\n \n for var in [pressure, velocity, temperature]:\n \n file.write(var, self.time)", "def save_solution(self, solution_path, file_path):\n node_info = \"\"\n with open(file_path, \"w\") as file:\n file.write(\n \"Strategy: \"\n + str(self.strategy)\n + \"\\n Max Depth: \"\n + str(self.max_depth)\n + \"\\n Depth Increment: \"\n + str(self.depth_increment)\n + \"\\n Pruning: \"\n + str(self.pruning)\n + \"\\n ---SOLUTION---: \"\n )\n for node in solution_path:\n node_info = \"\\n\\n ID: \" + str(node.id)\n if node.last_action != None:\n node_info += \"\\n Action: \" + str(node.last_action)\n node_info += (\n \"\\n Cost: \"\n + str(node.cost)\n + \"\\n Depth: \"\n + str(node.node_depth)\n + \"\\n Heuristic: \"\n + str(node.state.entropy())\n + \"\\n F value: \"\n + str(node.f)\n + \"\\n Node: \"\n + str(node.state.create_md5())\n )\n file.write(node_info)\n node_info = \"\"\n\n file.write(\n \"\\n TOTAL COST: \" + str(solution_path[len(solution_path) - 1].cost)\n )", "def make_outputfile(self, solved_status, filename):\n filename = filename.split(\".\")\n filename[0] = filename[0].replace(\"Input\",\"Output\")\n str_filename = \".\"\n str_filename = str_filename.join(filename)\n # print(str_filename)\n\n f = open(str_filename,\"w+\")\n\n if(solved_status):\n string_rep = self.values_to_grid()\n ptr = 0\n for row in range(0,9):\n for col in range(0,9):\n f.write(string_rep[ptr]+ \" \")\n ptr += 1\n f.write(\"\\r\\n\") #windows compatiable formatting...\n else:\n f.write(\"Unable to solve this puzzle.\")\n\n f.close()", "def writeslxsol(self, name, *values):\n with open(name, \"w\") as slx:\n for i, sol in enumerate(values):\n slx.write(\"NAME solution%d\\n\" % i)\n for name, value in sol:\n slx.write(f\" C {name} {value:.16f}\\n\")\n slx.write(\"ENDATA\\n\")", "def write_file(self):\n if self._write_file == None:\n return\n\n try:\n out = file(self._write_file, \"w\")\n except IOError, e:\n print e\n sys.exit(1)\n out.writelines(\"A cases\") \n out.close()", "def write(self, filename=None):\n if filename == None:\n filename = self.ofilename\n\n ofile = open(filename, 'w')\n\n ofile.write('# Susceptibility: %E d(susc): %E Coercivity: %E d(coer): %E\\n' % (self.susceptibility_mean, self.susceptibility_std, self.coercivity_mean, self.coercivity_std) )\n ofile.write('# H[] M[] Mfit[]\\n')\n\n #for i in range(len(self.h)):\n # ofile.write(\" %12.10f %12.10f %12.10f\\n\" % ( self.h[i], self.m[i], self.m_fit[i] ) )\n\n ofile.close()", "def writejsonsol(self,filename_): # 3\n res = self.__obj.writejsonsol(filename_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def write_to_file(board, output_file = \"solution.sud\"):\n with open(output_file, \"w\") as f:\n for i in range(n):\n if i and i%3==0:\n f.write(\"------+-------+------\\n\")\n for j in range(n): \n if j and j%3==0:\n f.write(\"| \")\n if len(board[i][j]) == 1:\n f.write(str(board[i][j][0]) + \" \")\n else:\n f.write(\". \")\n elif j==8:\n if len(board[i][j]) == 1:\n f.write(str(board[i][j][0]) + \"\\n\")\n else:\n f.write(\".\\n\")\n else:\n if len(board[i][j]) == 1:\n f.write(str(board[i][j][0]) + \" \")\n else:\n f.write(\". \")\n return 0", "def write_to_file(self, filename: str) -> None:", "def write_solution(n,solution, output_file=\"out.csv\", delimiter=','):\n to_print = solution[int(n):-2]\n with open(output_file,'w') as _file:\n for i in range(len(to_print)):\n s = str(i+1) + delimiter + str(to_print[i]) + str(\"\\n\")\n _file.write(s)\n _file.close()", "def write(self, filename):\n pass", "def write(self, filename):\n pass", "def save(self, filename):\n if self.model.convert_to_format == \"python\":\n # We currently cannot save models in the 'python' format\n raise NotImplementedError(\n \"\"\"\n Cannot save simulation if model format is python.\n Set model.convert_to_format = 'casadi' instead.\n \"\"\"\n )\n # Clear solver problem (not pickle-able, will automatically be recomputed)\n if (\n isinstance(self._solver, pybamm.CasadiSolver)\n and self._solver.integrator_specs != {}\n ):\n self._solver.integrator_specs = {}\n\n if self.op_conds_to_built_solvers is not None:\n for solver in self.op_conds_to_built_solvers.values():\n if (\n isinstance(solver, pybamm.CasadiSolver)\n and solver.integrator_specs != {}\n ):\n solver.integrator_specs = {}\n\n with open(filename, \"wb\") as f:\n pickle.dump(self, f, pickle.HIGHEST_PROTOCOL)", "def write(model, original=True):\n g_model, vars = model\n with open(g_model.getAttr(\"ModelName\") + \".sol\", \"w\") as file:\n file.write(\"{0} {1}\\n\".format(g_model.objval, int(original and g_model.status == grb.GRB.status.OPTIMAL)))\n for var in vars:\n file.write(\"{0} \".format(int(var.x)))\n file.write(\"\\n\")", "def save_problem(problem, filepath):\n with open(filepath, \"w\") as out:\n print \"Vectors count: %s\" % (len(problem))\n for vector in problem:\n out.write(\"%s \" % (vector[0]))\n for index, value in sorted(vector[1].iteritems()):\n out.write(\"%s:%s \" % (index, value))\n out.write(\"\\n\")", "def write_file(self):\n\n running_time = str(self.running_time_end - self.running_time_start)\n rounded_running_time = '{:.10}'.format(running_time)\n output = 'path_to_goal: ' + str(self.path_to_goal) + '\\n'\n output += 'cost_of_path: ' + str(self.cost_of_path) + '\\n'\n output += 'nodes_expanded: ' + str(self.nodes_expanded) + '\\n'\n output += 'fringe_size: ' + str(self.fringe_size) + '\\n'\n output += 'max_fringe_size: ' + str(self.max_fringe_size) + '\\n'\n output += 'search_depth: ' + str(self.search_depth) + '\\n'\n output += 'max_search_depth: ' + str(self.max_search_depth) + '\\n'\n output += 'running_time: ' + rounded_running_time + '\\n'\n\n system_name = system()\n if system_name == 'Windows':\n output += 'max_ram_usage: (Not available on Windows OS)'\n elif system_name == 'Linux':\n output += 'max_ram_usage: ' + \\\n str(getrusage(RUSAGE_SELF).ru_maxrss / 1024) + '\\n'\n\n file = open('output.txt', 'w+')\n file.write(output)\n print(output)", "def write_one(paths_out, solutii, current_fis, note=\"\"):\n f = open(f\"{paths_out[current_fis]}\", \"a\")\n f.write(note)\n for s in solutii:\n f.write(s)", "def readsolution(self,whichsol_,filename_): # 3\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res = self.__obj.readsolution(whichsol_,filename_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def write_solution(mm):\n\n m = mm.model\n\n solution_file = \"{0}_sol.csv\".format(mm.filename)\n\n harv_data = []\n harv_data.append([\"Harvest data\"])\n harv_data.append([\"Species\", \"Region\", \"Period\", \"Value\"])\n # write harv variable solution values\n harv = pg.get_variables(m, \"harv\")\n for h in harv:\n name = h.varName.split(\",\")\n species = name[0].split(\"[\")[1]\n region = name[1]\n period = name[-1][:-1]\n harv_data.append(\n [species, region, period, h.X])\n\n age_data = []\n age_data.append([\"Age data\"])\n age_data.append([\"Region\", \"Period\", \"Value\"])\n age = pg.get_variables(m, \"age\")\n for a in age:\n name = a.varName.split(\",\")\n region = name[0].split(\"[\")[1]\n period = name[-1][:-1]\n age_data.append(\n [region, period, a.X])\n\n with open(solution_file, \"w+\") as wrf:\n wf = csv.writer(wrf)\n wf.writerows(harv_data)\n wf.writerows(age_data)", "def write_first_stage_solution(self, solution_file_name,\n first_stage_solution_writer=first_stage_nonant_writer):\n if not self._ran:\n raise RuntimeError(\"Need to call WheelSpinner.run() before querying solutions.\")\n winner = self._determine_innerbound_winner()\n if winner:\n self.spcomm.opt.write_first_stage_solution(solution_file_name,first_stage_solution_writer)", "def write(self, f):\n if self.best_mhc_align:\n mhc_align_str = self.best_mhc_align.subject_str()\n mhc_score_str = str(self.best_mhc_align.bit_score)\n else:\n mhc_align_str = \".\"\n mhc_score_str = \"0\"\n\n if self.best_non_mhc_align:\n non_mhc_align_str = self.best_non_mhc_align.subject_str()\n non_mhc_score_str = str(self.best_non_mhc_align.bit_score)\n else:\n non_mhc_align_str = \".\"\n non_mhc_score_str = \"0\"\n \n f.write(\"\\t\".join([self.locus, self.short_samp_id, self.name,\n str(self.length), mhc_align_str, non_mhc_align_str,\n mhc_score_str, non_mhc_score_str,\n str(self.n_mhc_align), str(self.n_non_mhc_align)]) + \"\\n\")", "def readsolution(self,whichsol_,filename_):\n if isinstance(filename_,unicode):\n filename_ = filename_.encode(\"utf-8\",errors=\"replace\")\n res = __library__.MSK_XX_readsolution(self.__nativep,whichsol_,filename_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def write(self, fname):\n pass", "def write_to_txt(self):\r\n file = open(self.output_path, 'w')\r\n for question_id in self.question_ids:\r\n file.write(self.questions[question_id].question_string+str(self.questions[question_id].answer)+'\\n')\r\n file.close()", "def write_to(self, filename):\n with open(filename, 'w') as f:\n for xx, yy, zz, ww in zip(self.x, self.y, self.field, self.weight):\n f.write(\"%s %s %s %s\\n\" % (xx, yy, zz, ww))\n logger.info(\"Written data into file {0}\".format(filename))", "def export(self, f, delimiter=\",\"):\n if self.sol is None:\n raise Exception(\"Missing call to solve()\")\n\n np.savetxt(f, self.sol, header=\",\".join(self.__class__.CSV_ROW), delimiter=\",\")", "def write_tour(graph, tsp_model, filename):\n with open(filename, 'w') as file: # open the textfile\n for decision_variable in tsp_model.getVars(): # for every decision variable in the model\n if decision_variable.getAttr(\"X\"): # if the value is true\n variable_name = decision_variable.getAttr(\"VarName\") # get the variable name\n i, j = (int(num) for num in variable_name.split(\"_\")) # retrieve the node names\n file.write(\" \".join([str(i), str(j), str(graph[i][j])]) + \"\\n\") # store the edge in a new line\n # store the cost of the optimal tour as the final line\n file.write(\"The cost of the best tour is: \" + str(tsp_model.getAttr(\"ObjVal\")) + \"\\n\")", "def save_depfile(depdata,outname,is31=True): \n\n if outname==None:\n print('save_depfile requires a filename to save.')\n return\n try:\n fp=open(outname,'w')\n except IOError:\n print('save_depfile: invalid filename.')\n return data\n if is31:\n fp.write('Node Number = %d\\n' % len(depdata['node_num']) )\n for i in range(0,len(depdata['node_num'])):\n fp.write('%f %f %f\\n'% (depdata['x'][i],depdata['y'][i],depdata['h'][i]))\n fp.close()\n \n return" ]

[ "0.87910414", "0.7406337", "0.72049326", "0.7029762", "0.6727161", "0.6507342", "0.63737875", "0.6302796", "0.6293724", "0.6199659", "0.61895025", "0.6181373", "0.61070776", "0.61070776", "0.60899645", "0.60780126", "0.607718", "0.6065927", "0.60460186", "0.5984384", "0.5983138", "0.5958016", "0.59331405", "0.59311354", "0.59247166", "0.58966714", "0.5887481", "0.58850527", "0.5881214", "0.58669764" ]

[ "def writejsonsol(self,filename_): # 3\n res = self.__obj.writejsonsol(filename_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def write_json(self, filename):\n with open(filename, 'a+') as f:\n f.write(json.dumps(self.weights))\n f.write(\"\\n\")", "def SaveJSON(self, filename):\n data = {\n 'files': self._files,\n 'ebuilds': self._ebuilds,\n }\n json.dump(data, open(filename, 'w'))", "def write(self, _filepath=None):\n _json_txt = json.dumps(self.json_dict, indent=2)\n self._write_json_text(_json_txt, _filepath)", "def write_json_file(self, fname, content):\n pass", "def write_json_file(self, fname, content):\n pass", "def write(self):\r\n\r\n with open(self.filename + \".json\", mode='w') as json_file:\r\n json.dump(self.data, json_file, separators=(',', ':'))", "def _json_write(filename, res):\n with open(filename, 'w+') as file:\n return json.dump(res, file)", "def _write_json(self):\n with open(self._file_path, 'w') as f:\n json.dump(self._content, f, indent=4, separators=None,\n encoding='utf-8', sort_keys=False)", "def writef(self, fileName):\n\t\tf = open(fileName, \"w\")\n\t\tjson.dump(self.writed(self.__world), f)\n\t\tf.close()", "def to_json_file(self, json_file_path):\n with open(json_file_path, \"w\", encoding=\"utf-8\") as writer:\n writer.write(self.to_json_string())", "def to_json_file(self, json_file_path):\n with open(json_file_path, 'w', encoding='utf-8') as writer:\n writer.write(self.to_json_string())", "def write_json(self, filename):\n data = {\n \"fleets\": json.loads(self.manager_df.to_json(orient=\"records\")),\n \"transports\": json.loads(self.transport_df.to_json(orient=\"records\")),\n \"customers\": json.loads(self.customer_df.to_json(orient=\"records\")),\n \"stations\": json.loads(self.station_df.to_json(orient=\"records\")),\n \"simulation\": json.loads(self.df_avg.to_json(orient=\"records\"))\n }\n\n with open(filename, 'w') as f:\n f.seek(0)\n json.dump(data, f, indent=4)", "def to_json_file(self, json_file: str = None) -> None:\n\n if self.json:\n if not json_file:\n json_file = f\"{self.id}.json\"\n\n with open(json_file, \"w\") as f:\n f.write(self.json)", "def saveFile(self, filename=\"UQModelTest.json\"):\n sd = self.saveDict()\n with open(filename, \"w\") as f:\n json.dump(sd, f, indent=2)", "def save(self, filename):\n import json\n\n json = json.dumps(self.joint_limits)\n with open(filename, 'w') as f:\n f.write(json)", "def write_json(self, f, **kw_json):\n wntr.network.io.write_json(self, f, **kw_json)", "def json_writer():\n with open(\"{}.json\".format(sys.argv[3]), \"w+\") as new_json:\n print(\"uploading the jason file... \")\n json.dump(json_file, new_json)\n print(\"file is done\")", "def write_json(self, jsonfile):\n with open(jsonfile, 'w') as fp:\n json.dump(self.status, fp, sort_keys=True, indent=4)\n fp.close()", "def to_json_file(self, path):\n with open(path, 'w') as f:\n f.write(self.to_json())", "def write(self):\n self.json_o.write()", "def write_json(dictionary, filename):\r\n with open(filename, 'w') as data_file:\r\n json.dump(dictionary, data_file, indent=4, sort_keys=True)\r\n print('--> Wrote ' + os.path.basename(filename))", "def save(self, filename):\n data = {\"sizes\": self.sizes,\n \"weights\": [w.tolist() for w in self.weights],\n \"biases\": [b.tolist() for b in self.biases],\n \"cost\": str(self.cost.__name__)}\n f = open(filename, \"w\")\n json.dump(data, f)\n f.close()", "def writeJSON(filename):\n if not filename.endswith('.json'):\n filename += '.json'\n with open(filename, 'w') as f:\n for x in range(numRows):\n scores = quizScores()\n types = getTypes(scores)\n row = { 'id': x,\n 'challenger': types[0], 'collaborator': types[1],\n 'communicator': types[2], 'contributor': types[3],\n 'q1': scores[0], 'q2': scores[1], 'q3': scores[2],\n 'q4': scores[3], 'q5': scores[4], 'q6': scores[5],\n 'q7': scores[6], 'q8': scores[7], 'q9': scores[8],\n 'q10': scores[9], 'q11': scores[10], 'q12': scores[11],\n 'q13': scores[12], 'q14': scores[13], 'q15': scores[14],\n 'q16': scores[15], 'q17': scores[16], 'q18': scores[17]\n }\n json.dump(row, f, sort_keys=True)", "def save_to_json(self, file_name: str) -> bool:\n flag = True\n with open(file_name, \"w\") as jsonFile:\n try:\n d = {\"Edges\": [], \"Nodes\": []}\n for src in self._graph.out_edges.keys():\n for dst, w in self._graph.all_out_edges_of_node(src).items():\n d[\"Edges\"].append({\"src\": src, \"w\": w.weight, \"dest\": dst})\n for key, value in self._graph.nodes.items():\n if value.location is None:\n d[\"Nodes\"].append({\"id\": key})\n else:\n d[\"Nodes\"].append({\"pos\": str(value.location), \"id\": key})\n s = d.__str__()\n s = s.replace(\" \", \"\")\n s = s.replace(\"'\", \"\\\"\")\n jsonFile.write(s)\n # print(\"Save Json was succeeded \")\n except Exception as e:\n print(\"Save Json was failed \")\n print(e)\n flag = False\n finally:\n return flag", "def save_json(self, file):\n with open(file, 'w', encoding='utf8') as f:\n json.dump(self, f, ensure_ascii=False)", "def save_as_json(self, json_name):\r\n with open(json_name, \"w\") as outfile:\r\n json.dump(self.pet_file, outfile) # save create json file with current information\r\n self.pet_file_name = json_name # set name to passed name\r", "def save_json(self, file: Union[str, TextIO]) -> None:\n if hasattr(file, 'write'):\n file_ctx = nullcontext(file)\n else:\n file_ctx = open(file, 'w')\n\n with file_ctx as fp:\n for d in self:\n json.dump(d.dict(), fp)\n fp.write('\\n')", "def save(self, filename):\n content = self.to_dict()\n with open(filename, 'w') as f:\n json.dump(content, f)", "def write_data_to_json(self, filename):\n with open(filename, 'w') as f:\n json.dump(self.data, f, indent=2, separators=(',', ':'), cls=DatasetJSONEncoder)\n print 'Saved dataset to {}'.format(filename)" ]

[ "0.87295", "0.74165505", "0.72694594", "0.71959645", "0.7129806", "0.7129806", "0.71224076", "0.7121763", "0.7065886", "0.70466745", "0.70419866", "0.70303464", "0.700208", "0.69454724", "0.6898068", "0.6853677", "0.68358284", "0.6761869", "0.6744697", "0.671354", "0.6709994", "0.6690325", "0.66673696", "0.66541046", "0.6646401", "0.66375244", "0.6632134", "0.662929", "0.66059923", "0.660565" ]

[ "def primalsensitivity(self,subi,marki,subj,markj,leftpricei,rightpricei,leftrangei,rightrangei,leftpricej,rightpricej,leftrangej,rightrangej): # 3\n numi_ = None\n if numi_ is None:\n numi_ = len(subi)\n elif numi_ != len(subi):\n raise IndexError(\"Inconsistent length of array subi\")\n if numi_ is None:\n numi_ = len(marki)\n elif numi_ != len(marki):\n raise IndexError(\"Inconsistent length of array marki\")\n if numi_ is None: numi_ = 0\n if subi is None: raise TypeError(\"Invalid type for argument subi\")\n if subi is None:\n subi_ = None\n else:\n try:\n subi_ = memoryview(subi)\n except TypeError:\n try:\n _tmparr_subi = array.array(\"i\",subi)\n except TypeError:\n raise TypeError(\"Argument subi has wrong type\")\n else:\n subi_ = memoryview(_tmparr_subi)\n \n else:\n if subi_.format != \"i\":\n subi_ = memoryview(array.array(\"i\",subi))\n \n if marki is None: raise TypeError(\"Invalid type for argument marki\")\n if marki is None:\n marki_ = None\n else:\n try:\n marki_ = memoryview(marki)\n except TypeError:\n try:\n _tmparr_marki = array.array(\"i\",marki)\n except TypeError:\n raise TypeError(\"Argument marki has wrong type\")\n else:\n marki_ = memoryview(_tmparr_marki)\n \n else:\n if marki_.format != \"i\":\n marki_ = memoryview(array.array(\"i\",marki))\n \n numj_ = None\n if numj_ is None:\n numj_ = len(subj)\n elif numj_ != len(subj):\n raise IndexError(\"Inconsistent length of array subj\")\n if numj_ is None:\n numj_ = len(markj)\n elif numj_ != len(markj):\n raise IndexError(\"Inconsistent length of array markj\")\n if numj_ is None: numj_ = 0\n if subj is None: raise TypeError(\"Invalid type for argument subj\")\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n \n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n \n if markj is None: raise TypeError(\"Invalid type for argument markj\")\n if markj is None:\n markj_ = None\n else:\n try:\n markj_ = memoryview(markj)\n except TypeError:\n try:\n _tmparr_markj = array.array(\"i\",markj)\n except TypeError:\n raise TypeError(\"Argument markj has wrong type\")\n else:\n markj_ = memoryview(_tmparr_markj)\n \n else:\n if markj_.format != \"i\":\n markj_ = memoryview(array.array(\"i\",markj))\n \n _copyback_leftpricei = False\n if leftpricei is None:\n leftpricei_ = None\n else:\n try:\n leftpricei_ = memoryview(leftpricei)\n except TypeError:\n try:\n _tmparr_leftpricei = array.array(\"d\",leftpricei)\n except TypeError:\n raise TypeError(\"Argument leftpricei has wrong type\")\n else:\n leftpricei_ = memoryview(_tmparr_leftpricei)\n _copyback_leftpricei = True\n else:\n if leftpricei_.format != \"d\":\n leftpricei_ = memoryview(array.array(\"d\",leftpricei))\n _copyback_leftpricei = True\n if leftpricei_ is not None and len(leftpricei_) != (numi_):\n raise ValueError(\"Array argument leftpricei has wrong length\")\n _copyback_rightpricei = False\n if rightpricei is None:\n rightpricei_ = None\n else:\n try:\n rightpricei_ = memoryview(rightpricei)\n except TypeError:\n try:\n _tmparr_rightpricei = array.array(\"d\",rightpricei)\n except TypeError:\n raise TypeError(\"Argument rightpricei has wrong type\")\n else:\n rightpricei_ = memoryview(_tmparr_rightpricei)\n _copyback_rightpricei = True\n else:\n if rightpricei_.format != \"d\":\n rightpricei_ = memoryview(array.array(\"d\",rightpricei))\n _copyback_rightpricei = True\n if rightpricei_ is not None and len(rightpricei_) != (numi_):\n raise ValueError(\"Array argument rightpricei has wrong length\")\n _copyback_leftrangei = False\n if leftrangei is None:\n leftrangei_ = None\n else:\n try:\n leftrangei_ = memoryview(leftrangei)\n except TypeError:\n try:\n _tmparr_leftrangei = array.array(\"d\",leftrangei)\n except TypeError:\n raise TypeError(\"Argument leftrangei has wrong type\")\n else:\n leftrangei_ = memoryview(_tmparr_leftrangei)\n _copyback_leftrangei = True\n else:\n if leftrangei_.format != \"d\":\n leftrangei_ = memoryview(array.array(\"d\",leftrangei))\n _copyback_leftrangei = True\n if leftrangei_ is not None and len(leftrangei_) != (numi_):\n raise ValueError(\"Array argument leftrangei has wrong length\")\n _copyback_rightrangei = False\n if rightrangei is None:\n rightrangei_ = None\n else:\n try:\n rightrangei_ = memoryview(rightrangei)\n except TypeError:\n try:\n _tmparr_rightrangei = array.array(\"d\",rightrangei)\n except TypeError:\n raise TypeError(\"Argument rightrangei has wrong type\")\n else:\n rightrangei_ = memoryview(_tmparr_rightrangei)\n _copyback_rightrangei = True\n else:\n if rightrangei_.format != \"d\":\n rightrangei_ = memoryview(array.array(\"d\",rightrangei))\n _copyback_rightrangei = True\n if rightrangei_ is not None and len(rightrangei_) != (numi_):\n raise ValueError(\"Array argument rightrangei has wrong length\")\n _copyback_leftpricej = False\n if leftpricej is None:\n leftpricej_ = None\n else:\n try:\n leftpricej_ = memoryview(leftpricej)\n except TypeError:\n try:\n _tmparr_leftpricej = array.array(\"d\",leftpricej)\n except TypeError:\n raise TypeError(\"Argument leftpricej has wrong type\")\n else:\n leftpricej_ = memoryview(_tmparr_leftpricej)\n _copyback_leftpricej = True\n else:\n if leftpricej_.format != \"d\":\n leftpricej_ = memoryview(array.array(\"d\",leftpricej))\n _copyback_leftpricej = True\n if leftpricej_ is not None and len(leftpricej_) != (numj_):\n raise ValueError(\"Array argument leftpricej has wrong length\")\n _copyback_rightpricej = False\n if rightpricej is None:\n rightpricej_ = None\n else:\n try:\n rightpricej_ = memoryview(rightpricej)\n except TypeError:\n try:\n _tmparr_rightpricej = array.array(\"d\",rightpricej)\n except TypeError:\n raise TypeError(\"Argument rightpricej has wrong type\")\n else:\n rightpricej_ = memoryview(_tmparr_rightpricej)\n _copyback_rightpricej = True\n else:\n if rightpricej_.format != \"d\":\n rightpricej_ = memoryview(array.array(\"d\",rightpricej))\n _copyback_rightpricej = True\n if rightpricej_ is not None and len(rightpricej_) != (numj_):\n raise ValueError(\"Array argument rightpricej has wrong length\")\n _copyback_leftrangej = False\n if leftrangej is None:\n leftrangej_ = None\n else:\n try:\n leftrangej_ = memoryview(leftrangej)\n except TypeError:\n try:\n _tmparr_leftrangej = array.array(\"d\",leftrangej)\n except TypeError:\n raise TypeError(\"Argument leftrangej has wrong type\")\n else:\n leftrangej_ = memoryview(_tmparr_leftrangej)\n _copyback_leftrangej = True\n else:\n if leftrangej_.format != \"d\":\n leftrangej_ = memoryview(array.array(\"d\",leftrangej))\n _copyback_leftrangej = True\n if leftrangej_ is not None and len(leftrangej_) != (numj_):\n raise ValueError(\"Array argument leftrangej has wrong length\")\n _copyback_rightrangej = False\n if rightrangej is None:\n rightrangej_ = None\n else:\n try:\n rightrangej_ = memoryview(rightrangej)\n except TypeError:\n try:\n _tmparr_rightrangej = array.array(\"d\",rightrangej)\n except TypeError:\n raise TypeError(\"Argument rightrangej has wrong type\")\n else:\n rightrangej_ = memoryview(_tmparr_rightrangej)\n _copyback_rightrangej = True\n else:\n if rightrangej_.format != \"d\":\n rightrangej_ = memoryview(array.array(\"d\",rightrangej))\n _copyback_rightrangej = True\n if rightrangej_ is not None and len(rightrangej_) != (numj_):\n raise ValueError(\"Array argument rightrangej has wrong length\")\n res = self.__obj.primalsensitivity(numi_,subi_,marki_,numj_,subj_,markj_,leftpricei_,rightpricei_,leftrangei_,rightrangei_,leftpricej_,rightpricej_,leftrangej_,rightrangej_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_rightrangej:\n rightrangej[:] = _tmparr_rightrangej\n if _copyback_leftrangej:\n leftrangej[:] = _tmparr_leftrangej\n if _copyback_rightpricej:\n rightpricej[:] = _tmparr_rightpricej\n if _copyback_leftpricej:\n leftpricej[:] = _tmparr_leftpricej\n if _copyback_rightrangei:\n rightrangei[:] = _tmparr_rightrangei\n if _copyback_leftrangei:\n leftrangei[:] = _tmparr_leftrangei\n if _copyback_rightpricei:\n rightpricei[:] = _tmparr_rightpricei\n if _copyback_leftpricei:\n leftpricei[:] = _tmparr_leftpricei", "def dualsensitivity(self,subj,leftpricej,rightpricej,leftrangej,rightrangej): # 3\n numj_ = None\n if numj_ is None:\n numj_ = len(subj)\n elif numj_ != len(subj):\n raise IndexError(\"Inconsistent length of array subj\")\n if numj_ is None: numj_ = 0\n if subj is None: raise TypeError(\"Invalid type for argument subj\")\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n \n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n \n _copyback_leftpricej = False\n if leftpricej is None:\n leftpricej_ = None\n else:\n try:\n leftpricej_ = memoryview(leftpricej)\n except TypeError:\n try:\n _tmparr_leftpricej = array.array(\"d\",leftpricej)\n except TypeError:\n raise TypeError(\"Argument leftpricej has wrong type\")\n else:\n leftpricej_ = memoryview(_tmparr_leftpricej)\n _copyback_leftpricej = True\n else:\n if leftpricej_.format != \"d\":\n leftpricej_ = memoryview(array.array(\"d\",leftpricej))\n _copyback_leftpricej = True\n if leftpricej_ is not None and len(leftpricej_) != (numj_):\n raise ValueError(\"Array argument leftpricej has wrong length\")\n _copyback_rightpricej = False\n if rightpricej is None:\n rightpricej_ = None\n else:\n try:\n rightpricej_ = memoryview(rightpricej)\n except TypeError:\n try:\n _tmparr_rightpricej = array.array(\"d\",rightpricej)\n except TypeError:\n raise TypeError(\"Argument rightpricej has wrong type\")\n else:\n rightpricej_ = memoryview(_tmparr_rightpricej)\n _copyback_rightpricej = True\n else:\n if rightpricej_.format != \"d\":\n rightpricej_ = memoryview(array.array(\"d\",rightpricej))\n _copyback_rightpricej = True\n if rightpricej_ is not None and len(rightpricej_) != (numj_):\n raise ValueError(\"Array argument rightpricej has wrong length\")\n _copyback_leftrangej = False\n if leftrangej is None:\n leftrangej_ = None\n else:\n try:\n leftrangej_ = memoryview(leftrangej)\n except TypeError:\n try:\n _tmparr_leftrangej = array.array(\"d\",leftrangej)\n except TypeError:\n raise TypeError(\"Argument leftrangej has wrong type\")\n else:\n leftrangej_ = memoryview(_tmparr_leftrangej)\n _copyback_leftrangej = True\n else:\n if leftrangej_.format != \"d\":\n leftrangej_ = memoryview(array.array(\"d\",leftrangej))\n _copyback_leftrangej = True\n if leftrangej_ is not None and len(leftrangej_) != (numj_):\n raise ValueError(\"Array argument leftrangej has wrong length\")\n _copyback_rightrangej = False\n if rightrangej is None:\n rightrangej_ = None\n else:\n try:\n rightrangej_ = memoryview(rightrangej)\n except TypeError:\n try:\n _tmparr_rightrangej = array.array(\"d\",rightrangej)\n except TypeError:\n raise TypeError(\"Argument rightrangej has wrong type\")\n else:\n rightrangej_ = memoryview(_tmparr_rightrangej)\n _copyback_rightrangej = True\n else:\n if rightrangej_.format != \"d\":\n rightrangej_ = memoryview(array.array(\"d\",rightrangej))\n _copyback_rightrangej = True\n if rightrangej_ is not None and len(rightrangej_) != (numj_):\n raise ValueError(\"Array argument rightrangej has wrong length\")\n res = self.__obj.dualsensitivity(numj_,subj_,leftpricej_,rightpricej_,leftrangej_,rightrangej_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_rightrangej:\n rightrangej[:] = _tmparr_rightrangej\n if _copyback_leftrangej:\n leftrangej[:] = _tmparr_leftrangej\n if _copyback_rightpricej:\n rightpricej[:] = _tmparr_rightpricej\n if _copyback_leftpricej:\n leftpricej[:] = _tmparr_leftpricej", "def dualsensitivity(self,subj_,leftpricej_,rightpricej_,leftrangej_,rightrangej_):\n numj_ = None\n if numj_ is None:\n numj_ = len(subj_)\n elif numj_ != len(subj_):\n raise IndexError(\"Inconsistent length of array subj\")\n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n _leftpricej_minlength = (numj_)\n if (numj_) > 0 and leftpricej_ is not None and len(leftpricej_) != (numj_):\n raise ValueError(\"Array argument leftpricej is not long enough: Is %d, expected %d\" % (len(leftpricej_),(numj_)))\n if isinstance(leftpricej_,numpy.ndarray) and not leftpricej_.flags.writeable:\n raise ValueError(\"Argument leftpricej must be writable\")\n if isinstance(leftpricej_, numpy.ndarray) and leftpricej_.dtype is numpy.dtype(numpy.float64) and leftpricej_.flags.contiguous:\n _leftpricej_copyarray = False\n _leftpricej_tmp = ctypes.cast(leftpricej_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif leftpricej_ is not None:\n _leftpricej_copyarray = True\n _leftpricej_np_tmp = numpy.zeros(len(leftpricej_),numpy.dtype(numpy.float64))\n _leftpricej_np_tmp[:] = leftpricej_\n assert _leftpricej_np_tmp.flags.contiguous\n _leftpricej_tmp = ctypes.cast(_leftpricej_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _leftpricej_copyarray = False\n _leftpricej_tmp = None\n \n _rightpricej_minlength = (numj_)\n if (numj_) > 0 and rightpricej_ is not None and len(rightpricej_) != (numj_):\n raise ValueError(\"Array argument rightpricej is not long enough: Is %d, expected %d\" % (len(rightpricej_),(numj_)))\n if isinstance(rightpricej_,numpy.ndarray) and not rightpricej_.flags.writeable:\n raise ValueError(\"Argument rightpricej must be writable\")\n if isinstance(rightpricej_, numpy.ndarray) and rightpricej_.dtype is numpy.dtype(numpy.float64) and rightpricej_.flags.contiguous:\n _rightpricej_copyarray = False\n _rightpricej_tmp = ctypes.cast(rightpricej_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif rightpricej_ is not None:\n _rightpricej_copyarray = True\n _rightpricej_np_tmp = numpy.zeros(len(rightpricej_),numpy.dtype(numpy.float64))\n _rightpricej_np_tmp[:] = rightpricej_\n assert _rightpricej_np_tmp.flags.contiguous\n _rightpricej_tmp = ctypes.cast(_rightpricej_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _rightpricej_copyarray = False\n _rightpricej_tmp = None\n \n _leftrangej_minlength = (numj_)\n if (numj_) > 0 and leftrangej_ is not None and len(leftrangej_) != (numj_):\n raise ValueError(\"Array argument leftrangej is not long enough: Is %d, expected %d\" % (len(leftrangej_),(numj_)))\n if isinstance(leftrangej_,numpy.ndarray) and not leftrangej_.flags.writeable:\n raise ValueError(\"Argument leftrangej must be writable\")\n if isinstance(leftrangej_, numpy.ndarray) and leftrangej_.dtype is numpy.dtype(numpy.float64) and leftrangej_.flags.contiguous:\n _leftrangej_copyarray = False\n _leftrangej_tmp = ctypes.cast(leftrangej_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif leftrangej_ is not None:\n _leftrangej_copyarray = True\n _leftrangej_np_tmp = numpy.zeros(len(leftrangej_),numpy.dtype(numpy.float64))\n _leftrangej_np_tmp[:] = leftrangej_\n assert _leftrangej_np_tmp.flags.contiguous\n _leftrangej_tmp = ctypes.cast(_leftrangej_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _leftrangej_copyarray = False\n _leftrangej_tmp = None\n \n _rightrangej_minlength = (numj_)\n if (numj_) > 0 and rightrangej_ is not None and len(rightrangej_) != (numj_):\n raise ValueError(\"Array argument rightrangej is not long enough: Is %d, expected %d\" % (len(rightrangej_),(numj_)))\n if isinstance(rightrangej_,numpy.ndarray) and not rightrangej_.flags.writeable:\n raise ValueError(\"Argument rightrangej must be writable\")\n if isinstance(rightrangej_, numpy.ndarray) and rightrangej_.dtype is numpy.dtype(numpy.float64) and rightrangej_.flags.contiguous:\n _rightrangej_copyarray = False\n _rightrangej_tmp = ctypes.cast(rightrangej_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif rightrangej_ is not None:\n _rightrangej_copyarray = True\n _rightrangej_np_tmp = numpy.zeros(len(rightrangej_),numpy.dtype(numpy.float64))\n _rightrangej_np_tmp[:] = rightrangej_\n assert _rightrangej_np_tmp.flags.contiguous\n _rightrangej_tmp = ctypes.cast(_rightrangej_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _rightrangej_copyarray = False\n _rightrangej_tmp = None\n \n res = __library__.MSK_XX_dualsensitivity(self.__nativep,numj_,_subj_tmp,_leftpricej_tmp,_rightpricej_tmp,_leftrangej_tmp,_rightrangej_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _leftpricej_copyarray:\n leftpricej_[:] = _leftpricej_np_tmp\n if _rightpricej_copyarray:\n rightpricej_[:] = _rightpricej_np_tmp\n if _leftrangej_copyarray:\n leftrangej_[:] = _leftrangej_np_tmp\n if _rightrangej_copyarray:\n rightrangej_[:] = _rightrangej_np_tmp", "def sensitivity(self):\n return self.recall", "def sensitivity(self):\n return self.__sensitivity", "def test_sensitivity():\n n_ons = np.arange(0.1, 10, 0.3)\n n_offs = np.arange(0.1, 10, 0.3)\n alphas = np.array([1e-3, 1e-2, 0.1, 1, 10])\n for n_on in n_ons:\n for n_off in n_offs:\n for alpha in alphas:\n for method in ['simple', 'lima']:\n significance = significance_on_off(n_on, n_off, alpha, method=method)\n excess = sensitivity_on_off(n_off, alpha, significance, method=method)\n n_on2 = excess + alpha * n_off\n assert_allclose(n_on, n_on2, decimal=3)", "def input_sensitivity(self):\r\n\r\n if not hasattr(self, 'kern'):\r\n raise ValueError, \"this model has no kernel\"\r\n\r\n k = [p for p in self.kern.parts if p.name in ['rbf', 'linear', 'rbf_inv']]\r\n if (not len(k) == 1) or (not k[0].ARD):\r\n raise ValueError, \"cannot determine sensitivity for this kernel\"\r\n k = k[0]\r\n\r\n if k.name == 'rbf':\r\n return 1. / k.lengthscale\r\n elif k.name == 'rbf_inv':\r\n return k.inv_lengthscale\r\n elif k.name == 'linear':\r\n return k.variances", "def SetPRCatConstraint(self, model ) :\n tot = np.multiply(self.wish, self.dispo)\n for line in tot :\n for val in line :\n if not val : continue\n if self.bound>0 : model += val <= self.valBound\n elif self.bound<0 : model += val >= self.valBound", "def input_sensitivity(self):\n\n if not hasattr(self,'kern'):\n raise ValueError, \"this model has no kernel\"\n\n k = [p for p in self.kern.parts if p.name in ['rbf','linear']]\n if (not len(k)==1) or (not k[0].ARD):\n raise ValueError, \"cannot determine sensitivity for this kernel\"\n k = k[0]\n\n if k.name=='rbf':\n return k.lengthscale\n elif k.name=='linear':\n return 1./k.variances", "def finish_sensitivity(self):\n # do at most 1000 features\n idx = torch.randperm(self._features.shape[1])[:100]\n self._features = self._features[:, idx]\n\n weight = self.module.weight.data\n num_features_in = weight.shape[1]\n selected_in = torch.zeros(num_features_in).bool()\n\n # greedy approach to rank in features\n for rank in reversed(range(num_features_in)):\n error_best = torch.Tensor([np.Inf])\n best = None\n\n # loop through remaining features to see which to add next\n for idx_in in range(num_features_in):\n # it's already in the set, no need trying to add it...\n if selected_in[idx_in]:\n continue\n\n # try adding in feature j and compute error\n selected_in[idx_in] = 1\n error_with_j = (\n self._features[selected_in].sum(dim=0) ** 2\n ).sum()\n\n # see if it's better than previous best\n if error_with_j < error_best:\n error_best = error_with_j\n best = idx_in\n\n # remove j from selectedIn for now\n selected_in[idx_in] = 0\n\n # add best one from this round to selectedIn\n selected_in[best] = 1\n\n # also note the rank of best in the sensitivities\n self.sensitivity_in[best] = rank", "def calculate_sensitivity(self, x_train, y_train):\n model_f_activations = self.model_f.predict(x_train)\n reshaped_labels = np.array(y_train).reshape((x_train.shape[0], 1))\n tf_y_labels = tf.convert_to_tensor(reshaped_labels, dtype=np.float32)\n loss = k.binary_crossentropy(tf_y_labels, self.model_h.output)\n grad = k.gradients(loss, self.model_h.input)\n gradient_func = k.function([self.model_h.input], grad)\n calc_grad = gradient_func([model_f_activations])[0]\n sensitivity = np.dot(calc_grad, self.cav)\n self.sensitivity = sensitivity\n self.y_labels = y_train", "def evaluation(model_path, threshold):\n classifier = joblib.load(model_path)\n\n positive = np.load(\"./processed_data/validation/positive.npy\")\n unlabeled = np.load(\"./processed_data/validation/unlabeled.npy\")\n\n p_result = np.array(classifier.predict_proba(positive[:, :-1])[:, 1])\n plt.hist(p_result, bins=300)\n plt.show()\n\n tp_rate = np.where(p_result >= threshold, 1, 0).sum() / p_result.shape[0]\n print(tp_rate)\n\n u_result = np.array(classifier.predict_proba(unlabeled[:, :-1])[:, 1])\n plt.hist(u_result, bins=300)\n plt.show()\n\n\n # the following steps aim to filter 'possible' negative instances in the evaluation-unlabeled set\n stageone_classifier = joblib.load(\"./solver_result/liblinear/0.01/logistic.pkl\")\n stgone_result = np.array(stageone_classifier.predict_proba(unlabeled[:,:-1])[:, 1])\n possibly_negative = unlabeled[np.where(stgone_result <= _negative_threshold)]\n print(positive.shape)\n print(unlabeled.shape)\n print(possibly_negative.shape)\n possi_ng_result = np.array(classifier.predict_proba(possibly_negative[:, :-1])[:, 1])\n fp_rate = np.where(possi_ng_result >= threshold, 1, 0).sum() / possi_ng_result.shape[0]\n plt.hist(possi_ng_result, bins=300)\n plt.show()\n\n print(fp_rate)\n print(\"TP: \" + str(tp_rate) + \" FP: \" + str(fp_rate) + \" GMean: \" + str(math.sqrt(tp_rate * (1 - fp_rate))))", "def SetPRBinConstraint(self, model ) :\n tot = np.multiply(self.wish, self.dispo)\n for val in tot :\n if not val : continue\n if self.bound>0 : model += val <= self.valBound\n elif self.bound<0 : model += val >= self.valBound", "def model(self,sample):\n\n lca = self.lca\n \n self.amount_tech = lca.tech_params['amount']\n self.amount_bio = lca.bio_params['amount']\n\n self.i_sample = 0\n self.replace_non_parameterized_exchanges(sample)\n self.replace_parameterized_exchanges(sample)\n\n lca.rebuild_technosphere_matrix(self.amount_tech)\n lca.rebuild_biosphere_matrix(self.amount_bio)\n\n score = (sum(lca.characterization_matrix)*lca.biosphere_matrix) * \\\n spsolve(lca.technosphere_matrix,lca.demand_array)\n\n np.append(self.scores, score)\n\n return score", "def get_sensitive_hits(primers,\n input_fasta_files,\n percent_match,\n sequence_length,\n region_slice):\n\n seq_count=0\n for n in input_fasta_files:\n seq_total_target=get_sequence_count(n)\n deletion_threshold=get_deletion_threshold(percent_match,\n seq_total_target)\n fasta_f=open(n,'U')\n for label,seq in MinimalFastaParser(fasta_f):\n seq_count+=1\n unaligned_seq = seq.replace(\"-\",\"\")\n unaligned_seq = unaligned_seq.replace(\".\",\"\")\n unaligned_seq = unaligned_seq.upper()\n unaligned_seq = unaligned_seq.replace(\"U\",\"T\")\n integer_mapped_seq = convert_to_numeric(unaligned_seq)\n primers=find_sensitive_primer_matches(primers, integer_mapped_seq,\n deletion_threshold, seq_count, sequence_length,\n label,unaligned_seq, region_slice, seq)\n fasta_f.close()\n \n return primers", "def sensitivity(self, ad, amp, i, pa):\n\n sens = ct.c_float()\n ad, amp, i, pa = ct.c_int(ad), ct.c_int(amp), ct.c_int(i), ct.c_int(pa)\n self.lib.GetSensitivity(ad, amp, i, pa, ct.pointer(sens))\n return sens.value", "def penalty_calc(self):\n self.p_budget = (self.tx_oma_min - self.rx_unstressed_sensitivity - self.fiber_conn_loss)*self.l_1\n\n # fiber attenuation,\n self.p_atten = self.alpha*self.length # column B\n\n # calculate bandwidth for RIN test (exclude transmitter)\n rin_inverse_bw = np.sqrt(np.square(1.0/self.bw_cd) + np.square(1.0/self.bw_md) + (0.477/(self.rx_bw**2))*self.l_1)\n rin_bw = 1.0 / rin_inverse_bw\n\n # see FC-MSQS-2 equation B.47 in Annex B.4 for the following k_rin = math.sqrt(2.0/math.pi)*erfinv(0.8)\n k_rin = 0.7\n\n # v_rin,\n self.v_rin = (k_rin*1E6*(self.rin_test_isi**2)*rin_bw*\n math.pow(10.0,0.1*self.rin)) # column AK\n\n # Prin,\n print('v_rin: ', self.v_rin)\n print('Q: ',self.Q)\n print('isi_dj_refl_closed :', self.isi_dj_refl_closed)\n self.p_rin = -10.0*np.log10(np.sqrt(1.0-np.multiply(self.v_rin, np.square(self.Q/self.isi_dj_refl_closed)))) # column R\n print(\"P_rin : \", self.p_rin)\n self.beta = (3.14159E-6*self.speedup*self.br_nominal *self.delta_lambda*self.d1*self.length) # column O\n self.sigma_mpn = (self.k_mpn/math.sqrt(2.0)*(self.l_1 -np.exp(-np.square(self.beta)))) # column P\n self.p_mpn = (-10.0*np.log10(np.sqrt(self.l_1 - (self.Q**2)*np.square(self.sigma_mpn)))) # column Q\n self.p_blw = (-10.0*math.log10(math.sqrt(1.0- ((self.Q*self.sigma_blw)/ self.isi_tp4_rx)**2))*self.l_1) # cell T13\n self.p_reflection = -10.0*np.log10(self.isi_reflection) # column N\n self.v_mn = (((1.0-math.pow(10.0,-0.2*self.pmn))/ (self.Q)**2)*self.l_1) # cell AG7\n print(\"isi_center : \", self.isi_center)\n\n self.p_isi_center = -10.0*np.log10(self.isi_center) # column J\n\n self.p_isi_corners = (-10.0*np.log10(self.isi_corners) - self.p_isi_center) # column K\n self.p_isi_dj_center = (-10.0*np.log10(self.isi_dj_refl_closed) - self.p_isi_center) # column L\n self.p_isi_dj_corners = (-10.0*np.log10(self.isi_dj_corners) -self.p_isi_center -self.p_isi_corners) # column M\n\n\n # calculate the \"cross\" penalty contribution, column S\n arg1 = ((self.sigma_blw**2 + self.v_rin)/ np.square(self.isi_dj_refl_closed))\n arg2 = self.l_1 - (self.Q**2)*(arg1 + self.v_mn + np.square(self.sigma_mpn))\n arg3 = (-10.0*np.log10(np.multiply(self.isi_dj_refl_closed, np.sqrt(arg2))))\n self.p_cross_center = ( # column S\n arg3\n - self.p_blw # cell T13\n - self.p_isi_center # column J\n - self.p_isi_dj_center # column L\n - self.p_mpn # column Q\n - self.p_reflection # column N\n - self.p_rin # column R\n - self.pmn*self.l_1) # cell G13\n print('p_isi_center: ', self.p_isi_center)\n\n # calculate the total power budget evaluated at the center of the eye\n self.p_total_center = ( # column T\n self.p_isi_center # column J\n + self.p_isi_dj_center # column L\n + self.p_atten # column B\n + self.p_mpn # column Q\n + self.p_reflection # column N\n + self.p_rin # column R\n + self.p_cross_center # column S\n + self.pmn*self.l_1) # cell G13\n # calculate the total power budget evaluated at the corner of the eye\n self.p_total_corners = (\n self.p_isi_center # column J\n + self.p_isi_corners # column K\n + self.p_atten # column B\n + self.p_mpn # column Q\n + self.p_reflection # column N\n + self.p_rin # column R\n + self.p_cross_center # column S\n + self.pmn*self.l_1 # cell G13\n + self.p_isi_dj_corners)# column M\n\n # receiver stressed sensitivity\n self.margin = ( self.p_budget\n - self.p_total_center) # column W\n\n self.rx_stressed_sensitivity = (\n self.tx_oma_min*self.l_1\n - self.chil\n - self.p_mpn\n - self.p_reflection\n - self.p_rin\n - 0.5*self.p_cross_center\n - self.pmn*self.l_1\n - self.margin[self.lnum//2]*self.l_1)\n\n\n # end of GbE10.penalty_calc\n #======================================================================+", "def sensitivity(y_test, y_pred):\n\tmatrix = confusion_matrix(y_test, y_pred)\n\treturn matrix[0][0] / (matrix[0][0] + matrix[0][1])", "def sensitivity(base_case,init_df):\n SA_df = init_df.copy()\n M = init_df.index.size\n categories=list(init_df)\n N = len(categories)\n row = 0\n for x in range(M): \n if init_df.index[x] == base_case: \n basecase_index = row\n row += 1\n for x in range(M): \n if init_df.index[x] == base_case: \n for y in range(N): \n SA_df.iloc[x,y] = 0\n else: \n for y in range(N): \n if float(init_df.iloc[basecase_index,y]) == 0: \n SA_df.iloc[x,y] = np.nan\n else:\n SA_df.iloc[x,y] = (init_df.iloc[x,y]-init_df.iloc[basecase_index,y])/init_df.iloc[basecase_index,y]*100 \n return SA_df", "def first_stage_test(solver, sub_u_rate):\n bins_num = 100\n\n classifier = joblib.load(join(join(_result_path, solver), str(sub_u_rate)) + '/logistic.pkl')\n\n # evaluate positive set, which contains spies\n positive = np.load(\"./processed_data/train/raw/train_p.npy\")\n positive_x = positive[:, : -1]\n result_p = np.array(classifier.predict_proba(positive_x)[:, 1])\n plt.hist(result_p, bins=bins_num)\n plt.savefig(join(join(_result_path, solver), str(sub_u_rate)) + '/positive.png')\n plt.show()\n print(\"\\npositive set results: average: \" + str(np.mean(result_p)) + \" variance:\" + str(np.var(result_p)))\n print(\"max: \" + str(result_p.max()) + \" min: \" + str(result_p.min()))\n\n # evaluate spy set\n spy = np.load(_spy_path)\n spy_x = spy[:, : -1]\n result_spy = np.array(classifier.predict_proba(spy_x)[:, 1])\n plt.hist(result_spy, bins=bins_num)\n plt.savefig(join(join(_result_path, solver), str(sub_u_rate)) + '/spy.png')\n plt.show()\n print(\"\\nspy set results: average: \" + str(np.mean(result_spy)) + \" variance:\" + str(np.var(result_spy)))\n print(\"max: \" + str(result_spy.max()) + \" min: \" + str(result_spy.min()))\n\n # evaluate sub-unlabeled set\n sub_u = np.load(\"./processed_data/train/sub_u_\" + str(sub_u_rate) + \".npy\")\n sub_u_x = sub_u[:, :-1]\n result_sub_u = np.array(classifier.predict_proba(sub_u_x)[:, 1])\n plt.hist(result_sub_u, bins=bins_num)\n plt.savefig(join(join(_result_path, solver), str(sub_u_rate)) + '/sub-u.png')\n plt.show()\n print(\"\\nsub-unlabeled set results: average: \" + str(np.mean(result_sub_u)) + \" variance:\" + str(np.var(result_sub_u)))\n print(\"max: \" + str(result_sub_u.max()) + \" min: \" + str(result_sub_u.min()))\n\n # evaluate the whole unlabeled set\n unlabeled = np.load(\"./processed_data/train/raw/train_u.npy\")\n unlabeled_x = unlabeled[:, :-1]\n result_unlabeled = np.array(classifier.predict_proba(unlabeled_x)[:, 1])\n plt.hist(result_unlabeled, bins=bins_num)\n plt.savefig(join(join(_result_path, solver), str(sub_u_rate)) + '/unlabeled.png')\n plt.show()\n print(\"\\nunlabeled set results: average: \" + str(np.mean(result_unlabeled)) + \" variance:\" + str(\n np.var(result_unlabeled)))\n print(\"max: \" + str(result_unlabeled.max()) + \" min: \" + str(result_unlabeled.min()))", "def overall_sensitivity(self):\n if self.mod1:\n s = torch.max(torch.max(self.weight, -1)[0], -1)[0].item()\n else:\n s = torch.max(torch.sqrt(torch.sum(self.weight * self.weight, -1)))[0].item()\n s *= np.sqrt(2. / np.e)\n return s", "def SetPRBinCatConstraint( self, model ) :\n tot = np.dot( self.wish.T, self.dispo )\n for val in tot :\n if not val : continue\n if self.bound>0 : model += val <= self.valBound\n elif self.bound<0 : model += val >= self.valBound", "def test_model(model_name, save_dir, postive_file, negative_file, measure=\"SPC\", measure_threshold=0.95):\n print model_name\n postive_scores = get_model_scores(postive_file)\n negative_scores = get_model_scores(negative_file)\n all_scores = postive_scores+negative_scores\n # print all_scores\n\n if len(negative_scores) == 0:\n return {\"roc_auc\":0, \"threshold\":min(postive_scores)}\n\n y_true = [1]*len(postive_scores) + [0]*len(negative_scores)\n y_score = np.array(all_scores)\n\n fpr, tpr, thresholds = roc_curve(y_true, y_score)\n roc_auc = auc(fpr, tpr)\n\n best_threshold, thresholds, values = calcualte_threshold(\n postive_scores, \n negative_scores, \n measure=measure,\n measure_threshold=measure_threshold, \n thresholds=reversed(thresholds))\n\n\n pp = PdfPages(os.path.join(save_dir, \"{}_model_evaluation.pdf\".format(model_name)))\n\n sns.set(style=\"darkgrid\")\n f, axes = plt.subplots(3)\n trans = f.transFigure.inverted()\n colors = sns.color_palette(\"Set2\", 7)\n\n sns.kdeplot(np.array(postive_scores), shade=True, color=sns.xkcd_rgb[\"denim blue\"], label=\"Scores for postive examples\", ax=axes[0])\n sns.kdeplot(np.array(negative_scores), shade=True, color=sns.xkcd_rgb[\"pale red\"], label=\"Scores for negative examples\", ax=axes[0])\n axes[0].set_xlabel(\"Bit score\")\n axes[0].set_ylabel(\"Density\")\n axes[0].legend(loc=\"upper left\")\n #axes[0].set_title(\"Kernel Density of Scores\")\n axes[1].set_xlim([0, 1.0])\n axes[1].set_ylim([0.0, 1.05])\n\n \n axes[1].plot(fpr,tpr, color=colors[0], lw=3., label=\"ROC (AUC: {})\".format(roc_auc))\n axes[1].set_xlabel(\"False Positive Rate\")\n axes[1].set_ylabel(\"True Positive Rate\")\n axes[1].legend(loc=\"lower right\")\n axes[1].set_xlim([-0.05, 1.0])\n axes[1].set_ylim([0.0, 1.05])\n #axes[1].set_title(\"ROC\")\n \n for i, (measure, values) in enumerate(values.iteritems()):\n label = \"SPC: (>={})\".format(best_threshold) if measure==\"SPC\" else measure\n axes[2].plot(list(thresholds), values, label=label, linewidth=2, color=colors[i])\n axes[2].axvline(best_threshold)\n\n axes[2].legend()\n #axes[2].set_title(\"Coosing Cutoff\")\n axes[2].set_ylabel(\"Rate\")\n axes[2].set_xlabel(\"Threshold\")\n\n f.suptitle(\"{} Model Evaluation\".format(model_name), fontsize=20)\n\n pp.savefig()\n pp.close()\n\n return {\"roc_auc\":roc_auc, \"threshold\":best_threshold}", "def propabilityLVQ(self):\n self.labels = self.labelingLVQ()\n for i in range(self.labels.shape[0]):\n for j in range(self.labels.shape[1]):\n for k in range(self.labels.shape[2]):\n total = sum(self.labels[i, j, k] for i in range(self.labels.shape[0]))\n if total == 0. :\n continue\n else:\n self.propa[i, j, k] = self.labels[i, j, k] / total\n self.propa[i, j, k] = round(self.propa[i, j, k], 2)\n return self.propa", "def prediction(self, v, imu_meas):\n # YOUR CODE HERE\n pass", "def _compute_penalty(self):\n raise ValueError('Implement in a child class')", "def find_optimum_thresholds(search_method, subscripts, knowledge_model,\n samples):\n if len(samples) == 0:\n return None\n\n exercise_name = samples[0][idx.exercise]\n if exercise_name not in knowledge_model[\"thetas\"]:\n return None\n else:\n thetas = knowledge_model[\"thetas\"][exercise_name]\n\n # Convert CSV features into proper float-array representation\n correct, features, _ = parse_features(samples, False, [\"custom\", \"random\"])\n\n # Compute predctions based on features\n predictions = regression_util.sigmoid(np.dot(features, thetas))\n\n thresholds = {}\n for subscript in subscripts:\n if search_method.lower() == \"brute\":\n optimum_threshold = scipy.optimize.brute(f_score,\n ranges=((0.0, 1.0),), Ns=101, args=(subscript, correct,\n predictions),\n full_output=True)\n\n # Transform to standard format\n optimum_threshold = {\n \"max_score\": -optimum_threshold[1],\n \"success\": True,\n \"threshold\": optimum_threshold[0][0],\n }\n\n elif search_method.lower() == \"minimize_scalar\":\n optimum_threshold = scipy.optimize.minimize_scalar(f_score,\n method=\"bounded\", bounds=(0.0, 1.0), args=(subscript,\n correct, predictions))\n\n # Transform to standard format\n optimum_threshold = {\n \"max_score\": -optimum_threshold.fun,\n \"success\": optimum_threshold.success,\n \"threshold\": optimum_threshold.x,\n }\n\n else:\n raise ValueError(\"Did not understand search method %s\" %\n search_method)\n\n if not optimum_threshold[\"success\"]:\n print >>sys.stderr, \"Optimization failed for\", subscript\n\n # Augment the result object with the number of samples\n optimum_threshold[\"samples\"] = len(samples)\n\n thresholds[subscript] = optimum_threshold\n return thresholds", "def print_sensitivity(self):\n if type(self.y_labels) == list:\n self.y_labels = np.array(self.y_labels)\n print(\n \"The sensitivity of class 1 is \",\n str(\n np.sum(self.sensitivity[np.where(self.y_labels == 1)[0]] > 0)\n / np.where(self.y_labels == 1)[0].shape[0]\n ),\n )\n print(\n \"The sensitivity of class 0 is \",\n str(\n np.sum(self.sensitivity[np.where(self.y_labels == 0)[0]] > 0)\n / np.where(self.y_labels == 0)[0].shape[0]\n ),\n )", "def score(self,x,**kwargs):\r\n if self.kfun != 'matrix' and len(self.sv): \r\n k = self.kfun(x,self.sv,**self.cparam)\r\n #print \"Kernel after test: \", k\r\n else:\r\n k = x\r\n \r\n \r\n self.W=self.alphas \r\n self.mat=self.kfun(np.array([self.sv[1]]), self.sv,**self.cparam) \r\n self.bias=self.svLabels[1]- np.dot((self.alphas*self.svLabels).T,self.mat.T) \r\n z=np.dot((self.alphas*self.svLabels).T,k.T)+self.bias\r\n \r\n #print \"bias: \", self.bias, \"\\nZ: \",z\r\n \r\n \r\n return z", "def optimize(cls, trials, score, evals_rounds, mon_cons, categorical):\n raise NotImplementedError" ]

[ "0.8516134", "0.61320364", "0.6070157", "0.5505288", "0.5244543", "0.5228772", "0.51506406", "0.50980216", "0.50866646", "0.5006929", "0.5002792", "0.49891976", "0.49730042", "0.49282205", "0.49270394", "0.49166498", "0.49019164", "0.48693013", "0.48592722", "0.48548737", "0.48502716", "0.4845829", "0.48288587", "0.48229182", "0.4819024", "0.4794723", "0.47763416", "0.47659943", "0.47603166", "0.47544014" ]

[ "def sensitivityreport(self,whichstream_): # 3\n if not isinstance(whichstream_,streamtype): raise TypeError(\"Argument whichstream has wrong type\")\n res = self.__obj.sensitivityreport(whichstream_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def sero_reporter(self, metadata, analysistype, reportpath):\n logging.info('Creating {} report'.format(analysistype))\n metadata = self.serotype_escherichia(metadata=metadata,\n analysistype=analysistype)\n # Create the path in which the reports are stored\n make_path(reportpath)\n header = 'Strain,Serotype\\n'\n data = ''\n with open(os.path.join(reportpath, '{}.csv'.format(analysistype)), 'w') as report:\n for sample in metadata:\n if sample.general.bestassemblyfile != 'NA':\n data += sample.name + ','\n if sample[analysistype].blastresults:\n # Set the O-type as either the appropriate attribute, or O-untypable\n if ';'.join(sample.serosippr.o_set) == '-':\n otype = 'O-untypeable'\n else:\n otype = '{oset} ({opid})'.format(oset=';'.join(sample.serosippr.o_set),\n opid=sample.serosippr.best_o_pid)\n # Same as above, but for the H-type\n if ';'.join(sample.serosippr.h_set) == '-':\n htype = 'H-untypeable'\n\n else:\n htype = '{hset} ({hpid})'.format(hset=';'.join(sample.serosippr.h_set),\n hpid=sample.serosippr.best_h_pid)\n serotype = '{otype}:{htype}'.format(otype=otype,\n htype=htype)\n # Populate the data string\n data += serotype if serotype != 'O-untypeable:H-untypeable' else 'ND'\n data += '\\n'\n else:\n data += '\\n'\n report.write(header)\n report.write(data)\n return metadata", "def sirv_report_txt(self):\n return op.join(self.root_dir, 'SIRV_evaluation_summary.txt')", "def create_report(cls):\n try: \n report = f\"{sysname}_statistics.csv\"\n file_exists = os.path.isfile(report)\n fieldnames = ['timestampt','total_ram','free_ram','used_ram','cpu_total','cpu_loadavg','acs_8080','acs_8181','acs_8443','mysql','oracle','iis_ram','iis_cpu','java_ram','java_cpu','mysqld_ram','mysqld_cpu']\n data = SystemInformation.evaluate_data()\n with open(report, 'a', newline='') as csvreport:\n write = csv.DictWriter(csvreport, delimiter=',', lineterminator='\\n', fieldnames=fieldnames)\n if not file_exists:\n write.writeheader()\n write.writerow(data)\n logging.info(f\"Done. Report saved to file {report}\")\n except Exception as e:\n logging.exception(f\"EXCEPTION: {e} \\n Full stack trace: \\n\", exc_info=1)", "def solutionsummary(self,whichstream_):\n res = __library__.MSK_XX_solutionsummary(self.__nativep,whichstream_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def _generate_report(self):\n raise NotImplementedError", "def solutionsummary(self,whichstream_): # 3\n if not isinstance(whichstream_,streamtype): raise TypeError(\"Argument whichstream has wrong type\")\n res = self.__obj.solutionsummary(whichstream_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def make_ts_report(self):\n self.ts_report = ''\n if self.chosen_ts_method is not None:\n self.ts_report += 'TS method summary for {0} in {1}\\n'.format(self.label, self.rxn_label)\n self.ts_report += 'Methods that successfully generated a TS guess:\\n'\n if self.successful_methods:\n for successful_method in self.successful_methods:\n self.ts_report += successful_method + ','\n if self.unsuccessful_methods:\n self.ts_report += '\\nMethods that were unsuccessfully in generating a TS guess:\\n'\n for unsuccessful_method in self.unsuccessful_methods:\n self.ts_report += unsuccessful_method + ','\n self.ts_report += '\\nThe method that generated the best TS guess and its output used for the' \\\n ' optimization: {0}'.format(self.chosen_ts_method)", "def generate_report(self) -> Report:\n # equity_curve = self._generate_equity_curve()\n # summary_stats = self._generate_summary_stats(equity_curve)\n # return Report(equity_curve, summary_stats)\n pass", "def _calculate_strehl(self):\n\n self.strehl = np.exp(-1*((2*np.pi/self.science_wavelength)*self.high_order_wfe)**2)", "def report(self, stream):\n from collections import OrderedDict\n self.stats['total'] = sum(self.stats.values())\n for group in self.report_data.values():\n group.stats['total'] = sum(group.stats.values())\n self.report_file.write(self.jinja.get_template('report.html').render(\n report=OrderedDict(sorted(self.report_data.items())),\n stats=self.stats,\n ))\n self.report_file.close()\n if self.config.verbosity > 1:\n stream.writeln(\"-\" * 70)\n stream.writeln(\"HTML: %s\" % self.report_file.name)", "def print_sensitivity(self):\n if type(self.y_labels) == list:\n self.y_labels = np.array(self.y_labels)\n print(\n \"The sensitivity of class 1 is \",\n str(\n np.sum(self.sensitivity[np.where(self.y_labels == 1)[0]] > 0)\n / np.where(self.y_labels == 1)[0].shape[0]\n ),\n )\n print(\n \"The sensitivity of class 0 is \",\n str(\n np.sum(self.sensitivity[np.where(self.y_labels == 0)[0]] > 0)\n / np.where(self.y_labels == 0)[0].shape[0]\n ),\n )", "def write_stats(self, filestream):\n if not self.summary:\n self.summarize()\n\n print(self.scores, file=filestream)", "def create_vuln_report():", "def output(self,file):\n peep=len(self.findProID())\n f=open(file,'w')\n f.writelines(\" Apache Point Observatory\\n\"\\\n \" 3.5m Telescope Night Log\\n\")\n f.writelines(\" \"+self.link.GetLabel()+'\\n')\n #f.writelines('\\n'+self.userHeader.GetLabel()+'\\n')\n f.writelines(\"\\n ACTUAL\\n\"\\\n \" ASTRONOMER OBSERVER(S) INSTRUMENT START FINISH\\n\"\\\n \"--------------------------------------------------------------------\\n\")\n f.writelines('%s%s%s%s%s\\n' % (self.usastr0.GetValue().ljust(18),self.usobs0.GetValue().ljust(22),self.usinst0.GetValue().ljust(15),self.usstart0.GetValue().ljust(8), self.usend0.GetValue().ljust(8)))\n if oneVar==1:\n f.writelines('%s%s%s%s%s\\n' % (self.usastr0b.GetValue().ljust(18),self.usobs0b.GetValue().ljust(22),self.usinst0b.GetValue().ljust(15),self.usstart0b.GetValue().ljust(8), self.usend0b.GetValue()))\n f.writelines('%s%s%s%s%s\\n' % (self.usastr1.GetValue().ljust(18), self.usobs1.GetValue().ljust(22),self.usinst1.GetValue().ljust(15),self.usstart1.GetValue().ljust(8), self.usend1.GetValue()))\n if twoVar==1:\n f.writelines('%s%s%s%s%s\\n' % (self.usastr1b.GetValue().ljust(18),self.usobs1b.GetValue().ljust(22),self.usinst1b.GetValue().ljust(15),self.usstart1b.GetValue().ljust(8), self.usend1b.GetValue()))\n if peep > 2:\n f.writelines('%s%s%s%s%s\\n' % (self.usastr2.GetValue().ljust(18), self.usobs2.GetValue().ljust(22),self.usinst2.GetValue().ljust(15),self.usstart2.GetValue().ljust(8), self.usend2.GetValue()))\n if threeVar==1:\n f.writelines('%s%s%s%s%s\\n' % (self.usastr2b.GetValue().ljust(18),self.usobs2b.GetValue().ljust(22),self.usinst2b.GetValue().ljust(15),self.usstart2b.GetValue().ljust(8), self.usend2b.GetValue()))\n if peep > 3:\n f.writelines('%s%s%s%s%s\\n' % (self.usastr3.GetValue().ljust(18), self.usobs3.GetValue().ljust(22), self.usinst3.GetValue().ljust(15),self.usstart3.GetValue().ljust(8), self.usend3.GetValue()))\n if fourVar==1:\n f.writelines('%s%s%s%s%s\\n' % (self.usastr3b.GetValue().ljust(18),self.usobs3b.GetValue().ljust(22),self.usinst3b.GetValue().ljust(15),self.usstart3b.GetValue().ljust(8), self.usend3b.GetValue()))\n\n f.writelines('\\n' + self.schedHalf.GetLabel())\n f.writelines(\" ----------------------------------------------------------------\\n\")\n f.writelines('%s\\n' % self.sc1.GetValue())\n f.writelines('%s\\n' % self.sc2.GetValue())\n if peep > 2:\n f.writelines('%s\\n' %self.sc3.GetValue())\n if peep > 3:\n f.writelines('%s\\n' % self.sc4.GetValue())\n f.writelines(\"\\nnote: scheduled times listed include instrument change time\\n\\n\"\\\n \" ------------- ACTIVITY LOG --------------\\n\")\n f.writelines(self.obsspec.GetLabel()+'\\n\\n')\n f.writelines(self.actText.GetValue()+'\\n')\n f.writelines(\"\\n ------- FAILURE LOG -------\\n\"\\\n \"\\n\"\\\n \"PROG INST FAILURE MODE TIME\\n\"\\\n \" (SEDFNVOG) TI/SHU START FINISH DESCRIPTION\\n\"\\\n \"----------------------------------------------------------------------\\n\")\n f.writelines(self.failLog.GetValue()+'\\n')\n f.writelines('\\n'+self.focus.GetLabel()+'\\n')\n f.writelines(self.focusLog.GetValue()+'\\n')\n f.writelines(self.weathText.GetValue()+'\\n')\n f.writelines(' Note: the wind was coming from the azimuth listed.\\n'\\\n ' The convention used is north=0 degrees, east=90 degrees.\\n'\\\n ' The dust count is particles > 1u per 0.1 cubic feet.\\n\\n')\n f.writelines(self.stat.GetLabel()+'\\n')\n f.writelines(\" Telescope drives operational. Current TCC version: \" + self.statTCCText.GetValue() + '\\n')\n f.writelines(\" Current TUI version: \" + self.statTUIText.GetValue() + '\\n') \n f.close()\n\n \"\"\"In safari save as page source with filename weather.html\n In firefox save as web page, html only with filename weather.html\n \"\"\"", "def sensitivity(self):\n return self.__sensitivity", "def report(self):\r\n # Compose the list of report_column names required for \r\n # summary_report.dsw.DictWriter()\r\n sr = self.summary_report\r\n dict_leader = sr.dict_leader\r\n dict_out = sr.dict_out\r\n report_column_names = []\r\n if dict_leader is not None and dict_out is not None:\r\n for key,value in dict_leader.iteritems():\r\n #print \"Adding report_column_name(from dict_leader)=\",key\r\n report_column_names.append(key)\r\n dict_out[key] = value\r\n # We have to initialize the DictWriter with the report_column_names\r\n # below. \r\n # Also need matched coord_val and var names for calling node_report()\r\n # below,\r\n # so we do this duplication of storage of names. \r\n coord_var_names = []\r\n coord_val_names = []\r\n for idx, column_name in enumerate(self.column_names):\r\n var_name = \"Var_%s\" % str(idx+1)\r\n report_column_names.append(var_name)\r\n coord_var_names.append(var_name)\r\n val_name = \"Val_%s\" % str(idx+1)\r\n report_column_names.append(val_name)\r\n coord_val_names.append(val_name)\r\n # Add the entry report_column_names\r\n report_column_names += self.EntryClass.report_column_names\r\n # Instantiate dsw.DictWriter with report column names\r\n # 4 lines follow for quick test output\r\n columns_string = \"\"; sep = \"\"\r\n for i,cn in enumerate(report_column_names):\r\n columns_string += sep + cn\r\n sep = \", \"\r\n if sr.dsw_full_report is not None:\r\n # Instantiate the dict writer to write only one-row at a time,\r\n # rather than buffer the entire report in memory before\r\n # outputting, to reduce memory footprint of \r\n # large reports.\r\n # The caller assumes responsibility to sort such a large report \r\n # as needed, and to produce a view of only the 'max_bad' rows, \r\n # if needed; for example, by loading the full report\r\n # into a sql table and after it is populated by this routine, \r\n # using its facilities to sort and manipulate the report rows.\r\n dict_writer = (self.summary_report.dsw_full_report\r\n .dict_writer(report_column_names))\r\n if sr.write_header: \r\n # write the header row\r\n dict_writer.writeheader()\r\n else:\r\n dict_writer = None\r\n # Accrue output data values for a buffered report, separate from a \r\n # report that node_report may write, row by row, using dict_writer. \r\n # The output collected here may be further quickly sorted and \r\n # examined without having to reread the file that dict_writer \r\n # writes to.\r\n # Coord data output is formatted in node_report().\r\n # node_report() adds final entries column data to dict_out for \r\n # node coords and entry, and\r\n # if an entry has output, calls dict_writer to write it.\r\n is_entry, outrows = self.node_report(\r\n self.grand, \r\n dict_out=self.summary_report.dict_out, \r\n dict_writer=dict_writer,\r\n coord_var_names=coord_var_names, \r\n coord_val_names=coord_val_names)\r\n return outrows", "def createReport(query):\n sentiments = get_sentiments(query)\n print(\"Based on the query, %s has an average sentiment value of %d\", query, sentiments)", "def onesolutionsummary(self,whichstream_,whichsol_): # 3\n if not isinstance(whichstream_,streamtype): raise TypeError(\"Argument whichstream has wrong type\")\n if not isinstance(whichsol_,soltype): raise TypeError(\"Argument whichsol has wrong type\")\n res = self.__obj.onesolutionsummary(whichstream_,whichsol_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def report(self, brief=True, sens=None):\n self.fail_modes.sort(key=lambda x: x.phi, reverse=True)\n sens = sens or SHOW_SENS\n title = f'ODH report for {self}'\n padding = len(title) + 10\n print('#'*padding)\n print(title)\n print('-'*padding)\n if brief:\n print('Printing brief ODH report')\n print(f'Only leaks with Fatality rate > {sens} are shown')\n for f_mode in self.fail_modes:\n if f_mode.phi >= sens or not brief:\n print()\n print(f' Source: {f_mode.source.name}')\n print(f' Failure: {f_mode.name}')\n print(f' Fatality rate: {f_mode.phi.to(1/ureg.hr):.2~}')\n print(f' Building is powered: {not f_mode.outage}')\n print(f' Oxygen concentration: {f_mode.O2_conc:.0%}, '\n f'{f_mode.O2_conc/0.21:.0%} percent of norm')\n print(f' Leak failure rate: {f_mode.leak_fr:.3g~}')\n print(' ODH protection PFD: '\n f'{(f_mode.P_i/f_mode.leak_fr).to(ureg.dimensionless):.2~}')\n print(f' Total failure rate: {f_mode.P_i.to(1/ureg.hr):.2~}')\n print(f' Leak rate: {f_mode.q_leak:.2~}')\n print(f' Event duration: {f_mode.tau:.2~}')\n print(f' Fans working: {f_mode.N_fan}')\n print(f' Fan rate: {f_mode.Q_fan:.2~}')\n print(f' Fatality prob: {f_mode.F_i:.0%}')", "def onesolutionsummary(self,whichstream_,whichsol_):\n res = __library__.MSK_XX_onesolutionsummary(self.__nativep,whichstream_,whichsol_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def report(self, stream):\n stats = {'errors': self.xunitstats[0], 'failures': self.xunitstats[1], 'passes': self.xunitstats[2], 'skipped': self.xunitstats[3] }\n stats['encoding'] = self.encoding\n stats['total'] = (stats['errors'] + stats['failures'] + stats['passes'] + stats['skipped'])\n stats['header'] = self.xunit_header\n if UNICODE_STRINGS:\n error_report_file = open(self.xunit_file, 'w', encoding=self.encoding)\n else:\n error_report_file = open(self.xunit_file, 'w')\n error_report_file.write(\n '<?xml version=\"1.0\" encoding=\"%(encoding)s\"?>'\n '<testsuite %(header)s tests=\"%(total)d\" '\n 'errors=\"%(errors)d\" failures=\"%(failures)d\" '\n 'skip=\"%(skipped)d\">' % stats)\n while len(self.xunitstream) > 0:\n error_report_file.write(self.xunitstream.pop(0))\n #error_report_file.write('<properties><property name=\"myproperty\" value=\"1.5\"/></properties>')\n error_report_file.write('</testsuite>')\n error_report_file.close()\n if self.config.verbosity > 1:\n stream.writeln(\"-\" * 70)\n stream.writeln(\"XML: %s\" % error_report_file.name)", "def report(self, output_dir):", "def print_report(self, stream):\n stream.write(ET.tostring(self.xml()))", "def generate_student_report(self):\n \n period_type = self.parameter_dict.get(\"period_type\", \"monthly\")\n insert_gender_markers = self.parameter_dict.get(\n \"insert_gender_markers\", False)\n period = [(self.start_date,self.end_date)]\n for student in self.students:\n self.table_data.append(self._generate_single_student_report_line(\n student,period, False))\n self.keys_list.append(\"\")\n self.table_descriptor = \\\n [('name','string','Name'),\n ('days_present','number', 'Days Present'),\n ('percent_present', 'number', '% Present')]", "def readsummary(self,whichstream_):\n res = __library__.MSK_XX_readsummary(self.__nativep,whichstream_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def virulencefinder_reporter(metadata, analysistype, reportpath):\n with open(os.path.join(reportpath, 'virulence.csv'), 'w') as report:\n header = 'Strain,Gene,PercentIdentity,PercentCovered,Contig,Location,Sequence\\n'\n data = ''\n for sample in metadata:\n if sample.general.bestassemblyfile != 'NA':\n if sample[analysistype].blastlist:\n data += '{},'.format(sample.name)\n multiple = False\n for result in sample[analysistype].blastlist:\n if analysistype == 'virulence':\n gene = result['subject_id'].split(':')[0]\n else:\n gene = result['subject_id']\n if multiple:\n data += ','\n data += '{},{},{},{},{}..{},{}\\n' \\\n .format(gene, result['percentidentity'], result['alignment_fraction'],\n result['query_id'], result['low'], result['high'], result['query_sequence'])\n # data += '\\n'\n multiple = True\n else:\n data += '{}\\n'.format(sample.name)\n else:\n data += '{}\\n'.format(sample.name)\n report.write(header)\n report.write(data)", "def analyzeproblem(self,whichstream_):\n res = __library__.MSK_XX_analyzeproblem(self.__nativep,whichstream_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def report_use_case(self, as_dict: bool=None, stylise: bool=None):\n as_dict = as_dict if isinstance(as_dict, bool) else False\n stylise = stylise if isinstance(stylise, bool) else True\n report = self.pm.report_use_case()\n if as_dict:\n return report\n report = pd.DataFrame(report, index=['values'])\n report = report.transpose().reset_index()\n report.columns = ['use_case', 'values']\n if stylise:\n return self._report(report, index_header='use_case')\n return report", "def report():\n pass" ]

[ "0.84275156", "0.53856975", "0.5332675", "0.52303034", "0.5183973", "0.50662196", "0.50250715", "0.50113016", "0.49753663", "0.49550012", "0.49106225", "0.49092293", "0.48568374", "0.48396832", "0.47451615", "0.473587", "0.47343108", "0.47330227", "0.4705782", "0.47011182", "0.4662522", "0.4659116", "0.46578777", "0.46375796", "0.4635574", "0.46293944", "0.4621855", "0.46191368", "0.46112004", "0.46049708" ]

[ "def dualsensitivity(self,subj,leftpricej,rightpricej,leftrangej,rightrangej): # 3\n numj_ = None\n if numj_ is None:\n numj_ = len(subj)\n elif numj_ != len(subj):\n raise IndexError(\"Inconsistent length of array subj\")\n if numj_ is None: numj_ = 0\n if subj is None: raise TypeError(\"Invalid type for argument subj\")\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n \n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n \n _copyback_leftpricej = False\n if leftpricej is None:\n leftpricej_ = None\n else:\n try:\n leftpricej_ = memoryview(leftpricej)\n except TypeError:\n try:\n _tmparr_leftpricej = array.array(\"d\",leftpricej)\n except TypeError:\n raise TypeError(\"Argument leftpricej has wrong type\")\n else:\n leftpricej_ = memoryview(_tmparr_leftpricej)\n _copyback_leftpricej = True\n else:\n if leftpricej_.format != \"d\":\n leftpricej_ = memoryview(array.array(\"d\",leftpricej))\n _copyback_leftpricej = True\n if leftpricej_ is not None and len(leftpricej_) != (numj_):\n raise ValueError(\"Array argument leftpricej has wrong length\")\n _copyback_rightpricej = False\n if rightpricej is None:\n rightpricej_ = None\n else:\n try:\n rightpricej_ = memoryview(rightpricej)\n except TypeError:\n try:\n _tmparr_rightpricej = array.array(\"d\",rightpricej)\n except TypeError:\n raise TypeError(\"Argument rightpricej has wrong type\")\n else:\n rightpricej_ = memoryview(_tmparr_rightpricej)\n _copyback_rightpricej = True\n else:\n if rightpricej_.format != \"d\":\n rightpricej_ = memoryview(array.array(\"d\",rightpricej))\n _copyback_rightpricej = True\n if rightpricej_ is not None and len(rightpricej_) != (numj_):\n raise ValueError(\"Array argument rightpricej has wrong length\")\n _copyback_leftrangej = False\n if leftrangej is None:\n leftrangej_ = None\n else:\n try:\n leftrangej_ = memoryview(leftrangej)\n except TypeError:\n try:\n _tmparr_leftrangej = array.array(\"d\",leftrangej)\n except TypeError:\n raise TypeError(\"Argument leftrangej has wrong type\")\n else:\n leftrangej_ = memoryview(_tmparr_leftrangej)\n _copyback_leftrangej = True\n else:\n if leftrangej_.format != \"d\":\n leftrangej_ = memoryview(array.array(\"d\",leftrangej))\n _copyback_leftrangej = True\n if leftrangej_ is not None and len(leftrangej_) != (numj_):\n raise ValueError(\"Array argument leftrangej has wrong length\")\n _copyback_rightrangej = False\n if rightrangej is None:\n rightrangej_ = None\n else:\n try:\n rightrangej_ = memoryview(rightrangej)\n except TypeError:\n try:\n _tmparr_rightrangej = array.array(\"d\",rightrangej)\n except TypeError:\n raise TypeError(\"Argument rightrangej has wrong type\")\n else:\n rightrangej_ = memoryview(_tmparr_rightrangej)\n _copyback_rightrangej = True\n else:\n if rightrangej_.format != \"d\":\n rightrangej_ = memoryview(array.array(\"d\",rightrangej))\n _copyback_rightrangej = True\n if rightrangej_ is not None and len(rightrangej_) != (numj_):\n raise ValueError(\"Array argument rightrangej has wrong length\")\n res = self.__obj.dualsensitivity(numj_,subj_,leftpricej_,rightpricej_,leftrangej_,rightrangej_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_rightrangej:\n rightrangej[:] = _tmparr_rightrangej\n if _copyback_leftrangej:\n leftrangej[:] = _tmparr_leftrangej\n if _copyback_rightpricej:\n rightpricej[:] = _tmparr_rightpricej\n if _copyback_leftpricej:\n leftpricej[:] = _tmparr_leftpricej", "def primalsensitivity(self,subi_,marki_,subj_,markj_,leftpricei_,rightpricei_,leftrangei_,rightrangei_,leftpricej_,rightpricej_,leftrangej_,rightrangej_):\n numi_ = None\n if numi_ is None:\n numi_ = len(subi_)\n elif numi_ != len(subi_):\n raise IndexError(\"Inconsistent length of array subi\")\n if numi_ is None:\n numi_ = len(marki_)\n elif numi_ != len(marki_):\n raise IndexError(\"Inconsistent length of array marki\")\n if subi_ is None:\n raise ValueError(\"Argument subi cannot be None\")\n if subi_ is None:\n raise ValueError(\"Argument subi may not be None\")\n if isinstance(subi_, numpy.ndarray) and subi_.dtype is numpy.dtype(numpy.int32) and subi_.flags.contiguous:\n _subi_copyarray = False\n _subi_tmp = ctypes.cast(subi_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subi_ is not None:\n _subi_copyarray = True\n _subi_np_tmp = numpy.zeros(len(subi_),numpy.dtype(numpy.int32))\n _subi_np_tmp[:] = subi_\n assert _subi_np_tmp.flags.contiguous\n _subi_tmp = ctypes.cast(_subi_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subi_copyarray = False\n _subi_tmp = None\n \n if marki_ is None:\n raise ValueError(\"Argument marki cannot be None\")\n if marki_ is None:\n raise ValueError(\"Argument marki may not be None\")\n if marki_ is not None:\n _marki_tmp = (ctypes.c_int32 * len(marki_))(*marki_)\n else:\n _marki_tmp = None\n numj_ = None\n if numj_ is None:\n numj_ = len(subj_)\n elif numj_ != len(subj_):\n raise IndexError(\"Inconsistent length of array subj\")\n if numj_ is None:\n numj_ = len(markj_)\n elif numj_ != len(markj_):\n raise IndexError(\"Inconsistent length of array markj\")\n if subj_ is None:\n raise ValueError(\"Argument subj cannot be None\")\n if subj_ is None:\n raise ValueError(\"Argument subj may not be None\")\n if isinstance(subj_, numpy.ndarray) and subj_.dtype is numpy.dtype(numpy.int32) and subj_.flags.contiguous:\n _subj_copyarray = False\n _subj_tmp = ctypes.cast(subj_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n elif subj_ is not None:\n _subj_copyarray = True\n _subj_np_tmp = numpy.zeros(len(subj_),numpy.dtype(numpy.int32))\n _subj_np_tmp[:] = subj_\n assert _subj_np_tmp.flags.contiguous\n _subj_tmp = ctypes.cast(_subj_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_int32))\n else:\n _subj_copyarray = False\n _subj_tmp = None\n \n if markj_ is None:\n raise ValueError(\"Argument markj cannot be None\")\n if markj_ is None:\n raise ValueError(\"Argument markj may not be None\")\n if markj_ is not None:\n _markj_tmp = (ctypes.c_int32 * len(markj_))(*markj_)\n else:\n _markj_tmp = None\n _leftpricei_minlength = (numi_)\n if (numi_) > 0 and leftpricei_ is not None and len(leftpricei_) != (numi_):\n raise ValueError(\"Array argument leftpricei is not long enough: Is %d, expected %d\" % (len(leftpricei_),(numi_)))\n if isinstance(leftpricei_,numpy.ndarray) and not leftpricei_.flags.writeable:\n raise ValueError(\"Argument leftpricei must be writable\")\n if isinstance(leftpricei_, numpy.ndarray) and leftpricei_.dtype is numpy.dtype(numpy.float64) and leftpricei_.flags.contiguous:\n _leftpricei_copyarray = False\n _leftpricei_tmp = ctypes.cast(leftpricei_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif leftpricei_ is not None:\n _leftpricei_copyarray = True\n _leftpricei_np_tmp = numpy.zeros(len(leftpricei_),numpy.dtype(numpy.float64))\n _leftpricei_np_tmp[:] = leftpricei_\n assert _leftpricei_np_tmp.flags.contiguous\n _leftpricei_tmp = ctypes.cast(_leftpricei_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _leftpricei_copyarray = False\n _leftpricei_tmp = None\n \n _rightpricei_minlength = (numi_)\n if (numi_) > 0 and rightpricei_ is not None and len(rightpricei_) != (numi_):\n raise ValueError(\"Array argument rightpricei is not long enough: Is %d, expected %d\" % (len(rightpricei_),(numi_)))\n if isinstance(rightpricei_,numpy.ndarray) and not rightpricei_.flags.writeable:\n raise ValueError(\"Argument rightpricei must be writable\")\n if isinstance(rightpricei_, numpy.ndarray) and rightpricei_.dtype is numpy.dtype(numpy.float64) and rightpricei_.flags.contiguous:\n _rightpricei_copyarray = False\n _rightpricei_tmp = ctypes.cast(rightpricei_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif rightpricei_ is not None:\n _rightpricei_copyarray = True\n _rightpricei_np_tmp = numpy.zeros(len(rightpricei_),numpy.dtype(numpy.float64))\n _rightpricei_np_tmp[:] = rightpricei_\n assert _rightpricei_np_tmp.flags.contiguous\n _rightpricei_tmp = ctypes.cast(_rightpricei_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _rightpricei_copyarray = False\n _rightpricei_tmp = None\n \n _leftrangei_minlength = (numi_)\n if (numi_) > 0 and leftrangei_ is not None and len(leftrangei_) != (numi_):\n raise ValueError(\"Array argument leftrangei is not long enough: Is %d, expected %d\" % (len(leftrangei_),(numi_)))\n if isinstance(leftrangei_,numpy.ndarray) and not leftrangei_.flags.writeable:\n raise ValueError(\"Argument leftrangei must be writable\")\n if isinstance(leftrangei_, numpy.ndarray) and leftrangei_.dtype is numpy.dtype(numpy.float64) and leftrangei_.flags.contiguous:\n _leftrangei_copyarray = False\n _leftrangei_tmp = ctypes.cast(leftrangei_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif leftrangei_ is not None:\n _leftrangei_copyarray = True\n _leftrangei_np_tmp = numpy.zeros(len(leftrangei_),numpy.dtype(numpy.float64))\n _leftrangei_np_tmp[:] = leftrangei_\n assert _leftrangei_np_tmp.flags.contiguous\n _leftrangei_tmp = ctypes.cast(_leftrangei_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _leftrangei_copyarray = False\n _leftrangei_tmp = None\n \n _rightrangei_minlength = (numi_)\n if (numi_) > 0 and rightrangei_ is not None and len(rightrangei_) != (numi_):\n raise ValueError(\"Array argument rightrangei is not long enough: Is %d, expected %d\" % (len(rightrangei_),(numi_)))\n if isinstance(rightrangei_,numpy.ndarray) and not rightrangei_.flags.writeable:\n raise ValueError(\"Argument rightrangei must be writable\")\n if isinstance(rightrangei_, numpy.ndarray) and rightrangei_.dtype is numpy.dtype(numpy.float64) and rightrangei_.flags.contiguous:\n _rightrangei_copyarray = False\n _rightrangei_tmp = ctypes.cast(rightrangei_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif rightrangei_ is not None:\n _rightrangei_copyarray = True\n _rightrangei_np_tmp = numpy.zeros(len(rightrangei_),numpy.dtype(numpy.float64))\n _rightrangei_np_tmp[:] = rightrangei_\n assert _rightrangei_np_tmp.flags.contiguous\n _rightrangei_tmp = ctypes.cast(_rightrangei_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _rightrangei_copyarray = False\n _rightrangei_tmp = None\n \n _leftpricej_minlength = (numj_)\n if (numj_) > 0 and leftpricej_ is not None and len(leftpricej_) != (numj_):\n raise ValueError(\"Array argument leftpricej is not long enough: Is %d, expected %d\" % (len(leftpricej_),(numj_)))\n if isinstance(leftpricej_,numpy.ndarray) and not leftpricej_.flags.writeable:\n raise ValueError(\"Argument leftpricej must be writable\")\n if isinstance(leftpricej_, numpy.ndarray) and leftpricej_.dtype is numpy.dtype(numpy.float64) and leftpricej_.flags.contiguous:\n _leftpricej_copyarray = False\n _leftpricej_tmp = ctypes.cast(leftpricej_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif leftpricej_ is not None:\n _leftpricej_copyarray = True\n _leftpricej_np_tmp = numpy.zeros(len(leftpricej_),numpy.dtype(numpy.float64))\n _leftpricej_np_tmp[:] = leftpricej_\n assert _leftpricej_np_tmp.flags.contiguous\n _leftpricej_tmp = ctypes.cast(_leftpricej_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _leftpricej_copyarray = False\n _leftpricej_tmp = None\n \n _rightpricej_minlength = (numj_)\n if (numj_) > 0 and rightpricej_ is not None and len(rightpricej_) != (numj_):\n raise ValueError(\"Array argument rightpricej is not long enough: Is %d, expected %d\" % (len(rightpricej_),(numj_)))\n if isinstance(rightpricej_,numpy.ndarray) and not rightpricej_.flags.writeable:\n raise ValueError(\"Argument rightpricej must be writable\")\n if isinstance(rightpricej_, numpy.ndarray) and rightpricej_.dtype is numpy.dtype(numpy.float64) and rightpricej_.flags.contiguous:\n _rightpricej_copyarray = False\n _rightpricej_tmp = ctypes.cast(rightpricej_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif rightpricej_ is not None:\n _rightpricej_copyarray = True\n _rightpricej_np_tmp = numpy.zeros(len(rightpricej_),numpy.dtype(numpy.float64))\n _rightpricej_np_tmp[:] = rightpricej_\n assert _rightpricej_np_tmp.flags.contiguous\n _rightpricej_tmp = ctypes.cast(_rightpricej_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _rightpricej_copyarray = False\n _rightpricej_tmp = None\n \n _leftrangej_minlength = (numj_)\n if (numj_) > 0 and leftrangej_ is not None and len(leftrangej_) != (numj_):\n raise ValueError(\"Array argument leftrangej is not long enough: Is %d, expected %d\" % (len(leftrangej_),(numj_)))\n if isinstance(leftrangej_,numpy.ndarray) and not leftrangej_.flags.writeable:\n raise ValueError(\"Argument leftrangej must be writable\")\n if isinstance(leftrangej_, numpy.ndarray) and leftrangej_.dtype is numpy.dtype(numpy.float64) and leftrangej_.flags.contiguous:\n _leftrangej_copyarray = False\n _leftrangej_tmp = ctypes.cast(leftrangej_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif leftrangej_ is not None:\n _leftrangej_copyarray = True\n _leftrangej_np_tmp = numpy.zeros(len(leftrangej_),numpy.dtype(numpy.float64))\n _leftrangej_np_tmp[:] = leftrangej_\n assert _leftrangej_np_tmp.flags.contiguous\n _leftrangej_tmp = ctypes.cast(_leftrangej_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _leftrangej_copyarray = False\n _leftrangej_tmp = None\n \n _rightrangej_minlength = (numj_)\n if (numj_) > 0 and rightrangej_ is not None and len(rightrangej_) != (numj_):\n raise ValueError(\"Array argument rightrangej is not long enough: Is %d, expected %d\" % (len(rightrangej_),(numj_)))\n if isinstance(rightrangej_,numpy.ndarray) and not rightrangej_.flags.writeable:\n raise ValueError(\"Argument rightrangej must be writable\")\n if isinstance(rightrangej_, numpy.ndarray) and rightrangej_.dtype is numpy.dtype(numpy.float64) and rightrangej_.flags.contiguous:\n _rightrangej_copyarray = False\n _rightrangej_tmp = ctypes.cast(rightrangej_.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n elif rightrangej_ is not None:\n _rightrangej_copyarray = True\n _rightrangej_np_tmp = numpy.zeros(len(rightrangej_),numpy.dtype(numpy.float64))\n _rightrangej_np_tmp[:] = rightrangej_\n assert _rightrangej_np_tmp.flags.contiguous\n _rightrangej_tmp = ctypes.cast(_rightrangej_np_tmp.ctypes._as_parameter_,ctypes.POINTER(ctypes.c_double))\n else:\n _rightrangej_copyarray = False\n _rightrangej_tmp = None\n \n res = __library__.MSK_XX_primalsensitivity(self.__nativep,numi_,_subi_tmp,_marki_tmp,numj_,_subj_tmp,_markj_tmp,_leftpricei_tmp,_rightpricei_tmp,_leftrangei_tmp,_rightrangei_tmp,_leftpricej_tmp,_rightpricej_tmp,_leftrangej_tmp,_rightrangej_tmp)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _leftpricei_copyarray:\n leftpricei_[:] = _leftpricei_np_tmp\n if _rightpricei_copyarray:\n rightpricei_[:] = _rightpricei_np_tmp\n if _leftrangei_copyarray:\n leftrangei_[:] = _leftrangei_np_tmp\n if _rightrangei_copyarray:\n rightrangei_[:] = _rightrangei_np_tmp\n if _leftpricej_copyarray:\n leftpricej_[:] = _leftpricej_np_tmp\n if _rightpricej_copyarray:\n rightpricej_[:] = _rightpricej_np_tmp\n if _leftrangej_copyarray:\n leftrangej_[:] = _leftrangej_np_tmp\n if _rightrangej_copyarray:\n rightrangej_[:] = _rightrangej_np_tmp", "def primalsensitivity(self,subi,marki,subj,markj,leftpricei,rightpricei,leftrangei,rightrangei,leftpricej,rightpricej,leftrangej,rightrangej): # 3\n numi_ = None\n if numi_ is None:\n numi_ = len(subi)\n elif numi_ != len(subi):\n raise IndexError(\"Inconsistent length of array subi\")\n if numi_ is None:\n numi_ = len(marki)\n elif numi_ != len(marki):\n raise IndexError(\"Inconsistent length of array marki\")\n if numi_ is None: numi_ = 0\n if subi is None: raise TypeError(\"Invalid type for argument subi\")\n if subi is None:\n subi_ = None\n else:\n try:\n subi_ = memoryview(subi)\n except TypeError:\n try:\n _tmparr_subi = array.array(\"i\",subi)\n except TypeError:\n raise TypeError(\"Argument subi has wrong type\")\n else:\n subi_ = memoryview(_tmparr_subi)\n \n else:\n if subi_.format != \"i\":\n subi_ = memoryview(array.array(\"i\",subi))\n \n if marki is None: raise TypeError(\"Invalid type for argument marki\")\n if marki is None:\n marki_ = None\n else:\n try:\n marki_ = memoryview(marki)\n except TypeError:\n try:\n _tmparr_marki = array.array(\"i\",marki)\n except TypeError:\n raise TypeError(\"Argument marki has wrong type\")\n else:\n marki_ = memoryview(_tmparr_marki)\n \n else:\n if marki_.format != \"i\":\n marki_ = memoryview(array.array(\"i\",marki))\n \n numj_ = None\n if numj_ is None:\n numj_ = len(subj)\n elif numj_ != len(subj):\n raise IndexError(\"Inconsistent length of array subj\")\n if numj_ is None:\n numj_ = len(markj)\n elif numj_ != len(markj):\n raise IndexError(\"Inconsistent length of array markj\")\n if numj_ is None: numj_ = 0\n if subj is None: raise TypeError(\"Invalid type for argument subj\")\n if subj is None:\n subj_ = None\n else:\n try:\n subj_ = memoryview(subj)\n except TypeError:\n try:\n _tmparr_subj = array.array(\"i\",subj)\n except TypeError:\n raise TypeError(\"Argument subj has wrong type\")\n else:\n subj_ = memoryview(_tmparr_subj)\n \n else:\n if subj_.format != \"i\":\n subj_ = memoryview(array.array(\"i\",subj))\n \n if markj is None: raise TypeError(\"Invalid type for argument markj\")\n if markj is None:\n markj_ = None\n else:\n try:\n markj_ = memoryview(markj)\n except TypeError:\n try:\n _tmparr_markj = array.array(\"i\",markj)\n except TypeError:\n raise TypeError(\"Argument markj has wrong type\")\n else:\n markj_ = memoryview(_tmparr_markj)\n \n else:\n if markj_.format != \"i\":\n markj_ = memoryview(array.array(\"i\",markj))\n \n _copyback_leftpricei = False\n if leftpricei is None:\n leftpricei_ = None\n else:\n try:\n leftpricei_ = memoryview(leftpricei)\n except TypeError:\n try:\n _tmparr_leftpricei = array.array(\"d\",leftpricei)\n except TypeError:\n raise TypeError(\"Argument leftpricei has wrong type\")\n else:\n leftpricei_ = memoryview(_tmparr_leftpricei)\n _copyback_leftpricei = True\n else:\n if leftpricei_.format != \"d\":\n leftpricei_ = memoryview(array.array(\"d\",leftpricei))\n _copyback_leftpricei = True\n if leftpricei_ is not None and len(leftpricei_) != (numi_):\n raise ValueError(\"Array argument leftpricei has wrong length\")\n _copyback_rightpricei = False\n if rightpricei is None:\n rightpricei_ = None\n else:\n try:\n rightpricei_ = memoryview(rightpricei)\n except TypeError:\n try:\n _tmparr_rightpricei = array.array(\"d\",rightpricei)\n except TypeError:\n raise TypeError(\"Argument rightpricei has wrong type\")\n else:\n rightpricei_ = memoryview(_tmparr_rightpricei)\n _copyback_rightpricei = True\n else:\n if rightpricei_.format != \"d\":\n rightpricei_ = memoryview(array.array(\"d\",rightpricei))\n _copyback_rightpricei = True\n if rightpricei_ is not None and len(rightpricei_) != (numi_):\n raise ValueError(\"Array argument rightpricei has wrong length\")\n _copyback_leftrangei = False\n if leftrangei is None:\n leftrangei_ = None\n else:\n try:\n leftrangei_ = memoryview(leftrangei)\n except TypeError:\n try:\n _tmparr_leftrangei = array.array(\"d\",leftrangei)\n except TypeError:\n raise TypeError(\"Argument leftrangei has wrong type\")\n else:\n leftrangei_ = memoryview(_tmparr_leftrangei)\n _copyback_leftrangei = True\n else:\n if leftrangei_.format != \"d\":\n leftrangei_ = memoryview(array.array(\"d\",leftrangei))\n _copyback_leftrangei = True\n if leftrangei_ is not None and len(leftrangei_) != (numi_):\n raise ValueError(\"Array argument leftrangei has wrong length\")\n _copyback_rightrangei = False\n if rightrangei is None:\n rightrangei_ = None\n else:\n try:\n rightrangei_ = memoryview(rightrangei)\n except TypeError:\n try:\n _tmparr_rightrangei = array.array(\"d\",rightrangei)\n except TypeError:\n raise TypeError(\"Argument rightrangei has wrong type\")\n else:\n rightrangei_ = memoryview(_tmparr_rightrangei)\n _copyback_rightrangei = True\n else:\n if rightrangei_.format != \"d\":\n rightrangei_ = memoryview(array.array(\"d\",rightrangei))\n _copyback_rightrangei = True\n if rightrangei_ is not None and len(rightrangei_) != (numi_):\n raise ValueError(\"Array argument rightrangei has wrong length\")\n _copyback_leftpricej = False\n if leftpricej is None:\n leftpricej_ = None\n else:\n try:\n leftpricej_ = memoryview(leftpricej)\n except TypeError:\n try:\n _tmparr_leftpricej = array.array(\"d\",leftpricej)\n except TypeError:\n raise TypeError(\"Argument leftpricej has wrong type\")\n else:\n leftpricej_ = memoryview(_tmparr_leftpricej)\n _copyback_leftpricej = True\n else:\n if leftpricej_.format != \"d\":\n leftpricej_ = memoryview(array.array(\"d\",leftpricej))\n _copyback_leftpricej = True\n if leftpricej_ is not None and len(leftpricej_) != (numj_):\n raise ValueError(\"Array argument leftpricej has wrong length\")\n _copyback_rightpricej = False\n if rightpricej is None:\n rightpricej_ = None\n else:\n try:\n rightpricej_ = memoryview(rightpricej)\n except TypeError:\n try:\n _tmparr_rightpricej = array.array(\"d\",rightpricej)\n except TypeError:\n raise TypeError(\"Argument rightpricej has wrong type\")\n else:\n rightpricej_ = memoryview(_tmparr_rightpricej)\n _copyback_rightpricej = True\n else:\n if rightpricej_.format != \"d\":\n rightpricej_ = memoryview(array.array(\"d\",rightpricej))\n _copyback_rightpricej = True\n if rightpricej_ is not None and len(rightpricej_) != (numj_):\n raise ValueError(\"Array argument rightpricej has wrong length\")\n _copyback_leftrangej = False\n if leftrangej is None:\n leftrangej_ = None\n else:\n try:\n leftrangej_ = memoryview(leftrangej)\n except TypeError:\n try:\n _tmparr_leftrangej = array.array(\"d\",leftrangej)\n except TypeError:\n raise TypeError(\"Argument leftrangej has wrong type\")\n else:\n leftrangej_ = memoryview(_tmparr_leftrangej)\n _copyback_leftrangej = True\n else:\n if leftrangej_.format != \"d\":\n leftrangej_ = memoryview(array.array(\"d\",leftrangej))\n _copyback_leftrangej = True\n if leftrangej_ is not None and len(leftrangej_) != (numj_):\n raise ValueError(\"Array argument leftrangej has wrong length\")\n _copyback_rightrangej = False\n if rightrangej is None:\n rightrangej_ = None\n else:\n try:\n rightrangej_ = memoryview(rightrangej)\n except TypeError:\n try:\n _tmparr_rightrangej = array.array(\"d\",rightrangej)\n except TypeError:\n raise TypeError(\"Argument rightrangej has wrong type\")\n else:\n rightrangej_ = memoryview(_tmparr_rightrangej)\n _copyback_rightrangej = True\n else:\n if rightrangej_.format != \"d\":\n rightrangej_ = memoryview(array.array(\"d\",rightrangej))\n _copyback_rightrangej = True\n if rightrangej_ is not None and len(rightrangej_) != (numj_):\n raise ValueError(\"Array argument rightrangej has wrong length\")\n res = self.__obj.primalsensitivity(numi_,subi_,marki_,numj_,subj_,markj_,leftpricei_,rightpricei_,leftrangei_,rightrangei_,leftpricej_,rightpricej_,leftrangej_,rightrangej_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n if _copyback_rightrangej:\n rightrangej[:] = _tmparr_rightrangej\n if _copyback_leftrangej:\n leftrangej[:] = _tmparr_leftrangej\n if _copyback_rightpricej:\n rightpricej[:] = _tmparr_rightpricej\n if _copyback_leftpricej:\n leftpricej[:] = _tmparr_leftpricej\n if _copyback_rightrangei:\n rightrangei[:] = _tmparr_rightrangei\n if _copyback_leftrangei:\n leftrangei[:] = _tmparr_leftrangei\n if _copyback_rightpricei:\n rightpricei[:] = _tmparr_rightpricei\n if _copyback_leftpricei:\n leftpricei[:] = _tmparr_leftpricei", "def stationary_distribution_sensitivity(T, j):\n\n n = len(T)\n\n lEV = numpy.ones(n)\n rEV = stationary_distribution(T)\n eVal = 1.0\n\n T = numpy.transpose(T)\n\n vecA = numpy.zeros(n)\n vecA[j] = 1.0\n\n matA = T - eVal * numpy.identity(n)\n # normalize s.t. sum is one using rEV which is constant\n matA = numpy.concatenate((matA, [lEV]))\n\n phi = numpy.linalg.lstsq(numpy.transpose(matA), vecA)\n phi = numpy.delete(phi[0], -1)\n\n sensitivity = -numpy.outer(rEV, phi) + numpy.dot(phi, rEV) * numpy.outer(rEV, lEV)\n\n return sensitivity", "def calculate_sensitivity(self, x_train, y_train):\n model_f_activations = self.model_f.predict(x_train)\n reshaped_labels = np.array(y_train).reshape((x_train.shape[0], 1))\n tf_y_labels = tf.convert_to_tensor(reshaped_labels, dtype=np.float32)\n loss = k.binary_crossentropy(tf_y_labels, self.model_h.output)\n grad = k.gradients(loss, self.model_h.input)\n gradient_func = k.function([self.model_h.input], grad)\n calc_grad = gradient_func([model_f_activations])[0]\n sensitivity = np.dot(calc_grad, self.cav)\n self.sensitivity = sensitivity\n self.y_labels = y_train", "def dual_objective(self, dual_coeffs):\n primal = self.model._sdca_primal_dual_relation(self.l_l2sq,\n dual_coeffs)\n prox_l2_value = 0.5 * self.l_l2sq * np.linalg.norm(primal) ** 2\n return self.model.dual_loss(dual_coeffs) - prox_l2_value", "def eigenvector_sensitivity(T, k, j, right=True):\n\n n = len(T)\n\n if not right:\n T = numpy.transpose(T)\n\n eValues, rightEigenvectors = numpy.linalg.eig(T)\n leftEigenvectors = numpy.linalg.inv(rightEigenvectors)\n perm = numpy.argsort(eValues)[::-1]\n\n eValues = eValues[perm]\n rightEigenvectors = rightEigenvectors[:, perm]\n leftEigenvectors = leftEigenvectors[perm]\n\n rEV = rightEigenvectors[:, k]\n lEV = leftEigenvectors[k]\n eVal = eValues[k]\n\n vecA = numpy.zeros(n)\n vecA[j] = 1.0\n\n matA = T - eVal * numpy.identity(n)\n # Use here rEV as additional condition, means that we assume the vector to be\n # orthogonal to rEV\n matA = numpy.concatenate((matA, [rEV]))\n\n phi = numpy.linalg.lstsq(numpy.transpose(matA), vecA)\n\n phi = numpy.delete(phi[0], -1)\n\n sensitivity = -numpy.outer(phi, rEV) + numpy.dot(phi, rEV) * numpy.outer(lEV, rEV)\n\n if not right:\n sensitivity = numpy.transpose(sensitivity)\n\n return sensitivity", "def solve_SVM_dual_CVXOPT(x_train, y_train, x_test, C=1):\n n = x_train.shape[0]\n #Solving the dual\n K = y_train[:, None] * x_train\n K = np.dot(K, K.T)\n P = matrix(K)\n q = -1*matrix(np.ones((n, 1)))\n G = -1*matrix(np.eye(n))\n h = matrix(np.zeros(n))\n A = matrix(y_train.reshape(1, -1))\n b = matrix(np.zeros(1))\n solvers.options['show_progress'] = False\n sol = solvers.qp(P, q, G, h, A, b)\n alphas = np.array(sol['x'])\n #getting weights\n w = np.sum(alphas * y_train[:, None] * x_train, axis = 0)\n # getting bias\n cond = (alphas > 1e-4).reshape(-1)\n b = y_train[cond] - np.dot(x_train[cond], w)\n bias = b[0]\n for i in range(x_test.shape[0]):\n y_test[i] = np.dot(w.T,x_test[i])+bias\n if(y_test[i]>=0):\n y_test[i] = 1\n else:\n y_test[i] = -1\n #Lagrange Multipliers\n alphas = alphas.reshape(n,)\n alphas_1 = np.zeros(n,)\n for i in range(n):\n if(alphas[i]>=0 and alphas[i]<=C):\n alphas_1[i] = alphas[i]\n return (y_test,alphas_1)", "def secJacobian(self, r,eps=(10**(-16))):\n jacobi=np.zeros([2,2], float)\n sqrt_eps=np.sqrt(eps)\n h_0=sqrt_eps*r[0]\n h_1=sqrt_eps*r[1] \n e_0=np.array([1,0])\n e_1=np.array([0,1])\n x_vec=np.array(r)\n jacobi[0][0]= (self.derFunc(x_vec+h_0*e_0)[0]-self.derFunc(x_vec))[0]/h_0\n jacobi[1][1]= (self.derFunc(x_vec+h_1*e_1)[1]-self.derFunc(x_vec))[1]/h_1\n jacobi[1][0]= (self.derFunc(x_vec+h_0*e_0)[1]-self.derFunc(x_vec))[1]/h_0\n jacobi[0][1]= (self.derFunc(x_vec+h_1*e_1)[0]-self.derFunc(x_vec))[0]/h_1\n #print \"secant jacobian is \",jacobi\n try:\n return mat.inv(jacobi)\n except:\n print \"singular jacobi not invertable\"\n return 0", "def overall_sensitivity(self):\n if self.mod1:\n s = torch.max(torch.max(self.weight, -1)[0], -1)[0].item()\n else:\n s = torch.max(torch.sqrt(torch.sum(self.weight * self.weight, -1)))[0].item()\n s *= np.sqrt(2. / np.e)\n return s", "def sensitivity(self):\n return self.__sensitivity", "def create_jac_sens(x_sp,sensitivity_sp,diffeq_params, integration_params,\n SDerivSymbolicJacParamsLambFun,SDerivSymbolicJacConcLambFun):\n\n # create state variables\n allVars = np.concatenate((x_sp,sensitivity_sp))\n\n #create RHS\n dSensSym = sp.Matrix(dSens(0,allVars,diffeq_params, integration_params,\n SDerivSymbolicJacParamsLambFun, SDerivSymbolicJacConcLambFun))\n dSensSymJac = dSensSym.jacobian(allVars)\n\n # generate jacobian\n dSensSymJacDenseMatLam = sp.lambdify(allVars,dSensSymJac)\n dSensSymJacSparseMatLamFun = lambda t,xs: sparse.csr_matrix(dSensSymJacDenseMatLam(*xs))\n\n return dSensSymJacSparseMatLamFun", "def _svm_loss_penalty_dual(name):\n return hp.choice(name, [\n ('hinge', 'l2', True),\n ('squared_hinge', 'l2', True),\n ('squared_hinge', 'l1', False),\n ('squared_hinge', 'l2', False)\n ])", "def sensitivity(y_test, y_pred):\n\tmatrix = confusion_matrix(y_test, y_pred)\n\treturn matrix[0][0] / (matrix[0][0] + matrix[0][1])", "def mfpt_sensitivity(T, target, j):\n\n n = len(T)\n\n matA = T - numpy.diag(numpy.ones((n)))\n matA[target] *= 0\n matA[target, target] = 1.0\n\n tVec = -1. * numpy.ones(n)\n tVec[target] = 0\n\n mfpt = numpy.linalg.solve(matA, tVec)\n aVec = numpy.zeros(n)\n aVec[j] = 1.0\n\n phiVec = numpy.linalg.solve(numpy.transpose(matA), aVec)\n\n # TODO: Check sign of sensitivity!\n\n sensitivity = -1.0 * numpy.outer(phiVec, mfpt)\n sensitivity[target] *= 0\n\n return sensitivity", "def compute_jacs(x_sp,params_sens_dict,integration_params,**kwargs):\n\n # check if sensitivity to all params\n if kwargs['diffeq_params'] is None:\n diffeq_params = params_sens_dict\n params_sensitivity_sp = list(params_sens_dict.values())\n\n else:\n diffeq_params = kwargs['diffeq_params'].copy()\n params_sensitivity_sp = list(params_sens_dict.values())\n for key,value in params_sens_dict.items():\n diffeq_params[key] = value\n\n SDerivSymbolic = sp.Matrix(SDeriv(0,x_sp,integration_params,diffeq_params))\n\n # derivative of rhs wrt params\n SDerivSymbolicJacParams = SDerivSymbolic.jacobian(params_sensitivity_sp)\n SDerivSymbolicJacParamsLamb = sp.lambdify((x_sp,params_sensitivity_sp), SDerivSymbolicJacParams,'numpy')\n SDerivSymbolicJacParamsLambFun = lambda t,x,params: SDerivSymbolicJacParamsLamb(x,params)\n\n # derivative of rhs wrt Conc\n SDerivSymbolicJacConc = SDerivSymbolic.jacobian(x_sp)\n SDerivSymbolicJacConcLamb = sp.lambdify((x_sp,params_sensitivity_sp),SDerivSymbolicJacConc,'numpy')\n SDerivSymbolicJacConcLambFun = lambda t,x,params: SDerivSymbolicJacConcLamb(x,params)\n\n return [SDerivSymbolicJacParamsLambFun,SDerivSymbolicJacConcLambFun]", "def perform_trials(self, evolver: 'Evolver'):\r\n\r\n approach_ind = evolver.approach[0]\r\n\r\n approach_params = evolver.approach_params.copy()\r\n approach_params[self.evolve_param.name] = self.checking\r\n\r\n sens_params = self.new_sensitives.copy()\r\n sens_params[self.sensitive[1].name] = self.sens_checking\r\n\r\n trial_best = float('-inf')\r\n trial_patience = evolver.settings.trial_patience\r\n trial_epsilon = evolver.settings.trial_epsilon\r\n trial_patience_used = 0\r\n trial_index = 0\r\n\r\n if self.sensitive[1].categorical:\r\n metric_store = self.sens_sweep[self.sens_checking]\r\n else:\r\n evolver.logger.debug('sens_sweep_pts=%s, sens_sweep_len=%s, sens_checking=%s', self.sens_sweep_pts, self.sens_sweep_len, self.sens_checking)\r\n insert_ind = (\r\n np.searchsorted(self.sens_sweep_pts[:self.sens_sweep_len], self.sens_checking)\r\n if self.sens_sweep_len > 0\r\n else 0\r\n )\r\n assert isinstance(insert_ind, (int, np.int32, np.int64)), f'insert_ind={insert_ind}, type(insert_ind)={type(insert_ind)}'\r\n if insert_ind < self.sens_sweep_len:\r\n self.sens_sweep_pts[insert_ind+1:self.sens_sweep_len+1] = (\r\n self.sens_sweep_pts[insert_ind:self.sens_sweep_len])\r\n self.sens_sweep_pts[insert_ind] = self.sens_checking\r\n\r\n self.sens_sweep[insert_ind+1:self.sens_sweep_len+1] = (\r\n self.sens_sweep[insert_ind:self.sens_sweep_len])\r\n self.sens_sweep[insert_ind, :] = 0\r\n else:\r\n self.sens_sweep_pts[insert_ind] = self.sens_checking\r\n metric_store = self.sens_sweep[insert_ind]\r\n\r\n while (trial_index < evolver.settings.max_trials\r\n and trial_patience_used < trial_patience):\r\n for worker in evolver.workers:\r\n worker.job_queue.put((approach_ind, approach_params.copy(), sens_params.copy()))\r\n evolver.logger.debug('dispatched jobs')\r\n\r\n for worker in evolver.workers:\r\n while True:\r\n try:\r\n result = worker.result_queue.get()\r\n break\r\n except InterruptedError:\r\n evolver.logger.critical('result_queue.get() was interrupted')\r\n\r\n if trial_index == evolver.settings.max_trials:\r\n continue\r\n result_metric = result[evolver.settings.metric_name]\r\n metric_store[trial_index] = result_metric\r\n trial_index += 1\r\n\r\n if result_metric - trial_epsilon > trial_best:\r\n evolver.logger.debug('got trial metric %s (improved old: %s)', result_metric, trial_best)\r\n trial_best = result_metric\r\n if trial_patience_used < trial_patience:\r\n trial_patience_used = 0\r\n elif trial_patience_used < trial_patience:\r\n trial_patience_used += 1\r\n evolver.logger.debug('got trial metric %s, exhausted patience %s/%s',\r\n result_metric, trial_patience_used, trial_patience)\r\n else:\r\n evolver.logger.debug('got trial metric %s (worse, but already out of patience)', result_metric)", "def solve_SVM_dual_SMO(x_train, y_train, x_test, C=1):\n n, d = x_train.shape[0], x_train.shape[1]\n alpha = np.zeros((n))\n count = 0\n while True:\n count += 1\n alpha_prev = np.copy(alpha)\n for j in range(0, n):\n # Getting random int i!=j\n i = j\n cnt=0\n while i == j and cnt<1000:\n i = rnd.randint(0,n-1)\n cnt=cnt+1\n x_i, x_j, y_i, y_j = x_train[i,:], x_train[j,:], y_train[i], y_train[j]\n k_ij = (np.dot(x_i, x_i.T)) + (np.dot(x_j, x_j.T) ) - (2 * np.dot(x_i, x_j.T))\n if k_ij <= 0:\n continue\n alpha_prime_j, alpha_prime_i = alpha[j], alpha[i]\n if(y_i != y_j):\n (L,H) = (max(0, alpha_prime_j - alpha_prime_i), min(C, C - alpha_prime_i + alpha_prime_j))\n else:\n (L,H) = (max(0, alpha_prime_i + alpha_prime_j - C), min(C, alpha_prime_i + alpha_prime_j))\n if(L==H):\n continue\n # Computing model parameters\n w = np.dot(x_train.T, np.multiply(alpha,y_train))\n b = np.mean(y_train - np.dot(w.T, x_train.T))\n E_i = np.sign(np.dot(w.T, x_i.T) + b).astype(int) - y_i\n E_j = np.sign(np.dot(w.T, x_j.T) + b).astype(int) - y_j\n # Setting new alpha values(Lagrange multipliers)\n alpha[j] = alpha_prime_j + float(y_j * (E_i - E_j))/k_ij\n alpha[j] = max(alpha[j], L)\n alpha[j] = min(alpha[j], H)\n alpha[i] = alpha_prime_i + y_i*y_j * (alpha_prime_j - alpha[j])\n # Checking for convergence\n diff = np.linalg.norm(alpha - alpha_prev)\n if diff < 0.000000001:\n break\n # Computing weights and bias\n b = np.mean(y_train-np.dot(w.T,x_train.T))\n w = np.dot(x_train.T, np.multiply(alpha,y_train))\n y_pred_test = (np.sign(np.dot(w.T, x_test.T) + b).astype(int))\n return (y_pred_test,alpha)", "def test_sensitivity():\n n_ons = np.arange(0.1, 10, 0.3)\n n_offs = np.arange(0.1, 10, 0.3)\n alphas = np.array([1e-3, 1e-2, 0.1, 1, 10])\n for n_on in n_ons:\n for n_off in n_offs:\n for alpha in alphas:\n for method in ['simple', 'lima']:\n significance = significance_on_off(n_on, n_off, alpha, method=method)\n excess = sensitivity_on_off(n_off, alpha, significance, method=method)\n n_on2 = excess + alpha * n_off\n assert_allclose(n_on, n_on2, decimal=3)", "def print_sensitivity(self):\n if type(self.y_labels) == list:\n self.y_labels = np.array(self.y_labels)\n print(\n \"The sensitivity of class 1 is \",\n str(\n np.sum(self.sensitivity[np.where(self.y_labels == 1)[0]] > 0)\n / np.where(self.y_labels == 1)[0].shape[0]\n ),\n )\n print(\n \"The sensitivity of class 0 is \",\n str(\n np.sum(self.sensitivity[np.where(self.y_labels == 0)[0]] > 0)\n / np.where(self.y_labels == 0)[0].shape[0]\n ),\n )", "def sensitivity(self):\n return self.recall", "def dual_problem(\n states: list[np.ndarray], probs: list[float] = None, dist_method=\"min-error\"\n) -> float:\n constraints = []\n meas = []\n\n dim_x, _ = states[0].shape\n\n y_var = cvxpy.Variable((dim_x, dim_x), hermitian=True)\n objective = cvxpy.Minimize(cvxpy.trace(cvxpy.real(y_var)))\n\n dim = int(np.log2(dim_x))\n dim_list = [2] * int(np.log2(dim_x))\n sys_list = list(range(1, dim, 2))\n # dim_list = [3, 3]\n\n if dist_method == \"min-error\":\n for i, _ in enumerate(states):\n meas.append(cvxpy.Variable((dim_x, dim_x), PSD=True))\n constraints.append(\n cvxpy.real(y_var - probs[i] * states[i])\n >> partial_transpose(meas[i], sys=sys_list, dim=dim_list)\n )\n\n if dist_method == \"unambiguous\":\n for j, _ in enumerate(states):\n sum_val = 0\n for i, _ in enumerate(states):\n if i != j:\n sum_val += cvxpy.real(cvxpy.Variable()) * probs[i] * states[i]\n meas.append(cvxpy.Variable((dim_x, dim_x), PSD=True))\n constraints.append(\n cvxpy.real(y_var - probs[j] * states[j] + sum_val)\n >> partial_transpose(meas[j], sys=sys_list, dim=dim_list)\n )\n\n meas.append(cvxpy.Variable((dim_x, dim_x), PSD=True))\n constraints.append(\n cvxpy.real(y_var) >> partial_transpose(meas[-1], sys=sys_list, dim=dim_list)\n )\n\n problem = cvxpy.Problem(objective, constraints)\n sol_default = problem.solve()\n\n # print(np.around(y_var.value, decimals=3))\n\n return sol_default", "def compare_J_terms(m, nix, srcclass=None, analytic_class=None, calced_class=None, \n only_calced_Cterms=False, fx=None):\n \n if fx is None:\n fx = fixtures.fixture_from_model(m)\n \n if srcclass is None:\n srcclass = FullSingleFieldSource\n \n if analytic_class is None:\n analytic_class = analyticsolution.NoPhaseBunchDaviesSolution\n if calced_class is None:\n calced_class = calcedsolution.NoPhaseBunchDaviesCalced\n \n asol = analytic_class(fx, srcclass)\n csol = calced_class(fx, srcclass)\n \n #Need to make analytic solution use 128 bit floats to avoid overruns\n asol.srceqns.k = np.float128(asol.srceqns.k)\n \n \n #Get background values\n bgvars = m.yresult[nix, 0:3, 0]\n a = m.ainit*np.exp(m.tresult[nix])\n #Get potentials\n potentials = m.potentials(np.array([bgvars[0]]), m.pot_params)\n \n #Set alpha and beta\n alpha = 1/(a*np.sqrt(2))\n beta = a*bgvars[2]\n \n dp1 = csol.get_dp1(csol.srceqns.fullk, alpha=alpha)\n dp1dot = csol.get_dp1dot(csol.srceqns.fullk, alpha=alpha, beta=beta)\n \n #Calculate dphi(q) and dphi(k-q)\n dp1_q = dp1[:csol.srceqns.k.shape[-1]]\n dp1dot_q = dp1dot[:csol.srceqns.k.shape[-1]] \n \n theta_terms = csol.srceqns.getthetaterms(dp1, dp1dot)\n csol.srceqns.k = np.float128(csol.srceqns.k)\n csol.srceqns.fullk = np.float128(csol.srceqns.fullk)\n \n calced_Cterms = csol.calculate_Cterms(bgvars, a, potentials) \n if only_calced_Cterms:\n analytic_Cterms = calced_Cterms\n else:\n analytic_Cterms = asol.calculate_Cterms(bgvars, a, potentials)\n \n results = {}\n \n for Jkey in csol.J_terms.iterkeys():\n afunc = asol.J_terms[Jkey]\n cfunc = csol.J_terms[Jkey]\n analytic_result = afunc(asol.srceqns.k, analytic_Cterms, alpha=alpha, beta=beta)\n calced_result = cfunc(theta_terms, dp1_q, dp1dot_q, calced_Cterms)\n diff = analytic_result - calced_result\n err = np.abs(diff)/np.abs(analytic_result)\n results[Jkey] = (diff, err, analytic_result, calced_result)\n \n return results", "def eigenvalue_sensitivity(T, k):\n\n eValues, rightEigenvectors = numpy.linalg.eig(T)\n leftEigenvectors = numpy.linalg.inv(rightEigenvectors)\n\n perm = numpy.argsort(eValues)[::-1]\n\n rightEigenvectors = rightEigenvectors[:, perm]\n leftEigenvectors = leftEigenvectors[perm]\n\n sensitivity = numpy.outer(leftEigenvectors[k], rightEigenvectors[:, k])\n\n return sensitivity", "def sensitivity_analysis(D, X, Y=None):\n if Y is None:\n dX = X\n else:\n dX = Y-X\n\n # make a column vector if it is not the case.\n if len(dX.shape) == 1:\n dX = dX.reshape(-1,1)\n\n if len(D.shape) == 1:\n D = np.transpose(np.vstack([D**n for n in range(len(D))]))\n\n print D\n Dinv = np.linalg.inv(D)\n print dX, Dinv\n S = np.dot(Dinv,dX)\n \n return S", "def ridge_regression(y, tx, lambda_):\n lambda_prime = lambda_ * 2*tx.shape[0]\n\n a = tx.T.dot(tx) + lambda_prime*np.eye(tx.shape[1])\n b = tx.T.dot(y)\n w_star = np.linalg.solve(a, b)\n\n loss = compute_loss(y, tx, w_star)\n\n return w_star, loss", "def get_objective(X_t, xattr, Y_t, s):\n return eloglik(X_t, xattr, Y_t, s) - (s['KL']).sum()", "def ridge_regression(y, tx, lambda_):\n x_t = tx.T\n lambd = lambda_ * 2 * len(y)\n w = np.linalg.solve (np.dot(x_t, tx) + lambd * np.eye(tx.shape[1]), np.dot(x_t,y)) \n loss = compute_mse(y, tx, w)\n\n return w,loss", "def score(self,x,**kwargs):\r\n if self.kfun != 'matrix' and len(self.sv): \r\n k = self.kfun(x,self.sv,**self.cparam)\r\n #print \"Kernel after test: \", k\r\n else:\r\n k = x\r\n \r\n \r\n self.W=self.alphas \r\n self.mat=self.kfun(np.array([self.sv[1]]), self.sv,**self.cparam) \r\n self.bias=self.svLabels[1]- np.dot((self.alphas*self.svLabels).T,self.mat.T) \r\n z=np.dot((self.alphas*self.svLabels).T,k.T)+self.bias\r\n \r\n #print \"bias: \", self.bias, \"\\nZ: \",z\r\n \r\n \r\n return z", "def objective(self,w):\n l = 0\n for i in range(len(self.x)):\n # Each example contributes log(sigma(y_i * x_i . w))\n l -= log(sigmoid(self.y[i] * np.dot(w, self.x[i,:])))\n # regularisation 1/2 * alpha * ||w||^2\n l += 0.5 * self.alpha * np.dot(w,w)\n return l" ]

[ "0.8261849", "0.6106334", "0.6104639", "0.58061737", "0.57007354", "0.55202544", "0.53209084", "0.524894", "0.52469116", "0.5143102", "0.5046833", "0.50303704", "0.49652806", "0.49631125", "0.4957558", "0.49527156", "0.49477494", "0.49313244", "0.49223545", "0.49127185", "0.48985586", "0.48967382", "0.4887195", "0.4877972", "0.4864604", "0.48307377", "0.48204622", "0.48139405", "0.4810256", "0.4806549" ]

[ "def optimizermt(self,server_,port_): # 3\n res,resargs = self.__obj.optimizermt(server_,port_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _trmcode_return_value = resargs\n _trmcode_return_value = rescode(_trmcode_return_value)\n return _trmcode_return_value", "def asyncoptimize(self,server_,port_): # 3\n arr_token = array.array(\"b\",[0]*(33))\n memview_arr_token = memoryview(arr_token)\n res,resargs = self.__obj.asyncoptimize(server_,port_,memview_arr_token)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n retarg_token = resargs\n retarg_token = arr_token.tobytes()[:-1].decode(\"utf-8\",errors=\"ignore\")\n return retarg_token", "def update_optimizer(self, context, optimizer, host):\n pass", "def reset_optimizer(self, opt = tfk.optimizers.Adam):\n self.optimizer = opt(1e-4)\n return", "def __init__(self, ip, port, beam_manager):\n self._beam_manager = beam_manager\n self._reference_target = None\n super(DelayConfigurationServer, self).__init__(ip,port)", "def populate_server(self, target_server):\n for uid in self.servers_online:\n server = self.all_servers[uid]\n if server == target_server:\n pass\n else:\n if len(server.jobs)>1:\n shifting_task = server.jobs.pop(-1)\n self.servers_jobs_list[server.server_id].remove(shifting_task)\n server.waiting_time-=shifting_task.task_time\n self.schedule_task(shifting_task)", "def _stop_server(cls, address):\n\n print('_stop_server: please override me.')", "def restartserver(self, port=None):\n if port is not None:\n if port < 0: #code to try a random port\n self.parameters['port'] = random.randint(2223,50000)\n else:\n self.parameters['port'] = port\n return self.startserver()", "def delete_optimizer(self, context, optimizer, host):\n pass", "def do_local(self, host=\"localhost\", port=8000):\n port = int(port)\n if host == \"off\":\n self._local_endpoint = None\n else:\n self._local_endpoint = (host, port)\n self.onecmd(\"use %s\" % self.engine.region)", "def asyncstop(self,server_,port_,token_): # 3\n res = self.__obj.asyncstop(server_,port_,token_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def _stop_server(cls, server):\n\n try:\n server.kill()\n except Exception as error:\n print('ERROR stop enip server: ', error)", "def beta_create_TaskManager_server(servicer, pool=None, pool_size=None, default_timeout=None, maximum_timeout=None):\n request_deserializers = {\n ('gogrpcspec.TaskManager', 'AddTask'): Task.FromString,\n ('gogrpcspec.TaskManager', 'AddTasks'): Task.FromString,\n ('gogrpcspec.TaskManager', 'ChangeToDone'): Task.FromString,\n ('gogrpcspec.TaskManager', 'GetSummary'): Employee.FromString,\n ('gogrpcspec.TaskManager', 'GetTasks'): Employee.FromString,\n }\n response_serializers = {\n ('gogrpcspec.TaskManager', 'AddTask'): SpecificSummary.SerializeToString,\n ('gogrpcspec.TaskManager', 'AddTasks'): Summary.SerializeToString,\n ('gogrpcspec.TaskManager', 'ChangeToDone'): Task.SerializeToString,\n ('gogrpcspec.TaskManager', 'GetSummary'): SpecificSummary.SerializeToString,\n ('gogrpcspec.TaskManager', 'GetTasks'): Task.SerializeToString,\n }\n method_implementations = {\n ('gogrpcspec.TaskManager', 'AddTask'): face_utilities.unary_unary_inline(servicer.AddTask),\n ('gogrpcspec.TaskManager', 'AddTasks'): face_utilities.stream_unary_inline(servicer.AddTasks),\n ('gogrpcspec.TaskManager', 'ChangeToDone'): face_utilities.stream_stream_inline(servicer.ChangeToDone),\n ('gogrpcspec.TaskManager', 'GetSummary'): face_utilities.unary_unary_inline(servicer.GetSummary),\n ('gogrpcspec.TaskManager', 'GetTasks'): face_utilities.unary_stream_inline(servicer.GetTasks),\n }\n server_options = beta_implementations.server_options(request_deserializers=request_deserializers, response_serializers=response_serializers, thread_pool=pool, thread_pool_size=pool_size, default_timeout=default_timeout, maximum_timeout=maximum_timeout)\n return beta_implementations.server(method_implementations, options=server_options)", "def init_optimizer_for_pruning(cls, optimizer):\n assert (cls.__optimizer is None), \"ASP has initialized optimizer already.\"\n assert (cls.__calculate_mask is not None), \"Called ASP.init_optimizer_for_pruning before ASP.init_model_for_pruning.\"\n\n # store pointer to original optimizer step method\n cls.__optimizer = optimizer\n cls.__optimizer.__step = optimizer.step\n\n def __step(opt_self, *args, **kwargs):\n # prune gradients before step method\n with torch.no_grad():\n for module_name, module, p_name, p, mask, pruned in cls.__sparse_parameters:\n p.grad.mul_(mask)\n # call original optimizer step method\n rval = opt_self.__step(*args, **kwargs)\n # prune parameters after step method\n with torch.no_grad():\n for module_name, module, p_name, p, mask, pruned in cls.__sparse_parameters:\n p.mul_(mask)\n return rval\n cls.__optimizer.step = types.MethodType(__step, cls.__optimizer)", "def optimize(opt, target, n_agents, n_variables, n_iterations, lb, ub, hyperparams):\n\n # Creating the SearchSpace\n space = SearchSpace(n_agents=n_agents, n_variables=n_variables,\n n_iterations=n_iterations, lower_bound=lb, upper_bound=ub)\n\n # Creating the Function\n function = Function(pointer=target)\n\n # Creating Optimizer\n if opt.__name__ is not 'BH':\n optimizer = opt(hyperparams=hyperparams)\n else:\n optimizer = opt()\n\n # Creating the optimization task\n task = Opytimizer(space=space, optimizer=optimizer, function=function)\n\n return task.start(store_best_only=True)", "def get_task_optimizer(self) -> torch.optim.Optimizer:\n pass", "def asyncoptimize(self,server_,port_):\n if isinstance(server_,unicode):\n server_ = server_.encode(\"utf-8\",errors=\"replace\")\n if isinstance(port_,unicode):\n port_ = port_.encode(\"utf-8\",errors=\"replace\")\n token_ = (ctypes.c_char * 33)()\n res = __library__.MSK_XX_asyncoptimize(self.__nativep,server_,port_,token_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _token_retval = token_.value.decode(\"utf-8\",errors=\"replace\")\n return (_token_retval)", "def setSolverTau(*argv):", "def set_target(self, host, port):\r\n pass", "def reset(self):\n self.solver = None", "def shutdown(self):\n path = self.opt.get('model_file', None)\n if path is not None and hasattr(self, 'optimizer'):\n self.save(path + '.shutdown_state')\n super().shutdown()", "def server_pool(self, server_pool):\n\n self._server_pool = server_pool", "def stop_server(self): # pragma: no cover\n if not self.sql_server is None:\n loop = asyncio.get_event_loop()\n assert loop\n loop.create_task(self.sql_server.shutdown())\n\n self.sql_server = None", "def offline_server_garbler_phase(env, storage, num_relus):\n\n # key generation \n now = env.now\n yield env.timeout(utils.off_client_compute_keygen) # client generates key\n yield env.timeout(utils.off_client_write_key / bandwidth) # client sends key to server\n # simulate linear layers\n for i in range(len(utils.off_client_compute_he_encrypt)): # for i in range(linear layers)....\n yield env.timeout(utils.off_client_compute_he_encrypt[i]) # client encrypts random share for layer i\n yield env.timeout(utils.off_client_write_linear[i] / bandwidth) # client sends encrypted share to server\n yield env.timeout(utils.off_server_compute_he_eval[i]) # server performs linear HE op to obtain output\n yield env.timeout(utils.off_server_write_linear[i] / bandwidth) # server sends encrypted output to client\n yield env.timeout(utils.off_client_compute_he_decrypt[i]) # client decrypts output\n\n # simulate ReLU layers\n yield env.timeout(utils.off_server_compute_garble) # server garbles ReLU\n yield env.timeout(utils.off_server_compute_encode) # server encodes labels\n yield env.timeout(utils.off_server_write_garbled_c / bandwidth) # server sends garbled circuit to client\n \n # oblivious transfer protocol (protocol 4 of https://eprint.iacr.org/2016/602)\n yield env.timeout(utils.off_client_write_base_ot / bandwidth) # client sends labels (k_0, k_1)..... BASE OT\n yield env.timeout(utils.off_client_write_ext_ot_setup / bandwidth) # client sends u_i to server ..... EXT OT\n yield env.timeout(utils.off_server_write_ext_ot_send / bandwidth) # server sends (y_0, y_1) to client.. EXT OT\n yield storage.put(num_relus)", "def asyncstop(self,server_,port_,token_):\n if isinstance(server_,unicode):\n server_ = server_.encode(\"utf-8\",errors=\"replace\")\n if isinstance(port_,unicode):\n port_ = port_.encode(\"utf-8\",errors=\"replace\")\n if isinstance(token_,unicode):\n token_ = token_.encode(\"utf-8\",errors=\"replace\")\n res = __library__.MSK_XX_asyncstop(self.__nativep,server_,port_,token_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def run_onnx_optimizer(onnx_model):\n try:\n onnx_polish_model = onnx.utils.polish_model\n except AttributeError:\n pass\n else:\n return onnx_polish_model(onnx_model)\n\n try:\n # pylint: disable=import-outside-toplevel\n import onnxoptimizer\n except ImportError:\n pass\n else:\n return onnxoptimizer.optimize(onnx_model)\n\n return onnx_model", "def stop_server(self):\r\n # TODO-SDH Add way to stop the server from running.\r", "def set_optimizer(self, config):\r\n self.optimizer = optim.Adam(self.net.parameters(), config.lr)\r\n self.scheduler = optim.lr_scheduler.ExponentialLR(self.optimizer, config.lr_decay)", "def optimize(self):\n self.check_is_ready()\n self.check_infeasibility()\n solution_graph, obj_val = self.find_shortest_network_with_ADH((self.old_network_graph is not None))\n self.solution_graph = gnx.GeoMultiGraph(solution_graph, crs=self.optimization_graph.crs)", "def optimize_for_dagit(self, statement_timeout):" ]

[ "0.63646656", "0.5643291", "0.5561428", "0.512266", "0.51177776", "0.5061218", "0.50380147", "0.49591717", "0.4938649", "0.49026117", "0.49010754", "0.48953548", "0.48889136", "0.48848823", "0.4877491", "0.4876011", "0.48473015", "0.48344618", "0.4819269", "0.48074353", "0.47920346", "0.47879", "0.4761904", "0.47536215", "0.47479716", "0.47445187", "0.47368392", "0.47311974", "0.47265717", "0.47109693" ]

[ "def asyncoptimize(self,server_,port_): # 3\n arr_token = array.array(\"b\",[0]*(33))\n memview_arr_token = memoryview(arr_token)\n res,resargs = self.__obj.asyncoptimize(server_,port_,memview_arr_token)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n retarg_token = resargs\n retarg_token = arr_token.tobytes()[:-1].decode(\"utf-8\",errors=\"ignore\")\n return retarg_token", "def populate_server(self, target_server):\n for uid in self.servers_online:\n server = self.all_servers[uid]\n if server == target_server:\n pass\n else:\n if len(server.jobs)>1:\n shifting_task = server.jobs.pop(-1)\n self.servers_jobs_list[server.server_id].remove(shifting_task)\n server.waiting_time-=shifting_task.task_time\n self.schedule_task(shifting_task)", "def optimizermt(self,server_,port_): # 3\n res,resargs = self.__obj.optimizermt(server_,port_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _trmcode_return_value = resargs\n _trmcode_return_value = rescode(_trmcode_return_value)\n return _trmcode_return_value", "def optimize(self):\n self.check_is_ready()\n self.check_infeasibility()\n solution_graph, obj_val = self.find_shortest_network_with_ADH((self.old_network_graph is not None))\n self.solution_graph = gnx.GeoMultiGraph(solution_graph, crs=self.optimization_graph.crs)", "def server_pool(self, server_pool):\n\n self._server_pool = server_pool", "def update_optimizer(self, context, optimizer, host):\n pass", "def _update_objective(self):\n # rewrap the cost if the solver has been run\n self.Finalize()\n return", "def schedule_task(self, task):\n if self.time_based:\n minimum_wait_server = float('inf')\n for uid, server in self.all_servers.items():\n if server.status:\n if minimum_wait_server > server.waiting_time:\n target_server = server\n minimum_wait_server = server.waiting_time\n try:\n target_server.jobs.append(task)\n target_server.waiting_time += task.task_time\n self.servers_jobs_list[target_server.server_id].append(task)\n except Exception:\n print(\"There are no servers left to reassign\")\n raise Exception(\"################# CHAOS MONKEY WON ####################\")\n else:\n minimum_jobs = float('inf')\n for uid, server in self.all_servers.items():\n if server.status:\n if minimum_jobs > len(server.jobs):\n minimum_jobs = len(server.jobs)\n target_server = server\n try:\n target_server.jobs.append(task)\n target_server.waiting_time += task.task_time\n self.servers_jobs_list[target_server.server_id].append(task)\n except Exception:\n print(\"There are no servers left to reassign\")\n raise Exception(\"################# CHAOS MONKEY WON ####################\")", "def _stop_server(cls, address):\n\n print('_stop_server: please override me.')", "def optimizermt(self,server_,port_):\n if isinstance(server_,unicode):\n server_ = server_.encode(\"utf-8\",errors=\"replace\")\n if isinstance(port_,unicode):\n port_ = port_.encode(\"utf-8\",errors=\"replace\")\n trmcode_ = ctypes.c_int32()\n res = __library__.MSK_XX_optimizermt(self.__nativep,server_,port_,ctypes.byref(trmcode_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _trmcode_return_value = rescode(trmcode_.value)\n return (_trmcode_return_value)", "def runSolve(self, solver, timeout=5):\n try:\n self.pool.apply_async(self.solve, [solver,]).get(timeout)\n self.assertTrue(solver.gm.isWon())\n except TimeoutError:\n raise Exception(\"Timed out: %s\" % inspect.stack()[1][3])", "def asyncstop(self,server_,port_,token_): # 3\n res = self.__obj.asyncstop(server_,port_,token_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def run_onnx_optimizer(onnx_model):\n try:\n onnx_polish_model = onnx.utils.polish_model\n except AttributeError:\n pass\n else:\n return onnx_polish_model(onnx_model)\n\n try:\n # pylint: disable=import-outside-toplevel\n import onnxoptimizer\n except ImportError:\n pass\n else:\n return onnxoptimizer.optimize(onnx_model)\n\n return onnx_model", "def setSolverTau(*argv):", "def __solve(self) -> None:\n pyo.TransformationFactory(\"contrib.detect_fixed_vars\").apply_to(self.model) # type: ignore\n pyo.TransformationFactory(\"contrib.deactivate_trivial_constraints\").apply_to(self.model) # type: ignore\n\n # initialise the solver object\n self._logger.debug(\"[ModelSolver] Solver object initiated...\")\n solver = Config.OPTIMISATION_MODEL_CONFIG['SOLVER_TYPE']\n opt = pyo.SolverFactory(solver)\n if Config.OPTIMISATION_MODEL_CONFIG['SOLVER_OPTION'].get(solver) is not None:\n for k, v in Config.OPTIMISATION_MODEL_CONFIG['SOLVER_OPTION'].get(solver).items():\n opt.options[k] = v\n\n try:\n start_time = datetime.now()\n self._logger.debug(\"[ModelSolver] Solver starting...\")\n results = opt.solve(self.model, tee=True)\n self.results = results\n end_time = datetime.now()\n self._logger.info(f\"[ModelSolver] Solver completed in {end_time - start_time}.\")\n except Exception as e:\n raise Exception(f\"Model optimisation failed with {solver} with error message {e}.\")\n\n if (results.solver.status == SolverStatus.ok) and (results.solver.termination_condition == TerminationCondition.optimal):\n self._logger.info(\"Solution is feasible and optimal\")\n results.write()\n elif results.solver.termination_condition == TerminationCondition.infeasible:\n raise ValueError(\"Model optimisation resulted into an infeasible solution\")\n\n self.model.optimised = True", "def update_server(DisableAutomatedBackup=None, BackupRetentionCount=None, ServerName=None, PreferredMaintenanceWindow=None, PreferredBackupWindow=None):\n pass", "def update(self):\n for uid, server in self.servers_online.items():\n if len(server.jobs):\n self.populate_server(server)\n for uid, server in self.servers_online.items():\n if server.jobs:\n server.jobs[0].task_time -= time_interval\n server.waiting_time -= time_interval\n if server.jobs[0].task_time <= 0:\n completed_task = server.jobs.pop(0)\n print(f\"Task '{completed_task.description}' completed\")\n self.all_tasks.remove(completed_task)\n self.servers_jobs_list[uid].pop(0)\n for uid, server in self.all_servers.items():\n if server.status:\n print(f\"{server.server_name} has {len(set(server.jobs))} job(s)\")\n else:\n print(f\"{server.server_name} is offline\")", "def execute_cb(self, goal):\n print(\"Action server\")\n loop_rate = rospy.Rate(10)\n \n actual_path = Path()\n actual_path.header.frame_id = \"map\"\n self.set_plan(goal.path)\n while not rospy.is_shutdown():\n rospy.logwarn_throttle(2.0,\"ExecCb\")\n map_pose, odom_pose = self.robot.get_pose()\n if map_pose == None:\n rospy.logerr(\"Robot pose could not retreived\")\n continue\n \n if self.goal_reached(map_pose.pose):\n # Robot completed the task\n self.result.success = True\n self.action_server.set_succeeded(self.result)\n rospy.logerr(\"Goal reached\")\n return\n \n if self.action_server.is_preempt_requested():\n print(\"Preempt requested\")\n self.result.success = False\n self.action_server.publish_feedback(self.feedback)\n self.action_server.set_preempted(result=self.result)\n return\n\n\n\n res, cmd_vel = self.compute_velociy_commands()\n \n if res:\n self.robot.command(cmd_vel)\n\n actual_path.header.stamp = rospy.Time.now()\n actual_path.poses.append(map_pose)\n self.desired_pub.publish(goal.path)\n self.actual_pub.publish(actual_path)\n loop_rate.sleep()", "def asyncstop(self,server_,port_,token_):\n if isinstance(server_,unicode):\n server_ = server_.encode(\"utf-8\",errors=\"replace\")\n if isinstance(port_,unicode):\n port_ = port_.encode(\"utf-8\",errors=\"replace\")\n if isinstance(token_,unicode):\n token_ = token_.encode(\"utf-8\",errors=\"replace\")\n res = __library__.MSK_XX_asyncstop(self.__nativep,server_,port_,token_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def server_activate(self):\n\t\tself.socket.listen(self.request_queue_size)", "def noparallel():\n if GLOBAL['DASK_CLIENT'] is not None:\n GLOBAL['DASK_CLIENT'].shutdown()\n del GLOBAL['DASK_CLIENT']\n GLOBAL['DASK_CLIENT'] = None", "def _rebuild_server(self, context, server, preserve_ephemeral):\n\n self.driver.rebuild(context, server, preserve_ephemeral)", "def _PQConEnd(self, m):\n self.server = True", "async def return_to_pool(self, sock):\n ...", "async def _arun(self, query: str) -> str:\n raise NotImplementedError(\"PythonReplTool does not support async\")", "async def _arun(self, query: str) -> str:\n raise NotImplementedError(\"PythonReplTool does not support async\")", "def perform_overset_connectivity(self):\n for ss in self.solvers:\n ss.pre_overset_conn_work()\n\n tg = self.tioga\n if self.has_amr:\n tg.preprocess_amr_data()\n\n tg.profile()\n tg.perform_connectivity()\n if self.has_amr:\n tg.perform_connectivity_amr()\n\n for ss in self.solvers:\n ss.post_overset_conn_work()", "def _RestartServer( self ):\n with self._gocode_lock:\n self._StopServer()\n self._StartServer()", "def sync_remote(self, otp_params, local_params, server_nonce, required_answers, timeout=1):\n # Construct URLs\n responses = []\n dqueue = queue.Queue()\n for row in self.db.get_queue(otp_params['modified'], server_nonce):\n url = '%(server)s?otp=%(otp)s&modified=%(modified)s' % row\n url += '&' + row['info'].split(',')[0]\n _thread = threading.Thread(target=self._fetch_remote,\n args=(dqueue, row['server'], url, timeout))\n _thread.daemon = True\n _thread.start()\n loop_start = time.time()\n while len(responses) < required_answers and time.time() < loop_start + timeout * 1.5:\n try:\n resp = dqueue.get(timeout=0.2)\n responses.append(resp)\n # Delete entry from table\n self.db.remove_from_queue(resp['server'], otp_params['modified'], server_nonce)\n except queue.Empty:\n pass\n\n answers = len(responses)\n # Parse response\n valid_answers = 0\n for resp in responses:\n resp_params = resp['params']\n logger.debug('[%s] local DB contains %s',\n otp_params['yk_publicname'], local_params)\n logger.debug('[%s] response contains %s',\n otp_params['yk_publicname'], resp_params)\n logger.debug('[%s] OTP contains %s',\n otp_params['yk_publicname'], otp_params)\n # Update Internal DB (conditional)\n self.db.update_db_counters(resp_params)\n # Check for Warnings\n # https://developers.yubico.com/yubikey-val/doc/ServerReplicationProtocol.html\n # NOTE: We use local_params for validationParams comparison since they are actually\n # the same in this situation and we have them at hand.\n if counters_gt(local_params, resp_params):\n logger.warning('[%(yk_publicname)s] Remote server out of sync', otp_params)\n if counters_gt(resp_params, local_params):\n logger.warning('[%(yk_publicname)s] Local server out of sync', otp_params)\n if counters_eq(resp_params, local_params) \\\n and resp_params['nonce'] != local_params['nonce']:\n logger.warning('[%(yk_publicname)s] Servers out of sync. '\n 'Nonce differs.', otp_params)\n if counters_eq(resp_params, local_params) \\\n and resp_params['modified'] != local_params['modified']:\n logger.warning('[%(yk_publicname)s] Servers out of sync. '\n 'Modified differs.', otp_params)\n if counters_gt(resp_params, otp_params):\n logger.warning('[%(yk_publicname)s] OTP is replayed. '\n 'Sync response counters higher than OTP counters.', otp_params)\n elif counters_eq(resp_params, otp_params) \\\n and resp_params['nonce'] != otp_params['nonce']:\n logger.warning('[%(yk_publicname)s] OTP is replayed. Sync '\n 'response counters equal to OTP counters and nonce '\n 'differs.', otp_params)\n else:\n # The answer is ok since a REPLAY was not indicated\n valid_answers += 1\n if required_answers == valid_answers:\n break\n\n # NULL queued_time for remaining entries in queue, to allow\n # daemon to take care of them as soon as possible.\n self.db.null_queue(server_nonce)\n return {'answers': answers, 'valid_answers': valid_answers}", "def solve_optimisation(model, exe_path, project_dir, poses) -> None:\n opt = SolverFactory(\n 'ipopt',\n executable=exe_path\n )\n\n # solver options\n opt.options[\"print_level\"] = 5\n opt.options[\"max_iter\"] = 400\n opt.options[\"max_cpu_time\"] = 3600\n opt.options[\"tol\"] = 1e-1\n opt.options[\"OF_print_timing_statistics\"] = \"yes\"\n opt.options[\"OF_print_frequency_iter\"] = 10\n opt.options[\"OF_hessian_approximation\"] = \"limited-memory\"\n #opt.options[\"linear_solver\"] = \"ma86\"\n\n LOG_DIR = 'C://Users//user-pc//Documents//Scripts//FYP//logs'\n\n # --- This step may take a while! ---\n results = opt.solve(\n model, tee=True, \n keepfiles=True, \n logfile=os.path.join(LOG_DIR, \"solver.log\")\n )\n\n result_dir = os.path.join(project_dir, \"results\")\n save_data(model, file_path=os.path.join(result_dir, 'traj_results.pickle'), poses=poses)" ]

[ "0.6602464", "0.5121303", "0.5121116", "0.50080115", "0.48946133", "0.47785783", "0.47696742", "0.46918422", "0.46745405", "0.46690065", "0.46030888", "0.45859867", "0.45737752", "0.4560305", "0.45586684", "0.4554464", "0.45519593", "0.45377553", "0.45205483", "0.44452822", "0.44266963", "0.44256276", "0.44061604", "0.44012165", "0.43991485", "0.43991485", "0.43966553", "0.43831846", "0.43741718", "0.43702382" ]

[ "def asyncstop(self,server_,port_,token_): # 3\n res = self.__obj.asyncstop(server_,port_,token_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)", "def stop_server(self):\r\n # TODO-SDH Add way to stop the server from running.\r", "def stop():\n server = current_server()\n server.stop()", "def stop():\n global server_handle\n server_handle.kill()\n server_handle = None", "async def stop(self):", "def stop(self):\n if self._server_thread is None:\n return\n self._stopping.set()\n self._server_thread = None\n self._stopped.wait()", "def stop(self, *args):\n # logging.debug(\"Stopping....\")\n self.has_been_stopped.set()\n self.server.stop(grace=1)", "async def stop_server(self):\n t = time.time()\n success = await self._stop()\n t = time.time() - t\n if success:\n await self.send('Server stopped in {time:.3f}s'.format(time=t))\n else:\n await self.send('Server timed out and was killed')\n await self.set_trigger('control', None)\n await self.set_trigger('chat', None)\n await self.set_trigger('chat_init', None)", "def _stop_server(cls, server):\n\n try:\n server.kill()\n except Exception as error:\n print('ERROR stop enip server: ', error)", "def stop_server(self, server, name):\n # Spin down the requested server\n server.stop()", "async def _stop(self):\n return", "def stop_server(request):\n def stop_callback():\n global process\n process.terminate()\n request.addfinalizer(stop_callback)", "async def _stop(self) -> None:\n self._stopped.set()", "def _stop(self, host):\n pass", "def terminate_server(self, port):\n proc = self.processes.pop(port, None)\n if proc is None:\n raise ValueError(f\"Server for port {port} does not exists.\"\n \"It might have been closed already.\"\n )\n proc.terminate()", "def terminate(self):\n print('Terminating Revshell thread.')\n self.server.close()", "def stop() -> None:\n global _server\n if _server:\n try:\n _server.shutdown()\n except Exception:\n pass", "def do_stop(self):\n debug(\"CBA4.do_stop()\")\n if (self.__thread and self.__thread.isAlive()):\n self.__thread.stop()\n self.__thread.join(None)\n self.__thread = None\n\n if (self.is_valid()):\n tx = bytearray(16)\n tx[0] = 0x53\n tx[1] = 1\n self.get_status_response(tx)\n #end do_stop()", "def _stop_server(cls, server):\n\n try:\n server.kill()\n except Exception as error:\n print('ERROR stop modbus server: ', error)", "async def stop(self):\n self._stopped.set()", "def stop(self):\n self._isAlive = False\n logger.debug(\"Threaded Server has been stopped.\")", "def stop(self) -> None:\n self._client.terminate_job(jobId = self.id, reason = self.STOP_REASON)", "def stop():\n\n tidyUp()\n shutdown_server()\n return \"Stopping server\"", "def stop():\n\n tidyUp()\n shutdown_server()\n return \"Stopping server\"", "def stop():\n tidyup()\n shutdown_server()\n return \"Parando Servidor\"", "def stop():\n tidyup()\n shutdown_server()\n return \"Parando Servidor\"", "def stop():\n tidyup()\n shutdown_server()\n return \"Parando Servidor\"", "def stop(self):\n if self.started:\n try:\n self.server.shutdown()\n self.server.server_close()\n self.server_thread.join()\n self.server_thread = None\n except AttributeError:\n pass\n self.started = False\n self.server = None", "def stop(self):\n self.conn.stop()", "def stop(self):\n\n self._stop_server = True\n\n self.join()\n self.httpd.server_close()" ]

[ "0.85108995", "0.6669743", "0.6566155", "0.6552622", "0.6488106", "0.64568543", "0.6397652", "0.6383138", "0.636795", "0.63649935", "0.63017964", "0.62986946", "0.62985563", "0.62593824", "0.62358534", "0.62237436", "0.6220952", "0.61999017", "0.6176527", "0.61644346", "0.61298037", "0.6122053", "0.61197245", "0.61197245", "0.60896564", "0.60896564", "0.60896564", "0.6081251", "0.6074791", "0.60591924" ]

[ "def asyncpoll(self,server_,port_,token_): # 3\n res,resargs = self.__obj.asyncpoll(server_,port_,token_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _respavailable_return_value,_resp_return_value,_trm_return_value = resargs\n _trm_return_value = rescode(_trm_return_value)\n _resp_return_value = rescode(_resp_return_value)\n return _respavailable_return_value,_resp_return_value,_trm_return_value", "def asyncgetresult(self,server_,port_,token_):\n if isinstance(server_,unicode):\n server_ = server_.encode(\"utf-8\",errors=\"replace\")\n if isinstance(port_,unicode):\n port_ = port_.encode(\"utf-8\",errors=\"replace\")\n if isinstance(token_,unicode):\n token_ = token_.encode(\"utf-8\",errors=\"replace\")\n respavailable_ = ctypes.c_int32()\n resp_ = ctypes.c_int32()\n trm_ = ctypes.c_int32()\n res = __library__.MSK_XX_asyncgetresult(self.__nativep,server_,port_,token_,ctypes.byref(respavailable_),ctypes.byref(resp_),ctypes.byref(trm_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n respavailable_ = respavailable_.value\n _respavailable_return_value = respavailable_\n _resp_return_value = rescode(resp_.value)\n _trm_return_value = rescode(trm_.value)\n return (_respavailable_return_value,_resp_return_value,_trm_return_value)", "def remote_status():", "def asyncgetresult(self,server_,port_,token_): # 3\n res,resargs = self.__obj.asyncgetresult(server_,port_,token_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _respavailable_return_value,_resp_return_value,_trm_return_value = resargs\n _trm_return_value = rescode(_trm_return_value)\n _resp_return_value = rescode(_resp_return_value)\n return _respavailable_return_value,_resp_return_value,_trm_return_value", "def fetch_status():\n try:\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n s.connect((GEARMAND_HOST, GEARMAND_PORT))\n log_verbose('Connected to Gearmand at %s:%s' % (GEARMAND_HOST, GEARMAND_PORT))\n except socket.error, e:\n collectd.error('gearmand_info plugin: Error connecting to %s:%d - %r'\n % (GEARMAND_HOST, GEARMAND_PORT, e))\n return None\n fp = s.makefile('r')\n log_verbose('Sending info command')\n s.sendall('status\\r\\n')\n\n status = {}\n while True:\n data = fp.readline().strip()\n log_verbose('Received data: %r' % data)\n if not data or data == '.':\n break\n function, total, running, available_workers = data.split('\\t')\n status[function] = {\n 'total': total,\n 'running': running,\n 'available_workers': available_workers}\n\n s.close()\n return status", "async def get_status():", "def _fetch_remote(self, dqueue, server, url, timeout):\n try:\n req = requests.get(url, timeout=timeout)\n if req.status_code == 200:\n try:\n resp_params = parse_sync_response(req.text)\n dqueue.put({'server': server, 'params': resp_params})\n except ValueError as err:\n logger.error('Failed to parse response of %s: %s', server, err)\n else:\n logger.warning('Recieved status code %s for %s', req.status_code, url)\n except Exception as err:\n logger.warning('Failed to retrieve %s: %s', url, err)", "def get_status(self):\n\t\treturn call_sdk_function('PrlJob_GetStatus', self.handle)", "def thread_status():\n global dataSession\n return jsonify(dict(status=('finished' if len(dataSession) > 1 else 'running')))", "def server_status(self, timeout):\n _abstract()", "def server_status(self, timeout):\n _abstract()", "def mmo_cluster_serverStatus(self, mmo_connection, inc_mongos, poll=False):\n serverStatus = self.mmo_execute_on_cluster(mmo_connection, \"serverStatus\", inc_mongos)\n if os.path.exists(\"/tmp/server_status.p\"):\n os.rename(\"/tmp/server_status.p\", \"/tmp/server_status.previous\")\n pickle.dump(serverStatus, open(\"/tmp/server_status.p\", \"wb\"))\n return serverStatus", "def asyncoptimize(self,server_,port_):\n if isinstance(server_,unicode):\n server_ = server_.encode(\"utf-8\",errors=\"replace\")\n if isinstance(port_,unicode):\n port_ = port_.encode(\"utf-8\",errors=\"replace\")\n token_ = (ctypes.c_char * 33)()\n res = __library__.MSK_XX_asyncoptimize(self.__nativep,server_,port_,token_)\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _token_retval = token_.value.decode(\"utf-8\",errors=\"replace\")\n return (_token_retval)", "def ProcessRemoteCommandsRequest(self):\n return (200, '')", "def _set_status(self, action, status):\n cmd = \"curl http://{}:{}@{}/{}s.cgi?led={}\".format(self.config['username'],\n self.config['password'],\n self.config['host'],\n action,\n status)\n self.log.info(\"PDU cmd: {}\".format(cmd))\n utils.start_standing_subprocess(cmd)\n time.sleep(10)", "def get_status(self):\n if self.status:\n print(f\"Server '{self.server_name}' is online\")\n else:\n print(f\"Server '{self.server_name}' is offline\")", "def get_status(self):\n url = \"data_request?id=jobstatus&job=%d&plugin=zwave\" % self.id\n return self.vera.get(url)", "def _status(self, host):\n pass", "def server_status(self, timeout=None):\n return self._call('server_status', timeout=timeout)", "def poll_for_active_status(self, server_id, req_status=\"ACTIVE\"):\n status = \"BUILDING\"\n iteration = 30\n while status.upper() != req_status.upper() \\\n or status.upper() != \"ERROR\":\n server_info = self.show_server(server_id)\n if not isinstance(server_info, dict):\n return\n status = server_info['status']\n LOG_OBJ.debug(\"Server status : %s\" % status)\n if status.upper() in [req_status.upper(), 'ERROR']:\n break\n LOG_OBJ.debug(\"Waiting till server goes to %s state...\"\n % req_status)\n time.sleep(20)\n iteration -= 1\n if not iteration:\n err_msg = \"The server:%s is NOT in %s state\" \\\n \"within 10 minutes\" % (server_id, status)\n LOG_OBJ.error(err_msg)\n return \"POLL_TIME_EXCEEDED\"\n\n LOG_OBJ.debug(\"Server becomes %s\" % status)\n\n return status", "def get_status(chronos_url, statuses=False):\n if statuses:\n print('Jobs on ' + chronos_url)\n connection = http.client.HTTPConnection(chronos_url)\n connection.request(\"GET\", \"/scheduler/jobs\")\n response_str = connection.getresponse().read().decode(\"utf-8\")\n jobs_dict = json.loads(response_str)\n\n connection.request(\"GET\", \"/scheduler/graph/csv\")\n response_str = connection.getresponse().read().decode(\"utf-8\")\n reader = csv.reader(StringIO(response_str), delimiter=',')\n jobs_csv = {}\n for row in reader:\n if row[0] == 'link':\n continue\n jobs_csv[row[1]] = row\n\n # last_status: ['fresh', 'failure', 'success']\n # state: ['idle', 'queued', 'running']\n\n job_status = {}\n job_status['running'] = []\n job_status['failure'] = []\n job_status['fresh'] = []\n job_status['all'] = []\n for job in jobs_dict:\n jname = job['name']\n if jname not in jobs_csv:\n continue\n nerror = job['errorCount']\n nsuccess = job['successCount']\n #command = job['command']\n if statuses:\n print('\\t'.join([jobs_csv[jname][2], jobs_csv[jname][3], str(nerror),\n str(nsuccess), jname]))\n job_status['all'] = job_status['all'] + [jname]\n if jobs_csv[jname][3] == 'running':\n job_status['running'] = job_status['running'] + [jname]\n elif jobs_csv[jname][2] == 'failure':\n job_status['failure'] = job_status['failure'] + [jname]\n elif jobs_csv[jname][2] == 'fresh':\n job_status['fresh'] = job_status['fresh'] + [jname]\n return job_status", "def refresh_queue_status(self):\n \n # Get the jobid and state for all jobs pending/running/completed for the current user\n qacct_stdout=self.run_grid_command_resubmit([\"qacct\",\"-o\",getpass.getuser(),\"-j\",\"*\"])\n \n # info list should include jobid, state, cpus, time, and maxrss\n info=[]\n job_status=[]\n for line in qacct_stdout.split(\"\\n\"):\n if line.startswith(\"jobnumber\") or line.startswith(\"job_number\"):\n if job_status:\n info.append(job_status)\n job_status=[line.rstrip().split()[-1],\"NA\",\"NA\",\"NA\",\"NA\"]\n # get the states for completed jobs\n elif line.startswith(\"failed\"):\n failed_code = line.rstrip().split()[1]\n if failed_code != \"0\":\n if failed_code in [\"37\",\"100\"]:\n job_status[1]=self.job_code_terminated\n else:\n job_status[1]=self.job_code_error\n elif line.startswith(\"deleted_by\"):\n if line.rstrip().split()[-1] != \"NONE\" and job_status[1] == self.job_code_terminated:\n job_status[1]=self.job_code_deleted\n elif line.startswith(\"exit_status\"):\n # only record if status has not yet been set\n if job_status[1] == \"NA\":\n exit_status = line.rstrip().split()[-1]\n if exit_status == \"0\":\n job_status[1]=self.job_code_completed\n elif exit_status == \"137\":\n job_status[1]=self.job_code_terminated\n else:\n job_status[1]=self.job_code_error\n # get the current state for running jobs\n elif line.startswith(\"job_state\"):\n job_status[1]=line.rstrip().split()[-1]\n elif line.startswith(\"slots\"):\n job_status[2]=line.rstrip().split()[-1]\n elif line.startswith(\"ru_wallclock\"):\n try:\n # get the elapsed time in minutes\n job_status[3]=str(float(line.rstrip().split()[-1])/60.0)\n except ValueError:\n job_status[3]=\"NA\"\n elif line.startswith(\"ru_maxrss\"):\n job_status[4]=line.rstrip().split()[-1]+\"K\"\n \n if job_status:\n info.append(job_status)\n\n return info", "async def _async_status_request(self) -> None:\n try:\n # status_response = await self._hass.async_add_executor_job(\n # self._mc_status.status, self._MAX_RETRIES_STATUS\n # )\n if self.access_token:\n if (time.time() - self.last_request) > 1800:\n phantom = await self._hass.async_add_executor_job(\n self._phantom_load\n )\n if phantom.status_code == HTTP_OK:\n self.phantom_load = round(phantom.json().get(\"power\") / 1000, 3)\n else:\n _LOGGER.warning(phantom.content)\n\n # Got answer to request, update properties.\n live = await self._hass.async_add_executor_job(self._live_data)\n\n if live.status_code == HTTP_OK:\n self.power_usage = round(abs(live.json().get(\"power\")) / 1000, 3)\n else:\n _LOGGER.warning(live.content)\n\n self.last_request = time.time()\n self._last_status_request_failed = False\n except OSError as error:\n # No answer to request, set all properties to unknown.\n self.power_usage = None\n self.phantom_load = None\n\n # Inform user once about failed update if necessary.\n if not self._last_status_request_failed:\n _LOGGER.warning(\n \"Updating the properties of '%s' failed - OSError: %s\",\n self.unique_id,\n error,\n )\n self._last_status_request_failed = True", "def readresp(self, cmd):\n\t\tdata = self.read(22)\n\t\tresponse = data[0]\n\t\t#print \"laser response\", self.mylaser, response\n\t\tgstt.lstt_dacanswers[self.mylaser] = response\n\t\tcmdR = data[1]\n\t\tstatus = Status(data[2:])\n\t\tr.set('/lack/'+str(self.mylaser), response)\n\n\t\tif cmdR != cmd:\n\t\t\traise ProtocolError(\"expected resp for %r, got %r\"\n\t\t\t\t% (cmd, cmdR))\n\n\t\tif response != \"a\":\n\t\t\traise ProtocolError(\"expected ACK, got %r\"\n\t\t\t\t% (response, ))\n\n\t\tself.last_status = status\n\t\treturn status", "def sipserver_status(self) -> str:", "def update(self, **kwargs):\n self.status = status.parse(status.get(host=self._host, port=self._port))", "def checkOutNewTask(LoadBalanceServerAddress):\n url = LoadBalanceServerAddress+'/api/requestnewtask/'+ SERVERNAME\n response = requests.get(url)\n if response.status_code == 200:\n return response.text\n\n print(response.text)\n return ''", "def get_status(job_id):\n job = fetch_data.AsyncResult(job_id, app=app)\n return jsonify({'job_id': job_id, 'status': job.status})", "def asyncoptimize(self,server_,port_): # 3\n arr_token = array.array(\"b\",[0]*(33))\n memview_arr_token = memoryview(arr_token)\n res,resargs = self.__obj.asyncoptimize(server_,port_,memview_arr_token)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n retarg_token = resargs\n retarg_token = arr_token.tobytes()[:-1].decode(\"utf-8\",errors=\"ignore\")\n return retarg_token", "def mcstatus(self, irc, msg, args):\n prefix = self.registryValue('prefix')\n suffix = self.registryValue('suffix')\n\n separator = self.registryValue('separator')\n\n svprefix = self.registryValue('service.prefix')\n svsuffix = self.registryValue('service.suffix')\n\n stonline = self.registryValue('status.online')\n stoffline = self.registryValue('status.offline')\n\n\n json_data = urllib2.urlopen(self.registryValue('statusURL')).read()\n data = json.loads(json_data)\n services = []\n\n for pair in data:\n service, status = pair.keys()[0], pair.values()[0]\n services.append('%s%s%s%s' % (svprefix, service, svsuffix,\n stonline if status == 'green' else \\\n stoffline))\n\n irc.reply('%s%s%s' % (prefix, separator.join(services), suffix))" ]

[ "0.7130952", "0.63021195", "0.6182163", "0.607293", "0.5802242", "0.5686539", "0.5662773", "0.55745256", "0.5566215", "0.5563341", "0.5563341", "0.55591416", "0.552535", "0.55108213", "0.54052144", "0.5399891", "0.5390036", "0.5387752", "0.5369794", "0.5271437", "0.5226669", "0.52074784", "0.5192871", "0.5182731", "0.5174264", "0.51635695", "0.51320195", "0.5131731", "0.5106181", "0.50741124" ]

[ "def asyncgetresult(self,server_,port_,token_): # 3\n res,resargs = self.__obj.asyncgetresult(server_,port_,token_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _respavailable_return_value,_resp_return_value,_trm_return_value = resargs\n _trm_return_value = rescode(_trm_return_value)\n _resp_return_value = rescode(_resp_return_value)\n return _respavailable_return_value,_resp_return_value,_trm_return_value", "def GetResult(jobid, g_params): # {{{\n # retrieving result from the remote server for this job\n gen_logfile = g_params['gen_logfile']\n gen_errfile = g_params['gen_errfile']\n\n webcom.loginfo(f\"GetResult for {jobid}.\\n\", gen_logfile)\n\n path_static = g_params['path_static']\n path_result = os.path.join(path_static, 'result')\n path_cache = g_params['path_cache']\n finished_date_db = g_params['finished_date_db']\n name_server = g_params['name_server']\n\n rstdir = os.path.join(path_result, jobid)\n runjob_logfile = os.path.join(rstdir, \"runjob.log\")\n runjob_errfile = os.path.join(rstdir, \"runjob.err\")\n outpath_result = os.path.join(rstdir, jobid)\n if not os.path.exists(outpath_result):\n os.mkdir(outpath_result)\n\n remotequeue_idx_file = os.path.join(rstdir, \"remotequeue_seqindex.txt\")\n\n torun_idx_file = os.path.join(rstdir, \"torun_seqindex.txt\")\n finished_idx_file = os.path.join(rstdir, \"finished_seqindex.txt\")\n query_parafile = os.path.join(rstdir, \"query.para.txt\")\n\n query_para = {}\n if os.path.exists(query_parafile):\n content = myfunc.ReadFile(query_parafile)\n if content != \"\":\n try:\n query_para = json.loads(content)\n except ValueError:\n query_para = {}\n failed_idx_file = os.path.join(rstdir, \"failed_seqindex.txt\")\n\n starttagfile = os.path.join(rstdir, \"runjob.start\")\n cnttry_idx_file = os.path.join(rstdir, \"cntsubmittry_seqindex.txt\") # index file to keep log of tries\n tmpdir = os.path.join(rstdir, \"tmpdir\")\n finished_seq_file = os.path.join(outpath_result, \"finished_seqs.txt\")\n\n if not os.path.exists(tmpdir):\n os.mkdir(tmpdir)\n\n finished_info_list = [] # [info for finished record]\n finished_idx_list = [] # [origIndex]\n failed_idx_list = [] # [origIndex]\n resubmit_idx_list = [] # [origIndex]\n keep_queueline_list = [] # [line] still in queue\n\n cntTryDict = {}\n if os.path.exists(cnttry_idx_file):\n with open(cnttry_idx_file, 'r') as fpin:\n try:\n cntTryDict = json.load(fpin)\n except Exception:\n cntTryDict = {}\n\n # in case of missing queries, if remotequeue_idx_file is empty but the job\n # is still not finished, force recreating torun_idx_file\n if 'DEBUG' in g_params and g_params['DEBUG']:\n try:\n webcom.loginfo(\"DEBUG: %s: remotequeue_idx_file=%s, size(remotequeue_idx_file)=%d, content=\\\"%s\\\"\\n\" %(jobid, remotequeue_idx_file, os.path.getsize(remotequeue_idx_file), myfunc.ReadFile(remotequeue_idx_file)), gen_logfile)\n except Exception:\n pass\n if ((not os.path.exists(remotequeue_idx_file) or # {{{\n os.path.getsize(remotequeue_idx_file) < 1)):\n idlist1 = []\n idlist2 = []\n if os.path.exists(finished_idx_file):\n idlist1 = myfunc.ReadIDList(finished_idx_file)\n if os.path.exists(failed_idx_file):\n idlist2 = myfunc.ReadIDList(failed_idx_file)\n\n completed_idx_set = set(idlist1 + idlist2)\n\n jobinfofile = os.path.join(rstdir, \"jobinfo\")\n jobinfo = myfunc.ReadFile(jobinfofile).strip()\n jobinfolist = jobinfo.split(\"\\t\")\n if len(jobinfolist) >= 8:\n numseq = int(jobinfolist[3])\n\n if 'DEBUG' in g_params and g_params['DEBUG']:\n webcom.loginfo(\"DEBUG: len(completed_idx_set)=%d+%d=%d, numseq=%d\\n\"%(len(idlist1), len(idlist2), len(completed_idx_set), numseq), gen_logfile)\n\n if len(completed_idx_set) < numseq:\n all_idx_list = [str(x) for x in range(numseq)]\n torun_idx_str_list = list(set(all_idx_list)-completed_idx_set)\n for idx in torun_idx_str_list:\n try:\n cntTryDict[int(idx)] += 1\n except (ValueError, IndexError, KeyError):\n cntTryDict[int(idx)] = 1\n myfunc.WriteFile(\"\\n\".join(torun_idx_str_list)+\"\\n\", torun_idx_file, \"w\", True)\n\n if 'DEBUG' in g_params and g_params['DEBUG']:\n webcom.loginfo(\"recreate torun_idx_file: jobid = %s, numseq=%d, len(completed_idx_set)=%d, len(torun_idx_str_list)=%d\\n\"%(jobid, numseq, len(completed_idx_set), len(torun_idx_str_list)), gen_logfile)\n else:\n myfunc.WriteFile(\"\", torun_idx_file, \"w\", True)\n else:\n if 'DEBUG' in g_params and g_params['DEBUG']:\n webcom.loginfo(\"DEBUG: %s: remotequeue_idx_file %s is not empty\\n\" %(jobid, remotequeue_idx_file), gen_logfile)\n# }}}\n\n text = \"\"\n if os.path.exists(remotequeue_idx_file):\n text = myfunc.ReadFile(remotequeue_idx_file)\n if text == \"\":\n return 1\n lines = text.split(\"\\n\")\n\n nodeSet = set([])\n for i in range(len(lines)):\n line = lines[i]\n if not line or line[0] == \"#\":\n continue\n strs = line.split(\"\\t\")\n if len(strs) != 6:\n continue\n node = strs[1]\n nodeSet.add(node)\n\n myclientDict = {}\n for node in nodeSet:\n wsdl_url = f\"http://{node}/pred/api_submitseq/?wsdl\"\n try:\n myclient = Client(wsdl_url, cache=None, timeout=30)\n myclientDict[node] = myclient\n except Exception as e:\n webcom.loginfo(f\"Failed to access {wsdl_url} with errmsg {e}\", gen_logfile)\n pass\n\n for i in range(len(lines)): # {{{\n line = lines[i]\n\n if 'DEBUG' in g_params and g_params['DEBUG']:\n myfunc.WriteFile(f\"Process {line}\\n\", gen_logfile, \"a\", True)\n if not line or line[0] == \"#\":\n if 'DEBUG' in g_params and g_params['DEBUG']:\n webcom.loginfo(\"DEBUG: line empty or line[0] = '#', ignore\", gen_logfile)\n continue\n strs = line.split(\"\\t\")\n if len(strs) != 6:\n if 'DEBUG' in g_params and g_params['DEBUG']:\n webcom.loginfo(\"DEBUG: len(strs)=%d (!=6), ignore\\n\"%(len(strs)), gen_logfile)\n continue\n origIndex = int(strs[0])\n node = strs[1]\n remote_jobid = strs[2]\n description = strs[3]\n seq = strs[4]\n submit_time_epoch = float(strs[5])\n subfoldername_this_seq = f\"seq_{origIndex}\"\n outpath_this_seq = os.path.join(outpath_result, subfoldername_this_seq)\n\n try:\n myclient = myclientDict[node]\n except KeyError:\n if 'DEBUG' in g_params and g_params['DEBUG']:\n webcom.loginfo(\"DEBUG: node (%s) not found in myclientDict, ignore\"%(node), gen_logfile)\n keep_queueline_list.append(line)\n continue\n try:\n rtValue = myclient.service.checkjob(remote_jobid)\n except Exception as e:\n msg = \"checkjob(%s) at node %s failed with errmsg %s\"%(remote_jobid, node, str(e))\n webcom.loginfo(msg, gen_logfile)\n rtValue = []\n pass\n isSuccess = False\n isFinish_remote = False\n status = \"\"\n if len(rtValue) >= 1:\n ss2 = rtValue[0]\n if len(ss2) >= 3:\n status = ss2[0]\n result_url = ss2[1]\n errinfo = ss2[2]\n\n if errinfo and errinfo.find(\"does not exist\") != -1:\n if 'DEBUG' in g_params and g_params['DEBUG']:\n msg = \"Failed for remote_jobid %s with errmsg %s\"%(remote_jobid, str(errinfo))\n webcom.loginfo(msg, gen_logfile)\n\n isFinish_remote = True\n\n if status == \"Finished\": # {{{\n isFinish_remote = True\n outfile_zip = f\"{tmpdir}/{remote_jobid}.zip\"\n isRetrieveSuccess = False\n myfunc.WriteFile(\"\\tFetching result for %s/seq_%d from %s \" % (\n jobid, origIndex, result_url), gen_logfile, \"a\", True)\n if myfunc.IsURLExist(result_url, timeout=5):\n try:\n myfunc.urlretrieve(result_url, outfile_zip, timeout=10)\n isRetrieveSuccess = True\n myfunc.WriteFile(f\" succeeded on node {node}\\n\", gen_logfile, \"a\", True)\n except Exception as e:\n myfunc.WriteFile(\" failed with %s\\n\"%(str(e)), gen_logfile, \"a\", True)\n pass\n if os.path.exists(outfile_zip) and isRetrieveSuccess:\n cmd = [\"unzip\", outfile_zip, \"-d\", tmpdir]\n webcom.RunCmd(cmd, gen_logfile, gen_errfile)\n rst_fetched = os.path.join(tmpdir, remote_jobid)\n if name_server.lower() == \"pconsc3\":\n rst_this_seq = rst_fetched\n elif name_server.lower() == \"boctopus2\":\n rst_this_seq = os.path.join(rst_fetched, \"seq_0\", \"seq_0\")\n rst_this_seq_parent = os.path.join(rst_fetched, \"seq_0\")\n else:\n rst_this_seq = os.path.join(rst_fetched, \"seq_0\")\n\n if os.path.islink(outpath_this_seq):\n os.unlink(outpath_this_seq)\n elif os.path.exists(outpath_this_seq):\n shutil.rmtree(outpath_this_seq)\n\n if os.path.exists(rst_this_seq) and not os.path.exists(outpath_this_seq):\n cmd = [\"mv\", \"-f\", rst_this_seq, outpath_this_seq]\n webcom.RunCmd(cmd, gen_logfile, gen_errfile)\n if name_server.lower() == \"boctopus2\":\n # move also seq.fa and time.txt for boctopus2\n file1 = os.path.join(rst_this_seq_parent, \"seq.fa\")\n file2 = os.path.join(rst_this_seq_parent, \"time.txt\")\n for f in [file1, file2]:\n if os.path.exists(f):\n try:\n shutil.move(f, outpath_this_seq)\n except:\n pass\n\n fafile_this_seq = os.path.join(outpath_this_seq, \"seq.fa\")\n if webcom.IsCheckPredictionPassed(outpath_this_seq, name_server):\n # relpace the seq.fa with original description\n myfunc.WriteFile('>%s\\n%s\\n'%(description, seq), fafile_this_seq, 'w', True)\n isSuccess = True\n\n if isSuccess:\n # delete the data on the remote server\n try:\n rtValue2 = myclient.service.deletejob(remote_jobid)\n except Exception as e:\n msg = \"Failed to deletejob(%s) on node %s with errmsg %s\"%(remote_jobid, node, str(e))\n webcom.loginfo(msg, gen_logfile)\n rtValue2 = []\n pass\n\n logmsg = \"\"\n if len(rtValue2) >= 1:\n ss2 = rtValue2[0]\n if len(ss2) >= 2:\n status = ss2[0]\n errmsg = ss2[1]\n if status == \"Succeeded\":\n logmsg = \"Successfully deleted data on %s \"\\\n \"for %s\"%(node, remote_jobid)\n else:\n logmsg = \"Failed to delete data on %s for \"\\\n \"%s\\nError message:\\n%s\\n\"%(node, remote_jobid, errmsg)\n else:\n logmsg = \"Failed to call deletejob %s via WSDL on %s\\n\"%(remote_jobid, node)\n\n # delete the downloaded temporary zip file and\n # extracted file\n if os.path.exists(outfile_zip):\n os.remove(outfile_zip)\n if os.path.exists(rst_fetched):\n shutil.rmtree(rst_fetched)\n\n # create or update the md5 cache\n if name_server.lower() == \"prodres\" and query_para != {}:\n md5_key = hashlib.md5((seq+str(query_para)).encode('utf-8')).hexdigest()\n else:\n md5_key = hashlib.md5(seq.encode('utf-8')).hexdigest()\n subfoldername = md5_key[:2]\n md5_subfolder = \"%s/%s\"%(path_cache, subfoldername)\n cachedir = \"%s/%s/%s\"%(path_cache, subfoldername, md5_key)\n\n # copy the zipped folder to the cache path\n origpath = os.getcwd()\n os.chdir(outpath_result)\n shutil.copytree(\"seq_%d\"%(origIndex), md5_key)\n cmd = [\"zip\", \"-rq\", \"%s.zip\"%(md5_key), md5_key]\n webcom.RunCmd(cmd, runjob_logfile, runjob_errfile)\n if not os.path.exists(md5_subfolder):\n os.makedirs(md5_subfolder)\n shutil.move(\"%s.zip\"%(md5_key), \"%s.zip\"%(cachedir))\n shutil.rmtree(md5_key) # delete the temp folder named as md5 hash\n os.chdir(origpath)\n\n # Add the finished date to the database\n date_str = time.strftime(g_params['FORMAT_DATETIME'])\n MAX_TRY_INSERT_DB = 3\n cnttry = 0\n while cnttry < MAX_TRY_INSERT_DB:\n t_rv = webcom.InsertFinishDateToDB(date_str, md5_key, seq, finished_date_db)\n if t_rv == 0:\n break\n cnttry += 1\n time.sleep(random.random()/1.0)\n\n# }}}\n elif status in [\"Failed\", \"None\"]:\n # the job is failed for this sequence, try to resubmit\n isFinish_remote = True\n if 'DEBUG' in g_params and g_params['DEBUG']:\n webcom.loginfo(\"DEBUG: %s, status = %s\\n\"%(remote_jobid, status), gen_logfile)\n\n if status != \"Wait\" and not os.path.exists(starttagfile):\n webcom.WriteDateTimeTagFile(starttagfile, runjob_logfile, runjob_errfile)\n\n if isSuccess: # {{{\n time_now = time.time()\n runtime1 = time_now - submit_time_epoch # in seconds\n timefile = os.path.join(outpath_this_seq, \"time.txt\")\n runtime = webcom.ReadRuntimeFromFile(timefile, default_runtime=runtime1)\n info_finish = webcom.GetInfoFinish(\n name_server, outpath_this_seq,\n origIndex, len(seq), description,\n source_result=\"newrun\", runtime=runtime)\n finished_info_list.append(\"\\t\".join(info_finish))\n finished_idx_list.append(str(origIndex))\n # }}}\n\n # if the job is finished on the remote but the prediction is failed,\n # try resubmit a few times and if all failed, add the origIndex to the\n # failed_idx_file\n if isFinish_remote and not isSuccess:\n cnttry = 1\n try:\n cnttry = cntTryDict[int(origIndex)]\n except KeyError:\n cnttry = 1\n if cnttry < g_params['MAX_RESUBMIT']:\n resubmit_idx_list.append(str(origIndex))\n cntTryDict[int(origIndex)] = cnttry+1\n else:\n failed_idx_list.append(str(origIndex))\n\n if not isFinish_remote:\n time_in_remote_queue = time.time() - submit_time_epoch\n # for jobs queued in the remote queue more than one day (but not\n # running) delete it and try to resubmit it. This solved the\n # problem of dead jobs in the remote server due to server\n # rebooting)\n if (\n status != \"Running\"\n and status != \"\"\n and time_in_remote_queue > g_params['MAX_TIME_IN_REMOTE_QUEUE']):\n # delete the remote job on the remote server\n try:\n rtValue2 = myclient.service.deletejob(remote_jobid)\n except Exception as e:\n webcom.loginfo(\"Failed to run myclient.service.deletejob(%s) on node %s with msg %s\"%(remote_jobid, node, str(e)), gen_logfile)\n rtValue2 = []\n pass\n else:\n keep_queueline_list.append(line)\n# }}}\n # Finally, write log files\n finished_idx_list = list(set(finished_idx_list))\n failed_idx_list = list(set(failed_idx_list))\n resubmit_idx_list = list(set(resubmit_idx_list))\n\n if len(finished_info_list) > 0:\n myfunc.WriteFile(\"\\n\".join(finished_info_list)+\"\\n\", finished_seq_file,\n \"a\", True)\n if len(finished_idx_list) > 0:\n myfunc.WriteFile(\"\\n\".join(finished_idx_list)+\"\\n\", finished_idx_file,\n \"a\", True)\n if len(failed_idx_list) > 0:\n myfunc.WriteFile(\"\\n\".join(failed_idx_list)+\"\\n\", failed_idx_file, \"a\",\n True)\n if len(resubmit_idx_list) > 0:\n myfunc.WriteFile(\"\\n\".join(resubmit_idx_list)+\"\\n\", torun_idx_file,\n \"a\", True)\n\n if len(keep_queueline_list) > 0:\n keep_queueline_list = list(set(keep_queueline_list))\n myfunc.WriteFile(\"\\n\".join(keep_queueline_list)+\"\\n\",\n remotequeue_idx_file, \"w\", True)\n else:\n myfunc.WriteFile(\"\", remotequeue_idx_file, \"w\", True)\n\n with open(cnttry_idx_file, 'w') as fpout:\n json.dump(cntTryDict, fpout)\n\n return 0", "def result():\n # Retrieve JSON parameters data.\n data = request.get_json() or {}\n data.update(dict(request.values))\n tid = data.get(\"tid\")\n if not tid:\n raise abort(400, \"missing 'tid' data\")\n\n # Get the result (if exists and finished).\n result = tasks.process_message.AsyncResult(tid)\n # Return status and result if available.\n resp = {\n \"status\": result.status,\n \"result\": None,\n }\n if result.ready():\n resp[\"result\"] = result.get()\n return resp", "def _fetch_remote(self, dqueue, server, url, timeout):\n try:\n req = requests.get(url, timeout=timeout)\n if req.status_code == 200:\n try:\n resp_params = parse_sync_response(req.text)\n dqueue.put({'server': server, 'params': resp_params})\n except ValueError as err:\n logger.error('Failed to parse response of %s: %s', server, err)\n else:\n logger.warning('Recieved status code %s for %s', req.status_code, url)\n except Exception as err:\n logger.warning('Failed to retrieve %s: %s', url, err)", "async def _perform_get_results(self, login_token, result_token):\n data = {\"resultSetToken\": result_token, \"token\": login_token}\n return await self._perform_request(\"get-results\", data, lambda r: r.json())", "def get_result(self):\n\t\treturn handle_to_object(call_sdk_function('PrlJob_GetResult', self.handle))", "def on_get(self, req, resp, task_id):\n task_result = AsyncResult(task_id)\n result = {'status': task_result.status, 'result': task_result.result}\n resp.status = falcon.HTTP_200\n resp.body = json.dumps(result)", "def view_result(job_id):\n job = fetch_data.AsyncResult(job_id, app=app)\n if job.successful():\n result = job.result\n return jsonify({'job_id': job_id, 'result': job.result})\n else:\n result = 'job was not finished or was not successful'\n return jsonify({'job_id': job_id, 'result': result})", "def get_internal_result_from_server(self, server_name, timeout=4):\n start_time = time()\n while time() < start_time + timeout:\n for i in range(len(self.internal_result_queue)):\n if self.internal_result_queue[i].processed_by == server_name:\n return_result = copy.deepcopy(self.internal_result_queue[i])\n del self.internal_result_queue[i]\n return return_result", "def get_result(self, timeout):\n\n backend = self.parallel._backend\n\n if backend.supports_retrieve_callback:\n # We assume that the result has already been retrieved by the\n # callback thread, and is stored internally. It's just waiting to\n # be returned.\n return self._return_or_raise()\n\n # For other backends, the main thread needs to run the retrieval step.\n try:\n if backend.supports_timeout:\n result = self.job.get(timeout=timeout)\n else:\n result = self.job.get()\n outcome = dict(result=result, status=TASK_DONE)\n except BaseException as e:\n outcome = dict(result=e, status=TASK_ERROR)\n self._register_outcome(outcome)\n\n return self._return_or_raise()", "def request_result(job_id):\n result = _database_operations.get_results(job_id, Session())\n if result is None:\n flask.abort(404)\n else:\n return result", "def send_rpc_result(req, result):", "async def get_result(request):\n job_id = request.match_info['job_id']\n r = redis.Redis(\n host=os.environ['REDIS_HOST'],\n port=6379,\n decode_responses=True,\n )\n if not r.exists(job_id):\n return web.HTTPNotFound(text='Results are unavailable.')\n output_id = r.get(job_id)\n filename = output_id + '.json'\n try:\n with open(os.path.join(CACHE_DIR, filename), 'r') as f:\n response = json.load(f)\n except FileNotFoundError:\n # Redis is out-of-sync with file system. Remove the offending key.\n r.delete(job_id)\n return web.HTTPNotFound(text='Results are unavailable.')\n return web.json_response(response, dumps=functools.partial(json.dumps, indent=4))", "def _r_send_result(self, response, protocol):\n #print(\"Send result: %s\" % result)\n protocol.send_message(response)", "def remote_getResult(i=None):", "async def server_call_async(method, server, timeout=DEFAULT_TIMEOUT, verify_ssl=True, **parameters):\n if method is None:\n raise Exception(\"A method name must be specified\")\n if server is None:\n raise Exception(\"A server (eg. my3.geotab.com) must be specified\")\n parameters = api.process_parameters(parameters)\n return await _query(server, method, parameters, timeout=timeout, verify_ssl=verify_ssl)", "def asyncpoll(self,server_,port_,token_):\n if isinstance(server_,unicode):\n server_ = server_.encode(\"utf-8\",errors=\"replace\")\n if isinstance(port_,unicode):\n port_ = port_.encode(\"utf-8\",errors=\"replace\")\n if isinstance(token_,unicode):\n token_ = token_.encode(\"utf-8\",errors=\"replace\")\n respavailable_ = ctypes.c_int32()\n resp_ = ctypes.c_int32()\n trm_ = ctypes.c_int32()\n res = __library__.MSK_XX_asyncpoll(self.__nativep,server_,port_,token_,ctypes.byref(respavailable_),ctypes.byref(resp_),ctypes.byref(trm_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n respavailable_ = respavailable_.value\n _respavailable_return_value = respavailable_\n _resp_return_value = rescode(resp_.value)\n _trm_return_value = rescode(trm_.value)\n return (_respavailable_return_value,_resp_return_value,_trm_return_value)", "def get(self):\n\n response = PluginHelper.request_get(params=self.request.arguments)\n if (\n response.status_code == 200\n and response.json()[\"status\"] == \"ok\"\n ):\n result_json = {\n \"results\": response.json()[\"results\"],\n }\n else:\n raise exc.BadRequest(\"Bad host query: {}\".format(\n self.request.arguments\n ))\n\n self.success(result_json)", "async def get_task_result(task_id: TaskId):", "def _retrieve_result(self, out):\n try:\n result = self.parallel._backend.retrieve_result_callback(out)\n outcome = dict(status=TASK_DONE, result=result)\n except BaseException as e:\n # Avoid keeping references to parallel in the error.\n e.__traceback__ = None\n outcome = dict(result=e, status=TASK_ERROR)\n\n self._register_outcome(outcome)\n return outcome['status'] != TASK_ERROR", "def get(self, id):\n result_task = AsyncResult(id = id, app = backapp)\n state = result_task.state\n\n if state == states.STARTED:\n return { 'id':result_task.task_id, 'status': state }, 200\n # task still pending or unknown\n elif state == states.PENDING:\n return { 'id':result_task.task_id, 'status': state }, 200\n elif state == states.SUCCESS:\n return { 'id':result_task.task_id, 'status': state }, 303, {'Location': api.url_for(MathJobResult,id=result_task.task_id)}\n else:\n return error(result_task)", "def get_async_response(self,message): \n index = self.async_query_buffer.index(message)\n #print('**********')\n #print ('requesting ' + message + ' at index ' + str(index))\n b = True\n try:\n response = self.async_reply_buffer[index]\n if response.endswith('\\n'):\n response = self.async_reply_buffer.pop(index)\n else:\n b = False\n response = 'EMPTY'\n except IndexError: \n #print('response not available yet!!')\n response = 'EMPTY'\n b = False\n if b: \n #print('got reply:')\n #print(response)\n query = self.async_query_buffer.pop(index)\n #print('for query:')\n #print(query)\n #print('Buffers:')\n #print(self.async_reply_buffer)\n #print(self.async_query_buffer)\n #print('_________________')\n\n return response", "def asyncpoll(self,server_,port_,token_): # 3\n res,resargs = self.__obj.asyncpoll(server_,port_,token_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _respavailable_return_value,_resp_return_value,_trm_return_value = resargs\n _trm_return_value = rescode(_trm_return_value)\n _resp_return_value = rescode(_resp_return_value)\n return _respavailable_return_value,_resp_return_value,_trm_return_value", "def get(self):\n if not self.finished():\n self.wait()\n return self._result", "def _get_result(self):\r\n \r\n return self._result", "def get_koji_task_result(task_id, remote, ctx):\n py_cmd = ('import koji; '\n 'hub = koji.ClientSession(\"{kojihub_url}\"); '\n 'print(hub.getTaskResult({task_id}))')\n py_cmd = py_cmd.format(\n task_id=task_id,\n kojihub_url=config.kojihub_url\n )\n log.info(\"Querying kojihub for the result of task {0}\".format(task_id))\n task_result = _run_python_command(py_cmd, remote, ctx)\n return task_result", "def get_response(self, url):\n\n conn = http.client.HTTPConnection('localhost:8080')\n conn.request('GET', url)\n\n response = conn.getresponse()\n self.assertEqual(200, response.getcode())\n\n conn.close()\n\n return response", "def on_get(self, req, resp, task_id):\n task = celery_app.AsyncResult(task_id)\n\n resp.body = json.dumps(\n {'status': task.status, 'result': str(task.result)})\n resp.status = falcon.HTTP_200", "def get_response(command):\n connection = get_client()\n\n connection.send(command)\n\n data = connection.recv()\n connection.close()\n\n return data", "def get_response(self, request, decision, ext_port):\n if decision:\n return decision\n\n self.send_request(request, ext_port)\n\n return self.receive_response()" ]

[ "0.82163835", "0.6389818", "0.63313067", "0.60418195", "0.6038077", "0.5966368", "0.5915231", "0.5874337", "0.57946885", "0.57472014", "0.568589", "0.5681426", "0.5653823", "0.5595842", "0.5568461", "0.5545757", "0.5533846", "0.55289096", "0.55240965", "0.5513361", "0.5510967", "0.55078787", "0.5493625", "0.5481259", "0.5477579", "0.5451841", "0.5437847", "0.5411943", "0.5399875", "0.53905857" ]

[ "def is_variadic(self):\n if self.is_function() and self.args:\n return self.args[-1].endswith(\"...\")\n return False", "def check_args(args):\n for arg in vars(args):\n if getattr(args, arg):\n return True\n return False", "def takes_multiple_arguments(func):\n if func in ONE_ARITY_BUILTINS:\n return False\n elif func in MULTI_ARITY_BUILTINS:\n return True\n\n try:\n spec = getargspec(func)\n except:\n return False\n\n try:\n is_constructor = spec.args[0] == 'self' and isinstance(func, type)\n except:\n is_constructor = False\n\n if spec.varargs:\n return True\n\n if spec.defaults is None:\n return len(spec.args) - is_constructor != 1\n return len(spec.args) - len(spec.defaults) - is_constructor > 1", "def contains(self, *__args): # real signature unknown; restored from __doc__ with multiple overloads\r\n return False", "def contains(self, *__args): # real signature unknown; restored from __doc__ with multiple overloads\r\n return False", "def is_args_in_task(func):\n arg = inspect.getargs(func.func_code)\n return arg.varargs is not None", "def is_valide(self):\n if self.arguments:\n return True\n else:\n return False", "def is_function_variadic(self):\r\n assert self.kind == TypeKind.FUNCTIONPROTO\r\n\r\n return conf.lib.clang_isFunctionTypeVariadic(self)", "def isTrue(*args, **kwargs)->None:\n pass", "def _is_args_added(parser: CoreParser, custom_args: List[str]) -> bool:\n namespace, _ = parser.parser.parse_known_args()\n namespace_args = vars(namespace).keys()\n\n for arg in custom_args:\n if arg not in namespace_args:\n return False\n\n return True", "def __contains__(self, arg):\n # All arguments should have a default value of some sort\n if arg not in self.args:\n raise AttributeError(\n \"arg {} doesn't exist on {}\".format(arg, self.args)\n )\n # If the value is the sentinel then the argument was not provided AND\n # there is no default\n if getattr(self.args, arg) is NoArgument:\n return False\n\n if isinstance(getattr(self.args, arg), list):\n raise AttributeError(\n \"tried to check for presence of arg {} on {}, which is a \"\n \"list\".format(arg, self.args)\n )\n\n return True", "def has_args(iterable, args):\n\n try:\n return all(x in iterable for x in args)\n\n except TypeError:\n return False", "def valid_args(args):\n return args is not None and len(args) > 0", "def is_multi_commands(args: list) -> bool:\n for arg in args:\n if not isinstance(arg, list):\n return False\n # all elements must be lists\n return True", "def has_type_var(annotation) -> bool:\n return any(\n is_type_var(arg) or has_type_var(arg)\n for arg in getattr(annotation, \"__args__\", [])\n )", "def is_path_kwargs(self) -> bool:\n for key in self.kwargs.keys():\n return isinstance(key, tuple)\n return False", "def is_valid_arg(self, muts, arg):\n for mut in muts:\n if arg in mut.args():\n return True\n\n return False", "def call_has_args(*args, **kwargs) -> CallHasArgs:\n return CallHasArgs(*args, **kwargs)", "def has(self, *args):\n return _ida_hexrays.qvector_carg_t_has(self, *args)", "def __call__(self, *args):\n if self.fall or not args:\n return True\n elif self.value in args: # changed for v1.5\n self.fall = True\n return True\n else:\n return False", "def is_command_ancillary(args):\n # pylint: disable=bad-continuation\n if (\n # skip the parent check and only\n # determine if the parameter is present\n is_valid_executes(args, skip=True)\n ):\n return True\n return False", "def _can_perform_call(self, node, args, keywords):\n return (\n getattr(node, \"starargs\", None) is None\n and getattr(node, \"kwargs\", None) is None\n and all(isinstance(arg, KnownValue) for arg in args)\n and all(isinstance(arg, KnownValue) for _, arg in keywords)\n )", "def suitable_for(self, values, method):\n if self.methods is not None and method not in self.methods:\n return False\n\n valueset = set(values)\n\n for key in self.arguments:\n if key not in values:\n return False\n\n if self.arguments.issubset(valueset):\n return True\n\n return True", "def _true(*args):\n # pylint:disable=unused-argument\n return True", "def T(*args):\n return True", "def __is_args_new(self, *args, **kwargs):\n # if input size is different\n if len(args) != len(self.__cached_args) or len(kwargs) != len(self.__cached_kwargs):\n return True\n # check args and kwargs\n for a, ca in zip(args, self.__cached_args):\n if a != (ca() if isinstance(ca, wr.ReferenceType) else ca):\n return True\n for k in kwargs:\n if k not in self.__cached_kwargs:\n return True\n a = self.__cached_kwargs[k]\n if kwargs[k] != (a() if isinstance(a, wr.ReferenceType) else a):\n return True\n return False", "def is_call_arg_of(self, *args):\n return _ida_hexrays.cexpr_t_is_call_arg_of(self, *args)", "def accepts_type(self, *args):\n return _ida_hexrays.lvar_t_accepts_type(self, *args)", "def _check_args(self, args):\n if len(args) == 0:\n print(\"No parameters provided.\")\n return False\n else:\n return True", "def is_function(self):\n return self.args is not None" ]

[ "0.7789966", "0.6522471", "0.6384409", "0.6294994", "0.6294994", "0.62792814", "0.6124202", "0.6067434", "0.60662395", "0.6052541", "0.60394627", "0.60286164", "0.59856653", "0.59645915", "0.59602636", "0.5950267", "0.59254897", "0.5895693", "0.5832743", "0.5820968", "0.5788229", "0.57526684", "0.57464844", "0.573667", "0.57365954", "0.5720808", "0.57020617", "0.5679098", "0.56662494", "0.56641406" ]

[ "def is_number(self) -> bool:\n return False", "def isnumeric(self):\n return isnumeric(self)", "def is_numerable(self):\n return (self.is_unknown or self.is_byte or self.is_word\n or self.is_dword or self.is_qword)", "def is_number(self, value):\n if isinstance(value, (int, float, long, complex)): # noqa\n return True\n return False", "def is_numeric(self) -> bool:\n return False", "def is_numeric (self) :\n\n return self.__isnumeric__", "def ISNUMBER(value):\n return isinstance(value, numbers.Number)", "def isNumber(self):\n return _libsbml.ASTNode_isNumber(self)", "def is_number(self,val):\n try:\n float(val)\n return True\n except ValueError:\n return False", "def _is_number(value):\n try:\n float(value)\n return True\n except (TypeError, ValueError):\n return False", "def isNumeric(obj):\n # type: (Any) -> bool\n return isinstance(obj, numbers.Number)", "def is_numeric_type(self):\n row_type = self.get_type()\n is_numeric = row_type in (\n 'hidden decimal',\n 'decimal',\n 'hidden integer',\n 'integer',\n 'int',\n 'range',\n )\n return is_numeric", "def is_number(G):\n return True", "def is_number(value, allow_bool=False):\n if isinstance(value, bool):\n return allow_bool\n return isinstance(value, _Number)", "def is_numeric(number):\n\n if isinstance(number, bool):\n return False\n elif isinstance(number, int) or isinstance(number, float):\n return True\n else:\n return False", "def _is_number(self, symbol):\n if symbol.type == self.scanner.NUMBER:\n return True\n else:\n return False", "def isNumeric(obj):\n return isinstance(obj, (int, float, bool))", "def _usable_number(self, num):\n real = isinstance(num, numbers.Real)\n non_nan = not numpy.isnan(num)\n non_bool = not (num is True or num is False)\n return real and non_nan and non_bool", "def is_number(n):\n return isinstance(n, (int, float))", "def has_numeric_type(obj: _std_typing.Any) -> bool:\n return (not has_vector_type(obj)) and (not has_string_type(obj))", "def is_numberish(G):\n return True", "def is_numeric(obj):\n return isinstance(obj, (int, float, complex))", "def is_number(value):\n\n return isinstance(value, (int, long, float))", "def is_number(n):\n\ttry:\n\t\tfloat(n)\n\t\treturn True\n\texcept ValueError:\n\t\treturn False", "def _is_number(data):\n return len(data) and np.issubdtype(_to_ndarray(data).dtype, np.number)", "def is_numeric(x):\n if isinstance(x, NUMBER_TYPES):\n return True\n elif isinstance(x, np.ndarray):\n return x.dtype.type not in NUMPY_NON_TYPES\n return False", "def is_number(x):\n if isinstance(x, (int, float)):\n return True\n else:\n return False", "def is_numeric(space, w_obj):\n if w_obj.tp in [space.tp_float, space.tp_int]:\n return space.w_True\n if w_obj.tp == space.tp_str:\n return space.newbool(w_obj.is_really_valid_number(space))\n return space.w_False", "def could_be_number(val):\n if val == None:\n return False\n\n if isinstance(val, (float, int, long)):\n return True\n\n # allow coercion from str\n if isinstance(val, (str, unicode)):\n try:\n n = float(val)\n if not isinstance(n, float):\n raise ValueError\n else:\n return True\n except:\n return False\n\n #otherwise\n return False", "def is_real_number_type(self):\n raise exceptions.NotImplementedError()" ]

[ "0.7953751", "0.7412868", "0.7394168", "0.73898447", "0.72582585", "0.72320116", "0.7230378", "0.71296614", "0.71097714", "0.7034134", "0.70097035", "0.6974943", "0.69388425", "0.6865268", "0.68128484", "0.6790667", "0.6776975", "0.67644984", "0.66986984", "0.66595286", "0.6627337", "0.66240263", "0.66107243", "0.6572035", "0.6565456", "0.6540059", "0.65380543", "0.651633", "0.6513666", "0.6503694" ]

[ "def is_real(self):\n return all([isinstance(dim, Real) for dim in self.dimensions])", "def isReal(self):\n return _libsbml.ASTNode_isReal(self)", "def is_real(self):\r\n return self._imag.is_zero()", "def is_real(self) -> bool:\n return not any(self.v)", "def _is_real(symbol):\n return isa(symbol, float) or is_int(symbol)", "def _is_real_like(input):\n if type(input) is float:\n return True\n if isinstance(input, _ScalarConstant):\n if input.dtype in _float_types:\n return True\n return False", "def is_numerable(self):\n return (self.is_unknown or self.is_byte or self.is_word\n or self.is_dword or self.is_qword)", "def is_number(self) -> bool:\n return False", "def is_real(self):\n\n return self.purpose == 'real'", "def isnumeric(self):\n return isnumeric(self)", "def get_real_type(self):\n import numpy\n return numpy.float64", "def is_real(self) -> np.ndarray:\n return np.all(np.isclose(self.v, np.zeros_like(self.v)), axis=1)", "def is_numeric (self) :\n\n return self.__isnumeric__", "def is_numeric(obj):\n return isinstance(obj, (int, float, complex))", "def isrealnum(variable):\n return bool(math.isfinite(variable))", "def _usable_number(self, num):\n real = isinstance(num, numbers.Real)\n non_nan = not numpy.isnan(num)\n non_bool = not (num is True or num is False)\n return real and non_nan and non_bool", "def is_number_type(self):\n raise exceptions.NotImplementedError()", "def is_numeric_type(self):\n row_type = self.get_type()\n is_numeric = row_type in (\n 'hidden decimal',\n 'decimal',\n 'hidden integer',\n 'integer',\n 'int',\n 'range',\n )\n return is_numeric", "def is_numeric(self) -> bool:\n return False", "def isNumeric(obj):\n # type: (Any) -> bool\n return isinstance(obj, numbers.Number)", "def is_number(self, value):\n if isinstance(value, (int, float, long, complex)): # noqa\n return True\n return False", "def is_number(self,val):\n try:\n float(val)\n return True\n except ValueError:\n return False", "def has_numeric_type(obj: _std_typing.Any) -> bool:\n return (not has_vector_type(obj)) and (not has_string_type(obj))", "def isNumeric(obj):\n return isinstance(obj, (int, float, bool))", "def __eq__(self, value):\n return self.real != value", "def is_comparable(self):\n is_extended_real = self.is_extended_real\n if is_extended_real is False:\n return False\n if not self.is_number:\n return False\n # don't re-eval numbers that are already evaluated since\n # this will create spurious precision\n n, i = [p.evalf(2) if not p.is_Number else p\n for p in self.as_real_imag()]\n if not (i.is_Number and n.is_Number):\n return False\n if i:\n # if _prec = 1 we can't decide and if not,\n # the answer is False because numbers with\n # imaginary parts can't be compared\n # so return False\n return False\n else:\n return n._prec != 1", "def __ne__(self, value):\n return self.real == value", "def isRadian(self):\n return _libsbml.Unit_isRadian(self)", "def of_type(self, a):\n return type(a) == type(self.one)", "def _is_primitive(val):\n\n prims = [int, float, str, bool]\n for prim in prims:\n if isinstance(val, prim):\n return True\n return False" ]

[ "0.75589144", "0.7233921", "0.6978747", "0.681329", "0.67448235", "0.66748357", "0.66022563", "0.65728545", "0.65497756", "0.653825", "0.6438274", "0.6377211", "0.6347165", "0.62609124", "0.62475", "0.6229639", "0.62187284", "0.6214366", "0.6165807", "0.5998228", "0.59811014", "0.5938168", "0.5907508", "0.5889334", "0.5855985", "0.5829926", "0.5780416", "0.5774637", "0.57714957", "0.57584006" ]

[ "def isinstance_blender_object(self, b_obj):\n # lame and slow, but functional\n return b_obj in Blender.Object.Get()", "def verify_type(self, obj):\n return isinstance(obj, self.type_)", "def object_type_present(self, object_type):\n # Check input.\n if not isinstance(object_type, str):\n raise TypeError('object_type must be a string.')\n\n # Lookup object type and return.\n return object_type in self.model_map['object']", "def _isinstance(self, obj, raise_error=True):\n rv = isinstance(obj, self.__model__)\n if not rv and raise_error:\n raise ValueError('%s is not of type %s' % (obj, self.__model__))\n return rv", "def isNodeType(self, t):\n return isinstance(self, t)", "def is_object(obj):\n return (isinstance(obj, object) and\n type(obj) is not type and\n type(obj) is not types.FunctionType)", "def is_type(obj):\n return type(obj) is type or type(obj) is types.ClassType", "def object_type(self):\n return self._object_type", "def object_type(self):\n return self._object_type", "def object_type(self):\n return self._object_type", "def object_type(self):\n return self._object_type", "def object_type(self):\n return self._object_type", "def is_object(space, w_obj):\n return space.wrap(space.is_object(w_obj))", "def is_object(value, class_name):\n\n return isinstance(value, getattr(schema, class_name))", "def is_type(obj: Any) -> bool:\n return type(obj).__name__ == \"type\"", "def _isinstance(self, instance, raise_error=True):\n\n if isinstance(instance, self.__model__):\n return True\n elif raise_error:\n raise ValueError('{} is not of type {}.'.format(\n instance, self.__model__,\n ))\n else:\n return False", "def object_type(self):\n if not self.Flags & gdef.ACE_OBJECT_TYPE_PRESENT:\n return None\n return self.ObjectType", "def is_a(self, t):\n return isinstance(self._, t)", "def object_type(self) -> Optional[str]:\n return pulumi.get(self, \"object_type\")", "def object_type(self) -> Optional[str]:\n return pulumi.get(self, \"object_type\")", "def _is_vim_object(self, module):\n return isinstance(module, vim.Vim)", "def _valid_typable_object(ui_object, platform=Platform.ANDROID):\n if platform == Platform.ANDROID:\n return ui_object.obj_type in _TYPABLE_OBJECT_DESC.keys()\n else:\n assert False, 'Wrong Platform'", "def __bool__(self):\n return bool(self.obj)", "def applies(cls, obj):\n return type(obj) in cls.types", "def is_instance(self, thing: Any) -> bool:\n return isinstance(thing, self.underlying)", "def IsObject(object_id):\n return rhutil.coercerhinoobject(object_id, True, False) is not None", "def _is_typing_object(type_object):\n return type_object.__module__ == \"typing\"", "def is_object(self, name: str) -> bool:\r\n return os.path.exists(self._path_for_pickle(name))", "def match(self, cls):\n return isinstance(self, cls)", "def object_type(self) -> str:\n return self._object_type" ]

[ "0.7290644", "0.6918381", "0.6855239", "0.681674", "0.6812159", "0.66543907", "0.664714", "0.65621924", "0.65621924", "0.65621924", "0.65621924", "0.65621924", "0.6510388", "0.64790374", "0.64409184", "0.6381249", "0.63387465", "0.6333009", "0.62965983", "0.62965983", "0.62948096", "0.6294392", "0.62943906", "0.629393", "0.62864596", "0.6280199", "0.62784463", "0.6262339", "0.6201272", "0.6181189" ]

[ "def _is_any(typeval: Type) -> bool:\n return typeval in _ANY", "def any(self) -> bool:", "def is_a(self, t):\n return isinstance(self._, t)", "def of_type(self, a):\n return type(a) == type(self.one)", "def __bool__(self):\n return bool(self.obj)", "def any(x) -> bool:\n pass", "def __eq__(self, other: Any) -> bool:\n return isinstance(other, Nothing)", "def verify_type(self, obj):\n return isinstance(obj, self.type_)", "def isAny(self,test):\n for x in np.nditer(self.t, op_flags=['readonly']):\n if op(x):\n return True\n return False", "def is_simple(self) -> bool:\n return self.data in ('int', 'bool', 'float', 'str')", "def bool(self, obj):\n return True", "def bool(self, obj):\n return True", "def any(self) -> int:\n ...", "def _check_any(self) -> PossibleResult[T]:\n if _is_any(self.constructor):\n return self.obj # type: ignore\n return NO_RESULT", "def Any(cls):\n class Any(cls):\n def __eq__(self, other):\n return isinstance(other, cls)\n return Any()", "def __bool__(self):\n return any(p for p in self)", "def is_real(self) -> bool:\n return not any(self.v)", "def __bool__(self):\n return self is TRUE", "def is_bool(self):\n return False", "def is_instance(self, thing: Any) -> bool:\n return isinstance(thing, self.underlying)", "def is_type(obj: Any) -> bool:\n return type(obj).__name__ == \"type\"", "def any(self):\n return self.opt.AnyNumber(self._trg)", "def match(cls, kind: 'dsl.Any') -> bool:\n return isinstance(kind, cls)", "def __bool__(self):\n return self.__nonzero__()", "def applies(cls, obj):\n return type(obj) in cls.types", "def _isinstance(cls, x):\n return isinstance(x, cls.PYTHON_TYPE_CHECK)", "def any(self):\n return self.__node_a", "def __bool__(self):\r\n raise TypeError('cannot use secure type in Boolean expressions')", "def __bool__(self):\n\n return not self.is_empty()", "def __eq__(self, other: Any) -> bool:\n if not isinstance(other, type(self)):\n return NotImplemented\n return True" ]

[ "0.7405247", "0.6943971", "0.65161765", "0.6286378", "0.6231235", "0.6206884", "0.61998576", "0.61801803", "0.6145843", "0.61362964", "0.6085742", "0.6085742", "0.60733044", "0.60606396", "0.6040771", "0.6012931", "0.6012655", "0.5969699", "0.59533924", "0.5941362", "0.5928864", "0.5918363", "0.58871806", "0.5875387", "0.5875182", "0.58577967", "0.58559126", "0.58342254", "0.58290863", "0.5805151" ]

[ "def is_interface(self, file, i):\n\n # Get doc_str for class\n doc_str = self.get_doc_str(file, i)\n\n # Check if line specifies interface\n class_type = None\n\n # Iterate over lines in docstring\n for line in doc_str:\n\n # Search for string match \"class type: interface\"\n if \"class type: interface\" in line:\n # Set class type to interface if found\n class_type = \"interface\"\n\n # If line matches class definition and class_type is interface\n # Then return True, else False\n if self.is_cls(file, i) and class_type == \"interface\":\n return True\n return False", "def interfaceType(self): # pylint: disable=invalid-name\n return self.interface_type", "def __implements__(component, interface):\n return issubclass(component, interface)", "def implements(cls, interface):\n if isinstance(interface, type):\n interface = interface.__namespace__\n if interface in cls.__interfaces__:\n return True\n for implemented in cls.__interfaces__:\n target = PROXY_TYPES.get(implemented)\n if target is not None:\n if target.implements(interface):\n return True\n return False", "def _get_interface_type(self):\n return self.__interface_type", "def has_interfaces(node):\n if \"interfaces\" in node and len(node[\"interfaces\"]):\n return True\n else:\n return False", "def isService(self, serviceInterface: java.lang.Class) -> bool:\n ...", "def is_callback_interface_type(self):\n raise exceptions.NotImplementedError()", "def interface(self) -> type:\n return self.get_interface()", "def interface_class() -> Type[Interface]:\n raise NotImplementedError # pragma: no cover", "def is_annotated_type(self):\n raise exceptions.NotImplementedError()", "def is_integer_type(self):\n raise exceptions.NotImplementedError()", "def getIsType(self):\n return _libsbml.MultiCompartmentPlugin_getIsType(self)", "def hasUnoInterface( oObject, cInterfaceName ):\n\n # Get the Introspection service.\n oIntrospection = createUnoService( \"com.sun.star.beans.Introspection\" )\n\n # Now inspect the object to learn about it. \n oObjInfo = oIntrospection.inspect( oObject )\n \n # Obtain an array describing all methods of the object.\n oMethods = oObjInfo.getMethods( uno.getConstantByName( \"com.sun.star.beans.MethodConcept.ALL\" ) )\n # Now look at every method.\n for oMethod in oMethods:\n # Check the method's interface to see if\n # these aren't the droids you're looking for.\n cMethodInterfaceName = oMethod.getDeclaringClass().getName()\n if cMethodInterfaceName == cInterfaceName:\n return True\n return False", "def hasUnoInterface( oObject, cInterfaceName ):\n\n # Get the Introspection service.\n oIntrospection = createUnoService( \"com.sun.star.beans.Introspection\" )\n\n # Now inspect the object to learn about it.\n oObjInfo = oIntrospection.inspect( oObject )\n\n # Obtain an array describing all methods of the object.\n oMethods = oObjInfo.getMethods( uno.getConstantByName( \"com.sun.star.beans.MethodConcept.ALL\" ) )\n # Now look at every method.\n for oMethod in oMethods:\n # Check the method's interface to see if\n # these aren't the droids you're looking for.\n cMethodInterfaceName = oMethod.getDeclaringClass().getName()\n if cMethodInterfaceName == cInterfaceName:\n return True\n return False", "def has_type(self, item_type):\n raise NotImplementedError()", "def is_trait(schema_obj):\n\n return isinstance(schema_obj, schema.Trait)", "def is_enumeration_type(self):\n raise exceptions.NotImplementedError()", "def is_boolean_type(self):\n raise exceptions.NotImplementedError()", "def is_implemented(cls):\n return True", "def is_implemented(cls):\n return True", "def is_implemented(cls):\n return True", "def is_object_type(self):\n raise exceptions.NotImplementedError()", "def assertImplements(self, obj, interface):\n self.assertTrue(interface.providedBy(interface(obj, None)))", "def _isinstance(self, instance, raise_error=True):\n\n if isinstance(instance, self.__model__):\n return True\n elif raise_error:\n raise ValueError('{} is not of type {}.'.format(\n instance, self.__model__,\n ))\n else:\n return False", "def test_implementsInterfaces(self):\n self.assertTrue(IEvent.providedBy(self.obj))\n self.assertTrue(IEventRecurrence.providedBy(self.obj))\n self.assertTrue(IATEvent.providedBy(self.obj))\n self.assertTrue(IATEventRecurrence.providedBy(self.obj))\n\n self.assertTrue(IATEvent_ATCT.providedBy(self.obj))\n self.assertTrue(verifyObject(IATEvent_ATCT, self.obj))", "def test_implementsInterfaces(self):\n self.assertTrue(IEvent.providedBy(self.obj))\n self.assertTrue(IEventRecurrence.providedBy(self.obj))\n self.assertTrue(IATEvent.providedBy(self.obj))\n self.assertTrue(IATEventRecurrence.providedBy(self.obj))\n\n self.assertTrue(IATEvent_ATCT.providedBy(self.obj))\n self.assertTrue(verifyObject(IATEvent_ATCT, self.obj))", "def interfacetype(self, interfacetype):\n\n self._interfacetype = interfacetype", "def container_interface(self):\r\n return self._container_if", "def interface(self):\n return self._interface" ]

[ "0.7116907", "0.69529134", "0.6768456", "0.65538967", "0.6530441", "0.6227138", "0.61110246", "0.6041694", "0.60142016", "0.59563255", "0.58967304", "0.5890976", "0.588442", "0.58537626", "0.5843988", "0.579155", "0.5761401", "0.5754417", "0.5736469", "0.5722448", "0.5722448", "0.5722448", "0.56808305", "0.5672339", "0.56602097", "0.56471705", "0.56471705", "0.56456745", "0.56341565", "0.56300646" ]

[ "def hasNamespaceNS(self, *args):\n return _libsbml.XMLToken_hasNamespaceNS(self, *args)", "def isSBMLNamespace(*args):\n return _libsbml.SBMLNamespaces_isSBMLNamespace(*args)", "def hasNS(self, *args):\n return _libsbml.XMLNamespaces_hasNS(self, *args)", "def hasNamespaceURI(self, *args):\n return _libsbml.XMLToken_hasNamespaceURI(self, *args)", "def namespaces(self) -> NamespacesType:\n return self.schema.namespaces", "def SBMLNamespaces_isSBMLNamespace(*args):\n return _libsbml.SBMLNamespaces_isSBMLNamespace(*args)", "def is_typespace(schema_obj):\n\n return isinstance(schema_obj, schema.Typespace)", "def is_default_namespace(self):\n return self.db.get_default_namespace() == self", "def namespace(self):\n assert self._namespace\n return self._namespace", "def hasTargetNamespaces(self):\n return _libsbml.ConversionProperties_hasTargetNamespaces(self)", "def IsNamespaceDecl(self):\n ret = libxml2mod.xmlTextReaderIsNamespaceDecl(self._o)\n return ret", "def isNamespacesEmpty(self):\n return _libsbml.XMLToken_isNamespacesEmpty(self)", "def inScopeNamespaces (self):\n return self.__inScopeNamespaces", "async def namespace_exists(self, namespace: str) -> bool:\n return await self.AD.state.namespace_exists(namespace)", "def isNodeType(self, t):\n return isinstance(self, t)", "def all_namespaces(self) -> Optional[pulumi.Input[bool]]:\n return pulumi.get(self, \"all_namespaces\")", "def all_namespaces(self) -> Optional[pulumi.Input[bool]]:\n return pulumi.get(self, \"all_namespaces\")", "def _namespace_requested(self, namespace):\r\n if namespace is None:\r\n return False\r\n namespace_tuple = self._tuplefy_namespace(namespace)\r\n if namespace_tuple[0] in IGNORE_DBS:\r\n return False\r\n elif namespace_tuple[1] in IGNORE_COLLECTIONS:\r\n return False\r\n else:\r\n return self._tuple_requested(namespace_tuple)", "def GetNamespace(self, namespace_name):\n return self.type_namespaces_map.get(namespace_name, None)", "def XmlTypeNamespace(self) -> str:", "def namespace(self):\n return self._namespace", "def isEmpty(self):\n return _libsbml.XMLNamespaces_isEmpty(self)", "def namespace(self) -> _iomanagers.Namespace:\n # It cannot set self.__namespace,\n # but it can function as a setter to the namespace variables.\n return self.__namespace", "def namespace (self) :\n\n return self.__namespace__", "def matchesSBMLNamespaces(self, *args):\n return _libsbml.SBase_matchesSBMLNamespaces(self, *args)", "def hasURI(self, *args):\n return _libsbml.XMLNamespaces_hasURI(self, *args)", "def namespace(self):\n return Namespace(self)", "def namespaces(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\n return pulumi.get(self, \"namespaces\")", "def hasNamespacePrefix(self, *args):\n return _libsbml.XMLToken_hasNamespacePrefix(self, *args)", "def is_my_case(self, type_):\n return (\n isinstance(self.__apply_sequence(type_), self.declaration_class)\n )" ]

[ "0.6642418", "0.65552014", "0.64188397", "0.63565373", "0.6347774", "0.6345759", "0.6091694", "0.60831106", "0.6014322", "0.5938113", "0.5913756", "0.58826727", "0.5857198", "0.58479625", "0.58289057", "0.5818701", "0.5818701", "0.5781182", "0.56841654", "0.5679502", "0.56740314", "0.5673584", "0.56371826", "0.55904615", "0.5559601", "0.5543992", "0.55239755", "0.54953367", "0.5453439", "0.5433988" ]

[ "def is_callback_function_type(self):\n raise exceptions.NotImplementedError()", "def isCall(self) -> bool:\n ...", "def is_function_type(self, objtype):\n # return self.__cfuncptrt == type(objtype)\n return issubclass(objtype, self.__cfuncptrt)\n # return isinstance(objtype, self.__cfuncptrt)", "def __bool__(self) -> bool:\n return self._connected_event.is_set()", "def can_callback(ir):\n return isinstance(ir, Call) and ir.can_reenter()", "def is_registered(self, event_type, callback, details_filter=None):\n listeners = self._topics.get(event_type, [])\n for listener in listeners:\n if listener.is_equivalent(callback, details_filter=details_filter):\n return True\n return False", "def __bool__(self) -> bool:\n return not self._disconnected", "def isService(self, serviceInterface: java.lang.Class) -> bool:\n ...", "def is_incall_connected(self) -> bool:", "def is_interface_type(self):\n raise exceptions.NotImplementedError()", "def __bool__(self) -> bool:\n return self._rpc is not None", "def callback_interface(self):\n raise exceptions.NotImplementedError()", "def has_signal(self, signal_type):\n if signal_type in self.signals:\n return True\n return False", "def is_on(self) -> bool:\n raise NotImplementedError(\"Device subclass needs to implement this.\")", "def isconnected(self) -> bool:\n ...", "def __instancecheck__(self, instance):\n\n if isinstance(instance, ObjCInstance):\n return bool(instance.conformsToProtocol(self))\n else:\n return False", "def can_be_registered(self, event_type):\n return True", "def is_function(self):\n return self.args is not None", "def isconnected(self) -> bool:", "def is_function(self):\n return False", "def is_boolean_type(self):\n raise exceptions.NotImplementedError()", "def is_incall_dialing(self) -> bool:", "def isConnected(self):\n return self._isConnected", "def is_connected(self):\n return self.factory.is_connected", "def is_call_ended(self) -> bool:", "def isEmpty(self):\n\n return len(self.callbacks) == 0", "def is_callable(o):\n return isinstance(o, collections.Callable)", "def isFlow(self) -> bool:\n ...", "def can_be_registered(self, event_type):\n return (event_type in self._watchable_events or\n (event_type == self.ANY and self._allow_any))", "def get_callback(self):\n return self.callbacks[self.type]" ]

[ "0.72134316", "0.6319314", "0.6022175", "0.59459776", "0.59300095", "0.5850091", "0.5819407", "0.57849604", "0.57714933", "0.5738899", "0.5683553", "0.56677616", "0.5630323", "0.5536595", "0.5504375", "0.5490046", "0.546221", "0.5431004", "0.5413289", "0.54089355", "0.5399478", "0.5381337", "0.5369459", "0.5365296", "0.5357188", "0.53569156", "0.53135264", "0.530938", "0.53029424", "0.5284436" ]

[ "def is_function_type(self, objtype):\n # return self.__cfuncptrt == type(objtype)\n return issubclass(objtype, self.__cfuncptrt)\n # return isinstance(objtype, self.__cfuncptrt)", "def is_function(self):\n return self.args is not None", "def is_function(self):\n return False", "def is_function(self):\n return self.type == 'STT_FUNC'", "def isfunction(object):\r\n return isinstance(object, types.FunctionType)", "def isCall(self) -> bool:\n ...", "def isFunction(self):\n return _libsbml.ASTNode_isFunction(self)", "def isValidFunction(self):\n for token in self.value:\n if token.type == 'defFunction' or token.type == 'callFunction':\n if token.value.split('(')[0] == self.name:\n return False\n return True", "def is_callback_interface_type(self):\n raise exceptions.NotImplementedError()", "def is_callable(o):\n return isinstance(o, collections.Callable)", "def is_function(obj):\n return isinstance(obj, (types.FunctionType, types.MethodType,\n types.LambdaType))", "def is_function(self):\n line = self.line.strip()\n if line.startswith('fu'):\n if line.startswith('function') is False:\n return True", "def is_callable(o):\n return callable(o)", "def is_function(self, function: str) -> bool:\n return function in self.function_converter", "def is_callable_type(typevar: Union[Callable, callable, TypeVar]) -> bool:\n if typevar == callable or typevar == Callable:\n return True\n # This return is split in 2 parts to calm down pycharms static analyzer.\n if hasattr(typevar, \"__origin__\"):\n # noinspection PyUnresolvedReferences\n return typevar.__origin__ == Callable.__origin__\n return False", "def can_callback(ir):\n return isinstance(ir, Call) and ir.can_reenter()", "def is_callable(func: Any) -> bool:\n # noinspection PyTypeChecker\n return isinstance(func, (types.FunctionType, types.BuiltinFunctionType,\n types.MethodType, functools.partial))", "def isFunction(self, *args):\n return _libsbml.ASTBasePlugin_isFunction(self, *args)", "def __isFastener(f):\n\n if type(f) != Fastener:\n raise TypeError(\"FastnerGroups may contain only Fasteners\")\n else:\n return True", "def is_callable(obj):\n return callable(obj)", "def callable(obj):\n return bool(_PyCallable_Check(_py_object(obj)))", "def is_function(obj):\n if type(obj) is types.FunctionType:\n return True\n if not is_object(obj):\n return False\n if not hasattr(obj, '__class__'):\n return False\n module = obj.__class__.__module__\n name = obj.__class__.__name__\n return (module == '__builtin__' and\n name in ('function',\n 'builtin_function_or_method',\n 'instancemethod',\n 'method-wrapper'))", "def callable(obj): # pylint: disable=redefined-builtin\n return bool(PyCallable_Check(py_object(obj)))", "def is_registered(self, event_type, callback, details_filter=None):\n listeners = self._topics.get(event_type, [])\n for listener in listeners:\n if listener.is_equivalent(callback, details_filter=details_filter):\n return True\n return False", "def __bool__(self) -> bool:\n return self._connected_event.is_set()", "def isUserFunction(self):\n return _libsbml.ASTNode_isUserFunction(self)", "def iscoroutinefunction(func):\n return getattr(func, \"_is_compat_coroutine\", False)", "def is_equivalent(self, callback, details_filter=None):\n cb = self.callback\n if cb is None and callback is not None:\n return False\n if cb is not None and callback is None:\n return False\n if cb is not None and callback is not None \\\n and not reflection.is_same_callback(cb, callback):\n return False\n if details_filter is not None:\n if self._details_filter is None:\n return False\n else:\n return reflection.is_same_callback(self._details_filter,\n details_filter)\n else:\n return self._details_filter is None", "def _is_function(self, words):\n if words[0] == 'function':\n if len(words) != 3:\n raise SyntaxError(\"File line {}: Invalid number of arguments for C_FUNCTION command.\".format(self._file_line))\n return True\n else:\n return False", "def has_self(func):\n\treturn 'self' in inspect.signature(func).parameters" ]

[ "0.7153936", "0.6989906", "0.6942195", "0.6871938", "0.6591295", "0.64573854", "0.64336544", "0.64194083", "0.6401891", "0.62902033", "0.62851524", "0.61820483", "0.61599416", "0.6106562", "0.59508336", "0.5926797", "0.5912897", "0.5894191", "0.5893967", "0.5843545", "0.5829968", "0.5815556", "0.5735496", "0.56781423", "0.56543124", "0.5605569", "0.56042945", "0.5598035", "0.55876464", "0.55790025" ]

[ "def is_void(self):\n return False", "def IsVoid(self, *args):\n return _Bnd.Bnd_Box_IsVoid(self, *args)", "def is_void(type):\n return remove_alias(type) in create_cv_types(cpptypes.void_t())", "def IsVoid(self, *args):\n return _Bnd.Bnd_Box2d_IsVoid(self, *args)", "def is_pointer_to_void_type(self, objtype):\n # FIXME: DOCME what is that _subtype_ case\n if hasattr(objtype, '_subtype_'):\n if isinstance(None, objtype._subtype_):\n return True\n # FIXME: DOCME what are these cases ? not auto-loading ?\n # self.POINTER(None) is required, because sometimes, c_void_p !=\n # c_void_p :)\n return objtype in [self.c_char_p, self.c_wchar_p, self.c_void_p, self.POINTER(None)]", "def is_void_pointer(type):\n return is_same(type, cpptypes.pointer_t(cpptypes.void_t()))", "def is_bool(self):\n return False", "def ok(self) -> bool:\n # pylint:disable=invalid-name\n raise NotImplementedError # pragma: no cover", "def __bool__(self):\n return self.__nonzero__()", "def __bool__(self):\n return bool(self.obj)", "def __bool__(self):\n return self is TRUE", "def __bool__(self):\n return not self.undefine", "def __bool__(self):\n raise ValueError(\"bool() not permitted\")", "def __nonzero__(self): # real signature unknown; restored from __doc__\n pass", "def __nonzero__(self): # real signature unknown; restored from __doc__\n pass", "def __nonzero__(self): # real signature unknown; restored from __doc__\n pass", "def __nonzero__(self): # real signature unknown; restored from __doc__\n pass", "def __bool__(self):\n\n return not self.is_empty()", "def is_any_type(self):\n raise exceptions.NotImplementedError()", "def __nonzero__(self): # real signature unknown; restored from __doc__\r\n pass", "def __nonzero__(self): # real signature unknown; restored from __doc__\r\n pass", "def is_nullable_type(self):\n raise exceptions.NotImplementedError()", "def __bool__(self) -> bool:\n return self.return_code == 0", "def __bool__(self) -> bool:\n return self.return_code == 0", "def __bool__(self) -> bool:\n return self.return_code == 0", "def is_pointer(self):\n return False", "def bool(self, obj):\n return True", "def bool(self, obj):\n return True", "def is_null(self):\n return self._internal_handle() == 0", "def is_null(self):\n return self._internal_handle() == 0" ]

[ "0.82286865", "0.7611015", "0.74208486", "0.68909454", "0.6569038", "0.6109725", "0.593446", "0.581228", "0.5786375", "0.5749218", "0.57407707", "0.57311565", "0.57092", "0.5671437", "0.5671437", "0.5671437", "0.5671437", "0.5667571", "0.56453985", "0.5642926", "0.5642926", "0.56386036", "0.5631662", "0.5631662", "0.5631662", "0.5624807", "0.5615253", "0.5615253", "0.5596645", "0.5596645" ]

[ "def is_annotated_type(self) -> bool:\n return get_origin(self.type) is Annotated", "def is_typing_annotation(node: ast.AST, ctx: 'model.Documentable') -> bool:\n return is_using_annotations(node, TYPING_ALIAS, ctx) or \\\n is_using_annotations(node, SUBSCRIPTABLE_CLASSES_PEP585, ctx)", "def _is_simple_type(cls):\n return all([\n AnnotationWrapper(anno).is_simple_in_opt_and_not_opt\n for anno in cls._used_annotations()\n ])", "def is_a(self, t):\n return isinstance(self._, t)", "def isSetAnnotation(self):\n return _libsbml.SBase_isSetAnnotation(self)", "def verify_type(self, obj):\n return isinstance(obj, self.type_)", "def is_boolean_type(self):\n raise exceptions.NotImplementedError()", "def __bool__(self):\n return bool(self.obj)", "def of_type(self, a):\n return type(a) == type(self.one)", "def is_event_annotated(self, name):\n return name in self._annotations.keys()", "def isNodeType(self, t):\n return isinstance(self, t)", "def __bool__(self):\n return self is TRUE", "def _isinstance(cls, x):\n return isinstance(x, cls.PYTHON_TYPE_CHECK)", "def is_my_case(self, type_):\n return (\n isinstance(self.__apply_sequence(type_), self.declaration_class)\n )", "def as_bool(self):\n return self.as_type(bool)", "def check_type(self):\n return True", "def match(self, cls):\n return isinstance(self, cls)", "def class_is(cls: Class) -> bool:\n pass", "def needsAnnotationsDictionary(self):\n return self.needs_annotations_dict", "def are_any_attributes_visible(self):\n\n for attribute_name, type_instance in inspect.getmembers(self):\n\n if attribute_name.startswith('__') or inspect.ismethod(type_instance):\n continue\n\n if isinstance(type_instance, bool) and type_instance == True:\n return True\n elif isinstance(type_instance, self.__class__) and \\\n type_instance.are_all_attributes_visible() == True:\n return True\n\n return False", "def is_simple(self) -> bool:\n return self.data in ('int', 'bool', 'float', 'str')", "def is_registered(self, type):\n attr = self._type_to_attr(type)\n return getattr(self, attr, None) is not None", "def is_generic(annotation) -> bool:\n return (\n isinstance(annotation, type)\n and issubclass(annotation, typing.Generic) # type:ignore\n or isinstance(annotation, typing._GenericAlias) # type:ignore\n and annotation.__origin__\n not in (\n list,\n typing.Union,\n tuple,\n typing.ClassVar,\n collections.abc.AsyncGenerator,\n )\n )", "def is_flag(self):\n return (self.__type & NODE_TAG) and isinstance(self.__value, bool)", "def is_type(self, typ):\n return typ == self.__class__.__name__", "def is_enabled_type(self):\r\n registry = queryUtility(IRegistry) \r\n if registry is None: \r\n # Don't show if the registry is not found\r\n return False\r\n settings = registry.forInterface(IIPnextViewletBlogSettings, \r\n check=False) \r\n _types = getattr(settings, 'allowed_types', '')\r\n this_type = self.context.Type()\r\n \r\n return this_type in _types", "def is_type_var(annotation) -> bool:\n\n return isinstance(annotation, typing.TypeVar) # type:ignore", "def has_type_var(annotation) -> bool:\n return any(\n is_type_var(arg) or has_type_var(arg)\n for arg in getattr(annotation, \"__args__\", [])\n )", "def bool(self, obj):\n return True", "def bool(self, obj):\n return True" ]

[ "0.8426096", "0.6878035", "0.67209965", "0.6478677", "0.614293", "0.6041532", "0.60102296", "0.6004325", "0.5994643", "0.5971596", "0.5964204", "0.5907197", "0.58609444", "0.5856965", "0.5855007", "0.58441585", "0.58273065", "0.582121", "0.57902175", "0.57866234", "0.57857174", "0.5779488", "0.5777543", "0.57709545", "0.5757013", "0.57555014", "0.57300234", "0.57277954", "0.57271576", "0.57271576" ]

[ "def __bool__(self):\n return self.is_successful", "def is_task(self):\n from .tasks import Task\n return isinstance(self, Task)", "def __bool__(self):\n return bool(self.obj)", "def in_progress(self: \"Status\") -> bool:\n return isinstance(self, InProgress)", "def is_async(self) -> bool:\n return self.__is_async", "def __bool__(self):\n return self is TRUE", "def is_async(self) -> bool:", "def __bool__(self) -> bool:\n return self._rpc is not None", "def __bool__(self):\n return self.wait(0)", "def is_complete(self) -> bool:\n raise NotImplementedError(\"Base method not implemented\")", "def __bool__(self):\n return self.isValid()", "def __bool__(self) -> bool:\n return self.return_code == 0", "def __bool__(self) -> bool:\n return self.return_code == 0", "def __bool__(self) -> bool:\n return self.return_code == 0", "def __bool__(self):\n return bool(self.get_value())", "def is_P(self):\n return isinstance(self,P)", "def IsCompleted(self) -> bool:", "def IsCompleted(self) -> bool:", "def IsCompleted(self) -> bool:", "def isFuture(self):\n return (self._t > time())", "def is_resolved(self) -> bool:\n return self._target_object is not None", "def __bool__(self):\n return bool(self._value)", "def bool(self, obj):\n return True", "def bool(self, obj):\n return True", "def as_bool(self):\n return self.as_type(bool)", "def is_a(self, t):\n return isinstance(self._, t)", "def is_bool(self):\n return False", "def is_success(self):\n if self.status == NotificationError.SUCCESS:\n return True\n return False", "def __bool__(self):\n raise ValueError(\"bool() not permitted\")", "def __bool__(self):\n return self.is_valid" ]

[ "0.60700285", "0.6006526", "0.600101", "0.5995107", "0.5994056", "0.59527934", "0.59483606", "0.59320986", "0.5809807", "0.574972", "0.56372863", "0.5637197", "0.5637197", "0.5637197", "0.5628994", "0.56172556", "0.56081927", "0.56081927", "0.56081927", "0.55745226", "0.55606854", "0.55476594", "0.54990625", "0.54990625", "0.548062", "0.5479423", "0.5477241", "0.5474923", "0.5465335", "0.5463754" ]

[ "def supports_book_record_type(self, book_record_type=None):\n if book_record_type is None:\n raise NullArgument()\n return False", "def supports_catalog_record_type(self, catalog_record_type=None):\n if catalog_record_type is None:\n raise NullArgument()\n return False", "def ISREF(value):\n return isinstance(value, Record)", "def is_recording(self):\n return self._isrecording", "def record_class_for_type(cls, rec_type):\n if rec_type == PptRecordCurrentUser.TYPE:\n return PptRecordCurrentUser, True\n elif rec_type == PptRecordExOleObjAtom.TYPE:\n return PptRecordExOleObjAtom, True\n elif rec_type == PptRecordExOleVbaActiveXAtom.TYPE:\n return PptRecordExOleVbaActiveXAtom, True\n\n try:\n record_name = RECORD_TYPES[rec_type]\n if record_name.endswith('Container'):\n is_container = True\n elif record_name.endswith('Atom'):\n is_container = False\n elif record_name.endswith('Blob'):\n is_container = False\n elif record_name == 'CString':\n is_container = False\n else:\n logging.warning('Unexpected name for record type \"{0}\". typo?'\n .format(record_name))\n is_container = False\n\n if is_container:\n return PptContainerRecord, True\n else:\n return PptRecord, False\n except KeyError:\n return PptRecord, False", "def is_a(self, t):\n return isinstance(self._, t)", "def predicate(obj):\n return inspect.isclass(obj) and issubclass(obj, MafColumnRecord)", "def bool(self) -> bool:\n if isinstance(self, ps.DataFrame):\n df = self\n elif isinstance(self, ps.Series):\n df = self.to_dataframe()\n return df.head(2)._to_internal_pandas().bool()", "def is_recording(self):\n return self._recording_status in RECORDING_STATUS", "def supports_comment_record_type(self, comment_record_type=None):\n if comment_record_type is None:\n raise NullArgument()\n return False", "def recording(self) -> bool:\n\t\treturn self._raw_result['data']['recording']", "def _isinstance(self, instance, raise_error=True):\n\n if isinstance(instance, self.__model__):\n return True\n elif raise_error:\n raise ValueError('{} is not of type {}.'.format(\n instance, self.__model__,\n ))\n else:\n return False", "def __bool__(self):\n return bool(self.obj)", "def supports_book_search_record_type(self, book_search_record_type=None):\n if book_search_record_type is None:\n raise NullArgument()\n return False", "def _is_record_status(self, status_id):\n return status_id == self.record_status", "def can_record(self, variable):\n raise NotImplementedError", "def isRecording(self):\n if not self.proxy:\n self.proxy = self.session.service(\"ALVideoRecorder\")\n return self.proxy.isRecording()", "def match(self, cls):\n return isinstance(self, cls)", "def validate(self, record, records):\n if not record or self.field not in record.props:\n return False\n handle_id = record.props[self.field].val\n # Make sure the format of handle id is equivalent to all other handles\n # e.g. '0x123' will become '0x0123'.\n handle_id = '0x{:04X}'.format(int(handle_id, 16))\n if handle_id not in records:\n return False\n if records[handle_id].type_id != self.type_id:\n return False\n return True", "def is_playfield(cls):\n return True", "def store(self, record: ModelledTable) -> bool:\n\n return self.model.store(self.cursor, record)", "def supports_catalog_search_record_type(self, catalog_search_record_type=None):\n if catalog_search_record_type is None:\n raise NullArgument()\n return False", "def is_Q(self):\n return isinstance(self,Q)", "def bool(self, obj):\n return True", "def bool(self, obj):\n return True", "def _isinstance(self, obj, raise_error=True):\n rv = isinstance(obj, self.__model__)\n if not rv and raise_error:\n raise ValueError('%s is not of type %s' % (obj, self.__model__))\n return rv", "def identify(self, record):\n if record.record_type in self.record_type_iders:\n ident = self.record_type_iders[record.record_type](record)\n if ident:\n return [record.record_type] + ident\n return [record.record_type, False]\n return False", "def list_record_types(self):\n return [RecordType.A]", "def is_simple(self) -> bool:\n return self.data in ('int', 'bool', 'float', 'str')", "def is_active(self):\n return self._is_record_status(self.ACTIVE)" ]

[ "0.657329", "0.6553393", "0.6409709", "0.6220432", "0.6149315", "0.6113821", "0.61134356", "0.6092015", "0.603679", "0.5925932", "0.58947814", "0.5886511", "0.5882126", "0.5842346", "0.58168924", "0.5733191", "0.57082087", "0.5686609", "0.5648512", "0.56156236", "0.56152785", "0.56129754", "0.5606234", "0.5598683", "0.5598683", "0.559842", "0.5591155", "0.5578437", "0.5565238", "0.5560837" ]

[ "def is_sequence(self) -> bool:\n return isinstance(self.yaml_node, yaml.SequenceNode)", "def isSequence(obj):\n # type: (Any) -> bool\n return isinstance(obj, Sequence)", "def issequence(obj) -> bool:\n return hasattr(type(obj), '__iter__') and hasattr(type(obj), '__len__')", "def _is_sequence_like(self, data):\n return hasattr(data, \"__iter__\") and hasattr(data, \"__getitem__\")", "def is_sequence(item):\n return (not hasattr(item, \"strip\") and\n (hasattr(item, \"__getitem__\") or hasattr(item, \"__iter__\")))", "def is_seq_of(seq, expected_type, seq_type=None):\n if seq_type is None:\n exp_seq_type = abc.Sequence\n else:\n assert isinstance(seq_type, type)\n exp_seq_type = seq_type\n if not isinstance(seq, exp_seq_type):\n return False\n for item in seq:\n if not isinstance(item, expected_type):\n return False\n return True", "def _is_sequence(obj):\n return hasattr(obj, \"__iter__\") and not isinstance(obj, str)", "def __eq__(self, sequence):\n try:\n return self.seq == sequence.seq\n except AttributeError:\n return self.seq == sequence", "def is_sequence(value):\n return (hasattr(value, \"__iter__\") and not\n isinstance(value, (six.string_types, six.binary_type)))", "def is_a(self, t):\n return isinstance(self._, t)", "def _is_proper_sequence(seq):\n return (isinstance(seq, collections.abc.Sequence) and\n not isinstance(seq, str))", "def is_my_case(self, type_):\n return (\n isinstance(self.__apply_sequence(type_), self.declaration_class)\n )", "def sequence_type(self) -> str:\n raise NotImplementedError()", "def _check_sequence(self) -> PossibleResult[T]:\n if isinstance(self.constructor_origin, type) and issubclass(\n self.constructor_origin, Sequence\n ):\n if not isinstance(self.obj, Sequence):\n raise DeserializeError(\n Sequence, self.obj, self.new_depth, self.key\n )\n if self.constructor_args:\n _arg = self.constructor_args[0]\n else:\n _arg = Any # type: ignore\n return self.constructor_origin(\n Deserialize(\n obj=value,\n constructor=_arg,\n depth=self.new_depth,\n convert_primitives=self.convert_primitives,\n ).run()\n for value in self.obj\n ) # type: ignore\n return NO_RESULT", "def is_sequence(arg):\n return (not hasattr(arg, \"strip\") and\n hasattr(arg, \"__getitem__\") or\n hasattr(arg, \"__iter__\"))", "def same(seq: typing.Iterable[typing.Any]) -> bool:\n seq = iter(seq)\n first = type(next(seq))\n return all(isinstance(i, first) for i in seq)", "def is_sequence(arg):\n\n # np.float{16,32,64} and np.int types have __getitem__ defined\n # this is a long-standing bug in NumPy and unlikely to be fixed\n # todo: backport to qmmlpack, write tests\n if isinstance(arg, (str, bytes, np.number, dict, set)):\n return False\n\n return hasattr(arg, \"__getitem__\") or hasattr(arg, \"__iter__\")", "def is_assembly(cls, item: \"SeqFileTypes\") -> bool:\n if item in cls.list_assemblies(): return True;\n return False;", "def is_coding(self):\n return self.protein_seq is not None", "def sequence(self) -> Any:\n return self.__seq", "def is_sequence(x):\n return (not hasattr(x, 'strip') and\n hasattr(x, '__getitem__') or\n hasattr(x, '__iter__'))", "def isSetType(self):\n return _libsbml.Association_isSetType(self)", "def isNodeType(self, t):\n return isinstance(self, t)", "def validate_sequence(outcome):\n from collections.abc import Sequence\n if not isinstance(outcome, Sequence):\n raise ditException('Outcome class is not a sequence.')\n else:\n return True", "def of_type(self, a):\n return type(a) == type(self.one)", "def is_Q(self):\n return isinstance(self,Q)", "def isSetType(self):\n return _libsbml.Objective_isSetType(self)", "def match(self, cls):\n return isinstance(self, cls)", "def is_generator_or_sequence(x):\n builtin_iterators = (str, list, tuple, dict, set, frozenset)\n if isinstance(x, (tensor.Tensor, np.ndarray) + builtin_iterators):\n return False\n return (tf_inspect.isgenerator(x) or\n isinstance(x, Sequence) or\n isinstance(x, typing.Iterator))", "def is_structure(self) -> bool:\n return ATTRIBUTE.Structure.value in self.type_data.attributes" ]

[ "0.80957925", "0.71997213", "0.6930186", "0.6835188", "0.6770618", "0.64282125", "0.6424273", "0.64200723", "0.6333723", "0.63057405", "0.6290044", "0.60951614", "0.6089056", "0.60546523", "0.6032186", "0.6004696", "0.59916586", "0.5949501", "0.5874908", "0.586695", "0.58388746", "0.58330476", "0.5820246", "0.57702035", "0.57375914", "0.57089096", "0.56811416", "0.56808645", "0.5678858", "0.56590843" ]

[ "def _is_union(typeval: Type) -> bool:\n return get_origin(typeval) is Union", "def is_union(type_):\n if not is_class(type_):\n return False\n decl = class_traits.get_declaration(type_)\n return decl.class_type == class_declaration.CLASS_TYPES.UNION", "def is_Union(tp):\n if tp is Union:\n return True\n try:\n # Python 3.6\n return tp.__origin__ is Union\n except AttributeError:\n try:\n return isinstance(tp, typing.UnionMeta)\n except AttributeError:\n return False", "def is_union(self) -> bool:\n return False", "def is_union_type(self, objtype):\n # force ignore the longdouble construct\n if objtype == self.c_longdouble:\n return False\n # force ignore the CString construct\n #if objtype == self.CString:\n if self.is_cstring_type(objtype):\n return False\n return issubclass(objtype, self.get_real_ctypes_member('Union'))", "def is_union(self):\n return False", "def is_union_type(type_object):\n return _is_supported_generic(type_object) and type_object.__origin__ == typing.Union", "def is_union(self) -> bool:\n return bool(AnnotationWrapper.union_field_re.match(self.data))", "def is_union(annotation):\n\n annotation_origin = getattr(annotation, \"__origin__\", None)\n\n return annotation_origin == typing.Union", "def is_pointer_to_union_type(self, objtype):\n if hasattr(objtype, '_subtype_'):\n return self.is_union_type(objtype._subtype_)\n return self.is_pointer_type(objtype) and hasattr(objtype, '_type_') and self.is_union_type(objtype._type_)", "def _is_valid_union(content_type: str) -> bool:\n content_type = content_type.strip()\n\n if not content_type.startswith(\"pt:union\"):\n return False\n\n if not _has_matched_brackets(content_type):\n return False\n\n if not _has_brackets(content_type):\n return False\n\n sub_types = _get_sub_types_of_compositional_types(content_type)\n # check there are at least two subtypes in the union\n if len(sub_types) < 2:\n return False\n\n # check there are no duplicate subtypes in the union\n sub_types_set = set(sub_types)\n if len(sub_types) != len(sub_types_set):\n return False\n\n for sub_type in sub_types:\n if not (\n _is_valid_ct(sub_type)\n or _is_valid_pt(sub_type)\n or _is_valid_dict(sub_type)\n or _is_valid_list(sub_type)\n or _is_valid_set(sub_type)\n ):\n return False\n\n return True", "def _check_union(self) -> PossibleResult[T]:\n if _is_union(self.constructor):\n args = get_args(self.constructor)\n is_optional = len(args) == 2 and type(None) in args\n is_optional_property = len(args) == 2 and Undefined in args\n if is_optional and self.obj is None:\n return None # type: ignore\n if is_optional_property and self.obj is UNDEFINED:\n return UNDEFINED # type: ignore\n for argument in args:\n convert_primitives = self.convert_primitives and (\n (is_optional and argument != type(None))\n or (is_optional_property and argument != Undefined)\n )\n try:\n return Deserialize(\n obj=self.obj,\n constructor=argument,\n depth=self.new_depth,\n convert_primitives=convert_primitives,\n ).run()\n except DeserializeError:\n pass\n raise DeserializeError(\n self.constructor, self.obj, self.new_depth, self.key\n )\n return NO_RESULT", "def of_type(self, a):\n return type(a) == type(self.one)", "def _isinstance(self, value: Any, typ: Any) -> bool:\n typ_args = getattr(typ, '__args__', ())\n if hasattr(typ, '__origin__'):\n # Drop subscripted extra type parameters from generic type.\n # (e.g. Dict[str, str].__origin__ == dict)\n # See https://www.python.org/dev/peps/pep-0585 for more information.\n typ = typ.__origin__\n if typ == Union:\n return any(self._isinstance(value, t) for t in typ_args)\n else:\n return isinstance(value, typ)", "def is_structure(self) -> bool:\n return ATTRIBUTE.Structure.value in self.type_data.attributes", "def is_structure(self) -> bool:\n return ATTRIBUTE.Structure.value in self.type_data.attributes", "def is_struct(self):\n return False", "def is_struct_type(self, objtype):\n return issubclass(objtype, self.get_real_ctypes_member('Structure'))", "def is_simple(self) -> bool:\n return self.data in ('int', 'bool', 'float', 'str')", "def _assert_union_types_equal(self, type1, type2):\n self.assertEqual(type1.name, type2.name)\n self.assertEqual(type1.description, type2.description)\n self._assert_parent_types_equal(type1, type2)", "def is_enum(self):\n return self.is_complex and not self.is_class", "def isNodeType(self, t):\n return isinstance(self, t)", "def PyType_IsSubtype(space, a, b):\n w_type1 = from_ref(space, rffi.cast(PyObject, a))\n w_type2 = from_ref(space, rffi.cast(PyObject, b))\n return int(abstract_issubclass_w(space, w_type1, w_type2)) #XXX correct?", "def is_subtype(self, left: ProperType, right: ProperType) -> bool:\n if isinstance(right, AnyType):\n # trivial case\n return True\n if isinstance(right, UnionType) and not isinstance(left, UnionType):\n # Case that would be duplicated for each type, so we put it here.\n return any(self.is_subtype(left, right_elem) for right_elem in right.items)\n return left.accept(_SubtypeVisitor(self, right, self.is_subtype))", "def is_interpretable(self):\n return bool(self.as_date() or self.as_time())", "def verify_type(self, obj):\n return isinstance(obj, self.type_)", "def is_a(self, t):\n return isinstance(self._, t)", "def is_boolean_type(self):\n raise exceptions.NotImplementedError()", "def is_maybe_subtype(self, left: ProperType, right: ProperType) -> bool:\n if isinstance(right, AnyType):\n # trivial case\n return True\n if isinstance(right, UnionType) and not isinstance(left, UnionType):\n # Case that would be duplicated for each type, so we put it here.\n return any(\n self.is_maybe_subtype(left, right_elem) for right_elem in right.items\n )\n return left.accept(_MaybeSubtypeVisitor(self, right, self.is_maybe_subtype))", "def is_basic_type(self, objtype):\n if not hasattr(objtype, '_type_'):\n # could be python types\n return objtype in [int, long, float, bool]\n return self.is_basic_ctype(objtype)" ]

[ "0.8105555", "0.7861087", "0.7817273", "0.7705603", "0.7646727", "0.7483963", "0.7477847", "0.74555516", "0.69131905", "0.6808421", "0.65584767", "0.58904076", "0.5881067", "0.570471", "0.56558865", "0.56558865", "0.5652963", "0.562509", "0.5571252", "0.5558063", "0.5529574", "0.552538", "0.5461395", "0.5458151", "0.5456002", "0.5454994", "0.54280305", "0.54259115", "0.53721035", "0.53526014" ]

[ "def typenames(self):\n return tuple((item[0] for item in self))", "def visit_tuple_type(self, left: TupleType) -> T:", "def type(self) -> global___Type:", "def __tuple(self):\n return (self.__app, self.__namespace, self.__pairs)", "def _p_type(self):\n # FIXME: Something about self reference in structure fields from\n # ctypeslib.\n # Check if still used\n import inspect\n return dict(inspect.getmembers(self, inspect.isclass))[self]", "def as_tuple(self) -> tuple[typing.Any, ...]:\n return tuple(getattr(self, field.name) for field in Schema)", "def tuple(self) -> tuple:\n return tuple(self)", "def terminal_types(self):\n return (self,)", "def type_obj(self) -> Union[type, GenericAlias]:\n pass", "def Instance(self) -> TypeManager:", "def template_typedef(self, node, targs):\n typedefmap = []\n for typ in node.typedefs:\n oldtyp = typ.typemap\n typ.clone_post_class(targs)\n typedefmap.append( (oldtyp, typ.typemap) )\n node.typedef_map = typedefmap\n\n for function in node.functions:\n for arg in function.ast.declarator.params:\n ntypemap = arg.typemap\n for typedef in typedefmap:\n if ntypemap is typedef[0]:\n arg.typemap = typedef[1]\n break", "def classes(self) -> Tuple[Type, ...]:\n self._deprecation()\n return tuple(self.values())", "def lib(self):\n for name in dir(self):\n if not name.startswith('__') and type(getattr(self, name)) == tuple:\n yield name, getattr(self, name)[0]", "def tuple_ty(*tuple_types : MIRType) -> 'MIRTupleType':\n return MIRTupleType(list(tuple_types))", "def as_tuple(self):\n return (self.oid, self.type, self.value)", "def make_typedefs(self):\n type_dict = self.python_madz_types_dict + self.mangled_namespace\n res = \"{} = {{}}\\n\".format(type_dict)\n\n for node in self.description.declarations():\n varname = self.python_madz_types + self.mangled_namespace + \"___\" + node.name\n # Hack to get self referential top level structs.\n if (node.type.node_type() == pdl.TypeStruct):\n self._is_top_level = varname\n res += self.gen_type_string(node.type)\n res += \"\\n\"\n else:\n res += \"{} = {}\\n\".format(varname, self.gen_type_string(node.type))\n res += \"{}['{}'] = {}\\n\".format(type_dict, node.name, varname)\n return res", "def obj(self) -> (Symbol, int, int):\n return (self._symbol, self._start, self._end)", "def type_shapes(self):\n return self._type_shapes", "def etypes(self): # -> list[None]:\n ...", "def typedef(typedefs):\n\n\n for d in typedefs:\n\n\n type = map_type(d[\"type\"])\n typedef = d[\"typedef\"]\n\n MAPPINGS[typedef] = type", "def get_named_tuple_object(instance: Any) -> Tuple[object, Type[object]]:\n clz: Type = namedtuple(instance.__class__)\n instance_data = asdict(instance)\n for key, value in instance_data.items():\n if is_dataclass(value):\n obj, _ = get_named_tuple_object(value)\n instance_data[key] = obj\n\n # generics currently unsupported, maybe in later versions\n\n new_instance: clz = clz(**instance_data)\n return (new_instance, clz)", "def generate_from_descriptor(self, tuple_descriptor):\r\n t = Tuple()\r\n d = {}\r\n t.set_data(d)\r\n for alias in tuple_descriptor.aliases:\r\n fields = self.__tuple_descriptor.get_descriptor(alias).underlying_fields\r\n for field in fields:\r\n setattr(t, field, getattr(self, field))\r\n setattr(t, alias, getattr(self, alias))\r\n t.set_tuple_descriptor(tuple_descriptor)\r\n return t", "def Reference(cls):\n return type(cls.__name__, (Typed, ), {\"type\": cls})", "def ElementType(self) -> _n_0_t_1:", "def visit_Typedef(self, node):\n return str_node(node)", "def get_type_list(cls):\n\n from pygments.lexers import get_all_lexers\n return [(name, aliases[0]) for name, aliases, filetypes, mimetypes in get_all_lexers()]", "def create_type_tuple(*elems):\n tuple = ast.Tuple()\n\n tuple.elts = []\n for elem in elems:\n tuple.elts.append(elem)\n\n return tuple", "def return_type(self) -> global___Type:", "def get_items(self):\n return self._internal_type_mapping", "def boundIdentifiers(self):\n for type in self.metadata_type_markers:\n yield from self[:rdf.type:type]" ]

[ "0.6696826", "0.57943803", "0.57066506", "0.56916595", "0.56512207", "0.5642418", "0.5635808", "0.5627796", "0.55997837", "0.5572192", "0.5496701", "0.5446311", "0.5444676", "0.5443081", "0.5426574", "0.5398417", "0.53773963", "0.53727347", "0.5328607", "0.5304586", "0.52789986", "0.5270847", "0.5259667", "0.5214387", "0.5164338", "0.5157963", "0.5135883", "0.51356256", "0.51339346", "0.5115217" ]

[ "def permissive(self) -> bool:\n return self._permissive", "def privileged(self) -> Optional[pulumi.Input[bool]]:\n return pulumi.get(self, \"privileged\")", "def privileged(self) -> Optional[pulumi.Input[bool]]:\n return pulumi.get(self, \"privileged\")", "def _disallow_public_access(self) -> typing.Optional[bool]:\n return jsii.get(self, \"disallowPublicAccess\")", "def publicly_accessible(self) -> Optional[pulumi.Input[bool]]:\n return pulumi.get(self, \"publicly_accessible\")", "def publicly_accessible(self) -> Optional[pulumi.Input[bool]]:\n return pulumi.get(self, \"publicly_accessible\")", "def publicly_advertisable(self) -> Optional[pulumi.Input[bool]]:\n return pulumi.get(self, \"publicly_advertisable\")", "def publicly_advertisable(self) -> Optional[pulumi.Input[bool]]:\n return pulumi.get(self, \"publicly_advertisable\")", "def __bool__(self):\n return not self.undefine", "def is_restricted_download(self):\n return self.has_label(RESTRICTEDDOWNLOAD_LABEL)", "def publicly_advertisable(self) -> pulumi.Output[Optional[bool]]:\n return pulumi.get(self, \"publicly_advertisable\")", "def no_network_access_check(user):\n return not user.has_property(\"network_access\")", "def publicly_accessible(self) -> pulumi.Output[Optional[bool]]:\n return pulumi.get(self, \"publicly_accessible\")", "def give_me_a_boolean():\n return True\n pass", "def is_administrator(self):\n return False", "def request_access_enabled(self) -> Optional[pulumi.Input[bool]]:\n return pulumi.get(self, \"request_access_enabled\")", "def request_access_enabled(self) -> Optional[pulumi.Input[bool]]:\n return pulumi.get(self, \"request_access_enabled\")", "def restricted_bool(x):\n try:\n x = bool(x)\n except ValueError:\n raise argparse.ArgumentTypeError(\"%r not a bool literal\" % (x,))\n return x", "def can(self, unused_perm):\n return False", "def __bool__(self):\r\n raise TypeError('cannot use secure type in Boolean expressions')", "def check_passive(self) -> Optional[pulumi.Input[bool]]:\n return pulumi.get(self, \"check_passive\")", "def check_passive(self) -> Optional[pulumi.Input[bool]]:\n return pulumi.get(self, \"check_passive\")", "def request_access_enabled(self) -> pulumi.Output[bool]:\n return pulumi.get(self, \"request_access_enabled\")", "def is_allowed(self) -> bool:\n return self.effect == ALLOW_ACCESS", "def has_super_access():\n current_user = frappe.get_doc('User', frappe.session.user)\n roles = set([role.role for role in current_user.roles])\n return bool(roles & {'Administrator', 'Instructor', 'Education Manager', 'System Manager', 'Academic User'})", "def is_passkey_user_defined(self) -> bool:\n return pulumi.get(self, \"is_passkey_user_defined\")", "def __bool__(self):\n raise ValueError(\"bool() not permitted\")", "def can_be_disabled(self) -> bool:\n return True", "def check_passive(self) -> pulumi.Output[bool]:\n return pulumi.get(self, \"check_passive\")", "def hasVeryTrustedValue():\n return False" ]

[ "0.6544667", "0.638061", "0.638061", "0.6114495", "0.60935193", "0.60935193", "0.60431635", "0.60431635", "0.59647554", "0.5900266", "0.58141834", "0.5814022", "0.58053815", "0.57084066", "0.5697186", "0.56897306", "0.56897306", "0.5680323", "0.560833", "0.5602042", "0.55973583", "0.55973583", "0.55698436", "0.55628794", "0.5561314", "0.554693", "0.55393183", "0.5521127", "0.5517246", "0.5503781" ]

[ "def result_type(self) -> Optional[str]:\n if hasattr(self, \"_result_type\"):\n return self._result_type\n _args: list[Arg] = []\n _ctx = self._select(\"resultType\", _args)\n return _ctx.execute_sync(Optional[str])", "def result_type(self):\r\n if not hasattr(self, '_result_type'):\r\n self._result_type = conf.lib.clang_getResultType(self.type)\r\n\r\n return self._result_type", "def return_type(self) -> global___Type:", "def GetType(vDataSet):\r\n return imaris_types[str(vDataSet.GetType())]", "def result_type(self):\n\n anc = self.find_ancestor(ASTDeclarationNode) or self.find_ancestor(ASTAssignmentNode)\n if anc:\n return anc.type()\n return get_expression_type(self)", "def get_type(self) -> str:\n return self.row_dict['type']", "def type(self):\n # easy enough\n return self._dataset._pyre_id.type", "def type(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"type\")", "def type(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"type\")", "def type(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"type\")", "def type(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"type\")", "def type(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"type\")", "def type(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"type\")", "def type(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"type\")", "def type(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"type\")", "def type(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"type\")", "def type(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"type\")", "def type(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"type\")", "def type(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"type\")", "def type(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"type\")", "def type(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"type\")", "def type(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"type\")", "def type(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"type\")", "def type(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"type\")", "def type(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"type\")", "def type(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"type\")", "def type(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"type\")", "def type(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"type\")", "def type(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"type\")", "def type(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"type\")" ]

[ "0.7371791", "0.7227484", "0.70264876", "0.695912", "0.68952215", "0.67422324", "0.6730174", "0.6698153", "0.6698153", "0.6698153", "0.6698153", "0.6698153", "0.6698153", "0.6698153", "0.6698153", "0.6698153", "0.6698153", "0.6698153", "0.6698153", "0.6698153", "0.6698153", "0.6698153", "0.6698153", "0.6698153", "0.6698153", "0.6698153", "0.6698153", "0.6698153", "0.6698153", "0.6698153" ]

[ "def flattened_member_types(self):\n raise exceptions.NotImplementedError()", "def get_members():", "def typenames(self):\n return tuple((item[0] for item in self))", "def getTypeCode(self):\n return _libsbml.ListOfMembers_getTypeCode(self)", "def ntypes(self): # -> list[str]:\n ...", "def gettypes(self):\n return [str(self.sd.xlate(t[0])) for t in self.sd.types]", "def etypes(self): # -> list[str]:\n ...", "def types(self) -> List[str]:\n return self._types", "def _unicode_members(self):\n return [(m.name or m._as_rhs()) for m in self.members]", "def get_member_type(*args):\n return _ida_hexrays.get_member_type(*args)", "def get_list(self):\n return self._FF_TYPES", "def get_list(self):\n return self._FF_TYPES", "def members(cls) -> Mapping[str, Member]:\n return cls.__atom_members__", "def ntypes(self): # -> list[None]:\n ...", "def get_types(self):\n return self.types", "def getTypesList():\n return Gw2Spidy._request('types')['results']", "def getTypes():\n\t\n\ttranslationTable = []\n\tfor x in typePrimitive:\n\t\ttranslationTable.extend(x[0])\n\t\n\tid = 0\n\ttypes = []\n\tmax = 0\n\tfor x in typePrimitive:\n\t\t\n\t\tbinds = []\n\t\tfor y in x[2]:\n\t\t\tbinds.append(translationTable.index(y))\n\t\t\n\t\tif (x[4] != False) and (x[4] > max):\n\t\t\tmax = x[4]\n\t\t\t\n\t\t\n\t\ttypes.append({'name':x[0],'nSites':x[1],'binds':binds,'sym':x[3],'id':id,'max':x[4]})\n\t\tid+=1\n\t\n\treturn (max,types)", "def etypes(self): # -> list[None]:\n ...", "def get_types(self) :\n\n return list(self.types)[1:]", "def type_list():\n for type_ in orm.DataFlagType.select():\n click.echo(type_.name)", "def members(self) -> \"List[str]\":\n return self._attrs.get(\"members\")", "def members(self) -> \"List[str]\":\n return self._attrs.get(\"members\")", "def members(self) -> \"List[str]\":\n return self._attrs.get(\"members\")", "def get_type_list(cls):\n\n from pygments.lexers import get_all_lexers\n return [(name, aliases[0]) for name, aliases, filetypes, mimetypes in get_all_lexers()]", "def getMembers():", "def getMembers():", "def getMembers():", "def getMembers():", "def reflection_at_line(self) -> Tuple[int]:\n reflection_list = []\n # iterate over all subnodes in node\n for node in ast.walk(self.definition):\n # some subnodes does not has id attribute\n try:\n # append if node.id is 'isinstance' or 'type'\n if node.id in self.PYTHON_REFLECTION_EXPRESSIONS:\n line_number: int = node.lineno\n reflection_list.append(line_number)\n except: #nosec\n # skip if node.id is not exist\n continue\n # dont forget to convert to immutable type\n return tuple(reflection_list)", "def field_names(cls) -> tuple:\n return tuple((field.name for field in fields(cls)))" ]

[ "0.68188757", "0.63429254", "0.6320436", "0.628236", "0.6216023", "0.6130448", "0.606133", "0.6058626", "0.6038821", "0.6021799", "0.6015155", "0.6015155", "0.59637636", "0.5952403", "0.59175074", "0.5888799", "0.5880538", "0.5880081", "0.58317626", "0.583168", "0.5800849", "0.5800849", "0.5800849", "0.564485", "0.5642817", "0.5642817", "0.5642817", "0.5642817", "0.56365883", "0.55848634" ]

[ "def direct_descendant_type_set(self) -> Set[str]:\n return set(chain.from_iterable(seg.class_types for seg in self.segments))", "def descendant_type_set(self) -> Set[str]:\n return set(\n chain.from_iterable(\n seg.descendant_type_set | seg.class_types for seg in self.segments\n )\n )", "def types(cls, root):\r\n return cls._TYPES_BY_ROOT[root]", "def member_types(self):\n raise exceptions.NotImplementedError()", "def get_set_types(self):\n if not self._refreshed:\n self.refresh()\n return self._setTypes", "def get_types(self) :\n\n return list(self.types)[1:]", "def getmembers(klass, members=None):\n if members is None:\n members = []\n for k in klass.__bases__:\n print(k)\n getmembers(k, members)\n for m in dir(klass):\n print(m)\n if m not in members:\n members.append(m)\n return members", "def type_set(self) -> Set[str]:\n typs = {self.type}\n for s in self.segments:\n typs |= s.type_set()\n return typs", "def get_flat_type_info(cls):\n return _get_flat_type_info(cls, TypeInfo())", "def get_types(self):\n return self.types", "def gettypes(self):\n return [str(self.sd.xlate(t[0])) for t in self.sd.types]", "def get_child_types(self):\n types = set()\n for child in self.children:\n types.add(child.__class__)\n return types", "def get_children_types(self) -> set[FakeAnnotation]:\n result: set[FakeAnnotation] = set()\n for child in self.children:\n result.update(child.iterate_types())\n return result", "def get_children_typed_dicts(self: _R) -> set[_R]:\n result: set[_R] = set()\n children_types = self.get_children_types()\n for type_annotation in children_types:\n if not isinstance(type_annotation, self.__class__):\n continue\n result.add(type_annotation)\n\n return result", "def get_datatypes(self):\n datatypes = set()\n for element in itertools.chain(self.polygons, self.paths):\n datatypes.update(element.datatypes)\n for reference in self.references:\n datatypes.update(reference.ref_cell.get_datatypes())\n return datatypes", "def simple_reflections(self):\n return [s(self) for s in self.parent().simple_reflections()]", "def _inferred_type_levels(self) -> list[str]:\n return [i.inferred_type for i in self.levels]", "def get_datatypes(self):\n datatypes = set()\n for element in self.elements:\n if isinstance(element, PolygonSet):\n datatypes.update(element.datatypes)\n elif isinstance(element, CellReference) or isinstance(\n element, CellArray):\n datatypes.update(element.ref_cell.get_datatypes())\n return datatypes", "def _used_annotations(cls) -> set:\n return set(field.type for field in dataclasses.fields(cls))", "def get_local_types(self) -> list[FakeAnnotation]:\n return [self]", "def types(self) -> List[str]:\n return self._types", "def types(self) -> list:\n if self._types is None:\n fdist = self.fdist # ranked order\n types_ = list(fdist.type.values)\n self._types = types_\n return self._types", "def ntypes(self): # -> list[str]:\n ...", "def ntypes(self): # -> list[None]:\n ...", "def test_get_group_class_types(self):\n pass", "def all(cls):\n return [(k, v) for k, v in cls.__members__.items()]", "def get_members():", "def get_all_types(self) -> list[TypeInfo]:\n return list(self._types.values())", "def _unicode_members(self):\n return [(m.name or m._as_rhs()) for m in self.members]", "def gather_types(self):\n\n def gather_subfields(field: Field) -> List[Field]:\n fields = [field]\n\n if isinstance(field, CompositeField):\n for f in field.fields:\n fields.extend(gather_subfields(f))\n elif isinstance(field, ArrayField):\n fields = []\n fields.extend(gather_subfields(field.itemtype))\n\n return fields\n\n types = []\n for method in self.methods:\n types.extend([method.request, method.response])\n for field in method.request.fields:\n types.extend(gather_subfields(field))\n for field in method.response.fields:\n types.extend(gather_subfields(field))\n return types" ]

[ "0.6395949", "0.61097467", "0.6098791", "0.60910064", "0.6064942", "0.60378265", "0.60336834", "0.6015646", "0.5942752", "0.5909887", "0.5851412", "0.5845181", "0.5836261", "0.58322257", "0.57702565", "0.5755678", "0.572548", "0.5714109", "0.5702991", "0.5687666", "0.56698954", "0.5647705", "0.5608563", "0.558962", "0.5584847", "0.55575424", "0.5492886", "0.5483108", "0.54575574", "0.54563993" ]

[ "def partial ( index , func , x , h = 0 , I = 2 , err = False ) :\n \n if len(x) <= index :\n raise AttributeError(\"Invalid argument length/index %d/%d\" % ( len(x) , index ) )\n \n _x = [ float(a) for a in x ]\n \n ## create wrapper function \n def _wrap ( z ) :\n _z = _x[index] \n _x[index] = z\n _r = func ( *_x )\n _x[index] = _z\n return _r\n \n x_i = _x[ index ]\n return derivative ( _wrap , x = x_i , h = h , I = I , err = err )", "def dalf(x):\n return derivative(alf, x, dx=1e-6)", "def first_derivative(x, f):\n deriv = np.diff(f, n=1)\n deriv = np.insert(deriv, -1, 2 * deriv[-1] - deriv[-2])\n dx = x[1] - x[2]\n return deriv / dx", "def derivative(func: Callable, x: float, delta: float) -> float:\n return (func(x + delta) - func(x - delta)) / (2.0 * delta)", "def ddalf(x):\n return derivative(dalf, x, dx=1e-6)", "def ddalf(x):\n return derivative(dalf, x, dx=1e-6)", "def ddalf(x):\n return dalf_spl.derivatives(x)[1]", "def ddalf(x):\n return dalf_spl.derivatives(x)[1]", "def partial_derivative(self, parameter_location):\n self.decrement_parameter(parameter_location)\n cost_minus = self.evaluate_function()\n\n self.increment_parameter(parameter_location)\n self.increment_parameter(parameter_location)\n cost_plus = self.evaluate_function()\n\n derivative = self._derivative(cost_minus, cost_plus)\n\n self.decrement_parameter(parameter_location)\n return derivative", "def lie_derivative(h, f, x, n):\n if n == 0:\n return h\n elif n == 1:\n return h.jacobian(x) * f\n else:\n return lie_derivative(lie_derivative(h, f, x, 1), f, x, n - 1)", "def _derivative_(self, x, diff_param=None):\n return 2*exp(-x**2)/sqrt(pi)", "def test_partial_derivative_f1(self):\r\n # Verified with Wolfram Alpha.\r\n\r\n # f2 > 0\r\n obs = self.estimator1._partial_derivative_f1(2, 3, 10, 42)\r\n assert_almost_equal(obs, 1.22672908818)\r\n\r\n # f2 == 0\r\n obs = self.estimator1._partial_derivative_f1(2, 0, 10, 42)\r\n assert_almost_equal(obs, 1.272173492918482)\r\n\r\n # f1 == 0, f2 == 0\r\n obs = self.estimator1._partial_derivative_f1(0, 0, 10, 42)\r\n assert_almost_equal(obs, 1.2961664362634027)", "def derivative(f, x, epsilon = 1e-10):\n\n x_ = x + epsilon\n value = (f(x_) - f(x)) / epsilon\n\n return value", "def nth_derivative(f, x, n):\n h = 10e-2\n out_h = 1/(h**n)\n out = 0\n for k in range(0, n+1):\n out += (-1)**(k+n)*choose(n,k)*f(x +k*h)\n return out_h*out", "def eval_numerical_gradient(f, x, verbose = True, h = 0.00001):\n fx = f(x) # evaluate function value at original point\n grad = np.zeros_like(x) # iterate over all indexese in x\n it = np.nditer(x, flags = ['multi_index'], op_flags = ['readwrite'])\n while not it.finished:\n # evaluate function at x+h\n ix = it.multi_index\n oldval = x[ix]\n x[ix] = oldval + h # increment by h\n fxph = f(x) # evaluate f(x+h)\n x[ix] = oldval - h\n fxmh = f(x) # evaluate f(x-h)\n x[ix] = oldval # restore\n \n #compute the partial derivative with centered fromula.\n grad[ix] = (fxph - fxmh) / (2 * h)\n if verbose:\n print(ix, grad[ix])\n it.iternext()\n return grad", "def eval_numerical_gradient(f, x, verbose=True, h=0.00001):\n\n fx = f(x) # evaluate function value at original point\n grad = np.zeros_like(x)\n # iterate over all indexes in x\n it = np.nditer(x, flags=['multi_index'], op_flags=['readwrite'])\n while not it.finished:\n\n # evaluate function at x+h\n ix = it.multi_index\n oldval = x[ix]\n x[ix] = oldval + h # increment by h\n fxph = f(x) # evalute f(x + h)\n x[ix] = oldval - h\n fxmh = f(x) # evaluate f(x - h)\n x[ix] = oldval # restore\n\n # compute the partial derivative with centered formula\n grad[ix] = (fxph - fxmh) / (2 * h) # the slope\n if verbose:\n print(ix, grad[ix])\n it.iternext() # step to next dimension\n\n return grad", "def eval_numerical_gradient(f,x):\n\n\tgrad = np.zeros(x.shape)\n\th = 0.0001\n\n\t# iterate over all indexes in x\n\tit = np.nditer(x, flag = ['multi_index'], op_flags = ['readwrite'])\n\n\twhile not it.finished:\n\t\tix = it.multi_index\n\t\told_value = x[ix]\n\n\t\tx[ix] = old_value + h\n\t\tfxh_left = f(x)\n\n\t\tx[ix] = old_value - h\n\t\tfxh_right = f(x)\n\n\t\tx[ix] = old_value\n\n\t\t# compute the partial derivative\n\t\tgrad[ix] = (fxh_left - fxh_right) / (2 * h)\n\t\tit.iterate()\n\n\treturn grad", "def newton1d(f, df, ddf, x, niter=10):\n for i in xrange(niter):\n x_new = x - df(x)/ddf(x)\n x = x_new\n return x", "def fundemental_derivative(tab, spec, *XYf):\n if tab._backend != 'vdw':\n raise ValueError('This derived variable is only compatible with the vdw backend!')\n XYf_DT = XYf[:]\n rho = XYf_DT[0]\n units = EosUnits(tab.Pt_DT._requested_units, 'cgs')\n Pt = tab.get_table('P{s}_DT', spec)(*XYf_DT)*units.o2r('P')\n delta = tab.Pt_DT['delta']\n a = tab.Pt_DT['a']\n b = tab.Pt_DT['b']\n P_frac_1 = (Pt + a*rho**2)/(1./rho - b)**2\n P_frac_2 = rho*(Pt + a*rho**2)/(1./rho - b)\n num = (delta+1)*(delta+2) * P_frac_1 - 6*a*rho**4 \n denum = 2*(delta+1)*P_frac_2 - 4*a*rho**4\n return num/denum", "def func_deriv(x,remain):\n #df_x0 = -1*remain[0]/x[0]**2\n #df_x1 = -1*remain[2]/x[1]**2\n #df_x2 = -1*remain[1]/x[2]**2\n return np.array(-1*remain/x**2)", "def _partial_derivative_f1(self, f1, f2, m_star, n):\r\n if f1 > 0 and f2 > 0:\r\n a_0 = self._calculate_a_0(f1, f2, n)\r\n term1 = (m_star * a_0 ** (m_star - 1)) / n\r\n term2 = (f1 * (1 - a_0 ** m_star)) / f2\r\n return 1 - term1 + term2\r\n else:\r\n a_1 = self._calculate_a_1(f1, f2, n)\r\n term1 = (m_star * f1) * a_1 ** (m_star - 1)\r\n term2 = n * (f1 - 1)\r\n term3 = (f1 - 1) * (1 - a_1 ** m_star)\r\n term4 = 2 * (f2 + 1)\r\n term5 = f1 * (1 - a_1 ** m_star)\r\n return 1 - (term1 / term2) + (term3 / term4) + (term5 / term4)", "def ddx(n, dx, f):\n fx = np.zeros(n)\n for j in range(n):\n fx[j] = (f[get_index(j+1, n)]-f[get_index(j-1, n)])/(2*dx)\n return fx", "def _centred_first_derivs(self, f):\n return ((f[2:, 1:-1] - f[0:-2, 1:-1]) / (2 * self._dx),\n (f[1:-1, 2:] - f[1:-1, 0:-2]) / (2 * self._dy))", "def func_deriv(x, sign=1.0):\n dfdx0 = sign*(-2*x[0] + 2*x[1] + 2)\n dfdx1 = sign*(2*x[0] - 4*x[1])\n return np.array([ dfdx0, dfdx1 ])", "def newton1d(f, df, ddf, x, niter=10):\n\n x_0 = x\n x_k = x\n\n for i in xrange(niter):\n x_k1 = x_k - df(x_k)/ddf(x_k)\n x_k = x_k1\n\n return x_k", "def derivative_func(t, x, Approx_func):\n return x.dot(Approx_func)", "def get_partial_derivatives(self) -> List[Callable]:\n pass", "def firstderivative(func, x, samples):\n \n a = 0.5 * VERYSMALL \n \n fxmina = func(x - a, samples)\n fxplusa = func(x + a, samples)\n \n return (fxplusa - fxmina) / (2 * a)", "def newtons_method_1d(f, df_dx, x0, tol):\n # begin solution\n x = x0\n while abs(f(x)) > tol:\n x -= f(x) / df_dx(x)\n return x\n # end solution", "def derivative(self,x,dx=None):\n if dx is None:\n x = np.array(x,copy=False)\n if len(x.shape)>0:\n dx = np.convolve(x,[1,-1],mode='valid')\n dx = np.insert(dx,0,np.mean(dx))/2\n else:\n dx = 1\n return (self(x+dx)-self(x))/dx" ]

[ "0.75414956", "0.736744", "0.73278105", "0.7199806", "0.71724826", "0.71724826", "0.714068", "0.702706", "0.69684196", "0.6934339", "0.69137716", "0.688898", "0.6880599", "0.68199027", "0.68155897", "0.67816776", "0.67622113", "0.67365223", "0.6713649", "0.66943544", "0.65928864", "0.6592786", "0.65836066", "0.6558796", "0.6495569", "0.6489457", "0.6479412", "0.6471656", "0.64615464", "0.6449889" ]

[ "def jacobian_func(f):\n jacobian = jacfwd(f)\n return jacobian", "def jacobian(f, x):\n\n B, N = x.shape\n x.requires_grad = True\n in_ = torch.zeros(B, 1)\n \n y = f(in_, x)\n jacobian = list()\n \n for i in range(N):\n v = torch.zeros_like(y)\n v[:, i] = 1.\n dy_i_dx = torch.autograd.grad(y,\n x,\n grad_outputs=v,\n retain_graph=True,\n create_graph=True,\n allow_unused=True)[0] # shape [B, N]\n jacobian.append(dy_i_dx)\n\n jacobian = torch.stack(jacobian, dim=2).requires_grad_()\n\n return jacobian", "def jacobian(self, x):\n pass", "def jacobian(f, x, dx):\n x = np.atleast_1d(x)\n dx = np.atleast_1d(dx)\n nx = len(x)\n ny = 0\n jacobi = None\n e = np.zeros(nx)\n for ix in xrange(nx):\n e *= 0\n e[ix] = 1\n deriv = np.atleast_1d((f(x + e * dx) - f(x - e * dx)) / (2 * dx[ix]))\n if ix == 0:\n ny = len(deriv)\n jacobi = np.empty((ny, nx))\n jacobi[:, ix] = deriv\n return jacobi", "def jacobian(self,x,p,fun):\n n = self.n\n y = fun(x,p)\n h = 1e-4\n nout = np.size(y)\n dfdx = np.zeros((nout,n))\n for j in range(n):\n dx1 = np.zeros(n)\n dx2 = np.zeros(n)\n dx1[j] = -h\n dx2[j] = h\n dfdx[:,j] = (fun(x+dx2,p)-fun(x+dx1,p))/(2*h)\n return dfdx", "def JacobianFunction(p,x,y,z):\n \n n = len(x)\n \n J = np.array([ np.ones((n)),x,x**2,y,y**2,x*y ])\n \n return J", "def jacobian_i(self, x):\n return np.matrix([-x**3, -x**2, -x, -1])", "def jacobian_g(self, x, out=None, **kwargs):\n return self._base_nlp.jacobian_d(x, out=out)", "def jacobian_c(self, x, out=None, **kwargs):\n return self._base_nlp.jacobian_c(x, out=out, **kwargs)", "def jacobian_d(self, x, out=None, **kwargs):\n return self._base_nlp.jacobian_d(x, out=out, **kwargs)", "def jacobian_d(self, x, out=None, **kwargs):\n return self._base_nlp.jacobian_d(x, out=out, **kwargs)", "def jacobian(self, x):\n x_ = np.atleast_2d(x)\n if self.normalize:\n x_ = (x_ - self.sample_mean) / self.sample_std\n s_ = (self.samples - self.sample_mean) / self.sample_std\n else:\n s_ = self.samples\n\n fx, jf = self.reg_model(x_)\n rx, drdx = self.corr_model(x=x_, s=s_, params=self.corr_model_params, dx=True)\n y_grad = np.einsum('ikj,jm->ik', jf, self.beta) + np.einsum('ijk,jm->ki', drdx.T, self.gamma)\n if self.normalize:\n y_grad = y_grad * self.value_std / self.sample_std\n if x_.shape[1] == 1:\n y_grad = y_grad.flatten()\n return y_grad", "def jacobian(self, dt):\n return self._F_cache", "def jacobian_g(self, x, out=None, **kwargs):\n return self._base_nlp.jacobian_g(x, out=out, **kwargs)", "def jacobian(self, x1, x2, out=None):\n raise NotImplementedError", "def jacobian_c(self, x, out=None, **kwargs):\n return empty_matrix(0, self.nx)", "def jacobian(self,x,y,l,a):\n J = np.zeros([*x.shape,2,2])\n\n J = _jacobian(x,y,l,a,J)\n\n return J", "def numerical_jacobian (fhandle, x, **args):\n \n y = fhandle (x, **args)\n numRows, numCols = (len (y), len (x))\n J = np.zeros ((numRows, numCols))\n\n for col in range (0, numCols):\n xPrime = x.copy ()\n deltaX = max (1e-4*x[col], 1e-6)\n xPrime[col] += deltaX\n yPrime = fhandle (xPrime, **args)\n J[:, col] = (yPrime - y) / deltaX\n\n return J", "def jacobian(self, dt):\n raise NotImplementedError", "def jacobian(x, u):\n yaw = x[2, 0]\n v = u[0, 0]\n jac = np.array([\n [1.0, 0.0, -dt * v * math.sin(yaw), dt * math.cos(yaw)],\n [0.0, 1.0, dt * v * math.cos(yaw), dt * math.sin(yaw)],\n [0.0, 0.0, 1.0, 0.0],\n [0.0, 0.0, 0.0, 1.0]])\n\n return jac", "def jacobian(self, c):\n\n raise NotImplementedError", "def jacobian(self, xs):\n rx_list = []\n for nx,x in enumerate(xs):\n \n numpy.testing.assert_array_almost_equal(self.independentVariableShapeList[nx], numpy.shape(x), err_msg = '\\ntaped xs[%d].shape != forward xs[%d]\\n'%(nx,nx))\n rx = numpy.ravel(x)\n rx_list.append(rx)\n self.x = numpy.concatenate(rx_list)\n return wrapped_functions.jacobian(self.tape_tag, self.x)", "def jacobian(self, t, x, u, w):\n a= u[0]\n theta = x[2]\n v = x[3]\n fx = np.array([[0, 0, 0, 0],\n [0, 0, 0, 0],\n [-v*np.sin(theta), v*np.cos(theta), 0, 0],\n [np.cos(theta), np.sin(theta), 0, 0]])\n fu = np.array([[0, 0, 0, 1],\n [0, 0, 1, 0]])\n w = w * self.w_scale\n fw = np.array([[np.cos(theta), - np.sin(theta), 0, 0],\n [np.sin(theta), np.cos(theta), 0, 0],\n [0, 0, v, 0],\n [0, 0, 0, v]])\n return [fx, fu, fw]", "def newton_jacobian(f, x0, Jf, eps=1e-10):\n # Initialization\n globvar.ncalls = 0\n x = np.copy(x0)\n n = len(x)\n J = np.zeros((n, n), dtype='float64')\n fx = f(x)\n\n # Begin root search\n while True:\n globvar.ncalls += 1\n\n # Calculate Jacobian\n J = Jf(x)\n\n # Decompose and solve using Given's rotations\n decomp(J)\n Dx = -fx\n solve(J, Dx)\n\n # Begin backtracking linesearch\n lamb = 2.0\n while True: \n lamb /= 2\n y = x + Dx * lamb\n fy = f(y)\n\n fynorm = np.linalg.norm(fy)\n fxnorm = np.linalg.norm(fx)\n\n if (fynorm < (1 - lamb / 2) * fxnorm) or (lamb < (1 / 128.0)):\n break\n\n # Save latest approximation\n x = y\n fx = fy\n\n fxnorm = np.linalg.norm(fx)\n if fxnorm < eps:\n break\n\n return x", "def jacobian(self,var,g=None):\n if (g==None):g=self.g\n jac=np.zeros([self.n+1,self.n])\n for i in range(self.n):\n for j in range(self.n):\n if(i==j): jac[i][j]=2.*(var[i]+1.)-g*np.sum([self.XXZ.Z(i,k) for k in range(self.n) if k!=i])\n else: jac[i][j]=g*self.XXZ.Z(i,j)\n for i in range(self.n):\n jac[self.n][i]=1.\n return jac", "def jacobian(self, theta, force=False):\n \n # Update the internal solution\n self.solution_update(theta, force)\n \n # Run the internal jacobian calculation\n return self.compute_jacobian()", "def jacobian(self, b):\n \n # Substitute parameters in partial derivatives\n subs = [pd.subs(zip(self._b, b)) for pd in self._pderivs]\n # Evaluate substituted partial derivatives for all x-values\n vals = [sp.lambdify(self._x, sub, \"numpy\")(self.xvals) for sub in subs]\n # Arrange values in column-major order\n return np.column_stack(vals)", "def jacobian(self, dt):\n if dt not in self._F_cache:\n d = self._dimension\n with torch.no_grad():\n F = eye_like(self.sa2, d)\n F[: d // 2, d // 2 :] = dt * eye_like(self.sa2, d // 2)\n self._F_cache[dt] = F\n\n return self._F_cache[dt]", "def jacobianF_x(theta, delta_s, delta_theta):\n\n Fx = np.array(\n [[1, 0, -delta_s * np.sin(theta + delta_theta / 2)], [0, 1, delta_s * np.cos(theta + delta_theta / 2)],\n [0, 0, 1]])\n return Fx", "def fd_jacobian(self,y):\n res0 = self.residual(y)\n eps = 1e-6\n dofs = y.shape[0]\n jac_approx = np.zeros((dofs,dofs))\n for i in range(dofs):\n y_temp = np.copy(y)\n y_temp[i]+=eps\n\n r2 = self.residual(y_temp)\n dr = (r2-res0)/eps\n for j in range(dofs):\n jac_approx[j,i] = dr[j]\n \n return jac_approx" ]

[ "0.8722021", "0.8635077", "0.8538924", "0.84087753", "0.8155197", "0.801174", "0.79759496", "0.78006744", "0.7767167", "0.7756485", "0.7756485", "0.77275866", "0.7719678", "0.7650497", "0.76414347", "0.7623823", "0.7577455", "0.7467225", "0.7465109", "0.7456953", "0.7437073", "0.7405572", "0.7370815", "0.73680365", "0.7350347", "0.7300555", "0.7233878", "0.7083227", "0.7055335", "0.7046535" ]

[ "def calc_syndrome(codeword, n):\r\n sym = 0\r\n for i in range(1, n):\r\n if codeword[i]:\r\n sym ^= i\r\n extra_parity = calc_parity_vector(codeword)\r\n if extra_parity == codeword[0]:\r\n if sym == 0:\r\n return 0, sym\r\n else:\r\n return 2, sym\r\n else:\r\n if sym >= n:\r\n pass\r\n else:\r\n codeword[sym] ^= 1\r\n return 1, sym", "def parity(n):\n # bin(n) returns 0b.... \n # bin(n)[2:] trims \"ob\"\n return sum(int(x) for x in bin(n)[2:]) % 2", "def parity(it):\n \n return sum(it)%2", "def parity(p):\n\n f = dict(zip(sorted(p), p))\n seen, neven = set(), 0\n\n for x in p:\n if x in seen:\n continue\n\n c, l = x, 0\n while c not in seen:\n seen.add(c)\n l += 1\n c = f[c]\n\n neven += (l - 1) % 2\n\n return 1 if neven % 2 == 0 else -1", "def is_odd_parity(n=None):\n\tn=int(n,16)\n\tc = 0\n\twhile n:\n\t\tc += 1\n\t\tn &= n - 1\n\tif c%2: \n\t\treturn True\n\telse:\n\t\treturn False", "def parity(self):\n if self._cyclic_form is not None:\n return (self.size - len(self._cyclic_form)) % 2\n\n return perm_af_parity(self.array_form)", "def calc_parity_vector(parity_vector):\r\n return reduce(lambda x, y: x ^ y, parity_vector[1:])", "def parity_odd(x):\r\n\t\t\tx = x ^ (x >> 4)\r\n\t\t\tx = x ^ (x >> 2)\r\n\t\t\tx = x ^ (x >> 1)\r\n\t\t\treturn x & 1", "def xor(a, b):", "def gen_parity_oracle():\n secret = base64.b64decode(\"VGhhdCdzIHdoeSBJIGZvdW5kIHlvdSBkb24ndCBwbGF5IGF\"\n \"yb3VuZCB3aXRoIHRoZSBGdW5reSBDb2xkIE1lZGluYQ==\")\n r = rsa.Rsa(e=3, bits=1024)\n ciphertext = r.encrypt_bytes(secret)\n\n def parity_oracle(c):\n p = r.decrypt_bytes(c)\n return p[-1] % 2 == 0\n\n return parity_oracle, ciphertext, r.public_key()", "def odd_syn(v):\n coc = gc.vect_to_vintern(v)\n t = gc.syndrome_table[coc & 0x7ff ]\n return ( (1 << (t & 31)) ^ (1 << ((t >> 5) & 31)) ^\n (1 << ((t >> 10) & 31)) )", "def parity(x):\n\n res = 0\n while x:\n # XOR flips last bit\n res ^= 1\n # x & (x - 1) removes lowest set bit\n x &= x - 1\n return bool(res)", "def perm_af_parity(pi):\n n = len(pi)\n a = [0] * n\n c = 0\n for j in xrange(n):\n if a[j] == 0:\n c += 1\n a[j] = 1\n i = j\n while pi[i] != j:\n i = pi[i]\n a[i] = 1\n return (n - c) % 2", "def xor_columns(col, parity):\n result = []\n for i in range(len(col)):\n result.append(col[i] ^ parity[i])\n return result", "def parity64(x):\n\n x ^= x >> 32\n x ^= x >> 16\n x ^= x >> 8\n x ^= x >> 4\n x ^= x >> 2\n x ^= x >> 1\n return bool(x & 1)", "def solution(A):\n xor = 0\n for item in A:\n xor ^= item\n return xor", "def __parity_of_permutation(cls, lst: Iterable) -> int:\n\t\tparity = 1\n\t\tlst = list(lst)\n\t\tfor i in range(0, len(lst) - 1):\n\t\t\tif lst[i] != i:\n\t\t\t\tparity *= -1\n\t\t\t\tmn = SquareMatrix.__idx_of_minimum(lst[i:]) + i\n\t\t\t\t\n\t\t\t\tlst[i], lst[mn] = lst[mn], lst[i]\n\t\treturn parity", "def parity(n):\n if n%2==0:\n p=1\n else:\n p=-1\n return p", "def xor(it):\n return 0 if it[0]==it[1] else 1", "def xor_inplace(a,b):", "def stone_parity(self, board):\n computer_score = sum(sum(board == self.computer_num))\n opponent_score = sum(sum(board == self.opponent_num))\n return 100 * (computer_score - opponent_score) / (computer_score + opponent_score)", "def convolve(self, frame):\n\n parity_bits = ()\n for term in self.conv_code:\n parity = 0\n for idx, bit in enumerate(term):\n parity = parity ^ (bit and frame[idx])\n parity_bits += (parity,)\n\n return(tuple(parity_bits))", "def break_single_key_xor(b1):\n\n max_score = None\n result_plaintext = None\n key = None\n\n for i in range(256):\n b2 = [i] * len(b1)\n plaintext = bytes(xor(bytearray(b1), b2))\n line_score = score(plaintext)\n\n if line_score > max_score or not max_score:\n max_score = line_score\n result_plaintext = plaintext\n key = chr(i)\n return key, result_plaintext", "def double_xor(it):\n\n return [xor(it[2*i:2*i+2]) for i in range(len(it)/2)]", "def R(gammas, column, public_key):\n return reduce(_XOR, gammas[np.where(column == 1)], public_key.encrypt(0))", "def is_odd(self):\n return S(self.parity()).is_odd", "def xor(self):\n\n \"\"\" fisrt i pick element we need to xor each other and put theme in list\"\"\"\n bits_to_xor = []\n for i in self.xor_input:\n bits_to_xor.append(self.state[i])\n\n \"\"\" next xor the list elemet usin reduce with lambda func.\"\"\"\n res = reduce(lambda x, y: x ^ y, bits_to_xor)\n return res", "def __xor__(self,v2):\n\t\treturn np.cross(self._vec,v2._vec)", "def __xor__(self, other):\n a, b = Trits.match_length(self, other)\n return Trits([x ^ y for x, y in zip(a, b)])", "def __xor__(self, other):\r\n return self + other - 2 * self * other" ]

[ "0.7136234", "0.69508874", "0.683343", "0.6732784", "0.6722891", "0.6712324", "0.6674293", "0.6639559", "0.662268", "0.66004324", "0.65794253", "0.65109783", "0.64871514", "0.6413661", "0.6333544", "0.6296736", "0.6278239", "0.6260027", "0.6147197", "0.6048034", "0.59243625", "0.58562624", "0.5842636", "0.5841551", "0.58283645", "0.5806468", "0.57839704", "0.5767828", "0.5750372", "0.5743609" ]

[ "def generate_negative_sample_list(self, xc_start):\r\n return [self.get_random_Nb_sample(xc_start) for _ in range(self.Nb)]", "def _sample_negative(self, positives):\n max_val = self._H * self._W\n num_pos = len(positives)\n num_neg = int(num_pos * self._sample_ratio)\n positives = np.round(positives).astype(\"int\")\n positives = positives[:, :2]\n positives = np.ravel_multi_index((positives[:, 0], positives[:, 1]), (self._H, self._W))\n if self._sample_ratio < 70:\n negative_indices = []\n while len(negative_indices) < num_neg:\n negative = np.random.randint(0, max_val)\n if negative not in positives:\n negative_indices.append(negative)\n else:\n allowed = list(set(np.arange(0, max_val)) - set(positives.ravel()))\n np.random.shuffle(allowed)\n negative_indices = allowed[:num_neg]\n negative_indices = np.unravel_index(negative_indices, (self._H, self._W))\n return negative_indices", "def _sample_free_negative(self, kit_mask):\n max_val = self._H * self._W\n num_neg = int(100 * self._sample_ratio)\n negative_indices = []\n while len(negative_indices) < num_neg:\n negative_indices.append(np.random.randint(0, max_val))\n negative_indices = np.vstack(np.unravel_index(negative_indices, (self._H, self._W))).T\n idxs = np.random.choice(np.arange(len(kit_mask)), size=30, replace=False)\n inside = kit_mask[idxs]\n negative_indices = np.vstack([negative_indices, inside])\n return negative_indices", "def negative_sampling(self):\n \n self.train_arr = []\n sample_list = np.random.choice(list(range(self.item_count)), size = 10 * len(self.interactions) * self.num_ns)\n \n sample_idx = 0\n for user, pos_item, _ in self.interactions:\n ns_count = 0\n \n while True:\n neg_item = sample_list[sample_idx]\n if not is_visited(self.rating_mat, user, neg_item):\n self.train_arr.append((user, pos_item, neg_item))\n sample_idx += 1\n ns_count += 1\n if ns_count == self.num_ns:\n break\n \n sample_idx += 1", "def create_neg_sample_list(word_counts):\n negatives = []\n pow_freq = np.array(list(word_counts.values()))**0.75\n sum_pow_freq = np.sum(pow_freq)\n ratio = pow_freq / sum_pow_freq\n count = np.round(ratio * 1e6)\n max_sample_id = len(count)\n for wid, c in enumerate(count):\n negatives += [wid] * int(c)\n negatives = np.array(negatives)\n np.random.shuffle(negatives)\n return negatives", "def _sample_neg(self, assign_result, num_expected, **kwargs):\n neg_inds = torch.nonzero(assign_result.gt_inds == 0)\n if neg_inds.numel() != 0:\n neg_inds = neg_inds.squeeze(1)\n if len(neg_inds) <= num_expected:\n return neg_inds\n else:\n return self.random_choice(neg_inds, num_expected)", "def generate_negative_samples(self, data, sampled_data, zeros=[], validation=False):\n negative_sampled_data = []\n negative_sampled_indices = []\n for sample in sampled_data:\n i = data['pos'].index(sample) ## index of a particular move in a demo\n all_num = 0\n for which, num in enumerate(data['leng_pos']):\n all_num += num\n if all_num > i:\n which_demo = which ## index of a demo the move with index i comes from\n break\n\n sum_neg_lengths = sum(data['leng_neg'][:which_demo])\n\n key = sum_neg_lengths-1 \n value = sum_neg_lengths + data['leng_neg'][which_demo]\n demo_negative_data = data['neg'][key : value]\n state, action = sample\n for demo_state, demo_action in demo_negative_data:\n if demo_state == state:\n negative_sampled_data.extend([(demo_state, demo_action)])\n demo_index = data['neg'].index((demo_state, demo_action))\n negative_sampled_indices.append(demo_index)\n\n if not validation:\n num_pos = sum(self.pipeline_y == 1)\n num_neg = len(negative_sampled_data)\n pos_sample = self.pipeline_X[:num_pos, :]\n neg_sample = self.pipeline_X[num_pos + negative_sampled_indices, :]\n y_vector = [1] * num_pos + [0] * num_neg\n ######################### Mouselab ad-hc #########################\n ########################## Removing 0's ##########################\n non_zero = [self.pipeline_X[i, :] for i in range(num_pos)\n if i not in zeros]\n pos_sample = vstack(non_zero) if non_zero != [] else self.pipeline_X[0,:]\n num_pos = pos_sample.shape[0]\n y_vector = [1] * num_pos + [0] * num_neg\n ##################################################################\n\n self.pipeline_X = vstack((pos_sample, neg_sample))\n self.pipeline_y = np.array(y_vector, dtype='uint8')\n \n return negative_sampled_data", "def add_negative_samples(skipgram_data, unigrams_table, neg_examples_size=5):\n sg_neg_examples = []\n total_data = len(skipgram_data)\n for i, sg in tqdm(enumerate(skipgram_data), desc=\"Processing neg. samples ({} in total)\".format((total_data-1)),\n unit= \" neg. samples\"):\n for gram in sg:\n gram += negative_sampling(word_input=gram[0], target=gram[1],\n unigrams_table=unigrams_table, neg_examples_size=neg_examples_size)\n sg_neg_examples.append(gram)\n return sg_neg_examples", "def sample_data(self):\n print(\"Start data sampling...\")\n positives, negatives = [], []\n groups = self.group_data()\n for group in groups:\n positives.extend(list(combinations(group, 2)))\n for _ in range(len(positives)):\n group1, group2 = sample(groups, 2)\n negatives.append((sample(group1, 1)[0], sample(group2, 1)[0]))\n print(f\"\\x1b[32mSuccessfully completed data sampling ({len(positives)} x 2).\\x1b[0m\")\n return [negatives, positives]", "def get_negative_sample(context, num, prob, Gn):\n\tnegative_list = []\n\twhile len(negative_list) < Gn:\n\t\tnegative_sample = np.random.choice(num, p=prob.ravel())\n\t\tif negative_sample != context:\n\t\t\tnegative_list.append(negative_sample)\n\t\telse:\n\t\t\tpass\n\treturn np.array([negative_list])", "def sample(self):\n # For each row: round(random .* (max - min) + min, 0)\n random_array = prng.np_random.rand(self.num_discrete_space)\n return [int(x) for x in np.floor(np.multiply((self.high - self.low + 1.), random_array) + self.low)]", "def _sample_neg(self, assign_result, num_expected, **kwargs):\n neg_inds = torch.nonzero(assign_result.gt_inds == 0)\n if neg_inds.numel() != 0:\n neg_inds = neg_inds.squeeze(1)\n if len(neg_inds) <= num_expected:\n repeat_ = num_expected // neg_inds.numel()\n return torch.cat((neg_inds.repeat(repeat_), self.random_choice(neg_inds, num_expected % neg_inds.numel())))\n else:\n return self.random_choice(neg_inds, num_expected)", "def samplePositions(self):\n samples = []\n for i in range(self.sampleIter):\n x = random.randint(-self.sampleRange, self.sampleRange)\n y = random.randint(-self.sampleRange, self.sampleRange)\n x += self.currentPosition[0]\n y += self.currentPosition[1]\n if (x, y) in self.graph.keys():\n if self.graph[(x, y)] == 0:\n samples.append((x, y))\n return samples", "def __call__(self, *args):\n r = np.random.rand(*args)\n if type(r) is float:\n samples = self.values[(r < self.p).nonzero()[0][0]]\n elif type(r) is np.ndarray:\n samples = np.array(\n [self.values[np.nonzero(x < self.p)[0][0]] \n for x in r.flat]).reshape(r.shape)\n return samples", "def samples(self):\n return self._values[:self.nsamples]", "def test_no_duplicates_and_positives_in_negative_sample(self):\n model = PoincareModel(self.data_large, negative=3)\n positive_nodes = model.node_relations[0] # Positive nodes for node 0\n num_samples = 100 # Repeat experiment multiple times\n for i in range(num_samples):\n negatives = model._sample_negatives(0)\n self.assertFalse(positive_nodes & set(negatives))\n self.assertEqual(len(negatives), len(set(negatives)))", "def small_sample(num):\n sample = [0] * num\n for i in range(num):\n u = random.randint(0, 3)\n if u == 3:\n sample[i] = -1\n if u == 2:\n sample[i] = 1\n return sample", "def getNegativeSamples(target, dataset, K):\n\n indices = [None] * K\n for k in range(K):\n newidx = dataset.sampleTokenIdx()\n while newidx == target:\n newidx = dataset.sampleTokenIdx()\n indices[k] = newidx\n return indices", "def getNegativeSamples(target, dataset, K):\n\n indices = [None] * K\n for k in xrange(K):\n newidx = dataset.sampleTokenIdx()\n while newidx == target:\n newidx = dataset.sampleTokenIdx()\n indices[k] = newidx\n return indices", "def getNegativeSamples(target, dataset, K):\n\n indices = [None] * K\n for k in xrange(K):\n newidx = dataset.sampleTokenIdx()\n while newidx == target:\n newidx = dataset.sampleTokenIdx()\n indices[k] = newidx\n return indices", "def getNegativeSamples(target, dataset, K):\n\n indices = [None] * K\n for k in xrange(K):\n newidx = dataset.sampleTokenIdx()\n while newidx == target:\n newidx = dataset.sampleTokenIdx()\n indices[k] = newidx\n return indices", "def sampleNegativeImages(images, negativeSample, size=(64, 64), N=200):\n # Initialize internal state of the random number generator.\n random.seed(1)\n\n # Final image resolution.\n w, h = size[0], size[1]\n\n resizedImages = []\n \n for image in images:\n res = cv2.resize(image, dsize=(1728, 1152), interpolation=cv2.INTER_CUBIC)\n resizedImages.append(res)\n\n for image in resizedImages:\n images.append(image)\n\n # Read all images from the negative list.\n\n i = 0\n for image in images:\n\n if i > 4:\n N = 100\n for j in range(N):\n # random.random produced random number in [0,1) range\n y = int(random.random() * (len(image) - h))\n x = int(random.random() * (len(image[0]) - w))\n sample = image[y:y + h, x:x + w].copy()\n negativeSample.append(sample)\n\n # Create Afine transform\n afine_tf = tf.AffineTransform(shear = random.uniform(-0.2,0.2))\n # Apply transform to image data\n shearedImage = tf.warp(sample, inverse_map=afine_tf)\n negativeSample.append(shearedImage)\n i = i + 1\n\n return", "def under_sample(pos_nids, neg_nids, scale=1):\n index = np.arange(neg_nids.shape[0])\n index = np.random.RandomState().permutation(index)\n N = min(int(pos_nids.shape[0] * scale), neg_nids.shape[0])\n index = index[0: N]\n neg_sampled = neg_nids[index]\n sampled_nids = torch.cat((pos_nids, neg_sampled))\n\n return sampled_nids", "def negative_sampling(word_input, target, unigrams_table, neg_examples_size=5):\n negative_examples = []\n while len(negative_examples) is not neg_examples_size:\n neg_sample = np.random.choice(unigrams_table)\n # Make sure that the negative example is not the same as the training or as the target.\n # This will block if there only is one value within the unigram table\n if (neg_sample != word_input) and (neg_sample != target):negative_examples.append(neg_sample)\n else:pass\n return negative_examples", "def test_random_high_low_values(self):\n channel_count = 10\n low = -100\n high = 100\n gen = random_data(low=-100, high=100,\n channel_count=channel_count)\n data = [next(gen) for _ in range(100)]\n\n self.assertEqual(len(data), 100)\n\n for record in data:\n self.assertEqual(len(record), channel_count)\n for value in record:\n self.assertTrue(low <= value <= high)", "def samples(self):\n pass", "def get_data(self, t, st, total_data_number = 10000):\n \n X = []\n y = []\n num_pos = 0\n num_neg = 0\n num_other = 0\n for p in self.topic2phrs_zoo[t]:\n if num_pos > total_data_number // 2 and num_neg > total_data_number // 4:\n break\n if self.phrs2sub_topic[t][p][ self.topic2sub_topic[t][st] - 1 ] > 0:\n if num_pos >= -0:\n X.append( self.p2v[p] )\n y.append( 1 )\n num_pos += 1\n else:\n if num_neg <= num_pos // 2:\n X.append( self.p2v[p] )\n y.append( 0 )\n num_neg += 1\n labeled_sample = len(y)\n for i in range(len(y) // 3):\n p = self.topic2phrs_zoo['other'][np.random.randint(len(self.topic2phrs_zoo['other']))]\n X.append(self.p2v[p])\n y.append(0)\n num_other += 1;\n print(\"We get: \")\n print(num_pos, \" postive sample\")\n print(num_neg, \" sample of wrong intents\")\n print(num_other, \" sample of no intents\")\n \n return np.array(X), np.array(y)", "def samplePositiveImages(images, positiveSample, size=(64, 64), N=200):\n\n for image in images:\n \n rotated = imutils.rotate_bound(image, random.randint(-15,15))\n \n h, w, channels = rotated.shape\n cropped_img = rotated[w//2 - 64//2:w//2 + 64//2, h//2 - 64//2:h//2 + 64//2]\n\n positiveSample.append(image);\n positiveSample.append(cropped_img)\n positiveSample.append(np.fliplr(image))\n positiveSample.append(np.fliplr(cropped_img))\n \n supportList = []\n for img in positiveSample:\n supportList.append(img)\n\n for img in supportList:\n hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) #convert it to hsv\n hsv = hsv + 10\n img = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)\n positiveSample.append(img)\n \n hsv = hsv - 20\n img = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)\n positiveSample.append(img)\n\n return", "def sample_without_replacement(self, n=100):\n np.random.shuffle(self.logs)\n output = []\n for i in range(n):\n perf = self.logs[i]\n output.append(perf)\n return output", "def cut_samples_out( dataSet, fromValue, toValue ):\n newData = []\n for data in dataSet:\n newData.append( [] )\n\n for tup in zip(*dataSet):\n if fromValue <= tup[0] and tup[0] <= toValue:\n for i, val in enumerate( tup ):\n newData[i].append( val )\n \n return newData" ]

[ "0.67092043", "0.659947", "0.6404769", "0.6344144", "0.6319633", "0.6240499", "0.61978734", "0.61512685", "0.61288166", "0.6084549", "0.6071787", "0.6045156", "0.59752685", "0.59652776", "0.59609824", "0.59361213", "0.59042007", "0.58985853", "0.5882967", "0.5882967", "0.5882967", "0.5837222", "0.5816763", "0.5800822", "0.5795726", "0.57679117", "0.5764959", "0.57609", "0.5746065", "0.57175416" ]

[ "def _create_directory_tree(self, path, num_directories):\n assert num_directories >= 0\n if not num_directories:\n return\n\n self._create_directory(path)\n num_directories -= 1\n # Divide the remaining number of directories to create among 4\n # subdirectories in an approximate even fashion.\n for i in range(4, 0, -1):\n sub_dir_size = num_directories / i\n self._create_directory_tree(os.path.join(path, 'dir%d' % i), sub_dir_size)\n num_directories -= sub_dir_size", "def create_directories(path):\n directories = ['images', 'pdf', 'videos', 'audio', 'spreedsheet', 'text', 'scripts', 'docs', 'other']\n for directory in directories:\n create_directory(path, directory)", "def create_path(self, path):\n path_list = path.split(\"/\")\n done_path = self.parent_folder + \"/\"\n\n for directory in path_list:\n try:\n os.mkdir(done_path + directory + \"/\")\n except FileExistsError:\n done_path += directory + \"/\"", "def make_folders(path):\n path = extract_path_from_filepath(path)\n split_path = path.split('/')\n if (path[0] == '/'):\n\n path_inc = '/'\n else:\n path_inc = ''\n for ii in np.arange(len(split_path)):\n # if ii==0: path_inc=path_inc+split_path[ii]\n path_inc = path_inc + split_path[ii]\n if not os.path.exists(path_inc):\n os.makedirs(path_inc)\n path_inc = path_inc + '/'\n\n return", "def create_directory() -> None:\n slash_indexes = []\n for x in range(0, len(directory)):\n if directory[x] == \"/\" or directory[x] == \"\\\\\":\n slash_indexes.append(x)\n \n directories_to_create = []\n for x in range(0, len(slash_indexes)):\n if x == len(slash_indexes)-1:\n if os.path.isdir(directory[0:len(directory)]):\n existing_directory = directory[0:len(directory)]\n else:\n directories_to_create.append(directory[0:len(directory)])\n\n else: \n if os.path.isdir(directory[0:slash_indexes[x+1]]):\n existing_directory = directory[0:slash_indexes[x+1]]\n else:\n directories_to_create.append(directory[0:slash_indexes[x+1]])\n\n for _dir in directories_to_create:\n os.mkdir(_dir)", "def make_directories(path):\n\n os.mkdir('{}'.format(path))\n os.mkdir('{}/perturbed_cp'.format(path))\n os.mkdir('{}/perturbed_wing'.format(path))\n os.mkdir('{}/perturbed_wing/format_wing'.format(path))\n os.mkdir('{}/perturbed_wing/unformat_wing'.format(path))", "def mkdir(self, mdir, parents=False):\n assert mdir.startswith('/'), \"%s: invalid manta path\" % mdir\n parts = mdir.split('/')\n assert len(parts) > 3, \"%s: cannot create top-level dirs\" % mdir\n if not parents:\n self.put_directory(mdir)\n else:\n # Find the first non-existant dir: binary search. Because\n # PutDirectory doesn't error on 'mkdir .../already-exists' we\n # don't have a way to detect a miss on `start`. So basically we\n # keep doing the binary search until we hit and close the `start`\n # to `end` gap.\n # Example:\n # - mdir: /trent/stor/builds/a/b/c (need to mk a/b/c)\n # parts: ['', 'trent', 'stor', 'builds', 'a', 'b', 'c']\n # start: 4\n # end: 8\n # - idx: 6\n # d: /trent/stor/builds/a/b (put_directory fails)\n # end: 6\n # - idx: 5\n # d: /trent/stor/builds/a (put_directory succeeds)\n # start: 5\n # (break out of loop)\n # - for i in range(6, 8):\n # i=6 -> d: /trent/stor/builds/a/b\n # i=7 -> d: /trent/stor/builds/a/b/c\n end = len(parts) + 1\n start = 3 # Index of the first possible dir to create.\n while start < end - 1:\n idx = (end - start) / 2 + start\n d = '/'.join(parts[:idx])\n try:\n self.put_directory(d)\n except errors.MantaAPIError:\n _, ex, _ = sys.exc_info()\n if ex.code == 'DirectoryDoesNotExist':\n end = idx\n else:\n raise\n else:\n start = idx\n\n # Now need to create from (end-1, len(parts)].\n for i in range(end, len(parts) + 1):\n d = '/'.join(parts[:i])\n self.put_directory(d)", "def mkdirpath (dirpath):\n\n if os.path.isdir(dirpath):\n return\n\n incpath = \"\"\n for subdir in os.path.normpath(dirpath).split(os.path.sep):\n incpath = os.path.join(incpath, subdir)\n if not os.path.isdir(incpath):\n os.mkdir(incpath)", "def make_directories(file_path):\n logger.info(\"Create all directories in the path %s\", file_path)\n if not os.path.exists(file_path):\n os.makedirs(file_path, exist_ok=True)\n else:\n logger.warning(\"Cannot create directories %s. The directory already exists\", file_path)", "def rmkdir(path):\n t = []\n sep = os.path.sep\n if sep != \"/\":\n parts = path.replace(os.path.sep, \"/\").split(\"/\")\n else:\n parts = path.split(sep)\n \n if path[0] == \"/\":\n t = [\"/\" + parts[0]]\n parts = parts[1:]\n \n for p in parts:\n t.append(p)\n # I chose isdir so we'll get a helpful error if it exists but is a file\n if os.path.isdir(sep.join(t)): continue\n os.mkdir(sep.join(t))", "def create_tree(path, depth=DEPTH):\r\n os.mkdir(path)\r\n for i in range(NUM_FILES):\r\n filename = os.path.join(path, 'file{0:03}.txt'.format(i))\r\n with open(filename, 'wb') as f:\r\n f.write(b'foo')\r\n if depth <= 1:\r\n return\r\n for i in range(NUM_DIRS):\r\n dirname = os.path.join(path, 'dir{0:03}'.format(i))\r\n create_tree(dirname, depth - 1)", "def build_dirs(self, path):\n if not os.path.exists(path):\n os.makedirs(path)\n return path", "def remake_directories(*dirnames):\r\n for d in dirnames:\r\n d = path(d)\r\n if d.exists():\r\n d.rmtree()\r\n d.mkdir()\r\n return", "def createFoldersFromPath(path):\n create = dirname(realpath(path))\n if not os.path.exists(create):\n os.makedirs(create)", "def make_dirs(path):\n\tif not os.path.exists(path):\n\t\treturn os.makedirs(path)", "def make_dirs(path):\n if not os.path.exists(path):\n os.makedirs(path)", "def create_directories(path):\n try:\n os.makedirs(path)\n\n except OSError as e:\n\n if e.errno != errno.EEXIST:\n logging.error(str(e))\n raise", "def __make_dirs(path, mode=0o777):\n\n try:\n os.makedirs(path, mode=mode)\n except OSError as e:\n if e.errno != errno.EEXIST:\n raise Ai1wmError('error creating a directory: {}, error: {}'.format(path, e))\n return path", "def mkdirpath(path):\n parts = split_path(path)\n full_path = ''\n for part in parts:\n full_path = os.path.join(full_path, part)\n if not os.path.isdir(full_path):\n os.mkdir(full_path)", "def make_subdirs(self) -> None:\r\n\r\n # Pull off everything below the root.\r\n subpath = self.full_path[len(self.context.root):]\r\n logger.debug(f\"make_subdirs: subpath is {subpath}\")\r\n \r\n # Split on directory separators, but drop the last one, as it should\r\n # be the filename.\r\n dirs = subpath.split(os.sep)[:-1]\r\n logger.debug(f\"dirs is {dirs}\")\r\n current = self.context.root\r\n \r\n for dir in dirs:\r\n if dir:\r\n current = os.path.join(current, dir)\r\n if not os.path.isdir(current):\r\n os.mkdir(current, 0o700) # FIXME - This should be defined in the server startup\r", "def mkdirs(path):\n\tif not os.path.exists(path):\n\t\tos.makedirs(path)", "def create_sub_directories(main_directory_path: str, sub_directory_names_list: list):\n try:\n for sub_name in sub_directory_names_list:\n os.makedirs(f\"{main_directory_path}/{sub_name}\", exist_ok=True)\n os.makedirs(f\"{main_directory_path}/{sub_name}\", exist_ok=True)\n except Exception as err:\n logger.error(f\"Failed to write out the file: {err}\")", "def InitializeDirectories(directory_list):\n for dir_name in directory_list:\n if not os.path.exists(dir_name):\n os.makedirs(dir_name)", "def mkdir(path):\n path = expandPath(path).split(os.sep)[1:]\n tmp = os.sep\n for entry in path:\n tmp += '%s%s' % (entry,os.sep)\n try:\n os.mkdir(tmp)\n except OSError:\n pass", "def create_directories(self, path):\n os.makedirs(path)\n print('Directory created at:', path)\n return path", "def rm_n_mkdir(dir_path):\n if os.path.isdir(dir_path):\n shutil.rmtree(dir_path)\n os.makedirs(dir_path)", "def _create_paths(paths):\n for path in paths:\n _mkdir_if_not_exist(path)", "def increment_path(path, overwrite=False):\n path = Path(path)\n\n if (path.exists() and overwrite) or (not path.exists()):\n if not os.path.exists(str(path).split('/')[0]):\n os.mkdir(str(path).split('/')[0])\n if not path.exists():\n os.mkdir(path)\n return str(path)\n else:\n dirs = glob.glob(f\"{path}*\")\n matches = [re.search(rf\"%s(\\d+)\" % path.stem, d) for d in dirs]\n i = [int(m.groups()[0]) for m in matches if m]\n n = max(i) + 1 if i else 2\n path = f\"{path}{n}\"\n if not os.path.exists(path):\n os.mkdir(path)\n return path", "def create_folder(path):\n try:\n os.listdir(path)\n except:\n os.makedirs(path)\n else:\n shutil.rmtree(path)\n os.makedirs(path)\n return path", "def make_dirs(dirpath, debug=False):\n\tif not os.path.exists(dirpath):\n\t\ttry:\n\t\t\tos.mkdir(dirpath)\n\t\texcept OSError as e:\n\t\t\tif debug:\n\t\t\t\tprint(e)\n\t\t\t(head, tail) = os.path.split(dirpath)\n\t\t\tif '/' not in head or os.path.exists(head):\n\t\t\t\treturn False\n\t\t\telse:\n\t\t\t\tif(make_dirs(head)):\n\t\t\t\t\treturn make_dirs(dirpath)\n\treturn dirpath" ]

[ "0.7962767", "0.7113393", "0.6915076", "0.67396253", "0.6736857", "0.6665003", "0.65617204", "0.6539428", "0.6518947", "0.65147203", "0.6502605", "0.64700353", "0.6469974", "0.6457087", "0.6433749", "0.64269245", "0.6422392", "0.6416041", "0.63974875", "0.6356123", "0.63397115", "0.62771374", "0.62687796", "0.62651587", "0.62544215", "0.6252991", "0.62439257", "0.6178313", "0.61712193", "0.6156417" ]

[ "def test_subdirectory_deleted(self):\n path = self._create_directory('test')\n sub_path = self._create_directory('test/test2')\n self._watcher.start()\n\n self.assertEqual(\n set([sub_path]),\n self._watcher._directory_to_subdirs[path])\n os.rmdir(sub_path)\n self.assertEqual(\n set([sub_path]),\n self._watcher.changes())\n self.assertEqual(\n set(),\n self._watcher._directory_to_subdirs[path])\n\n os.rmdir(path)\n self.assertEqual(\n set([path]),\n self._watcher.changes())", "def delete(self): # DirObj.delete\n self.deleted=True\n for name, d in self.subdirs.iteritems():\n d.delete()\n for name, f in self.files.iteritems():\n f.delete()", "def tearDown(self):\n for d in os.listdir(tmp_dir_path):\n d_path = os.path.join(tmp_dir_path,d)\n try:\n os.remove(d_path)\n except:\n for f in os.listdir(d_path):\n f_path = os.path.join(d_path,f)\n os.remove(f_path)\n os.rmdir(d_path)\n assert os.listdir(tmp_dir_path) == []", "def purge_deleted_directories(self):\n registered = {safe_filename(obj.name) for obj in self}\n bad_directories = [\n self._base_data_dir / dirname\n for dirname in os.listdir(self._base_data_dir)\n if (self._base_data_dir / dirname).is_dir() and dirname not in registered\n ]\n\n for fp in bad_directories:\n shutil.rmtree(fp)\n\n return len(bad_directories)", "def test_filePathDeltaSubdir(self):\n self.assertEqual(\n filePathDelta(FilePath(\"/foo/bar\"), FilePath(\"/foo/bar/baz\")), [\"baz\"]\n )", "def clear_dir(self, subdir=''):\n if not os.path.isdir(self.file_system.mount_point):\n raise exceptions.ValidationError(\"mount_point %s is not a directory\" % self.file_system.mount_point)\n if not os.path.isdir(self.full_path()):\n raise exceptions.ValidationError(\"project %s is not a directory\" % self.full_path())\n path = self.subdir(subdir)\n if not os.path.isdir(path):\n raise exceptions.ValidationError(\"%s is not a directory\" % path)\n for root, dirs, files in os.walk(path):\n for f in files:\n os.unlink(os.path.join(root, f))\n for d in dirs:\n shutil.rmtree(os.path.join(root, d))\n return True", "def _testKeySubNsDel(self):\n if len(self._getKeyList()) == 0 and len(self._getSubNsList()) == 0:\n parent = self.parent()\n if parent:\n parent._delChild(self.path[-1])", "def test_rmtree(self, client, remote_mock_dir):\n\n dir_path = posixpath.join(remote_mock_dir, \"subdir\")\n assert client.exists(dir_path)\n\n with HdfsHook() as hook:\n hook.rmtree(dir_path)\n\n assert not client.exists(dir_path)", "def remove_empty ( self ):\n with self._lock:\n for key in tuple ( self._subdirs.keys() ):\n if self._subdirs [key].check_empty():\n del self._subdirs [key]", "async def expire_directories(parent_dir, expiry, dry_run=False):\n if isinstance(expiry, int):\n if expiry == 0 and not dry_run:\n app_log.warning(\"0 expiry; not deleting all data, assuming dry run\")\n dry_run = True\n now = datetime.now(timezone.utc)\n today = now - timedelta(hours=now.hour)\n expiry = today - timedelta(days=expiry)\n app_log.info(\n f\"Deleting subdirectories in {storage.fs_name}/{parent_dir} older than {expiry}\"\n )\n\n fs_client = storage.fs_clients[(storage.storage_account, storage.fs_name)]\n\n try:\n fs_client.get_directory_client(parent_dir).get_directory_properties()\n except azure.core.exceptions.ResourceNotFoundError:\n app_log.warning(\n f\"Nothing to delete in nonexistent {storage.fs_name}/{parent_dir}\"\n )\n return\n\n def process_one(path):\n dc = fs_client.get_directory_client(path)\n props = dc.get_directory_properties()\n if props.last_modified < expiry:\n app_log.info(\n f\"{'(not really) ' * dry_run}Deleting {dc.path_name} from {props.last_modified}\"\n )\n if not dry_run:\n dc.delete_directory()\n else:\n app_log.info(f\"Not deleting {dc.path_name} from {props.last_modified}\")\n\n done, pending = set(), set()\n with ThreadPoolExecutor(CONCURRENCY) as pool:\n for path in fs_client.get_paths(parent_dir, recursive=False):\n pending.add(asyncio.wrap_future(pool.submit(process_one, path)))\n done, pending = await asyncio.wait(pending, timeout=0.01)\n if done:\n await asyncio.gather(*done)\n\n if pending:\n await asyncio.gather(*pending)", "def tearDown(self):\n # unittest.TestCase.tearDown(self)\n\n root = os.path.join(\".\", \"files\")\n endingList = os.listdir(root)\n rmList = [fn for fn in endingList if fn not in self.startingList]\n\n if self.oldRoot == root:\n for fn in rmList:\n fnFullPath = os.path.join(root, fn)\n if os.path.isdir(fnFullPath):\n os.rmdir(fnFullPath)\n else:\n os.remove(fnFullPath)\n\n os.chdir(self.oldRoot)", "def test_filePathDeltaSubdir(self):\n self.assertEquals(filePathDelta(FilePath(\"/foo/bar\"),\n FilePath(\"/foo/bar/baz\")),\n [\"baz\"])", "def tearDown(self):\n for root, dirs, files in os.walk(TEMPDIR, topdown=False):\n for name in files:\n os.remove(os.path.join(root, name))\n for name in dirs:\n os.rmdir(os.path.join(root, name))\n os.rmdir(root)", "def test_team_template_folders_id_children_fk_delete(self):\n pass", "def tearDown(self):\n if self.rootdir and os.path.exists(self.rootdir):\n shutil.rmtree(self.rootdir)", "def test_recursive_delete(self):\n org1 = bonsai.LDAPEntry(\"ou=testusers,%s\" % self.basedn)\n org1.update({\"objectclass\" : ['organizationalUnit', 'top'],\n \"ou\" : \"testusers\"})\n org2 = bonsai.LDAPEntry(\"ou=tops,ou=testusers,%s\" % self.basedn)\n org2.update({\"objectclass\" : ['organizationalUnit', 'top'], \"ou\" : \"tops\"})\n entry = bonsai.LDAPEntry(\"cn=tester,ou=tops,ou=testusers,%s\" % self.basedn)\n entry.update({\"objectclass\" : [\"top\", \"inetorgperson\"],\n \"cn\" : \"tester\", \"sn\" : \"example\"})\n try:\n self.conn.add(org1)\n self.conn.add(org2)\n self.conn.add(entry)\n self.conn.delete(org1.dn, recursive=True)\n res = self.conn.search(org1.dn, 2)\n self.assertListEqual(res, [])\n except bonsai.LDAPError:\n self.fail(\"Recursive delete is failed.\")", "def test_change_non_empty_dir_to_file(self):\n dir0, dir1 = self.make_temp_dirs(2)\n self.write_file(dir0, \"foo/bar\", \"baz\")\n self.sync_all()\n self.assertFile(dir0, \"foo/bar\", \"baz\")\n self.assertFile(dir1, \"foo/bar\", \"baz\")\n\n self.delete_file(dir0, \"foo/bar\")\n self.delete_dir(dir0, \"foo\")\n self.write_file(dir0, \"foo\", \"bar\")\n self.sync_all()\n self.assertFile(dir0, \"foo\", \"bar\")\n self.assertFile(dir1, \"foo\", \"bar\")", "def rm_subdirs(path, onerror=None):\r\n\r\n # NOTE this code is adapted from the one in shutil.rmtree, and is\r\n # just as fast\r\n\r\n names = []\r\n try:\r\n names = os.listdir(path)\r\n except os.error as err:\r\n if onerror is not None:\r\n onerror(os.listdir, path, sys.exc_info())\r\n else:\r\n raise\r\n\r\n for name in names:\r\n fullname = os.path.join(path, name)\r\n if os.path.isdir(fullname):\r\n if onerror is not None:\r\n shutil.rmtree(fullname, False, onerror)\r\n else:\r\n # allow the rmtree to fail once, wait and re-try.\r\n # if the error is raised again, fail\r\n err_count = 0\r\n while True:\r\n try:\r\n shutil.rmtree(fullname, False, None)\r\n break\r\n except os.error:\r\n if err_count > 0:\r\n raise\r\n err_count += 1\r\n time.sleep(RM_SUBDIRS_RETRY_TIME)", "def test_skipped_dir_create(self):\n dir0, dir1, dir2 = self.make_temp_dirs(3)\n self.sync_all()\n\n # Make subdir in dir0 and sync dir0/dir1 but not dir2\n self.write_dir(dir0, \"subdir\")\n self.sync_dirs(dir0, dir1)\n self.assertDirPresent(dir0, \"subdir\")\n self.assertDirPresent(dir1, \"subdir\")\n self.assertFileAbsent(dir2, \"subdir\")\n\n # Sync all and subdir should be created in dir2 also\n self.sync_all()\n self.assertDirPresent(dir0, \"subdir\")\n self.assertDirPresent(dir1, \"subdir\")\n self.assertDirPresent(dir2, \"subdir\")", "def clean_directory():\n if os.path.exists('data'):\n shutil.rmtree('data')\n os.makedirs('data')\n\n if os.path.exists('returns'):\n shutil.rmtree('returns')\n os.makedirs('returns')", "def test_team_template_folders_id_delete(self):\n pass", "def _delete_root_dir(self):\n\n staf_request = ('DELETE ENTRY \"{0}\" RECURSE '\n 'CONFIRM '.format(unix_style_path(self._sut.bespoke_root)))\n\n result = self._staf_handle.submit(self._sut.network_address, 'fs', staf_request)\n\n if result.rc not in [result.Ok, result.DoesNotExist]:\n raise CoreError(result.result)", "def test_nested_directories(self):\n filesystem = {\n '/a/a/a': '',\n '/a/a/b': '',\n '/a/b/a': '',\n '/a/b/b': '',\n '/b/a/a': '',\n '/b/a/b': '',\n '/b/b/a': '',\n '/b/b/b': '',\n }\n self.mfs.add_entries(filesystem)\n\n for path in filesystem:\n self.assertTrue(os.path.isdir(os.path.dirname(path)))\n self.assertTrue(os.path.exists(path))\n self.assertTrue(os.path.isfile(path))", "def verifyDirectoryComparison(self, before, after, reverify=False):\n root = self.createHierarchy(before)\n\n config.DocumentRoot = root\n config.DataRoot = root\n\n (yield self.doUpgrade(config))\n self.assertTrue(self.verifyHierarchy(root, after))\n\n if reverify:\n # Ensure that repeating the process doesn't change anything\n (yield self.doUpgrade(config))\n self.assertTrue(self.verifyHierarchy(root, after))", "def rmdir(path):", "def test_file_update_delete_conflict(self):\n dir0, dir1 = self.make_temp_dirs(2)\n self.write_file(dir0, \"foo\", \"bar\")\n self.sync_all()\n\n self.write_file(dir0, \"foo\", \"baz\")\n self.delete_file(dir1, \"foo\")\n self.sync_all()\n self.assertFileAbsent(dir0, \"foo\")\n self.assertFileAbsent(dir1, \"foo\")", "def _clean_files(self, in_subdirectory=False):\n files = self._file_explorer.ls()\n if not in_subdirectory:\n LOG.info(f\"Cleaning {len(files)} file(s) on the device\")\n for file_ in files:\n try:\n self._file_explorer.rm(file_)\n except Exception as e:\n # Try to explore subdirectory\n LOG.info(f\"Attempting to clean directory {file_}\")\n self._file_explorer.cd(file_)\n self._clean_files(in_subdirectory=True)\n if in_subdirectory:\n self._file_explorer.cd('..')\n else:\n LOG.info(\"Done cleaning FS\")", "def test_cd():\n for i in range(0, 100, 1):\n os.mkdir('test_folder_%s' % i)\n os.chdir('test_folder_%s' % i)\n os.chdir('..')\n os.rmdir('test_folder_%s' % i)", "def tearDown(self):\n rmtree(getcwd(), ignore_errors=True)", "def tearDown(self):\n rmtree(getcwd(), ignore_errors=True)" ]

[ "0.84610265", "0.65147275", "0.6495986", "0.632336", "0.63160795", "0.6192951", "0.6191186", "0.61434877", "0.6129643", "0.6103901", "0.60978335", "0.608716", "0.6058954", "0.603675", "0.603421", "0.6027226", "0.6024294", "0.59999007", "0.59602183", "0.59490293", "0.5924875", "0.5869914", "0.58692116", "0.58565485", "0.5834363", "0.58250463", "0.57964355", "0.5791326", "0.5785126", "0.5785126" ]

[ "def analyze(movie_review_filename):\n client = language.LanguageServiceClient()\n\n with open(movie_review_filename, 'r') as review_file:\n # Instantiates a plain text document.\n content = review_file.read()\n\n document = types.Document(content=content,\n type=enums.Document.Type.PLAIN_TEXT)\n annotations = client.analyze_sentiment(document=document)\n # Print the results\n return annotations", "def analyze(text):\n client = language_service_client.LanguageServiceClient()\n\n # with open(movie_review_filename, 'r') as review_file:\n # Instantiates a plain text document.\n \n # content = text.read()\n content=text\n document = language_v1.types.Document(\n content=content,\n type=enums.Document.Type.PLAIN_TEXT,\n language='en'\n )\n # type='PLAIN_TEXT',\n # )\n \n try:\n response = client.analyze_sentiment(\n document=document,\n encoding_type='UTF32',\n )\n sentiment = response.document_sentiment\n return (sentiment.score)\n except InvalidArgument:\n sentiment=0.0\n return sentiment", "def AnalyzeSentiment(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details(\"Method not implemented!\")\n raise NotImplementedError(\"Method not implemented!\")", "def sentiment_analysis(self, text):\n\n body = {'text': text}\n body = json.dumps(body)\n url = self.base_url + '/language-service/phoenix-language/nlp/sentiment'\n headers = {\"ApiKey\": self.api_key, \"Content-type\": \"application/json\"}\n response = requests.post(url=url, data=body, headers=headers).json()\n return response", "def AnalyzeSentiment(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')", "def AnalyzeSentiment(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')", "def sentiment(self, text, method = \"vocabulary\"):\n assert method == \"vocabulary\" or method == \"rnn\"\n endpoint = method == \"vocabulary\" and \"sentiment\" or \"sentimentRNN\"\n return self._er.jsonRequestAnalytics(\"/api/v1/\" + endpoint, { \"text\": text })", "def vader_analyse(file_input):\n sentences = getdata_from_db(1000)\n print(\"Working on %d tweets\" % (len(sentences)))\n headers = ('text', 'label', 'score')\n analyzed_data = []\n sid = SentimentIntensityAnalyzer()\n for line in sentences:\n text = pre.clean(line)\n scores = sid.polarity_scores(text)\n analyzed_data.append((text, getlabel(scores), scores['compound']))\n save_data_to_db(analyzed_data)\n analyzed = Dataset(*analyzed_data, headers=headers)\n return analyzed", "def analyze_sentiment(test_files_list: list, classification_dict: dict):\n\n # Lexicon words used for sentiment analysis\n pos_lex_words = get_lexicon_words(POS_LEXICON_DIR_PATH)\n neg_lex_words = get_lexicon_words(NEG_LEXICON_DIR_PATH)\n\n classification_scores = []\n true_labels = []\n\n for file in test_files_list:\n \n # Read the file to analyze\n with open(file) as f:\n sentences = f.readlines()\n\n # tokenize the sentences in the file\n tokens = []\n for sentence in sentences:\n tokens += tokenize(sentence) # Do not want to remove duplicate words, so we have more data\n \n # Get number of positive and negative words found in the file\n positive_words, negative_words = get_pos_neg_word_count(tokens, pos_lex_words, neg_lex_words)\n \n # Keep an array of all the scores we have (negative, positive)\n classification_score = [negative_words, positive_words]\n classification_scores.append(classification_score)\n \n # Maintain the true answer (negative, positive)\n true_label = [0, 0]\n if file.split('/')[1] == 'pos': true_label[1] += 1\n else: true_label[0] += 1\n true_labels.append(true_label)\n\n # Print for submitting assignment\n if true_label[0]: #file is actually negative\n classification_dict['neg'][file.split('/')[2]] = 'neutral'\n if positive_words > negative_words: classification_dict['neg'][file.split('/')[2]] = 'positive'\n else: classification_dict['neg'][file.split('/')[2]] = 'negative'\n else:\n classification_dict['pos'][file.split('/')[2]] = 'neutral'\n if positive_words > negative_words: classification_dict['pos'][file.split('/')[2]] = 'positive'\n else: classification_dict['pos'][file.split('/')[2]] = 'negative'\n\n \n return np.array(classification_scores), np.array(true_labels)", "def sentiment(text):\n words = pattern_split.split(text.lower())\n sentiments = map(lambda word: afinn.get(word, 0), words)\n if sentiments:\n # How should you weight the individual word sentiments? \n # You could do N, sqrt(N) or 1 for example. Here I use sqrt(N)\n sentiment = float(sum(sentiments))/math.sqrt(len(sentiments))\n \n else:\n sentiment = 0\n return sentiment", "def run(self, input_type, file_name):\n data = self.get_data(file_name)\n\n sentiment = dict()\n mood = dict()\n emoticon = dict()\n\n for line in data:\n weight = 1\n # Twitter data has a weight defined before the |\n if input_type == \"Twitter\":\n columns = line.split(\"|\")\n weight += int(columns[0])\n # Everything but the weight at the beginning\n line = '|'.join(columns[1:])\n\n # Prepare data for analysis\n sentances = self.prepare_data(line)\n\n # Perform analysis\n sentiment_val = self.get_sentiment(sentances)\n mood_val = self.get_mood(sentances)\n emoticon_val = self.get_emoticons_value(line)\n\n # Add each sentiment value to a dictionary along with its weight\n sentiment[sentiment_val] = weight if sentiment_val not in sentiment else sentiment[sentiment_val] + weight\n # Add results to mood totals\n for m, count in mood_val.items():\n mood[m] = count if m not in mood else mood[m] + count\n # Add results to emote totals\n for e in emoticon_val:\n emoticon[e] = 1 if e not in emoticon else emoticon[e] + 1\n\n return sentiment, mood, emoticon", "def getSentiment(s):\n headers = {\"Ocp-Apim-Subscription-Key\" : \"4c28d3a67a12442cad6666a3200c49f5\",\n \"Content-Type\" : \"application/json\", \"Accept\" : \"application/json\"}\n url = \"https://westus.api.cognitive.microsoft.com/text/analytics/v2.0/sentiment\"\n json = {\"documents\": [{\"language\": \"en\", \"id\" : \"1\"}]}\n json['documents'][0]['text'] = s\n sentiment = r.post(url, headers = headers, json = json)\n sentiment = j.loads(sentiment.text)\n return sentiment['documents'][0]['score']", "def feed_sent_file(self, path):\n self.item_rb.feed_sent_score_result(path)", "def analyze(content):\r\n client = language.LanguageServiceClient()\r\n\r\n document = types.Document(\r\n content=content,\r\n type=enums.Document.Type.PLAIN_TEXT)\r\n annotations = client.analyze_sentiment(document=document)\r\n\r\n # Write results to GCS \r\n return annotations.document_sentiment.score", "def text_analytics(self):\n\n headers = {\n # Request headers\n 'Content-Type': 'application/json',\n 'Ocp-Apim-Subscription-Key': self.keys['text_analytics'],\n }\n \n sentiment_url = 'https://westus.api.cognitive.microsoft.com/text/analytics/v2.0/sentiment'\n \n raw_text = self.article_params['text']\n\n # Build post for sentiment\n try:\n sentences = tokenize.sent_tokenize(str(raw_text))\n content = []\n for i, sentence in enumerate(sentences):\n content.append({'id': str(i), 'language': 'en', 'text': sentence})\n body = json.dumps({\"documents\": content}).encode('utf-8')\n\n request = urllib.request.Request(sentiment_url, body, headers)\n response = urllib.request.urlopen(request)\n json_response = json.loads(response.read().decode('utf-8'))\n \n # A list of dictionaries, with each dictionary containing a sentence\n # sentiment score\n sentiments_list = json_response['documents']\n\n # Calculate the articles average sentiment from all the sentences\n cumulative_sentiment_score = 0\n for sent in sentiments_list:\n cumulative_sentiment_score += sent['score']\n avg_article_sentiment = cumulative_sentiment_score/len(sentiments_list)\n\n # Put article sentiments in bucket from 1 to 5, with 1 being very\n # negative and 5 being very positive\n if avg_article_sentiment < 0.2:\n sentiment = 1\n elif 0.2 <= avg_article_sentiment < 0.4:\n sentiment = 2\n elif 0.4 <= avg_article_sentiment < 0.6:\n sentiment = 3\n elif 0.6 <= avg_article_sentiment < 0.8:\n sentiment = 4\n else:\n sentiment = 5\n\n except Exception as e:\n print('Unable to process sentiment for article. Assuming '\n 'sentiment is neutral.')\n sentiment = 3\n\n return sentiment", "def get_sentiment_analysis(sender, instance, **kwargs):\n text_analysis = TextAnalysis(instance.text)\n\n # Prevent sentiment_analysis API call every time the document is saved\n if instance.sentiment_analysis is None:\n instance.get_sentiment_analysis()", "def sample_analyze_sentiment(text):\n\n client = language_v1.LanguageServiceClient()\n\n # Available types: PLAIN_TEXT, HTML\n type_ = enums.Document.Type.PLAIN_TEXT\n\n document = {\"content\": text, \"type\": type_}\n\n # Available values: NONE, UTF8, UTF16, UTF32\n encoding_type = enums.EncodingType.UTF8\n\n response = client.analyze_sentiment(document, encoding_type=encoding_type)\n\n # Get sentiment for all sentences in the document\n sentences = []\n\n # Get sentiment for all sentences in the document\n for sentence in response.sentences:\n print(u\"Sentence text: {}\".format(sentence.text.content))\n print(u\"Sentence sentiment score: {}\".format(sentence.sentiment.score))\n print(u\"Sentence sentiment magnitude: {}\".format(sentence.sentiment.magnitude))\n sentences.append({\n \"content\": sentence.text.content,\n \"textSentimentScore\": sentence.sentiment.score,\n \"textSentimentMagnitude\": sentence.sentiment.magnitude\n })\n\n # Get the language of the text, which will be the same as\n # the language specified in the request or, if not specified,\n # the automatically-detected language.\n print(u\"Language of the text: {}\".format(response.language))\n\n result = {\n \"success\": True,\n \"sentimentScore\": response.document_sentiment.score,\n \"sentimentMagnitude\": response.document_sentiment.magnitude,\n \"sentences\": sentences,\n }\n return result", "def run(self,infilename): \n ### initizlize the analysis\n self.init_analysis(infilename)\n ### run the analysis\n self.run_analysis()\n ### store selected results\n self.store_results()\n return", "def analyze_sentiment(self, lang: str = TARGET_LANG):\n if not self.translation and self.language != lang:\n self.translate()\n if not self.clean:\n return\n query = {\"documents\": [\n {\"id\": \"1\", \"language\": \"{}\".format(lang),\n \"text\": \"{}\".format(self.translation)}\n ]}\n response = requests.post(self.url_sentiment, headers=self.sentiment_headers, json=query)\n self.sentiment = response.json()['documents'][0]['sentiment']", "def parse_sentiment_file(self, file):\n\n file_sentiment = file['documentSentiment']\n file_entities = [x['name'] for x in file['entities']]\n file_entities = self.sentence_sep.join(file_entities)\n\n if self.extract_sentiment_text:\n file_sentences_text = [x['text']['content'] for x in\n file['sentences']]\n file_sentences_text = self.sentence_sep.join(file_sentences_text)\n file_sentences_sentiment = [x['sentiment'] for x in file['sentences']]\n\n file_sentences_sentiment = pd.DataFrame.from_dict(\n file_sentences_sentiment, orient='columns').sum()\n file_sentences_sentiment = file_sentences_sentiment.add_prefix(\n 'document_').to_dict()\n\n file_sentiment.update(file_sentences_sentiment)\n\n df_sentiment = pd.DataFrame.from_dict(file_sentiment, orient='index').T\n if self.extract_sentiment_text:\n df_sentiment['text'] = file_sentences_text\n\n df_sentiment['entities'] = file_entities\n df_sentiment = df_sentiment.add_prefix('sentiment_')\n\n return df_sentiment", "def parse_sentiment_file(self, file):\n \n file_sentiment = file['documentSentiment']\n file_entities = [x['name'] for x in file['entities']]\n file_entities = self.sentence_sep.join(file_entities)\n\n if self.extract_sentiment_text:\n file_sentences_text = [x['text']['content'] for x in file['sentences']]\n file_sentences_text = self.sentence_sep.join(file_sentences_text)\n file_sentences_sentiment = [x['sentiment'] for x in file['sentences']]\n \n file_sentences_sentiment = pd.DataFrame.from_dict(\n file_sentences_sentiment, orient='columns').sum()\n file_sentences_sentiment = file_sentences_sentiment.add_prefix('document_').to_dict()\n \n file_sentiment.update(file_sentences_sentiment)\n \n df_sentiment = pd.DataFrame.from_dict(file_sentiment, orient='index').T\n if self.extract_sentiment_text:\n df_sentiment['text'] = file_sentences_text\n \n df_sentiment['entities'] = file_entities\n df_sentiment = df_sentiment.add_prefix('sentiment_')\n \n return df_sentiment", "def sample_analyze_sentiment(text_content):\n\n client = language_v1.LanguageServiceClient()\n\n # text_content = 'I am so happy and joyful.'\n\n # Available types: PLAIN_TEXT, HTML\n type_ = enums.Document.Type.PLAIN_TEXT\n\n # Optional. If not specified, the language is automatically detected.\n # For list of supported languages:\n # https://cloud.google.com/natural-language/docs/languages\n language = \"en\"\n document = {\"content\": text_content, \"type\": type_, \"language\": language}\n\n # Available values: NONE, UTF8, UTF16, UTF32\n encoding_type = enums.EncodingType.UTF8\n\n resp = client.analyze_sentiment(document, encoding_type=encoding_type)\n # Get overall sentiment of the input document\n print(f\"Document sentiment score: {resp.document_sentiment.score}\")\n print(f\"Document sentiment magnitude: {resp.document_sentiment.magnitude}\")\n\n # Get sentiment for all sentences in the document\n for sentence in resp.sentences:\n print(f\"Sentence text: {sentence.text.content}\")\n print(f\"Sentence sentiment score: {sentence.sentiment.score}\")\n print(f\"Sentence sentiment magnitude: {sentence.sentiment.magnitude}\")\n\n # Get the language of the text, which will be the same as\n # the language specified in the request or, if not specified,\n # the automatically-detected language.\n print(f\"Language of the text: {resp.language}\")", "def analyze(self, text):\n tknzr = nltk.tokenize.casual.TweetTokenizer(preserve_case=True, reduce_len=False, strip_handles=False)\n tknTxt = tknzr.tokenize(text)\n sentiment = 0\n \n for i in range(len(tknTxt)):\n if tknTxt[i] in self.posTxt:\n #print(\"POS\")\n #print(tknTxt[i])\n sentiment += 1\n elif tknTxt[i] in self.negTxt:\n #print(\"NEG\")\n #print(tknTxt[i])\n sentiment -= 1\n \n return sentiment", "def analyze(self, text): #takes the text to be analyzed for sentiment\n #initialize inicial score to 0\n score = 0\n #Create tokenizer instance\n tokenizer = nltk.tokenize.TweetTokenizer()\n #create list of words in a tweets\n tokens = tokenizer.tokenize(text)\n \n #iterate over tokens(list of words)\n for word in tokens:\n #check if word is positive or negative\n if word.lower() in self.positives_words:\n score+=1\n if word.lower() in self.negatives_words:\n score-=1\n #neutral if its neither, doesnt add anything, 0\n return score", "def ocr_core(filename):\n\n\n #text = pytesseract.image_to_string(Image.open(filename)) # We'll use Pillow's Image class to open the image and pytesseract to detect the string in the image\n \n \n sentiment_dict= analyser.polarity_scores(text) \n \n \n \n \n # print(\"sentence was rated as \", sentiment_dict['neg']*100, \"% Negative\") \n # print(\"sentence was rated as \", sentiment_dict['neu']*100, \"% Neutral\") \n # print(\"sentence was rated as \", sentiment_dict['pos']*100, \"% Positive\") \n \n\n if sentiment_dict['compound'] >= 0.08 : \n Category.append('Positive') \n print('Positive') \n \n elif (sentiment_dict['compound'] > - 0.08) & (sentiment_dict['compound'] < 0.08): \n Category.append('Random')\n print('Random')\n \n elif (sentiment_dict['compound'] <= -0.08):\n Category.append('Negative')\n print('Negative')\n \n #return text", "def sentiment(self, text):\n\n response = self._send_request(\"sentiment\", dict(text=text))\n return response[self._layer]['sentiment']", "def AnalyzeEntitySentiment(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details(\"Method not implemented!\")\n raise NotImplementedError(\"Method not implemented!\")", "def sentimentAnalysis(fileName, city, outFileName):\n tweetTokenizer = TweetTokenizer()\n punct = list(string.punctuation)\n stopwordList = stopwords.words('english') + punct + ['rt', 'via', '...']\n vaderSent = vaderSentimentAnalysis(fileName, tweetTokenizer, stopwordList)\n vaderSent['city'] = city\n vaderSent = vaderSent[vaderSent['sentiment'] < 0]\n vaderSent.to_csv(outFileName)", "def AnalyzeEntitySentiment(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')", "def sentiment_analyzer(text):\n\n\tlower_text = text.lower()\n\t\t\n\thashtag_scaling = 0.3\n\texclamation_scaling = 0.5\n\tuppercase_scaling = 0.2\n\n\n\tsent_index = 0\n\n\tfor x in range(len(positive_words)):\n\t\tsent_index += lower_text.count(positive_words[x])\n\tfor x in range(len(negative_words)):\n\t\tsent_index -= lower_text.count(negative_words[x])\n\tif '!' in text:\n\t\tsent_index *= exclamation_scaling * lower_text.count('!') + 1\n\tif '#' in text:\n\t\tsent_index *= hashtag_scaling * lower_text.count('#') + 1\n\tsent_index *= uppercase_scaling * sum(1 for c in text if c.isupper())\n\t\t\n\treturn sent_index" ]

[ "0.69191617", "0.6760998", "0.6308087", "0.623117", "0.61973536", "0.61973536", "0.61879206", "0.61140317", "0.60523087", "0.60180354", "0.60157675", "0.6008048", "0.60011387", "0.5983024", "0.59410757", "0.5926585", "0.59205323", "0.59090775", "0.590305", "0.58944255", "0.5826811", "0.58144176", "0.58101904", "0.5793115", "0.5782408", "0.5772381", "0.57513595", "0.57402277", "0.5729624", "0.56955576" ]

[ "def update(self):\n q = self.M[self.state,:][0]\n self.state = random.choice(self.N,1,p = q)\n return self.state", "def update(self, next_state, reward):\n pass", "def update(self):\n if self.size() < 2: return\n idx = random.randint(0, 100) % 3\n if idx < 2:\n slot = self.slots[idx]\n if slot.get_state() == Slot.CLEAN:\n slot.set_state(Slot.DIRTY)\n # self.slots[idx] = slot", "def updateOne(self,ident):\n \tLOGGER.info(\"lazily updating {}\".format(ident))\n \tself.idToUpdate=ident\n \tself.newState=''\n \tself.save()", "def update(self):\n self._state = 23", "def random_state(state):\n old_state = RandomState()\n state.set_global()\n yield\n old_state.set_global()", "def update_state(self, a, obs, t):\n \n self.update_weights(a, obs, t) # only update weights, not particles \n self.update_running_average_weights(t) \n return None", "def update_random_state(self):\n self.random_state = RandomState()", "def update(self, t):\n self.state.send(t)", "def update(self, state, action, nextState, reward):\n self.qvals[(state, action)] = self.qvals[(state, action)] + self.alpha * (\n reward + self.discount * self.computevaluefromqvalues(nextState) - self.qvals[(state, action)])", "def update1(self, state, action, nextState, reward):\n util.raiseNotDefined()", "def update(self, state, action, nextState, reward):\n \"*** YOUR CODE HERE ***\"\n # print \"update\"\n oldValue = self.getQValue(state, action)\n sample = reward + self.discount*self.computeValueFromQValues(nextState)\n self.qValues[(state, action)] = (1-self.alpha)*oldValue + self.alpha*(sample)", "def update(self, state, action, nextState, reward):\n \"*** YOUR CODE HERE ***\"\n self.qValues[(state, action)] = ((1 - self.alpha) * self.getQValue(state, action)) + self.alpha \\\n * (reward + self.discount * self.computeValueFromQValues(nextState))", "def updateQValue(self, state, action, old_q, reward, future_rewards):\n self.q[(tuple(state), action)] = old_q + self.alpha * (reward + future_rewards - old_q)", "def _update(self, count=True, forced=False):", "def sync(self):\r\n\r\n # Ensure to rerun only once to avoid infinite loops\r\n # caused by a constantly changing state value at each run.\r\n #\r\n # Example: state.value += 1\r\n if self._state[\"is_rerun\"]:\r\n self._state[\"is_rerun\"] = False\r\n \r\n elif self._state[\"hash\"] is not None:\r\n if self._state[\"hash\"] != self._state[\"hasher\"].to_bytes(self._state[\"data\"], None):\r\n self._state[\"is_rerun\"] = True\r\n self._state[\"session\"].request_rerun()\r\n\r\n self._state[\"hash\"] = self._state[\"hasher\"].to_bytes(self._state[\"data\"], None)", "def update_store(self, value, index):\n if index == 1:\n self.state[self.M] = value\n else:\n self.state[-1] = value", "def update_qvals(self, state, action, reward):\n self.qvals[(state, action)] = 0", "def step(self, state, action, reward, done):\n\n self.memory.add(state, action, reward, done)\n if done and self.n_tau % self.update_freq == 0:\n self.n_tau += 1\n return self.update()\n return None", "def update(self, state, action, nextState, reward):\n \"\"\"Description:\n Use Q-Learning algoritm in slide 58 of MDP\n \"\"\"\n \"\"\" YOUR CODE HERE \"\"\"\n maxQns = self.getValue(nextState) # get max q-value of next state\n if maxQns == None:\n maxQns = 0\n Qsa = self.getQValue(state, action) #self.qValues[(state, action)]\n difference = reward + self.discountRate * maxQns - Qsa\n self.qValues[(state, action)] += self.alpha * difference\n \n self.vitCount[(state, action)] += 1\n \"\"\" END CODE \"\"\"", "def sync(self):\r\n\r\n # Ensure to rerun only once to avoid infinite loops\r\n # caused by a constantly changing state value at each run.\r\n #\r\n # Example: state.value += 1\r\n if self._state[\"is_rerun\"]:\r\n self._state[\"is_rerun\"] = False\r\n\r\n elif self._state[\"hash\"] is not None:\r\n if self._state[\"hash\"] != self._state[\"hasher\"].to_bytes(self._state[\"data\"], None):\r\n self._state[\"is_rerun\"] = True\r\n self._state[\"session\"].request_rerun()\r\n\r\n self._state[\"hash\"] = self._state[\"hasher\"].to_bytes(self._state[\"data\"], None)", "def update():", "def update():", "def make_q_update(self, reward, state: str, joint_action: Dict[int, str], next_state, alpha: float, gamma: float):\n previous_value = self.Q_t[state][(joint_action[0], joint_action[1])]\n if '(0, 0)' in next_state:\n max_future_reward = 0\n else:\n max_future_reward = max(self.Q_t[next_state].values())\n new_value = reward + gamma * max_future_reward\n\n self.Q_t[state][(joint_action[0], joint_action[1])] = (1 - alpha) * previous_value + alpha * new_value", "def update_Q(self, state, action, new_state, reward):\n \n self.Q[(state, action)] = self.Q[(state, action)] + self.alpha*(reward + self.gamma * self.max_value(new_state) - self.Q[(state, action)])", "def updateValue(self, state):\n return self.getQValue(state, self.policy[state[0], state[1]])", "def _update_state(self) -> None:\n raise NotImplementedError(\"\")", "def update_to_state(self, game_state):\n pass", "def sync(self):\n\n # Ensure to rerun only once to avoid infinite loops caused by a constantly changing state value at each run.\n # Example: state.value += 1\n\n if self._state[\"is_rerun\"]:\n self._state[\"is_rerun\"] = False\n\n elif self._state[\"hash\"] is not None:\n if self._state[\"hash\"] != self._state[\"hasher\"].to_bytes(\n self._state[\"data\"], None\n ):\n self._state[\"is_rerun\"] = True\n self._state[\"session\"].request_rerun()\n\n self._state[\"hash\"] = self._state[\"hasher\"].to_bytes(self._state[\"data\"], None)", "def update(self, state, action, nextState, reward):\n util.raiseNotDefined()" ]

[ "0.6271556", "0.61507183", "0.59220254", "0.5901581", "0.58661723", "0.58061427", "0.57873684", "0.5770999", "0.5711735", "0.56835014", "0.56593686", "0.5658279", "0.56573635", "0.5653307", "0.5647151", "0.56323695", "0.56268525", "0.5618667", "0.5605903", "0.5596673", "0.55906767", "0.5567487", "0.5567487", "0.55565715", "0.5548335", "0.55418766", "0.5540492", "0.5540164", "0.5534169", "0.55299693" ]

[ "def next_period_step(self):\n self.update_forces()\n element: Cell\n for element in self.cells:\n self.update_coordinates(element)\n self.update_volosity(element)", "def _update_positions(self, delta_t):\n\n for atom in self.atoms:\n atom.update(delta_t)", "def verletIntegration(self):\n for atom in range(0, self.numAtoms):\n \n # Update velocities\n self.atoms[atom].vx += (self.atoms[atom].fx/self.m)*self.dt\n self.atoms[atom].vy += (self.atoms[atom].fy/self.m)*self.dt\n self.atoms[atom].vz += (self.atoms[atom].fz/self.m)*self.dt\n \n \n # Update positions\n newX = self.atoms[atom].x + self.atoms[atom].vx*self.dt\n newY = self.atoms[atom].y + self.atoms[atom].vy*self.dt\n newZ = self.atoms[atom].z + self.atoms[atom].vz*self.dt\n\n # Update current positions (applying PBC)\n if newX < 0:\n self.atoms[atom].x = newX + self.lbox\n elif newX > self.lbox:\n self.atoms[atom].x = newX - self.lbox\n else:\n self.atoms[atom].x = newX\n \n if newY < 0:\n self.atoms[atom].y = newY + self.lbox\n elif newY > self.lbox:\n self.atoms[atom].y = newY - self.lbox\n else:\n self.atoms[atom].y = newY\n \n if newZ < 0:\n self.atoms[atom].z = newZ + self.lbox\n elif newZ > self.lbox:\n self.atoms[atom].z = newZ - self.lbox\n else:\n self.atoms[atom].z = newZ", "def update(self, iteration):\n pass", "def updateTemperature(self):\n sumv2 = 0\n for atom in self.atoms:\n sumv2 += atom.vx**2 + atom.vy**2 + atom.vz**2\n self.currentTemp = (self.m/(3*self.numAtoms*self.kb))*sumv2\n self.temperatures.append(self.currentTemp)", "def run(self, steps=50):\n f = self.atoms.get_forces()\n\n if not self.atoms.has('momenta'):\n self.atoms.set_momenta(np.zeros_like(f))\n\n for step in range(steps):\n f = self.step(f)\n self.nsteps += 1\n self.call_observers()", "def update(self, delta_time):\n for b in self.star_list:\n b.update()", "def update(self,dt):\n t1 = time()\n\n if SPLIT:\n self.check_refine()\n if AMALGAMATE:\n self.check_amalg(self.nl_default)\n\n t = time()\n self.rebuild_lists()\n self.timing['nlist rebuild time'] = time() - t\n\n # Is this derivative step required?\n t = time()\n self.derivatives()\n self.timing['deriv time'] = time() - t\n \n t = time()\n self.step(self.gather_state,self.derivatives, \\\n self.gather_derivatives,self.scatter_state,dt)\n self.timing['integrate time'] = time() - t\n \n self.box.apply(self)\n\n if self.thermostat:\n self.apply_thermostat(self.thermostat_temp)\n \n self.timing['update time'] = time() - t1\n self.steps += 1", "def run(self):\r\n for slot in self.slots:\r\n slot.work()\r\n self.increment()", "def updateMetaAtom (self):\r\n # print (\"Old state DNS: \\n\")\r\n # self.stateDanglingNodes()\r\n synchList = []\r\n synchListState = []\r\n for i in range(len(self.mol)):\r\n for j in range(len(self.mol[i].nodeArray)):\r\n synchList.append(self.mol[i].nodeArray[j])\r\n synchListState.append(synchList[i].state)\r\n #print (\"The original state is: \\n\" + str(synchListState) + \"\\n\")\r\n # Find new state for every node\r\n newStates = []\r\n for i in range(len(synchList)):\r\n oldState = synchList[i].state\r\n synchList[i].calculateState()\r\n newStates.append(synchList[i].state)\r\n synchList[i].state = oldState\r\n \r\n for i in range(len(synchList)):\r\n synchList[i].state = newStates[i]\r\n synchListState[i] = synchList[i].state\r\n \r\n offSet = 0 \r\n for i in range(len(self.mol)):\r\n for j in range(len(self.mol[i].nodeArray)):\r\n self.mol[i].nodeArray[j].state = synchListState[offSet]\r\n offSet += 1\r\n stateMol = []\r\n \r\n for i in range(len(self.mol)):\r\n for j in range(len(self.mol[i].nodeArray)):\r\n stateMol.append(self.mol[i].nodeArray[j].state)\r\n \r\n # print (\"The new state is: \\n\" + str(synchListState) + \"\\n\")\r\n # print (\"The state of the mol array is: \" + str(stateMol) + \"\\n\")\r\n #print (\"Post update \\n\")\r\n self.stateDanglingNodes()\r\n offSet = 0 \r\n oldStateNodes = [] # Store the old state of nodes in molecule\r\n newStateNodes = [] # Stores the new state\r\n # The code below goes through each metaspike and ensures that the dangling nodes have been updated with the correct\r\n # new state\r\n for i in range(len(self.metaSpikes)):\r\n if self.metaSpikes[i].typeSpike == 1:\r\n #print (\"Inside type 1 \\n\")\r\n #print (\"The number of DNs is: \" + str(len(self.metaSpikes[i].danglingNodeList)) + \"\\n\")\r\n for j in range(len(self.metaSpikes[i].danglingNodeList)):\r\n # Find the location of the dangling node in the synch list and change the dangling nodes state to match\r\n # state locted in the synch list\r\n if self.metaSpikes[i].danglingNodeList[j].node in synchList:\r\n oldStateNodes.append(self.metaSpikes[i].danglingNodeList[j].node.state)\r\n indexNode = synchList.index(self.metaSpikes[i].danglingNodeList[j].node)\r\n # print (\"The current value is: \" + str(self.metaSpikes[i].danglingNodeList[j].node.state) + \"\\n\")\r\n # print (\"The index of node is: \" + str(indexNode) + \"\\n\")\r\n # print (\"The new value should be: \" + str(synchListState[indexNode]) + \"\\n\")\r\n self.metaSpikes[i].danglingNodeList[j].changeState(synchListState[indexNode]) \r\n newStateNodes.append(self.metaSpikes[i].danglingNodeList[j].node.state)\r\n # print (\"Node in list \\n\")\r\n else:\r\n # print (\"The number of DTs is: \" + str(len(self.metaSpikes[i].danglingTailList)) + \"\\n\")\r\n #print (\"Inside type 2 \\n\")\r\n # With dangling tails we need an extra for loop to iterate across each nodelist of the tail\r\n for j in range(len(self.metaSpikes[i].danglingTailList)):\r\n for k in range(len(self.metaSpikes[i].danglingTailList[j].nodeList)):\r\n if self.metaSpikes[i].danglingTailList[j].nodeList[k].node in synchList:\r\n oldStateNodes.append(self.metaSpikes[i].danglingTailList[j].nodeList[k].state)\r\n indexNode = synchList.index(self.metaSpikes[i].danglingTailList[j].nodeList[k].node)\r\n self.metaSpikes[i].danglingTailList[j].nodeList[k].changeState(synchListState[indexNode]) \r\n newStateNodes.append(self.metaSpikes[i].danglingTailList[j].nodeList[k].state)\r\n #print (\"Node in list \\n\")\r\n \r\n # print (\"After running update code \\n\")\r\n self.stateDanglingNodes()\r\n # Recalculate the state of the metaatom\r\n self.calculateState()\r\n #print (\"The old state is:\\n\" + str(oldStateNodes) + \"\\n\")\r\n #print (\"The new state is:\\n\" + str(newStateNodes) + \"\\n\")\r\n \r\n \r\n # Next need to give each node in mol its state \r\n \r\n #print (\"Intensity before update: \" + str(self.metaSpikes[i].intensity) + \"\\n\")\r\n #print (\"Intensity after update: \" + str(self.metaSpikes[i].intensity) + \"\\n\")\r\n # Now need to recalculate state\r", "def update(self,dt):\n self.rebuild_lists()\n self.step(self.gather_state,self.derivatives, \\\n self.gather_derivatives,self.scatter_state,dt)\n self.box.apply(self)\n self.steps += 1", "def run(self):\n\t\t\n\t\twhile self.update():\n\t\t\tpass", "def step(self, sys):\n self._momentum_update(sys, 0.5*self.dt)\n self._position_update(sys, 0.5*self.dt)\n self._OU_update(sys, self.dt)\n self._position_update(sys, 0.5*self.dt)\n self._momentum_update(sys, 0.5*self.dt)", "def update_temperature(self):\n self.iteration += 1 \n self.T = self.T0 * 0.9935**self.iteration", "def run(self): # pragma: no cover\n while True:\n self.update()", "def updateForces(self):\n for atom1 in range(0, self.numAtoms-1):\n for atom2 in range(atom1+1, self.numAtoms):\n self.calculateForce(atom1, atom2)\n \n # Multiply by constants \n for atom in range(0, self.numAtoms):\n self.atoms[atom].fx *= 48*self.e\n self.atoms[atom].fy *= 48*self.e\n self.atoms[atom].fz *= 48*self.e\n self.atoms[atom].potential *= 4*self.e", "def update(self, time_step):\n a = [0,0]\n F = self.force()\n for i in [0,1]: # We have to update x and y\n a[i] = self.force()[i] / self.mass\n self.velocity[i] = self.velocity[i] + a[i]*time_step\n self.position[i] = self.position[i] + self.velocity[i]*time_step # I'm lazy\n self.turtle.goto(self.position) # Comment out the goto if you need the simulation to run really fast; you won't get the animation", "def step(self, f):\n\n NVTBerendsen.scale_velocities(self)\n self.scale_positions_and_cell()\n\n #one step velocity verlet\n atoms = self.atoms\n p = self.atoms.get_momenta()\n p += 0.5 * self.dt * f\n\n if self.fixcm:\n # calculate the center of mass\n # momentum and subtract it\n psum = p.sum(axis=0) / float(len(p))\n p = p - psum\n\n self.atoms.set_positions(self.atoms.get_positions() +\n self.dt * p / self.atoms.get_masses()[:,np.newaxis])\n\n # We need to store the momenta on the atoms before calculating\n # the forces, as in a parallel Asap calculation atoms may\n # migrate during force calculations, and the momenta need to\n # migrate along with the atoms. For the same reason, we\n # cannot use self.masses in the line above.\n\n self.atoms.set_momenta(p)\n f = self.atoms.get_forces()\n atoms.set_momenta(self.atoms.get_momenta() + 0.5 * self.dt * f)\n\n\n return f", "def updateNodeStates (self,listAtoms):\r\n \r\n for i in range(len(listAtoms)):\r\n for j in range(len(listAtoms[i].nodeArray)):\r\n self.mol[i].nodeArray[j].state = listAtoms[i].nodeArray[j].state", "def run(self):\r\n\r\n # t=0 is singular point\r\n\r\n print 'Time of laboratory clock Tw =', self.tick\r\n tt = self.tmp\r\n ll = self.lst\r\n car = self.interaction(self.carr)\r\n ll.item_run(tt, self.tick, car)\r\n tt = tt.next\r\n\r\n # run of local time\r\n\r\n while not tt is None:\r\n\r\n if tt.dedicated_node:\r\n self.tick = self.tick + 1\r\n print 'Time of laboratory clock Tw =', self.tick\r\n\r\n # self.move() # It is classical motion of particle (example).\r\n\r\n self.move_reset()\r\n car = self.interaction(self.carr)\r\n\r\n ll = self.lst\r\n while not ll is None:\r\n ll.item_run(tt, self.tick, car)\r\n ll = ll.right\r\n\r\n tt = tt.next", "def run_update_step(self, time, pids, hole_rating, observations):\n\t\treturn NotImplemented", "def update(self, delta_t):\n\n self.display.fill((255, 255, 255))\n for atom in self.atoms:\n atom.update(delta_t)\n self._collide()", "def step(self):\n updating_env = {} if self.independent_update else self.env\n for a in self.agents:\n if self.i % a.period == 0:\n action = a(self.env)\n if a.name is not None:\n updating_env[a.name] = action\n if self.independent_update:\n self.env.update(updating_env)\n self.i += 1", "def update(self, initial, follows):", "def update_afferents_ap(self,time):\n\t\t# Iterate over all dictionaries\n\t\tfor muscle in self.cells:\n\t\t\tfor cellName in self.cells[muscle]:\n\t\t\t\tif cellName in self._afferentsNames:\n\t\t\t\t\tfor cell in self.cells[muscle][cellName]:\n\t\t\t\t\t\tcell.update(time)", "def momentum (self):\n\n for planet in self.planets: #this loop takes a 'planet' from 'self.planets' and computes it linear momentum.\n planet.momentum = planet.mass * planet.velocity #Each body's resulting momentum is updated to the body's information defined in the Particle class.", "def increment_time_step(self):\n for grid in self.get_grid_list():\n try:\n self[grid].increment_time_step()\n except AttributeError:\n pass", "def step(self):\n\n for component in self.components:\n component.input(self.current_time)\n\n for component in self.components:\n component.fire()\n\n self.current_time = self.current_time + self.interval\n\n for component in self.components:\n component.output(self.current_time)\n\n return self.current_time", "def run(self):\n last = self.system.last_timestep\n start = last.timestep + 1 if last else 0\n del last\n end = self.system.cg_steps\n \n logging.info(\"running timesteps {} to {}\".format(start, end))\n \n for _ in range(start, end):\n self.system.begin_timestep()\n self.atomistic_step()\n self.cg_step()\n self.system.end_timestep()\n \n logging.info(\"completed all {} timesteps\".format(end-start))", "def run(self):\n while True:\n if self.job_q.empty():\n self.message_q.put(False)\n return\n try:\n smolecule = self.job_q.get()\n self.data_pointer[smolecule].get_adp(self.Temp)\n except IndexError:\n self.message_q.put((smolecule, None))\n try:\n self.message_q.put((smolecule, [j.adp['cart_int'] for j in self.data_pointer[smolecule].atoms]))\n except KeyError:\n # =======================================================\n # self.message_q.put((molecule,[0 for i in self.data_pointer[molecule].atoms]))\n #=======================================================\n pass" ]

[ "0.68202317", "0.6715678", "0.6414524", "0.628308", "0.6197439", "0.6184594", "0.61320144", "0.6113552", "0.60633576", "0.60368603", "0.6036309", "0.60067433", "0.59957993", "0.59397817", "0.5897747", "0.5897021", "0.5874688", "0.58444345", "0.5821056", "0.58185893", "0.58007413", "0.58007103", "0.5787963", "0.57846195", "0.5763084", "0.5744508", "0.57158357", "0.57111734", "0.5705929", "0.5693979" ]

[ "def compare_one_state_evolution(self, value, true_state, option='density'):\n\n fig = plt.figure(figsize=(16, 8), dpi=100)\n ax = fig.add_subplot(111)\n if option == 'density':\n ax.plot(np.squeeze(np.array(self.est_density[value, :])), 'r--', label='Estimated')\n elif option == 'flow':\n if value == 0:\n # plot qin\n index = self.x_index['qin']\n elif value == self.num_cells:\n # plot qout\n index = self.x_index['qout']\n elif value in self.x_index['onramp'].keys():\n # plot onramp flow\n index = self.x_index['onramp'][value]\n elif value in self.x_index['offramp'].keys():\n # plot offramp flow\n index = self.x_index['offramp'][value]\n else:\n raise Exception('KeyError: value must be the cell id')\n\n ax.plot(np.squeeze(np.array(self.est_state_all[index, :])), 'r--', label='Estimated')\n ax.plot(np.squeeze(np.array(true_state)), 'b', label='True')\n\n plt.title('{0} at {1}'.format(option, value))\n plt.xlabel('Time (step)')\n plt.ylabel('Value')\n\n plt.grid(True)\n plt.legend()\n\n plt.draw()", "def make_comparison_plot(\n nu,\n y_comp,\n y_ref,\n comp_label,\n ref_label,\n fig_title,\n fig_path,\n plot_type,\n y_range=None,\n comparison_range=None\n):\n if plot_type == \"sed\":\n # set the axes labels for an SED plot\n x_label = SED_X_LABEL\n y_label = SED_Y_LABEL\n deviation_label = SED_DEVIATION_LABEL\n elif plot_type == \"tau\":\n # set the axes labels for a tau plot\n x_label = TAU_X_LABEL\n y_label = TAU_Y_LABEL\n deviation_label = TAU_DEVIATION_LABEL\n else:\n # set a custom y label, keep the x-axis in frequency\n x_label = SED_X_LABEL\n y_label = plot_type\n deviation_label = f\"({plot_type} agnpy / {plot_type} ref.) - 1\"\n # make the plot\n fig, ax = plt.subplots(\n 2,\n sharex=True,\n gridspec_kw={\"height_ratios\": [2, 1], \"hspace\": 0.05},\n figsize=(8, 6),\n )\n\n # plot the SEDs or TAUs in the upper panel\n # plot the reference sed with a continuous line and agnpy sed with a dashed one\n ax[0].loglog(nu, y_ref, marker=\".\", ls=\"-\", color=\"k\", lw=1.5, label=ref_label)\n ax[0].loglog(\n nu, y_comp, marker=\".\", ls=\"--\", color=\"crimson\", lw=1.5, label=comp_label\n )\n ax[0].set_ylabel(y_label)\n ax[0].set_title(fig_title)\n ax[0].legend(loc=\"best\")\n if y_range is not None:\n ax[0].set_ylim(y_range)\n if comparison_range is not None:\n ax[0].axvline(comparison_range[0], ls=\"--\", color=\"dodgerblue\")\n ax[0].axvline(comparison_range[1], ls=\"--\", color=\"dodgerblue\")\n ax[0].grid(ls=\":\")\n\n # plot the deviation in the bottom panel\n deviation = y_comp / y_ref - 1\n ax[1].axhline(0, ls=\"-\", color=\"darkgray\")\n ax[1].axhline(0.2, ls=\"--\", color=\"darkgray\")\n ax[1].axhline(-0.2, ls=\"--\", color=\"darkgray\")\n ax[1].axhline(0.3, ls=\":\", color=\"darkgray\")\n ax[1].axhline(-0.3, ls=\":\", color=\"darkgray\")\n ax[1].set_ylim([-0.5, 0.5])\n ax[1].semilogx(\n nu,\n deviation,\n marker=\".\",\n ls=\"--\",\n color=\"crimson\",\n lw=1.5,\n label=deviation_label,\n )\n ax[1].set_xlabel(x_label)\n ax[1].legend(loc=\"best\")\n if comparison_range is not None:\n ax[1].axvline(comparison_range[0], ls=\"--\", color=\"dodgerblue\")\n ax[1].axvline(comparison_range[1], ls=\"--\", color=\"dodgerblue\")\n\n fig.savefig(f\"{fig_path}\")\n # avoid RuntimeWarning: More than 20 figures have been opened.\n plt.close(fig)", "def show_grid_policy(policy, states):\n\n actions = np.array([policy(s) for s in states])\n states = np.array(states)\n plt.quiver(states[:, 0], states[:, 1], actions[:, 0], actions[:, 1])\n plt.axis((min(states)[0], max(states)[0],\n min(states, key=lambda s: s[1])[1], max(states, key=lambda s: s[1])[1]))\n plt.show()", "def test_traj () :\n samples = getAllTraj()\n states = []\n for t in samples : \n states.extend([toInternalStateRep(s) for s, _, _ in t])\n states = np.stack(states)\n xRange = np.linspace(-np.pi, np.pi, 100)\n yRange = np.linspace(-np.pi, np.pi, 100)\n plotHist(states, xRange, yRange, 'theta1', 'theta2', 'S Count')", "def plot_results(self):\n experiment_utils.plot_exp_metric_comparison(self.experiments(reverse_sort=False))", "def display_comparison(self, X_val, y_val):\n import matplotlib.pyplot as plt\n x = []\n y = []\n for model_tuple in self.model_list:\n x.append(model_tuple[1])\n y.append(model_tuple[0].score(X_val, y_val))\n plt.scatter(x, y)\n plt.show()", "def plot_comparison(results):\n dfs = []\n for res in results:\n equity = (1 + res['equity']).cumprod()\n equity.name = 'equity'\n equity = equity.reset_index()\n equity['name'] = res['name']\n dfs.append(equity)\n data = pd.concat(dfs, axis=0)\n\n fig = px.line(data, x='time_idx', y='equity', color='name')\n fig.show()", "def plot(stats):\n global y1, y2, lines\n bars = redraw()\n\n if y1 == y2:\n print('plot equals case')\n add_line(y1)\n ax.set_title('Mean comparison against y = {}'.format(int(y1)))\n\n ttres = st.ttest_1samp(dfT, y1)\n ps = ttres[1]\n\n label_bars(ps, bars, lambda p, b: p_to_color_div(p, b.get_height() > y1), True)\n\n asc, desc = np.arange(0, 1, 0.2), np.arange(1, -0.1, -0.2)\n colors = [p_to_color_div(p, True) for p in asc] + [p_to_color_div(p, False) for p in desc]\n\n leg = add_legend(colors, np.around(np.append(asc, desc), 1))\n else:\n add_line(y1)\n add_line(y2)\n ymin, ymax = min(y1, y2), max(y1, y2)\n\n ax.set_title('Probability of population mean between {} and {}'.format(int(ymin), int(ymax)))\n\n lower = st.t.cdf(ymin, stats['dof'], loc=stats['mean'], scale=stats['stderr'])\n higher = st.t.cdf(ymax, stats['dof'], loc=stats['mean'], scale=stats['stderr'])\n density_in_range = higher - lower\n\n label_bars(density_in_range, bars, lambda p, b: p_to_color_seq(p), False)\n\n seq = np.arange(1.01, 0, -0.1)\n colors = [p_to_color_seq(p) for p in seq]\n\n leg = add_legend(colors, np.around(seq, 1))\n\n return bars", "def graph_count_comparison(df):\r\n # set the visual features of the graph\r\n sns.set(font_scale=2)\r\n sns.set_style(\"darkgrid\")\r\n fig, ax = plt.subplots()\r\n fig.set_size_inches(20, 10)\r\n ax.set_title(\"Distribution of Vehicle Police Deaths\")\r\n # create the two graphs of the data and overlap them\r\n plot1 = sns.violinplot(x='cause_short', y='count', data=df, inner=None, palette=\"winter_d\")\r\n plot2 = sns.swarmplot(x='cause_short', y='count', data=df, palette=\"Wistia\")\r\n # plt.show()\r\n # save the graph as an image\r\n fig.savefig(\"2_graph_count_comparison.png\")", "def plot_metric_results():\n from run_metric_comparison_experiments import (\n PIVECTOR_TEMPLATE,\n PIVECTOR_DISTANCE_MATRIX_TEMPLATE,\n DISCRIMINATOR_DISTANCE_MATRIX_TEMPLATE,\n GAUSSIAN_DISTANCE_MATRIX_TEMPLATE,\n ENCODER_DISTANCE_MATRIX_TEMPLATE,\n DISCRETIZATION_DISTANCE_MATRIX_TEMPLATE,\n NUM_TRAJECTORIES,\n NUM_COMPONENTS,\n NUM_REPETITIONS,\n REWARD_SCALES,\n ENVS\n )\n\n # Path-templates to each distance matrix to compare\n # BC = Behavioural Characteristication\n BC_DISTANCE_MATRIX_TEMPLATES = [\n PIVECTOR_DISTANCE_MATRIX_TEMPLATE,\n GAUSSIAN_DISTANCE_MATRIX_TEMPLATE,\n DISCRIMINATOR_DISTANCE_MATRIX_TEMPLATE,\n ENCODER_DISTANCE_MATRIX_TEMPLATE,\n DISCRETIZATION_DISTANCE_MATRIX_TEMPLATE\n ]\n\n BC_LEGEND_NAMES = [\n \"Supervector\",\n \"Gaussian\",\n \"Discriminator\",\n \"Encoder\",\n \"Discretization\"\n ]\n\n BC_PLOT_COLORS = [\n \"C0\",\n \"C1\",\n \"C2\",\n \"C3\",\n \"C4\"\n ]\n\n fig, axs = pyplot.subplots(\n figsize=[4.8 * 3 * 0.75, 4.8 * 0.75],\n nrows=1,\n ncols=3,\n )\n\n def get_policy_names(env):\n policy_names = glob(PIVECTOR_TEMPLATE.format(env=env, num_traj=\"*\", num_components=\"*\", policy_name=\"*\", repetition_num=\"*\"))\n policy_names = [\"_\".join(os.path.basename(x).split(\"_\")[-4:-2]) for x in policy_names]\n policy_names = sorted(list(set(policy_names)))\n return policy_names\n\n # For each different distance measurement\n for distance_matrix_template, plot_legend_name, plot_color in zip(BC_DISTANCE_MATRIX_TEMPLATES, BC_LEGEND_NAMES, BC_PLOT_COLORS):\n # These will be NUM_TRAJECTORY length lists\n average_scores = np.ones((len(NUM_TRAJECTORIES),))\n std_scores = np.ones((len(NUM_TRAJECTORIES),))\n for num_traj_idx, num_traj in enumerate(NUM_TRAJECTORIES):\n # Average over environments, policies and repetitions\n scores = []\n for env_i, env in enumerate(ENVS):\n if \"Bipedal\" in env and distance_matrix_template == DISCRETIZATION_DISTANCE_MATRIX_TEMPLATE:\n print(\"[Note] Skipping env {} for discretization distances (OOM)\".format(env))\n continue\n min_reward, max_reward = REWARD_SCALES[env]\n policy_names = get_policy_names(env)\n\n for policy_name in policy_names:\n for repetition in range(1, NUM_REPETITIONS + 1):\n # Ugh bit of messing around because I did not think this through...\n if distance_matrix_template == PIVECTOR_DISTANCE_MATRIX_TEMPLATE:\n file_path = distance_matrix_template.format(env=env, num_traj=num_traj, num_components=NUM_COMPONENTS, policy_name=policy_name, repetition_num=repetition)\n else:\n file_path = distance_matrix_template.format(env=env, num_traj=num_traj, policy_name=policy_name, repetition_num=repetition)\n\n data = np.load(file_path)\n distance_matrix = data[\"distance_matrix\"]\n rewards = data[\"average_episodic_rewards\"]\n\n raveled_reward_distances = np.abs(rewards - rewards[:, None])\n # Take upper diagonal, skip diagonal\n raveled_reward_distances = raveled_reward_distances[np.triu_indices(raveled_reward_distances.shape[0], 1)]\n raveled_distances = distance_matrix[np.triu_indices(distance_matrix.shape[0], 1)]\n\n # Score is correlation between the two\n correlation = np.corrcoef(raveled_distances, raveled_reward_distances)[0, 1]\n scores.append(correlation)\n\n scores = np.array(scores)\n average_score = np.mean(scores)\n std_score = np.std(scores)\n average_scores[num_traj_idx] = average_score\n std_scores[num_traj_idx] = std_score\n ax = axs[0]\n ax.plot(NUM_TRAJECTORIES, average_scores, c=plot_color, label=plot_legend_name)\n ax.scatter(NUM_TRAJECTORIES, average_scores, c=plot_color)\n #ax.fill_between(\n # NUM_TRAJECTORIES,\n # average_scores - std_scores,\n # average_scores + std_scores,\n # alpha=0.2,\n # color=plot_color,\n # edgecolor=\"none\",\n # linewidth=0.0\n #)\n ax.set_xticks(NUM_TRAJECTORIES)\n ax.tick_params(axis='both', which='both', labelsize=\"x-large\")\n ax.set_ylabel(\"Correlation with return-distances\", fontsize=\"x-large\")\n ax.set_xlabel(\"Number of trajectories\", fontsize=\"x-large\")\n ax.grid(alpha=0.2)\n\n # Amount of error to \"ground truth\" result,\n # where \"ground truth\" is one of the results with 100 trajectories of data.\n # Because of wonkyness of this, store list [#num-traj] of lists,\n # each storing results for that num-traj run\n per_trajectory_relative_errors = [[] for i in NUM_TRAJECTORIES]\n for env in ENVS:\n if \"Bipedal\" in env and distance_matrix_template == DISCRETIZATION_DISTANCE_MATRIX_TEMPLATE:\n print(\"[Note] Skipping env {} for discretization distances (OOM)\".format(env))\n continue\n policy_names = get_policy_names(env)\n for policy_name in policy_names:\n # The \"ground truth\" distances, will be filled with first\n # result with 100 trajectories.\n anchor_distance = None\n for traj_i, num_traj in enumerate(NUM_TRAJECTORIES):\n for repetition in range(1, NUM_REPETITIONS + 1):\n if distance_matrix_template == PIVECTOR_DISTANCE_MATRIX_TEMPLATE:\n file_path = distance_matrix_template.format(env=env, num_traj=num_traj, num_components=NUM_COMPONENTS, policy_name=policy_name, repetition_num=repetition)\n else:\n file_path = distance_matrix_template.format(env=env, num_traj=num_traj, policy_name=policy_name, repetition_num=repetition)\n distance_matrix = np.load(file_path)[\"distance_matrix\"]\n # Normalize to [0, 1]\n distance_matrix = (distance_matrix - distance_matrix.min()) / (distance_matrix.max() - distance_matrix.min())\n # Get only upper triangle as distance matrix is symmetric. Exlude diagonal\n raveled_distances = distance_matrix[np.triu_indices(distance_matrix.shape[0], 1)]\n # Check if we use this as the zero-point or compute relative error to\n if anchor_distance is None:\n assert num_traj == 100\n anchor_distance = raveled_distances\n else:\n per_trajectory_relative_errors[traj_i].append(\n np.mean(np.abs(raveled_distances - anchor_distance) / np.abs(anchor_distance))\n )\n # Lists are not of equal length, so can not just change into an array\n mean_average_errors = np.array([np.mean(np.array(results) * 100) for results in per_trajectory_relative_errors])\n std_average_errors = np.array([np.std(np.array(results) * 100) for results in per_trajectory_relative_errors])\n ax = axs[1]\n ax.plot(NUM_TRAJECTORIES, mean_average_errors, c=plot_color, label=plot_legend_name)\n ax.scatter(NUM_TRAJECTORIES, mean_average_errors, c=plot_color)\n #ax.fill_between(\n # NUM_TRAJECTORIES,\n # mean_average_errors - std_average_errors,\n # mean_average_errors + std_average_errors,\n # alpha=0.2,\n # color=plot_color,\n # edgecolor=\"none\",\n # linewidth=0.0\n #)\n ax.set_xticks(NUM_TRAJECTORIES)\n ax.tick_params(axis='both', which='both', labelsize=\"x-large\")\n ax.set_ylabel(\"Relative error to ground truth (%)\", fontsize=\"x-large\")\n ax.set_xlabel(\"Number of trajectories\", fontsize=\"x-large\")\n ax.grid(alpha=0.2)\n\n # Variation between results\n cv_means = np.ones((len(NUM_TRAJECTORIES,)))\n cv_stds = np.ones((len(NUM_TRAJECTORIES,)))\n for traj_i, num_traj in enumerate(NUM_TRAJECTORIES):\n traj_averaged_cvs = []\n for env in ENVS:\n if \"Bipedal\" in env and distance_matrix_template == DISCRETIZATION_DISTANCE_MATRIX_TEMPLATE:\n print(\"[Note] Skipping env {} for discretization distances (OOM)\".format(env))\n continue\n policy_names = get_policy_names(env)\n for policy_name in policy_names:\n # Compute std over repetitions\n distances = []\n for repetition in range(1, NUM_REPETITIONS + 1):\n if distance_matrix_template == PIVECTOR_DISTANCE_MATRIX_TEMPLATE:\n file_path = distance_matrix_template.format(env=env, num_traj=num_traj, num_components=NUM_COMPONENTS, policy_name=policy_name, repetition_num=repetition)\n else:\n file_path = distance_matrix_template.format(env=env, num_traj=num_traj, policy_name=policy_name, repetition_num=repetition)\n\n distance_matrix = np.load(file_path)[\"distance_matrix\"]\n # Normalize to [0, 1]\n distance_matrix = (distance_matrix - distance_matrix.min()) / (distance_matrix.max() - distance_matrix.min())\n # Get only upper triangle as distance matrix is symmetric. Exlude diagonal\n raveled_distances = distance_matrix[np.triu_indices(distance_matrix.shape[0], 1)]\n distances.append(raveled_distances)\n distances = np.stack(distances)\n # Coefficient of variance (std / mean)\n average_cv = np.mean(np.std(distances, axis=0) / np.mean(distances, axis=0))\n traj_averaged_cvs.append(average_cv)\n traj_averaged_cvs = np.array(traj_averaged_cvs)\n cv_means[traj_i] = np.mean(traj_averaged_cvs)\n cv_stds[traj_i] = np.std(traj_averaged_cvs)\n\n ax = axs[2]\n ax.plot(NUM_TRAJECTORIES, cv_means, c=plot_color, label=plot_legend_name)\n ax.scatter(NUM_TRAJECTORIES, cv_means, c=plot_color)\n #ax.fill_between(\n # NUM_TRAJECTORIES,\n # cv_means - cv_stds,\n # cv_means + cv_stds,\n # alpha=0.2,\n # color=plot_color,\n # edgecolor=\"none\",\n # linewidth=0.0\n #)\n ax.set_xticks(NUM_TRAJECTORIES)\n ax.tick_params(axis='both', which='both', labelsize=\"x-large\")\n ax.set_ylabel(\"Coefficient of variance $\\\\sigma/\\\\mu$\", fontsize=\"x-large\")\n ax.set_xlabel(\"Number of trajectories\", fontsize=\"x-large\")\n ax.grid(alpha=0.2)\n\n axs[1].legend(prop={\"size\": \"large\"})\n pyplot.tight_layout()\n pyplot.savefig(\"figures/metric_comparison.pdf\", bbox_inches=\"tight\", pad_inches=0.0)", "def plot_compare(benchmark_portvals,\n manual_strategy_portvals,\n strategy_learner_portval):\n final_df = pd.concat([benchmark_portvals, manual_strategy_portvals, strategy_learner_portval], axis=1)\n final_df.columns = ['Normalized Benchmark Portfolio Value',\n 'Normalized Manual Strategy Portfolio Value',\n 'Normalized Strategy Learner Portfolio Value']\n # print(final_df)\n # Plot final dataframe\n title = \"Strategy Learner vs Manual Strategy\"\n xlabel = \"Date\"\n ylabel = \"Portfolio Value\"\n \"\"\"Plot stock prices with a custom title and meaningful axis labels.\"\"\"\n ax = final_df.plot(title=title, fontsize=12, color=['g', 'r', 'b'], figsize=(10, 6))\n ax.set_xlabel(xlabel)\n ax.set_ylabel(ylabel)\n plt.axhline(y=1., color='m', linestyle=':')\n plt.show()\n return None", "def comp(a,b,av=None,bv=None,domatch=True,out=None) :\n if domatch :\n i1,i2=match.match(a['APOGEE_ID'],b['APOGEE_ID'])\n gd = np.where(a['NVISITS'][i1] == b['NVISITS'][i2])[0]\n a=a[i1[gd]]\n b=b[i2[gd]]\n\n fig = vscat(a)\n vscat(b,fig=fig,ls=':')\n if out is not None : \n fig[0].savefig(out+'_1.png')\n plt.close()\n\n if domatch :\n fig,ax=plots.multi(1,2)\n #plots.plotp(ax[0,0],a['SNR'],a['VHELIO_AVG']-b['VHELIO_AVG'],yr=[-3,3],yt=r'$\\Delta$ VHELIO_AVG')\n #plots.plotp(ax[0,1],a['SNR'],a['VHELIO_AVG']-b['VHELIO_AVG'],yr=[-50,50],yt=r'$\\Delta$ VHELIO_AVG')\n #plots.plotp(ax[1,0],a['SNR'],a['VSCATTER']-b['VSCATTER'],yr=[-0.5,0.5],yt=r'$\\Delta$ VSCATTER')\n #plots.plotp(ax[1,1],a['SNR'],a['VSCATTER']-b['VSCATTER'],yr=[-5,5],yt=r'$\\Delta$ VSCATTER')\n ax[0].hist(a['VHELIO_AVG']-b['VHELIO_AVG'],bins=np.arange(-0.5,0.5,0.02),histtype='step')\n ax[0].set_xlabel(r'$\\Delta$ VHELIO_AVG')\n ax[1].hist(a['VSCATTER']-b['VSCATTER'],bins=np.arange(-0.5,0.5,0.02),histtype='step')\n ax[1].set_xlabel(r'$\\Delta$ VSCATTER')\n if out is not None : \n fig.savefig(out+'_2.png')\n plt.close()\n\n return a,b", "def make_comparison_plot(args, res, keys, min_length):\n directory = args.directory\n\n # Build the plot.\n fig, ax = plt.subplots(figsize=(args.figSizeX, args.figSizeY))\n\n # Stack the results groups, thus, each must be the same shape.\n sns.tsplot(data = np.stack(res, axis=2), condition=keys, ax=ax, ci=[68, 95])\n \n # Save the plot.\n ax.set_title('Average Return by Group, N=' + str(min_length), fontsize=18)\n ax.set_xlabel('Bin', fontsize=18)\n ax.set_ylabel('Average Return', fontsize=18)\n ax.legend(fontsize=18)\n plt.tick_params(axis='both', which='major', labelsize=18)\n ax.xaxis.set_major_locator(ticker.MaxNLocator(integer=True))\n plt.savefig(os.path.join(directory, 'group_comparison.png'), \n bbox_inches='tight')", "def plot(self):\n\t\tself.plotOfHeatingCurrent().plot()", "def comparison_test(params):\n nx, ny, lx, ly, dx, dy, dt, nt, p, pmlc, source = params\n\n history_bpml = callers.call_bpml(nx, ny, nt, dx, dy, dt, p, pmlc, source)\n history_cpml = callers.call_cpml(nx, ny, nt, dx, dy, dt, p, pmlc, source)\n\n ny = int(params[1] / 2)\n b_snap = history_bpml[:, ny, -1]\n c_snap = history_cpml[:, ny, -1]\n\n names = [\"BPML\", \"CPML\"]\n labels = [\"Space [Cells]\", \"Hz [V/m]\"]\n snaps = [b_snap, c_snap]\n\n common.plot_snaps(names, labels, snaps)", "def obstab_plot_observable(yyyy: int, doy: int, gnss: str, dfprnobst: pd.DataFrame, dir_gfzplt: str, obstab_name: str, dt_first: datetime, dt_last: datetime, show_plot: bool = False, logger: logging.Logger = None) -> str:\n cFuncName = colored(os.path.basename(__file__), 'yellow') + ' - ' + colored(sys._getframe().f_code.co_name, 'green')\n\n amutils.logHeadTailDataFrame(df=dfprnobst, dfName='dfprnobst[{gnss:s}]'.format(gnss=gnss), logger=logger, callerName=cFuncName)\n\n # set up the plot\n plt.style.use('ggplot')\n # plt.style.use('seaborn-darkgrid')\n\n # determine index of first obst\n idx_PRN = dfprnobst.columns.get_loc('PRN') + 1\n nr_obsts = len(dfprnobst.columns[idx_PRN:])\n\n # used markers\n lst_markers = ['o', 'x', '+', '.', ',', 'v', '^', '<', '>', 's', 'd']\n\n # create 2 subplots with same axis if more than 1 obst, else only 1 subplot\n if nr_obsts == 1:\n fig, ax1 = plt.subplots(1, figsize=(10, 4))\n else:\n fig, (ax1, ax2) = plt.subplots(2, sharex=True, figsize=(10, 7), gridspec_kw={'height_ratios': [2, 1]})\n\n # create colormap with nrcolors discrete colors which is th efirst always present plot\n obst_colors, title_font = amutils.create_colormap_font(nrcolors=nr_obsts, font_size=12)\n obst_markers = lst_markers[:nr_obsts]\n for obst, obst_color, marker in zip(dfprnobst.columns[idx_PRN:], obst_colors, obst_markers):\n ax1.plot(dfprnobst['DATE_TIME'], dfprnobst[obst], color=obst_color, label=obst, alpha=0.6, linestyle='', marker=marker, markersize=2)\n\n # beautify plot\n ax1.xaxis.grid(b=True, which='major')\n ax1.yaxis.grid(b=True, which='major')\n\n ax1.set_ylabel(gfzc.dict_obstypes[dfprnobst.columns[idx_PRN][0]], fontdict=title_font)\n # ax1.yaxis.set_major_formatter(ticker.FormatStrFormatter('%d'))\n\n ax1.legend(loc='best', markerscale=4)\n\n # setticks on Y axis to represent the PRNs\n if dfprnobst.columns[idx_PRN][0] == 'S':\n ax1.set_yticks(np.arange(10, 61, 10))\n\n # this will be the bottom axis if only 1 obst available\n axis = ax1\n\n # add difference plot when there are more than 1 obst available\n if nr_obsts > 1:\n # add difference between observables\n diff_colors = []\n for i, color in enumerate(amutils.get_spaced_colors(nr_obsts)):\n diff_colors.append(tuple(rgb / 256. for rgb in color))\n\n obst_diff_markers = lst_markers[:nr_obsts]\n\n dfprnobstdiff = pd.DataFrame(dfprnobst['DATE_TIME'])\n for i, obst1 in enumerate(dfprnobst.columns[idx_PRN:-1]):\n for j, obst2 in enumerate(dfprnobst.columns[idx_PRN + (i + 1):]):\n obst_diff = '{obst1:s}-{obst2:s}'.format(obst1=obst1, obst2=obst2)\n\n dfprnobstdiff[obst_diff] = dfprnobst[obst1] - dfprnobst[obst2]\n\n marker = obst_diff_markers[i * len(dfprnobst.columns[idx_PRN:-1]) + j]\n ax2.plot(dfprnobstdiff['DATE_TIME'], dfprnobstdiff[obst_diff], label=obst_diff, alpha=0.6, linestyle='', marker=marker, markersize=2)\n\n # beutify this plot\n if dfprnobst.columns[idx_PRN][0] == 'S':\n ax2.set_ylim([-10, +10])\n if dfprnobst.columns[idx_PRN][0] == 'C':\n ax2.set_ylim([-20, +20])\n ax2.set_ylabel('Diff {obst:s}'.format(obst=gfzc.dict_obstypes[dfprnobst.columns[idx_PRN][0]]), fontdict=title_font)\n\n # this will be the bottom axis if more than 1 obst available\n axis = ax2\n\n # plot title\n plt.suptitle('{obst:s} for PRN {prn:s} on {yy:02d}/{doy:03d}'.format(obst=gfzc.dict_obstypes[dfprnobst.columns[idx_PRN][0]], prn=dfprnobst['PRN'].iloc[0], yy=(yyyy % 100), doy=doy))\n\n # beautify plot\n axis.set_xlabel('Time', fontdict=title_font)\n axis.yaxis.grid(b=True, which='major')\n axis.legend(loc='best', markerscale=3)\n\n # create the ticks for the time axis\n axis.set_xlim([dt_first, dt_last])\n dtFormat = plot_utils.determine_datetime_ticks(startDT=dt_first, endDT=dt_last)\n\n if dtFormat['minutes']:\n # ax.xaxis.set_major_locator(dates.MinuteLocator(byminute=range(10, 60, 10), interval=1))\n pass\n else:\n axis.xaxis.set_major_locator(dates.HourLocator(interval=dtFormat['hourInterval'])) # every 4 hours\n axis.xaxis.set_major_formatter(dates.DateFormatter('%H:%M')) # hours and minutes\n\n axis.xaxis.set_minor_locator(dates.DayLocator(interval=1)) # every day\n axis.xaxis.set_minor_formatter(dates.DateFormatter('\\n%d-%m-%Y'))\n\n axis.xaxis.set_tick_params(rotation=0)\n for tick in axis.xaxis.get_major_ticks():\n # tick.tick1line.set_markersize(0)\n # tick.tick2line.set_markersize(0)\n tick.label1.set_horizontalalignment('center')\n\n fig.tight_layout()\n\n # save the plot in subdir png of GNSSSystem\n plt_name = '{basen:s}-{gnss:s}-{PRN:s}-{obst:s}.pdf'.format(basen=obstab_name.split('.')[0], gnss=gnss, PRN=dfprnobst['PRN'].iloc[0], obst=gfzc.dict_obstypes[dfprnobst.columns[idx_PRN][0]])\n fig.savefig(os.path.join(dir_gfzplt, plt_name), dpi=200)\n logger.info('{func:s}: created plot {plot:s}'.format(func=cFuncName, plot=colored(plt_name, 'green')))\n\n # if show_plot:\n if show_plot:\n plt.show(block=True)\n else:\n plt.close(fig)\n\n return plt_name", "def _plot_comparison(xs, pan, other_program_name, **kw):\n\n pans = ['Bmax', 'Emax']\n units = ['(mG)', '(kV/m)']\n title_app = [', Max Magnetic Field', ', Max Electric Field']\n save_suf = ['-%s-comparison-Bmax' % other_program_name,\n '-%s-comparison-Emax' % other_program_name]\n\n for p,u,t,s in zip(pans, units, title_app, save_suf):\n #figure object and axes\n fig = plt.figure()\n ax_abs = fig.add_subplot(2,1,1)\n ax_per = ax_abs.twinx()\n ax_mag = fig.add_subplot(2,1,2)\n #Bmax\n #init handles and labels lists for legend\n kw['H'], kw['L'] = [], []\n _plot_comparison_repeatables(ax_abs, ax_per, ax_mag, pan, p, u,\n other_program_name, **kw)\n _plot_wires(ax_mag, xs.hot, xs.gnd, pan['emf.fields-results'][p], **kw)\n _check_und_conds([xs], [ax_mag], **kw)\n ax_abs.set_title('Absolute and Percent Difference' + t)\n ax_mag.set_ylabel(p + ' ' + u)\n ax_mag.set_title('Model Results' + t)\n ax_mag.legend(kw['H'], kw['L'], **_leg_kw)\n _color_twin_axes(ax_abs, mpl.rcParams['axes.labelcolor'], ax_per, 'firebrick')\n _format_line_axes_legends(ax_abs, ax_per, ax_mag)\n #_format_twin_axes(ax_abs, ax_per)\n _save_fig(xs.sheet + s, fig, **kw)", "def vis_difference(self):\n print(self.init_vec)\n\n init = self.init_output.numpy()\n\n alphas = np.linspace(0, 1, 20)\n for i, alpha in enumerate(alphas):\n\n display.clear_output(wait=True)\n norm = [torch.linalg.norm(torch.tensor(\n self.init_vec + alpha*self.eigen[i]), axis=1).detach().numpy() for i in range(2)]\n\n diff = np.array([self.compute_difference(\n alpha, self.eigen[i]) for i in range(2)])\n\n fig = plt.figure(figsize=(14, 12), tight_layout=True)\n fig.suptitle(\"Latent direction variation\", fontsize=20)\n gs = gridspec.GridSpec(2, 2)\n\n ax_temp = plt.subplot(gs[0, :])\n ax_temp.scatter(\n init[:, 0], init[:, 1])\n ax_temp.set_title(\"Initial Dataset\")\n ax_temp.set_xlim(-1, 1)\n ax_temp.set_ylim(-1, 1)\n [s.set_visible(False) for s in ax_temp.spines.values()]\n\n for j in range(2):\n ax_temp = plt.subplot(gs[1, j])\n sc = ax_temp.quiver(\n init[:, 0], init[:, 1], diff[j, :, 0], diff[j, :, 1], norm[j])\n sc.set_clim(np.min(norm[j]), np.max(norm[j]))\n plt.colorbar(sc)\n ax_temp.set_title(\n \"Direction: {}, alpha: {}\".format(j+1, alpha))\n ax_temp.set_xlim(-1, 1)\n ax_temp.set_ylim(-1, 1)\n [s.set_visible(False) for s in ax_temp.spines.values()]\n\n plt.savefig(\"frames_dir/fig_{}\".format(i))\n plt.show()", "def overview(self, minState=5):\n n = 600\n \n ### first plot: the RTOFFSETs and STATES\n plt.figure(10)\n plt.clf()\n plt.subplots_adjust(hspace=0.05, top=0.95, left=0.05,\n right=0.99, wspace=0.00, bottom=0.1)\n ax1 = plt.subplot(n+11)\n try:\n print self.insmode+' | pri:'+\\\n self.getKeyword('OCS PS ID')+' | sec:'+\\\n self.getKeyword('OCS SS ID')\n \n plt.title(self.filename+' | '+self.insmode+' | pri:'+\n self.getKeyword('OCS PS ID')+' | sec:'+\n self.getKeyword('OCS SS ID'))\n except:\n pass\n plt.plot(self.raw['OPDC'].data.field('TIME'),\n self.raw['OPDC'].data.field('FUOFFSET')*1e3,\n color=(1.0, 0.5, 0.0), label=self.DLtrack+' (FUOFFSET)',\n linewidth=3, alpha=0.5)\n plt.legend(prop={'size':9})\n plt.ylabel('(mm)')\n plt.xlim(0)\n \n plt.subplot(n+12, sharex=ax1) # == DDL movements\n \n plt.plot(self.raw['DOPDC'].data.field('TIME'),\n 1e3*self.raw['DOPDC'].data.field(self.DDLtrack),\n color=(0.0, 0.5, 1.0), linewidth=3, alpha=0.5,\n label=self.DDLtrack)\n plt.plot(self.raw['DOPDC'].data.field('TIME'),\n 1e3*self.raw['DOPDC'].data.field('PSP'),\n color=(0.0, 0.5, 1.0), linewidth=1, alpha=0.9,\n label='PSP', linestyle='dashed')\n plt.legend(prop={'size':9})\n plt.ylabel('(mm)')\n plt.xlim(0)\n \n plt.subplot(n+13, sharex=ax1) # == states\n plt.plot(self.raw['OPDC'].data.field('TIME'),\n self.raw['OPDC'].data.field('STATE'),\n color=(1.0, 0.5, 0.0), label='OPDC')\n plt.plot(self.raw['DOPDC'].data.field('TIME'),\n self.raw['DOPDC'].data.field('STATE'),\n color=(0.0, 0.5, 1.0), label='DOPDC')\n plt.legend(prop={'size':9})\n plt.ylabel('STATES')\n yl=plt.ylim()\n plt.ylim(yl[0]-1, yl[1]+1)\n plt.xlim(0)\n ### fluxes\n plt.subplot(n+14, sharex=ax1)\n try:\n fsua_dark = self.fsu_calib[('FSUA', 'DARK')][0,0]\n fsub_dark = self.fsu_calib[('FSUB', 'DARK')][0,0]\n fsua_alldark = self.fsu_calib[('FSUA', 'DARK')].sum(axis=1)[0]\n fsub_alldark = self.fsu_calib[('FSUB', 'DARK')].sum(axis=1)[0]\n except:\n print 'WARNING: there are no FSUs calibrations in the header'\n fsua_dark = 0.0\n fsub_dark = 0.0\n fsua_alldark = 0.0\n fsub_alldark = 0.0\n\n M0 = 17.5\n fluxa = (self.raw['IMAGING_DATA_FSUA'].data.field('DATA1')[:,0]+\n self.raw['IMAGING_DATA_FSUA'].data.field('DATA2')[:,0]+\n self.raw['IMAGING_DATA_FSUA'].data.field('DATA3')[:,0]+\n self.raw['IMAGING_DATA_FSUA'].data.field('DATA4')[:,0]-\n fsua_alldark)/\\\n (4*self.getKeyword('ISS PRI FSU1 DIT'))\n print 'FLUX FSUA (avg, rms):', round(fluxa.mean(), 0), 'ADU/s',\\\n round(100*fluxa.std()/fluxa.mean(), 0), '%'\n print ' -> pseudo mag = '+str(M0)+' - 2.5*log10(flux) =',\\\n round(M0-2.5*np.log10(fluxa.mean()),2)\n fluxb = (self.raw['IMAGING_DATA_FSUB'].data.field('DATA1')[:,0]+\n self.raw['IMAGING_DATA_FSUB'].data.field('DATA2')[:,0]+\n self.raw['IMAGING_DATA_FSUB'].data.field('DATA3')[:,0]+\n self.raw['IMAGING_DATA_FSUB'].data.field('DATA4')[:,0]-\n fsub_alldark)/\\\n (4*self.getKeyword('ISS PRI FSU2 DIT'))\n print 'FLUX FSUB (avg, rms):', round(fluxb.mean(), 0), 'ADU/s',\\\n round(100*fluxb.std()/fluxb.mean(), 0), '%'\n print ' -> pseudo mag = '+str(M0)+' - 2.5*log10(flux) =',\\\n round(M0-2.5*np.log10(fluxb.mean()),2)\n plt.plot(self.raw['IMAGING_DATA_FSUA'].data.field('TIME'),\\\n fluxa/1000, color='b', alpha=0.5, label='FSUA')\n plt.plot(self.raw['IMAGING_DATA_FSUB'].data.field('TIME'),\\\n fluxb/1000, color='r', alpha=0.5, label='FSUB')\n\n plt.ylim(1)\n plt.legend(prop={'size':9})\n plt.ylabel('flux - DARK (kADU)')\n plt.xlim(0)\n plt.subplot(n+15, sharex=ax1)\n try:\n # -- old data version\n plt.plot(self.raw['IMAGING_DATA_FSUA'].data.field('TIME'),\n self.raw['IMAGING_DATA_FSUA'].data.field('OPDSNR'),\n color='b', alpha=0.5, label='FSUA SNR')\n plt.plot(self.raw['IMAGING_DATA_FSUB'].data.field('TIME'),\n self.raw['IMAGING_DATA_FSUB'].data.field('OPDSNR'),\n color='r', alpha=0.5, label='FSUB SNR')\n except:\n plt.plot(self.raw['IMAGING_DATA_FSUA'].data.field('TIME'),\n self.raw['IMAGING_DATA_FSUA'].data.field(self.OPDSNR),\n color='b', alpha=0.5, label='FSUA SNR')\n plt.plot(self.raw['IMAGING_DATA_FSUB'].data.field('TIME'),\n self.raw['IMAGING_DATA_FSUB'].data.field(self.OPDSNR),\n color='r', alpha=0.5, label='FSUB SNR')\n plt.legend(prop={'size':9})\n \n A = (self.raw['IMAGING_DATA_FSUA'].data.field('DATA1')[:,0]-\n self.fsu_calib[('FSUA', 'DARK')][0,0])/\\\n (self.fsu_calib[('FSUA', 'FLAT')][0,0]-\n 2*self.fsu_calib[('FSUA', 'DARK')][0,0])\n B = (self.raw['IMAGING_DATA_FSUA'].data.field('DATA2')[:,0]-\n self.fsu_calib[('FSUA', 'DARK')][0,1])/\\\n (self.fsu_calib[('FSUA', 'FLAT')][0,1]-\n 2*self.fsu_calib[('FSUA', 'DARK')][0,1])\n C = (self.raw['IMAGING_DATA_FSUA'].data.field('DATA3')[:,0]-\n self.fsu_calib[('FSUA', 'DARK')][0,2])/\\\n (self.fsu_calib[('FSUA', 'FLAT')][0,2]-\n 2*self.fsu_calib[('FSUA', 'DARK')][0,2])\n D = (self.raw['IMAGING_DATA_FSUA'].data.field('DATA4')[:,0]-\n self.fsu_calib[('FSUA', 'DARK')][0,3])/\\\n (self.fsu_calib[('FSUA', 'FLAT')][0,3]-\n 2*self.fsu_calib[('FSUA', 'DARK')][0,3])\n snrABCD_a = ((A-C)**2+(B-D)**2)\n snrABCD_a /= ((A-C).std()**2+ (B-D).std()**2)\n #plt.plot(self.raw['IMAGING_DATA_FSUA'].data.field('TIME'),\n # snrABCD_a, color='b', alpha=0.5, linestyle='dashed')\n \n A = (self.raw['IMAGING_DATA_FSUB'].data.field('DATA1')[:,0]-\n self.fsu_calib[('FSUB', 'DARK')][0,0])/\\\n (self.fsu_calib[('FSUB', 'FLAT')][0,0]-\n 2*self.fsu_calib[('FSUB', 'DARK')][0,0])\n B = (self.raw['IMAGING_DATA_FSUB'].data.field('DATA2')[:,0]-\n self.fsu_calib[('FSUB', 'DARK')][0,1])/\\\n (self.fsu_calib[('FSUB', 'FLAT')][0,1]-\n 2*self.fsu_calib[('FSUB', 'DARK')][0,1])\n C = (self.raw['IMAGING_DATA_FSUB'].data.field('DATA3')[:,0]-\n self.fsu_calib[('FSUB', 'DARK')][0,2])/\\\n (self.fsu_calib[('FSUB', 'FLAT')][0,2]-\n 2*self.fsu_calib[('FSUB', 'DARK')][0,2])\n D = (self.raw['IMAGING_DATA_FSUB'].data.field('DATA4')[:,0]-\n self.fsu_calib[('FSUB', 'DARK')][0,3])/\\\n (self.fsu_calib[('FSUB', 'FLAT')][0,3]-\n 2*self.fsu_calib[('FSUB', 'DARK')][0,3])\n \n snrABCD_b = ((A-C)**2+(B-D)**2)\n snrABCD_b /= ((A-C).std()**2+ (B-D).std()**2)\n #plt.plot(self.raw['IMAGING_DATA_FSUB'].data.field('TIME'),\n # snrABCD_b, color='r', alpha=0.5, linestyle='dashed') \n \n # -- SNR levels:\n #plt.hlines([self.getKeyword('INS OPDC OPEN'),\n # self.getKeyword('INS OPDC CLOSE'),\n # self.getKeyword('INS OPDC DETECTION')],\n # self.raw['IMAGING_DATA_FSUB'].data.field('TIME').min(),\n # self.raw['IMAGING_DATA_FSUB'].data.field('TIME').max(),\n # color=(1.0, 0.5, 0.0))\n #plt.hlines([self.getKeyword('INS DOPDC OPEN'),\n # self.getKeyword('INS DOPDC CLOSE'),\n # self.getKeyword('INS DOPDC DETECTION')],\n # self.raw['IMAGING_DATA_FSUB'].data.field('TIME').min(),\n # self.raw['IMAGING_DATA_FSUB'].data.field('TIME').max(),\n # color=(0.0, 0.5, 1.0))\n # -- plot thresholds\n plt.ylabel('SNR')\n plt.xlim(0)\n \n if self.getKeyword('OCS DET IMGNAME')=='PACMAN_OBJ_ASTRO_':\n # == dual FTK\n plt.subplot(n+16, sharex=ax1)\n plt.ylabel('PRIMET ($\\mu$m)')\n #met = interp1d(np.float_(self.raw['METROLOGY_DATA'].\\\n # data.field('TIME')),\\\n # self.raw['METROLOGY_DATA'].data.field('DELTAL'),\\\n # kind = 'linear', bounds_error=False, fill_value=0.0)\n met = lambda x: np.interp(x,\n np.float_(self.raw['METROLOGY_DATA'].data.field('TIME')),\n self.raw['METROLOGY_DATA'].data.field('DELTAL'))\n metro = met(self.raw['DOPDC'].data.field('TIME'))*1e6\n n_ = min(len(self.raw['DOPDC'].data.field('TIME')),\n len(self.raw['OPDC'].data.field('TIME')))\n\n plt.plot(self.raw['DOPDC'].data.field('TIME'),\n metro, color=(0.5,0.5,0.), label='A-B')\n\n w1 = np.where((self.raw['OPDC'].data.field('STATE')[:n_]>=minState)*\\\n (self.raw['OPDC'].data.field('STATE')[:n_]<=7))\n try:\n print 'OPDC FTK stat:', round(100*len(w1[0])/float(n_), 1), '%'\n except:\n print 'OPDC FTK stat: 0%'\n\n w1 = np.where((self.raw['DOPDC'].data.field('STATE')[:n_]>=minState)*\\\n (self.raw['DOPDC'].data.field('STATE')[:n_]<=7))\n try:\n print 'DOPDC FTK stat:', round(100*len(w1[0])/float(n_), 1), '%'\n except:\n print 'DOPDC FTK stat: 0%'\n\n w = np.where((self.raw['DOPDC'].data.field('STATE')[:n_]>=minState)*\\\n (self.raw['DOPDC'].data.field('STATE')[:n_]<=7)*\\\n (self.raw['OPDC'].data.field('STATE')[:n_]>=minState)*\\\n (self.raw['OPDC'].data.field('STATE')[:n_]<=7))\n try:\n print 'DUAL FTK stat:', round(100*len(w[0])/float(n_),1), '%'\n except:\n print 'DUAL FTK stat: 0%'\n\n plt.xlim(0)\n plt.plot(self.raw['DOPDC'].data.field('TIME')[w],\n metro[w], '.g', linewidth=2,\n alpha=0.5, label='dual FTK')\n #plt.legend()\n if len(w[0])>10 and False:\n coef = np.polyfit(self.raw['DOPDC'].data.field('TIME')[w],\n metro[w], 2)\n plt.plot(self.raw['DOPDC'].data.field('TIME'),\n np.polyval(coef, self.raw['DOPDC'].\n data.field('TIME')),\n color='g')\n plt.ylabel('metrology')\n\n print 'PRIMET drift (polyfit) :', 1e6*coef[1], 'um/s'\n slope, rms, synth = NoisySlope(self.raw['DOPDC'].\n data.field('TIME')[w],\n metro[w], 3e6)\n plt.figure(10)\n yl = plt.ylim()\n plt.plot(self.raw['DOPDC'].data.field('TIME')[w],\n synth, color='r')\n plt.ylim(yl)\n print 'PRIMET drift (NoisySlope):',\\\n slope*1e6,'+/-', rms*1e6, 'um/s'\n else:\n # == scanning\n plt.subplot(n+16, sharex=ax1)\n fringesOPDC = \\\n self.raw['IMAGING_DATA_'+self.primary_fsu].data.field('DATA1')[:,0]-\\\n self.raw['IMAGING_DATA_'+self.primary_fsu].data.field('DATA3')[:,0]\n \n fringesDOPDC =\\\n self.raw['IMAGING_DATA_'+self.secondary_fsu].data.field('DATA1')[:,0]-\\\n self.raw['IMAGING_DATA_'+self.secondary_fsu].data.field('DATA3')[:,0]\n \n plt.plot(self.raw['IMAGING_DATA_'+self.primary_fsu].data.field('TIME'),\n scipy.signal.wiener(fringesOPDC/fringesOPDC.std()),\n color=(1.0, 0.5, 0.0), alpha=0.6,\n label=self.primary_fsu+'/OPDC')\n plt.plot(self.raw['IMAGING_DATA_'+self.secondary_fsu].data.field('TIME'),\n scipy.signal.wiener(fringesDOPDC/fringesDOPDC.std()),\n color=(0.0, 0.5, 1.0), alpha=0.6,\n label=self.secondary_fsu+'/DOPDC')\n plt.legend(prop={'size':9})\n plt.ylabel('A-C')\n plt.xlabel('time stamp ($\\mu$s)')\n return", "def plot_comparisons(self, exact, blocked, blockederr, axdelta=None):\n if axdelta is None:\n axdelta = plt.gca()\n delta = self.means - exact\n axdelta.errorbar(list(range(1, self.max_dets)), delta[0], yerr=self.stderr[0], label='independent')\n axdelta.errorbar(list(range(1, self.max_dets)), delta[1], yerr=self.stderr[1], label='correlated')\n axdelta.axhline(delta[0, 0], linestyle=':', color='grey', label='reference')\n axdelta.axhline(0, linestyle='-', linewidth=1, color='black')\n if blocked:\n axdelta.axhline(blocked-exact, linestyle='--', color='darkgreen', label='reblocked')\n if blockederr:\n axdelta.fill_between([0, self.max_dets], [blocked-exact-blockederr,blocked-exact-blockederr],\n [blocked-exact+blockederr,blocked-exact+blockederr], color='green', alpha=0.2)\n axdelta.set_xlabel('Number of determinants in estimator')\n axdelta.set_ylabel(r'$E-E_\\mathrm{CCSD}$ / ha')\n axdelta.legend()\n return axdelta", "def btn_equalize_hist_callback(self):\n self.show_as_waiting(True)\n self.image_proc_selected('Histogram Equalization')\n self.show_as_waiting(False)", "def plot_initial_state(input_file_name='initial_state.nc',\n output_file_name='initial_state.png'):\n\n # load mesh variables\n chunks = {'nCells': 32768, 'nEdges': 32768}\n ds = xarray.open_dataset(input_file_name, chunks=chunks)\n nCells = ds.sizes['nCells']\n nEdges = ds.sizes['nEdges']\n nVertLevels = ds.sizes['nVertLevels']\n\n fig = plt.figure()\n fig.set_size_inches(16.0, 12.0)\n plt.clf()\n\n print('plotting histograms of the initial condition')\n print('see: init/initial_state/initial_state.png')\n d = datetime.datetime.today()\n txt = \\\n 'MPAS-Ocean initial state\\n' + \\\n 'date: {}\\n'.format(d.strftime('%m/%d/%Y')) + \\\n 'number cells: {}\\n'.format(nCells) + \\\n 'number cells, millions: {:6.3f}\\n'.format(nCells / 1.e6) + \\\n 'number layers: {}\\n\\n'.format(nVertLevels) + \\\n ' min val max val variable name\\n'\n\n plt.subplot(3, 3, 2)\n varName = 'maxLevelCell'\n var = ds[varName]\n maxLevelCell = var.values - 1\n xarray.plot.hist(var, bins=nVertLevels - 4)\n plt.ylabel('frequency')\n plt.xlabel(varName)\n txt = '{}{:9.2e} {:9.2e} {}\\n'.format(txt, var.min().values,\n var.max().values, varName)\n\n plt.subplot(3, 3, 3)\n varName = 'bottomDepth'\n var = ds[varName]\n xarray.plot.hist(var, bins=nVertLevels - 4)\n plt.xlabel(varName)\n txt = '{}{:9.2e} {:9.2e} {}\\n'.format(txt, var.min().values,\n var.max().values, varName)\n\n cellsOnEdge = ds['cellsOnEdge'].values - 1\n cellMask = np.zeros((nCells, nVertLevels), bool)\n edgeMask = np.zeros((nEdges, nVertLevels), bool)\n for k in range(nVertLevels):\n cellMask[:, k] = k <= maxLevelCell\n cell0 = cellsOnEdge[:, 0]\n cell1 = cellsOnEdge[:, 1]\n edgeMask[:, k] = np.logical_and(np.logical_and(cellMask[cell0, k],\n cellMask[cell1, k]),\n np.logical_and(cell0 >= 0,\n cell1 >= 0))\n cellMask = xarray.DataArray(data=cellMask, dims=('nCells', 'nVertLevels'))\n edgeMask = xarray.DataArray(data=edgeMask, dims=('nEdges', 'nVertLevels'))\n\n plt.subplot(3, 3, 4)\n varName = 'temperature'\n var = ds[varName].isel(Time=0).where(cellMask)\n xarray.plot.hist(var, bins=100, log=True)\n plt.ylabel('frequency')\n plt.xlabel(varName)\n txt = '{}{:9.2e} {:9.2e} {}\\n'.format(txt, var.min().values,\n var.max().values, varName)\n\n plt.subplot(3, 3, 5)\n varName = 'salinity'\n var = ds[varName].isel(Time=0).where(cellMask)\n xarray.plot.hist(var, bins=100, log=True)\n plt.xlabel(varName)\n txt = '{}{:9.2e} {:9.2e} {}\\n'.format(txt, var.min().values,\n var.max().values, varName)\n\n plt.subplot(3, 3, 6)\n varName = 'layerThickness'\n var = ds[varName].isel(Time=0).where(cellMask)\n xarray.plot.hist(var, bins=100, log=True)\n plt.xlabel(varName)\n txt = '{}{:9.2e} {:9.2e} {}\\n'.format(txt, var.min().values,\n var.max().values, varName)\n\n plt.subplot(3, 3, 7)\n varName = 'rx1Edge'\n var = ds[varName].isel(Time=0).where(edgeMask)\n maxRx1Edge = var.max().values\n xarray.plot.hist(var, bins=100, log=True)\n plt.ylabel('frequency')\n plt.xlabel('Haney Number, max={:4.2f}'.format(maxRx1Edge))\n txt = '{}{:9.2e} {:9.2e} {}\\n'.format(txt, var.min().values,\n var.max().values, varName)\n\n font = FontProperties()\n font.set_family('monospace')\n font.set_size(12)\n print(txt)\n plt.subplot(3, 3, 1)\n plt.text(0, 1, txt, verticalalignment='top', fontproperties=font)\n plt.axis('off')\n\n plt.tight_layout(pad=4.0)\n\n plt.savefig(output_file_name, bbox_inches='tight', pad_inches=0.1)", "def get_stability_plot(self):\n fig, ax = plt.subplots()\n first_episode = self.get_convergence_episode()\n\n values = self.stats['return_stats']['episode_totals']\n _, _, (y_lower, _) = self._moving_average(\n values, window=_ROLLING_WINDOW, p=_CONFIDENCE_LEVEL)\n episodes = np.arange(len(values))\n unstable_episodes = np.where(\n np.logical_and(values < y_lower[-1], episodes > first_episode))[0]\n\n ax.plot(episodes, values, color='steelblue', lw=2, alpha=.9,\n label='Return')\n for i, episode in enumerate(unstable_episodes):\n ax.axvline(episode, color='salmon', lw=2,\n label='Unstable' if i == 0 else None)\n ax.axvline(first_episode, color='seagreen', lw=2, label='Converged')\n\n ax.set_title('Normalized instability = {:.3f}%'.format(\n self.get_normalized_instability() * 100.))\n ax.legend()\n ax.set_ylabel('Return')\n ax.set_xlabel('Episode')\n return fig", "def plot(decisionTree):\n def toString(decisionTree, indent=''):\n if decisionTree.results != None: # leaf node\n return str(decisionTree.results)\n else:\n if isinstance(decisionTree.value, int) or isinstance(decisionTree.value, float):\n decision = 'Column %s: x >= %s?' % (decisionTree.col, decisionTree.value)\n else:\n decision = 'Column %s: x == %s?' % (decisionTree.col, decisionTree.value)\n trueBranch = indent + 'yes -> ' + toString(decisionTree.trueBranch, indent + '\\t\\t')\n falseBranch = indent + 'no -> ' + toString(decisionTree.falseBranch, indent + '\\t\\t')\n return (decision + '\\n' + trueBranch + '\\n' + falseBranch)\n\n print(toString(decisionTree))", "def plotComparison(x, nt, nx, c, phi, phiExact, methodName):\n \n plt.figure()\n plt.plot(x, phiExact)\n\n plt.plot(x, phi)\n plt.ylim([-0.2, 1.4])\n plt.title(str(methodName)+\" scheme\\nExact vs Numerical solution \"\\\n \"nt=\"+str(nt)+\", nx=\"+str(nx)+\"\\n\"\n \"Courant number: \"+str(c))\n plt.show()", "def __draw(self, state:dict):\n _, ax = plt.subplots()\n ax.set_axis_off()\n tb = Table(ax, bbox=[0,0,1,1])\n\n width = height = 1.0 /9 \n\n\n for key in self.state.keys():\n # Add cells\n i,j = self.__display_table_map[key]\n tb.add_cell(i, j, width, height, text='{}'.format(state[key]), \n loc='center',facecolor= self.__color_map[key])\n\n ax.add_table(tb)\n plt.show()", "def compare(self, plot=False):\n oldConditionData, newConditionData = self.runSimulations()\n \n #For now make print statements about each none matching data\n conditionsBroken=[]\n variablesFailed=[]\n for i in range(len(oldConditionData)):\n timeOld, dataListOld = oldConditionData[i]\n timeNew, dataListNew = newConditionData[i]\n\n for variableOld, variableNew in zip(dataListOld, dataListNew):\n if not curvesSimilar(timeOld.data, variableOld.data, timeNew.data, variableNew.data, 0.05):\n if i not in conditionsBroken: conditionsBroken.append(i)\n\n if variableOld.species:\n label = variableOld.species.molecule[0].toSMILES()\n print \"Species profile for {0} does not match between the old model ({1}) and \\\n the new model ({2}) in condition {3:d}.\".format(variableOld.species.molecule[0].toSMILES(),\n variableOld.label, \n variableNew.label,\n i+1)\n else:\n label = variableOld.label\n print \"{0} does not match between the old model and \\\n the new model in condition {1:d}.\".format(variableOld.label, i+1)\n\n variablesFailed.append((self.conditions[i], label, variableOld, variableNew))\n print ''\n print 'The following reaction conditions were broken:'\n print ''\n for index in conditionsBroken:\n print \"Condition {0:d}:\"\n print str(self.conditions[index])\n print ''\n\n if plot:\n # Ignore Inerts\n inertList = ['[Ar]','[He]','[N#N]','[Ne]']\n for i in range(len(oldConditionData)):\n time, dataList = oldConditionData[i]\n speciesData = [data for data in dataList if data.species.molecule[0].toSMILES() not in inertList]\n oldSpeciesPlot = SimulationPlot(xVar=time, yVar=speciesData, ylabel='Mole Fraction')\n\n time, dataList = newConditionData[i]\n speciesData = [data for data in dataList if data.species.molecule[0].toSMILES() not in inertList]\n newSpeciesPlot = SimulationPlot(xVar=time, yVar=speciesData, ylabel='Mole Fraction')\n\n # Name after the index of the condition\n # though it may be better to name it after the actual conditions in T, P, etc\n oldSpeciesPlot.comparePlot(newSpeciesPlot,filename='simulation_condition_{0}.png'.format(i+1))\n\n return variablesFailed", "def show_dcr_results(dg):\n cycle = dg.fileDB['cycle'].values[0]\n df_dsp = pd.read_hdf(f'./temp_{cycle}.h5', 'opt_dcr')\n # print(df_dsp.describe()) \n\n # compare DCR and A/E distributions\n fig, (p0, p1) = plt.subplots(2, 1, figsize=(8, 8))\n \n elo, ehi, epb = 0, 25000, 100\n \n # aoe distribution\n # ylo, yhi, ypb = -1, 2, 0.1\n # ylo, yhi, ypb = -0.1, 0.3, 0.005\n ylo, yhi, ypb = 0.05, 0.08, 0.0005\n nbx = int((ehi-elo)/epb)\n nby = int((yhi-ylo)/ypb)\n h = p0.hist2d(df_dsp['trapEmax'], df_dsp['aoe'], bins=[nbx,nby],\n range=[[elo, ehi], [ylo, yhi]], cmap='jet',\n norm=LogNorm())\n # p0.set_xlabel('Energy (uncal)', ha='right', x=1)\n p0.set_ylabel('A/E', ha='right', y=1)\n\n # dcr distribution\n # ylo, yhi, ypb = -20, 20, 1 # dcr_raw\n # ylo, yhi, ypb = -5, 2.5, 0.1 # dcr = dcr_raw / trapEmax\n # ylo, yhi, ypb = -3, 2, 0.1\n ylo, yhi, ypb = 0.9, 1.08, 0.001\n ylo, yhi, ypb = 1.034, 1.0425, 0.00005 # best for 64.4 us pz\n # ylo, yhi, ypb = 1.05, 1.056, 0.00005 # best for 50 us pz\n # ylo, yhi, ypb = 1.016, 1.022, 0.00005 # best for 100 us pz\n nbx = int((ehi-elo)/epb)\n nby = int((yhi-ylo)/ypb)\n h = p1.hist2d(df_dsp['trapEmax'], df_dsp['dcr'], bins=[nbx,nby],\n range=[[elo, ehi], [ylo, yhi]], cmap='jet',\n norm=LogNorm())\n p1.set_xlabel('Energy (uncal)', ha='right', x=1)\n p1.set_ylabel('DCR', ha='right', y=1)\n \n # plt.show()\n plt.savefig(f'./plots/dcr_cyc{cycle}.png', dpi=300)\n plt.cla()", "def question_2():\r\n comparison_graph = er_algorithm(1000, random.uniform(0, 1))\r\n in_degree_dist = utility_graph.in_degree_distribution(comparison_graph)\r\n normalized_dist = utility_graph.normalize_distribution(in_degree_dist)\r\n\r\n utility_graph.plot_log_log_scatter(normalized_dist,\r\n 'ER Algorithm In-degree Distribution',\r\n 'in-degree log-base-10',\r\n 'normalized distribution log-base-10')", "def compare_plot_instances(data_causal):\n col_names = data_causal.columns.values # get the columns' names\n dimension = 2 # TODO: figure out better way to organize plots by location\n\n fig = plt.figure()\n i = 1\n for cond in col_names:\n ax = fig.add_subplot(len(col_names)/dimension, dimension, i)\n df_compare = data_causal.groupby(cond)[cond].count() # displays num instances assigned to each condition\n ax = df_compare.plot(kind='bar', title=cond)\n ax.set_xlabel(cond)\n ax.set_ylabel(\"count instances\")\n i += 1\n fig.tight_layout()\n plt.show()" ]

[ "0.627075", "0.5974408", "0.5943118", "0.5872826", "0.58601767", "0.5731816", "0.5659566", "0.5647532", "0.5573353", "0.55374664", "0.5527548", "0.5518267", "0.5488848", "0.54798263", "0.5470446", "0.54632896", "0.5436311", "0.5432833", "0.5427665", "0.5418614", "0.5414802", "0.54042244", "0.53870666", "0.53761315", "0.5373044", "0.53727794", "0.53493327", "0.5339281", "0.53383285", "0.5336862" ]

[ "def deposit_money():\n print(\"\\n\")\n print(messages.account_credentials)\n u_id = pyip.inputInt(\"Your Id: \", greaterThan=0)\n password = pyip.inputPassword(\"Your Password: \")\n\n credentials = {\"id\":u_id, \"password\":password}\n result = BankOperationsBackend.deposit_money(credentials)\n start_again() if result else BankOperationsUi.deposit_money()", "async def balance(self, ctx):\n try:\n cash = await ctx.bot.pool.fetchrow(f'select cash from wallet where id={ctx.author.id}')\n\n if cash is None:\n await ctx.bot.pool.execute(f'insert into wallet values ({ctx.author.id}, 0);')\n return await ctx.send('You do not have a wallet yet.')\n\n if cash[0] is None:\n return await ctx.send('You do not have a wallet yet.')\n\n await ctx.send(f'You have {cash[0]} robux.')\n except Exception as e:\n await ctx.send(e)", "def deposit(self, deposit_money):\r\n self.balance += deposit_money", "def cash_deposit(name, bank_id, password):\n amount = int(raw_input(\"Enter Amount to Deposit:\"))\n for i in range(0, len(MY_MEMBER)):\n if MY_MEMBER[i].Name == name and \\\n MY_MEMBER[i].Password == password and \\\n MY_MEMBER[i].BankID == bank_id:\n old_balance = MY_MEMBER[i].balance\n MY_MEMBER[i].balance += amount\n new_balance = MY_MEMBER[i].balance\n print\"*************************\"\n print\"****Depositing Cash******\"\n print\"your Old Bank balance: %r\" % old_balance\n print\"Amount Deposited: %r\" % amount\n print\"your New Bank balance: %r\" % new_balance\n print\"*************************\"\n what_to_do(name, bank_id, password)", "def deposit(account, amount):\n pass", "async def debit(ctx, *args):\n users_mentioned = ctx.message.mentions\n user_mention = ctx.author.mention\n debit = 0\n for arg in args:\n try:\n debit = float(arg)\n await ctx.message.channel.send(user_mention+\", we have successfully debited as you commanded.\")\n break\n except:\n pass\n bals = self.data[\"balances.json\"]\n for user in users_mentioned:\n if user.id in bals:\n bals[user.id] -= debit\n else:\n bals[user.id] = -debit", "def deposit(self, amount):\n self.balance += amount", "def deposit(self, amount):\n self.balance += amount", "def save(self, *args, **kwargs):\n wallet = self.wallet.withdraw(self.value)\n super(Payment, self).save(*args, **kwargs)", "def deposit(self, amount):\n self.balance += amount\n self.transactions.append((\"Deposit\", amount))\n print \"Your new balance is $%d.\" % self.balance", "def withdraw_money():\n print(\"\\n\")\n print(messages.account_credentials)\n u_id = pyip.inputInt(\"Your Id: \", greaterThan=0)\n password = pyip.inputPassword(\"Your Password: \")\n\n credentials = {\"id\":u_id, \"password\":password}\n result = BankOperationsBackend.withdraw_money(credentials)\n start_again() if result else BankOperationsUi.withdraw_money()", "async def deposit(ctx, money:int):\n author = ctx.message.author\n if str(author) in settings.BOT_ADMIN:\n database.add_pokedollars(author, money)\n await ctx.send(\"funds deposited\")\n else:\n await ctx.send(\"You are not the bot admin. Go awai.\")", "async def credit(ctx, *args):\n users_mentioned = ctx.message.mentions\n user_mention = ctx.author.mention\n credit = 0\n for arg in args:\n try:\n credit = float(arg)\n await ctx.message.channel.send(user_mention+\", we have successfully debited as you commanded.\")\n break\n except:\n pass\n bals = self.data[\"balances.json\"]\n for user in users_mentioned:\n if user.id in bals:\n bals[user.id] += credit\n else:\n bals[user.id] = credit", "def make_deposit(conn, userid, acctype, amount):\n print('\\n\\nUpdating account user:{}, type:{}, amount:{}'.format(userid, acctype, amount))\n with conn.cursor() as curs:\n res = curs.execute(\"\"\"UPDATE accounts\n SET balance=%s\n WHERE owner_id=%s AND type=%s\"\"\", (amount, userid, acctype))\n if res is not None:\n print(res)", "async def balance(self, ctx, name=None):\n if has_post_permission(ctx.guild.id, ctx.channel.id):\n user: User = ctx.user_object\n item = Item.objects.get(name=\"coins\")\n\n if name is None:\n amount = '{:,}'.format(user.get_item_by_item(COINS).amount)\n name = get_display_name(ctx.author)\n await ctx.send(f'{name} has {amount} coins')\n elif name == 'universe':\n await ctx.send('As all things should be.')\n else:\n user = User.objects.filter(Q(name__icontains=name) | Q(nick__icontains=name))\n if not user:\n await ctx.send(f'Name {name} not found in server.')\n elif len(user) > 1:\n await ctx.send(f'Input {name} can refer to multiple people.')#({members})')\n else:\n user = user[0]\n amount = '{:,}'.format(user.get_item_by_item(COINS).amount)\n await ctx.send(f'{user.plain_name} has {amount} coins')", "def deposit(self, account_number: int, deposit: float): \n self._accounts[account_number][1] += deposit", "def deposit(self, amount):\n connection = sqlite3.connect('/home/BorneAgain/Desktop/flasktest/accounts.db')\n\n cursor = connection.cursor()\n\n if self.getBalance() + amount > 0:\n cursor.execute(\"\"\"update accounts set amount=? where name =?;\"\"\", (amount+self.getBalance(), self.name))\n cursor.execute(\"\"\"insert into history (username,dt,amount) values (?,?,?);\"\"\", (self.name, datetime.utcnow(), amount))\n else:\n \n cursor.execute(\"\"\"update accounts set amount=? where name =?;\"\"\", (0, self.name))\n\n cursor.execute(\"\"\"insert into history (username,dt,amount) values (?,?,?);\"\"\", (self.name, datetime.utcnow(), amount))\n connection.commit()", "def deposit(self, amount, budget):\r\n if budget != \"Total Balance\":\r\n assert budget in self.budgets, \"Specified budget doesn't exist\"\r\n self.budgets[budget] += float(amount)\r\n self.balance += float(amount)", "def withdraw(account, amount):\n pass", "def deposit(self, amount):\n self.transactions += [('deposit', amount)]\n self.balance = self.balance + amount\n return self.balance", "def deposit(self, amount):\n self.balance += amount\n return self.balance", "def deposit(self, amount):\n self.balance += amount\n return self.balance", "def deposit(self, amount):\n self.balance += amount\n return self.balance", "def withdraw(self, user_id, money, **kwargs):\n user = User.objects(user_id=user_id).first()\n\n if money > 0:\n if user.balance >= money:\n print('Cantidad retirada: ', money)\n user.balance = float(user.balance) - float(money)\n user.save()\n else:\n print('No hay fondos suficientes para realizar el retiro.')\n else:\n print('No es posible retirar valores negativos.')", "def deposit(self, amount):\n self.balance = self.balance + amount\n return self.balance", "def deposit(self, amount):\n self.dep = amount\n self.balance += self.dep", "def deposit():\n\n if request.method == \"POST\":\n if not request.form.get(\"deposit\"):\n return apology(\"Must enter amount to deposit\")\n\n deposit = request.form.get(\"deposit\")\n entry = db.execute(\"SELECT * FROM users WHERE id=:id\",\n id=session['user_id'])\n user = entry[0]['username']\n cash = entry[0]['cash'] + float(deposit)\n\n db.execute(\"UPDATE users SET cash=:cash WHERE id = :id\",\n cash=cash, id=session['user_id'])\n\n return redirect(url_for(\"index\"))\n\n else:\n return render_template(\"deposit.html\")", "def deposit(self, amount):\r\n self.balance = self.balance + amount\r\n amount = abs(amount)\r\n self.transactions.append(+amount)\r\n return amount", "def earnCoin(self, amount):\n self.coins += amount", "def transfer_amount(self, conn, data_subtract, data_add):\n sql_subtract = 'UPDATE card SET balance = balance - ? WHERE number = ?;'\n sql_add = 'UPDATE card SET balance = balance + ? WHERE number = ?;'\n\n c = conn.cursor()\n c.execute(sql_subtract, data_subtract)\n conn.commit()\n\n c = conn.cursor()\n c.execute(sql_add, data_add)\n conn.commit()\n\n # print(f\"amount {data_add[0]} was added to account {data_add[1]}\")\n print(\"Success!\")\n self.menus()" ]

[ "0.7368084", "0.7210584", "0.71865326", "0.71241224", "0.7043473", "0.69961643", "0.6883361", "0.6883361", "0.6859989", "0.68431985", "0.68298614", "0.6818901", "0.6789854", "0.67864424", "0.6737196", "0.6642632", "0.66231024", "0.66116875", "0.6569632", "0.65545714", "0.65375346", "0.65375346", "0.65375346", "0.65355414", "0.6528473", "0.65134114", "0.6503926", "0.6472239", "0.6445341", "0.6432615" ]

[ "def random_image(context, person):\n collection = db['people']\n images = collection.find({'person': person})\n row = []\n for image in images:\n row.append(image['image_url'])\n rand_img = random.choice(list(row))\n return context.channel.send(rand_img)", "def get_a_picture_randomly(self):\n files = os.listdir(self.image_directory)\n if len(files) == 0:\n return None\n full_image_name = os.path.abspath(self.image_directory + random.choice(files))\n return full_image_name", "async def corona(self, context):\n\n await random_image(context, 'corona')", "def get_random_image():\r\n\r\n logging.debug('get_random_image()')\r\n\r\n choice = random.randint(1, 10)\r\n if choice < 7:\r\n return get_random_image_from_any_source()\r\n else:\r\n return get_random_image_from_database()", "def get_rand_img():\n import urllib\n import os\n import glob\n\n pics = glob.glob('/home/cody_techngs/PycharmProjects/ProjTest/ActiveAMT/ActiveAMT_FLASK/static/images/HITs/rand*')\n nums = []\n\n for pic in pics:\n nums.append(int(pic.split('rand_img')[1].split('.')[0]))\n\n unique_num = False\n new_rand_num = 0\n\n while not unique_num:\n new_rand_num = random.randrange(1, 2000)\n if new_rand_num not in nums:\n unique_num = True\n\n img_name = 'rand_img{}.jpg'.format(new_rand_num)\n dl_location = os.getcwd() + '/ActiveAMT/ActiveAMT_FLASK/static/images/HITs/' + img_name\n url = 'https://unsplash.it/400/300/?random'\n urllib.urlretrieve(url, dl_location)\n\n return 'static/images/HITs/{}'.format(img_name)", "def take_door_photo():\n\n # based on lukec's code in VHS.pm\n config = yaml.load(file('/etc/vhs.yaml'))\n short_hash = hashlib.sha256(str(datetime.datetime.now())).hexdigest()[0:6]\n pic_base = config.get('picture_base')\n if pic_base:\n filename = os.path.join(pic_base, '%s.jpeg' % short_hash)\n os.system('streamer -c /dev/video0 -b 16 -o %s >/dev/null 2>&1' % filename)\n short_file = os.path.splitext(filename)[0] + '.jpg'\n os.rename(filename, short_file)\n pic_uri_base = config.get('picture_uri_base') \n if pic_uri_base and os.path.exists(short_file):\n pic_uri = '%s/%s' % (pic_uri_base, os.path.basename(short_file))\n return (pic_uri, short_file)\n\n return None", "def get_cute_animal_picture(self, message=None):\n animal = None\n\n if message:\n animal = self._get_animal_from_message(message)\n\n if animal is None:\n animal = random.choice(self.animals)\n\n # Get a random image for the animal specified\n img_directory = '{}/{}'.format(settings.IMAGE_DIRECTORY, animal)\n img_filename = random.choice(os.listdir(img_directory))\n\n return '{}/{}'.format(img_directory, img_filename)", "def random_image():\n img_dir = \"./static\"\n img_list = os.listdir(img_dir)\n img_path = os.path.join(img_dir, random.choice(img_list))\n return img_path", "def user_random_avatar():\n avatar_names = os.listdir(PATH)\n avatar_path = random.choice([avatar_image for avatar_image in avatar_names\n if os.path.isfile(os.path.join(PATH,avatar_image))])\n return PATH_RELATIVE+avatar_path", "def random_img(path):\n fullpath = os.path.join(settings.MEDIA_ROOT, path)\n filenames = [f for f in os.listdir(fullpath) if is_image_file(f)]\n pick = random.choice(filenames)\n return posixpath.join(settings.MEDIA_URL, path, pick)", "def make_rand_task():\n rand_type = all_tasks.keys()[random.randint(0, len(all_tasks.keys()) - 1)]\n rand_hit = all_tasks[rand_type][random.randint(0, len(all_tasks[rand_type]) - 1)]\n\n if rand_hit['type'] == 'img':\n rand_hit['img_src'] = get_rand_img()\n\n return rand_hit", "def genrandimg(args) -> None:\n\n size = (int(args.x), int(args.y))\n fp = Image.new(\"RGB\", size)\n data = []\n\n if not args.c: # If color\n for i in range(size[0]*size[1]):\n r = random.choice([0x00, 0xff])\n data.append((r, r, r)) # Each RGB value is the same random value\n else: # Else black-and-white\n for i in range(size[0]*size[1]):\n r = [random.choice(range(0, 256)) for _ in range(0, 3)]\n r = (r[0], r[1], r[2]) # Choose 3 random numbers for different RGB values\n data.append(r)\n\n fp.putdata(data)\n print(\"Saving to %s...\" % args.o)\n fp.save(args.o)\n fp.close()", "def cmd_gallery_random(client, args):\n gallery_random = client.gallery_random(args.page)\n data = [item.__dict__ for item in gallery_random]\n generate_output({'gallery_random': data}, args.output_file)", "def pick(self, mess, args):\n return random.choice(args)", "def funny_command(update: Update, context: CallbackContext) -> None:\n try:\n r = randint(0, 100000000)\n context.bot.send_photo(chat_id=update.effective_chat.id,\n photo=f\"https://cataas.com/cat/cute?_nocache={r}\",\n caption=get_random_story(intro=choice([4, 4, 4, 8, 11])))\n except telegram.error.BadRequest:\n update.message.reply_text(BAD_REQUEST)", "def gallery_command(update: Update, context: CallbackContext) -> None:\n photo: Photo = get_random_photo()\n context.bot.send_photo(chat_id=update.effective_chat.id,\n photo=photo.file_id,\n caption=photo.description)", "async def lathow(self, context):\n\n await random_image(context, 'lathow')", "def main():\n images = Images()\n #print images.create_image_urls()\n print images.get_image_random()\n print images.get_image(12)", "def capture_photo(self, count):\n output_file_name = generate_image_filename()\n save_file_path = os.path.join(self.output_dir, output_file_name)\n\n self.take_photo(save_file_path)\n check_image_useful(save_file_path)", "def mpoint_pick_random(self, key_t, lts=False):\n \n errstr = \"\"\n logstr = clogger( \"\\nPicking a random {} key-point ...\".format(key_t), lts )\n\n # current public pool config needed no matter what...\n pool_cfg = self.secmgr['pool']\n\n # public poolkey pool or not? (ie, privkey pool)\n if 'pool' in key_t:\n pool_t = 'pool'\n additers = 0\n keypool_cfg = pool_cfg\n else:\n pool_t = key_t\n additers = pool_cfg.get('iters')\n keypool_cfg = self.secmgr[key_t]\n keypool_cfg.set_section_to('pool')\n keypool_cfg.read()\n\n args = keypool_cfg.get_as_args()\n args.iters = int(args.iters) + int(additers)\n\n ms = PyMScreen(args)\n\n logstr += clogger( ms.get_info(), lts )\n\n imgf = keypool_cfg.get('image')\n if not imgf or not path.exists(imgf):\n # generate pool image file from MScreen and attach to config file ...\n imgf = path.join( keypool_cfg.rootd, \"{}.png\".format(keypool_cfg.get('name')) )\n logstr += clogger( \"==> {} image not found. Making new one at\\n\".format(key_t) \n + \" -> {} ...\".format(imgf),\n lts )\n ms.gen_to_file(imgf)\n keypool_cfg.set_write( {'image': imgf,} )\n\n else:\n # ... else read PyMScreen object from image file\n logstr += clogger( \"Reading image file ...\", lts )\n try:\n ms.gen_mscreen_from_img(imgf)\n except Exception, err:\n errstr = clogger( \"\\n==> ERROR: {}\".format(str(err)), lts )\n\n # MScreen method returns a random MPoint object\n pt = ms.get_mpoint()\n logstr += clogger( \"\\n==> Point picked: ({0}, {1})\\n\".format( pt.real, pt.imag )\n + \" -> Index: [{0}, {1}]\".format( pt.Get_ix(), pt.Get_iy() ),\n lts )\n\n # update current *key config file\n self.secmgr[key_t].reset_section()\n self.secmgr[key_t].set_write( {'real': pt.real, 'imag': pt.imag,\n 'ix': pt.Get_ix(), 'iy': pt.Get_iy(),\n 'info': \"Randomly selected key-point.\",} )\n self.secmgr[key_t].read()\n\n return Arguments( {'log': logstr, 'err': errstr, 'mpoint': pt} )", "def get_shot_location():\n global LAST_SHOT\n available = [(x, y) for x in range(10) for y in range(10) if MY_SHOTS[x][y] is None]\n coords = random.choice(available)\n LAST_SHOT = coords\n return json.dumps(coords)", "def shoot(self):\n return self.bot_client.send_command(_Command.Shoot)", "def test_random_single_image():\n\n shap.image_plot(np.random.randn(3, 20, 20), np.random.randn(3, 20, 20), show=False)", "def generateRandomImage(size, lims=[0,255]):\n a,b = lims\n image_array = (b-a)*np.random.random(size) + a\n image = sitk.GetImageFromArray(image_array.astype(int))\n return image", "def random_image():\n\n # select random photo from sample table\n result = db.engine.execute(\"\"\"SELECT photo_id\n FROM sample\n ORDER BY rand() LIMIT 1\"\"\")\n photo_id = result.first()[0]\n\n # extract classification vector from database\n class_columns = \",\".join(\"Label{}\".format(i) for i in range(num_labels))\n result = db.engine.execute(\"\"\"SELECT yfcc.download_url, {}\n FROM placesCNN INNER JOIN yfcc\n ON placesCNN.photo_id = yfcc.photo_id\n WHERE yfcc.photo_id = {}\"\"\".format(class_columns,\n photo_id))\n\n row = result.first()\n download_url = row[0]\n classification = np.array(row[1:])\n\n return {\"suggested_tags\": predicted_tags(classification),\n \"classification_vector\": classification,\n \"image_url\": download_url}", "def take_picture(self):\n self.drone.take_picture()", "def take_picture(self):\n self.drone.take_picture()", "def shotgenerator():\n return random.randint(0, 9), random.randint(0, 9)", "def selectShot(self):\r\n self.weightBoard()\r\n self.printBoard()\r\n bestCoordinates = self.selectBestCoordinates()\r\n shot = self.mapToShot(bestCoordinates)\r\n logging.debug(\"select shot: %s\" % (shot))\r\n return shot", "def cute_command(update: Update, context: CallbackContext) -> None:\n try:\n r = randint(0, 100000000)\n context.bot.send_photo(chat_id=update.effective_chat.id, photo=f\"https://cataas.com/cat/cute?_nocache={r}\")\n except telegram.error.BadRequest:\n update.message.reply_text(BAD_REQUEST)" ]

[ "0.65586865", "0.6467759", "0.6397638", "0.6377332", "0.6242254", "0.60787094", "0.60604465", "0.6005842", "0.59123677", "0.5841395", "0.5834412", "0.57959676", "0.5688708", "0.5661508", "0.5629392", "0.5547989", "0.5545386", "0.55240476", "0.5522917", "0.55134743", "0.548782", "0.54836154", "0.5477818", "0.54717946", "0.5463484", "0.545942", "0.545942", "0.5458994", "0.5453871", "0.54457426" ]

[ "def random_image(context, person):\n collection = db['people']\n images = collection.find({'person': person})\n row = []\n for image in images:\n row.append(image['image_url'])\n rand_img = random.choice(list(row))\n return context.channel.send(rand_img)", "def random_img(path):\n fullpath = os.path.join(settings.MEDIA_ROOT, path)\n filenames = [f for f in os.listdir(fullpath) if is_image_file(f)]\n pick = random.choice(filenames)\n return posixpath.join(settings.MEDIA_URL, path, pick)", "async def corona(self, context):\n\n await random_image(context, 'corona')", "def get_random_image():\r\n\r\n logging.debug('get_random_image()')\r\n\r\n choice = random.randint(1, 10)\r\n if choice < 7:\r\n return get_random_image_from_any_source()\r\n else:\r\n return get_random_image_from_database()", "def random_entry(): \n\n files = list_entries()\n return random.choice(files)", "def funny_command(update: Update, context: CallbackContext) -> None:\n try:\n r = randint(0, 100000000)\n context.bot.send_photo(chat_id=update.effective_chat.id,\n photo=f\"https://cataas.com/cat/cute?_nocache={r}\",\n caption=get_random_story(intro=choice([4, 4, 4, 8, 11])))\n except telegram.error.BadRequest:\n update.message.reply_text(BAD_REQUEST)", "def get_cute_animal_picture(self, message=None):\n animal = None\n\n if message:\n animal = self._get_animal_from_message(message)\n\n if animal is None:\n animal = random.choice(self.animals)\n\n # Get a random image for the animal specified\n img_directory = '{}/{}'.format(settings.IMAGE_DIRECTORY, animal)\n img_filename = random.choice(os.listdir(img_directory))\n\n return '{}/{}'.format(img_directory, img_filename)", "def shootout_ran():\r\n return choice([\r\n \"https://im-a-dev.xyz/QqoZ2M6m.png\",\r\n \"https://im-a-dev.xyz/BvdekLII.png\",\r\n \"https://im-a-dev.xyz/MfSnYYAa.png\"\r\n ])", "def get_a_picture_randomly(self):\n files = os.listdir(self.image_directory)\n if len(files) == 0:\n return None\n full_image_name = os.path.abspath(self.image_directory + random.choice(files))\n return full_image_name", "def make_rand_task():\n rand_type = all_tasks.keys()[random.randint(0, len(all_tasks.keys()) - 1)]\n rand_hit = all_tasks[rand_type][random.randint(0, len(all_tasks[rand_type]) - 1)]\n\n if rand_hit['type'] == 'img':\n rand_hit['img_src'] = get_rand_img()\n\n return rand_hit", "def user_random_avatar():\n avatar_names = os.listdir(PATH)\n avatar_path = random.choice([avatar_image for avatar_image in avatar_names\n if os.path.isfile(os.path.join(PATH,avatar_image))])\n return PATH_RELATIVE+avatar_path", "def random_photos(self):\n return self.waypoints.filter(photo_id__isnull=False).order_by('?')[:10]", "def random_image():\n img_dir = \"./static\"\n img_list = os.listdir(img_dir)\n img_path = os.path.join(img_dir, random.choice(img_list))\n return img_path", "def cute_command(update: Update, context: CallbackContext) -> None:\n try:\n r = randint(0, 100000000)\n context.bot.send_photo(chat_id=update.effective_chat.id, photo=f\"https://cataas.com/cat/cute?_nocache={r}\")\n except telegram.error.BadRequest:\n update.message.reply_text(BAD_REQUEST)", "def get_random_file():\n\n return random.choice(File.get_files())", "def pick(self, mess, args):\n return random.choice(args)", "def cmd_gallery_random(client, args):\n gallery_random = client.gallery_random(args.page)\n data = [item.__dict__ for item in gallery_random]\n generate_output({'gallery_random': data}, args.output_file)", "def get_rand_img():\n import urllib\n import os\n import glob\n\n pics = glob.glob('/home/cody_techngs/PycharmProjects/ProjTest/ActiveAMT/ActiveAMT_FLASK/static/images/HITs/rand*')\n nums = []\n\n for pic in pics:\n nums.append(int(pic.split('rand_img')[1].split('.')[0]))\n\n unique_num = False\n new_rand_num = 0\n\n while not unique_num:\n new_rand_num = random.randrange(1, 2000)\n if new_rand_num not in nums:\n unique_num = True\n\n img_name = 'rand_img{}.jpg'.format(new_rand_num)\n dl_location = os.getcwd() + '/ActiveAMT/ActiveAMT_FLASK/static/images/HITs/' + img_name\n url = 'https://unsplash.it/400/300/?random'\n urllib.urlretrieve(url, dl_location)\n\n return 'static/images/HITs/{}'.format(img_name)", "def meme_rand():\n img = random.choice(imgs)\n quotes_list_one = random.choice(quotes)\n quote = random.choice(quotes_list_one)\n if quote and img:\n path = meme.make_meme(img, quote.body, quote.author)\n return render_template('meme.html', path=path)\n else:\n path = meme.make_meme('./_data/photos/dog/xander_1.jpg', \"Quote\", \"Author\")\n return render_template('meme.html', path=path)", "def test_move_to_trash(self):\n os.chdir(\"testimages/\")\n shutil.copyfile(\"arch_001.jpg\", \"image_to_edit.jpg\")\n filename = os.path.abspath(\"image_to_edit.jpg\")\n files = [filename]\n fileactions.move_to_trash(files, self.trashdir)\n trashed_file = os.path.join(self.trashdir, \"image_to_edit.jpg\")\n self.assertTrue(os.path.isfile(trashed_file))\n # Repeat, to check if backing up works\n shutil.copyfile(\"arch_001.jpg\", \"image_to_edit.jpg\")\n fileactions.move_to_trash(files, self.trashdir)\n trashed_file1 = os.path.join(self.trashdir, \"image_to_edit.jpg.1\")\n self.assertTrue(os.path.isfile(trashed_file1))\n shutil.copyfile(\"arch_001.jpg\", \"image_to_edit.jpg\")\n fileactions.move_to_trash(files, self.trashdir)\n trashed_file2 = os.path.join(self.trashdir, \"image_to_edit.jpg.2\")\n self.assertTrue(os.path.isfile(trashed_file2))\n # Clear the files\n os.remove(trashed_file)\n os.remove(trashed_file1)", "def random_choice(gallery, num):\n assert len(gallery) >= num\n if isinstance(gallery, list):\n gallery = np.array(gallery)\n cands = np.arange(len(gallery))\n np.random.shuffle(cands)\n rand_inds = cands[:num]\n if not isinstance(gallery, np.ndarray):\n rand_inds = torch.from_numpy(rand_inds).long().to(gallery.device)\n return gallery[rand_inds]", "def random_choice(gallery, num):\n assert len(gallery) >= num\n if isinstance(gallery, list):\n gallery = np.array(gallery)\n cands = np.arange(len(gallery))\n np.random.shuffle(cands)\n rand_inds = cands[:num]\n if not isinstance(gallery, np.ndarray):\n rand_inds = torch.from_numpy(rand_inds).long().to(gallery.device)\n return gallery[rand_inds]", "def _remove_wall_pic(self):\n # Retrieve the item that was selected\n key = self._listbox.get(ACTIVE)\n # Post a delete notice to the manager\n self._remove(key)", "async def imgran(self):\r\n search=\"random\"\r\n search = client.gallery()\r\n holder=[]\r\n for d in search:\r\n holder.append(d.link)\r\n await self.bot.say(random.choice(holder))", "def cat_command(update: Update, context: CallbackContext) -> None:\n try:\n r = randint(0, 100000000)\n context.bot.send_photo(chat_id=update.effective_chat.id, photo=f\"https://cataas.com/cat?_nocache={r}\")\n except telegram.error.BadRequest:\n update.message.reply_text(BAD_REQUEST)", "def get_random_image_from_database():\r\n\r\n logging.debug('get_random_image_from_database()')\r\n\r\n dir_path = os.path.join(os.environ['LOCALAPPDATA'],'WarietyWallpaperImages')\r\n os.makedirs(dir_path, exist_ok=True)\r\n db_file = os.path.join(dir_path,'wariety.db')\r\n full_image_path = \"\"\r\n conn = sqlite3.connect(db_file)\r\n c = conn.cursor()\r\n\r\n c.execute(\"SELECT id, ipath FROM wallpapers\")\r\n\r\n result = c.fetchall()\r\n conn.close()\r\n\r\n max = len(result)\r\n\r\n choice = random.randint(0, int(max+max*.5))\r\n try:\r\n full_image_path = os.path.abspath(result[choice][1])\r\n except:\r\n return get_random_image_from_any_source()\r\n logging.debug('get_random_image_from_database - full_image_path = {}'.format(full_image_path))\r\n return full_image_path", "def cmd_image_favorite(client, args):\n favorite_image = client.favorite_image(args.image_id)\n generate_output({'favorite_image': favorite_image})", "def do_command(self, args):\n imageops = dbops.Images()\n imageops.delete(args)", "def get_random_song(self):\n return random.choice(self.song_list)", "def gallery_command(update: Update, context: CallbackContext) -> None:\n photo: Photo = get_random_photo()\n context.bot.send_photo(chat_id=update.effective_chat.id,\n photo=photo.file_id,\n caption=photo.description)" ]

[ "0.6314875", "0.5989403", "0.5938749", "0.5929158", "0.5898837", "0.5824625", "0.5752863", "0.5730477", "0.5720863", "0.56660914", "0.5637061", "0.5596575", "0.55663687", "0.552755", "0.5518498", "0.55022883", "0.5477218", "0.54448146", "0.542192", "0.5327834", "0.5300046", "0.5300046", "0.5292745", "0.5291646", "0.52874374", "0.52507025", "0.52366436", "0.5234386", "0.5225985", "0.5215417" ]