Rename make_cfunc() -> cfunc().

doc/notebooks/NeuralODEs.ipynb

@@ -416,7 +416,7 @@
    "outputs": [],
    "source": [
     "# Assemble the r.h.s. for scipy-integration\n",
-    "rhs = hy.make_cfunc(fn = [it[1] for it in dyn], compact_mode=True, vars = state + list(phi.flatten())+ list(varphi.flatten()))"
+    "rhs = hy.cfunc(fn = [it[1] for it in dyn], compact_mode=True, vars = state + list(phi.flatten())+ list(varphi.flatten()))"
    ]
   },
   {

doc/notebooks/Optimal Control of the Lotka-Volterra equations.ipynb

@@ -196,7 +196,7 @@
     "\n",
     "# Introduce a compiled function\n",
     "# for the evaluation of the Hamiltonian.\n",
-    "H_cf = hy.make_cfunc([H], vars=[x, y, lx, ly, u])\n",
+    "H_cf = hy.cfunc([H], vars=[x, y, lx, ly, u])\n",
     "\n",
     "def _hamiltonian(x,y,lx,ly,p):\n",
     "    sw_v = switching_function(x,y,lx,ly,p)\n",

doc/notebooks/Outer Solar System.ipynb

@@ -141,7 +141,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "en_cf = hy.make_cfunc([hy.model.nbody_energy(6, masses = masses, Gconst = G)],\n",
+    "en_cf = hy.cfunc([hy.model.nbody_energy(6, masses = masses, Gconst = G)],\n",
     "                     # NOTE: the variables for the compiled function\n",
     "                     # are taken from the definition of the ODE system.\n",
     "                     vars=[_[0] for _ in sys])"

doc/notebooks/Periodic orbits in the CR3BP.ipynb

@@ -162,7 +162,7 @@
    "source": [
     "# Introduce a compiled function for the evaluation\n",
     "# of the dynamics equation for px.\n",
-    "px_dyn_cf = hy.make_cfunc([f[3]], vars=x)\n",
+    "px_dyn_cf = hy.cfunc([f[3]], vars=x)\n",
     "\n",
     "def compute_L_points(mu):\n",
     "    \"\"\"Computes The exact position of the Lagrangian points. To do so it finds the zeros of the\n",
@@ -651,7 +651,7 @@
    "source": [
     "# Introduce a compiled function for the evaluation\n",
     "# of the dynamics equations.\n",
-    "dyn_cf = hy.make_cfunc(f, vars=x)\n",
+    "dyn_cf = hy.cfunc(f, vars=x)\n",
     "\n",
     "def corrector(ta, x0):\n",
     "    \"\"\"\n",

doc/notebooks/Pseudo arc-length continuation in the CR3BP.ipynb

@@ -153,8 +153,8 @@
     "f = np.array(f)\n",
     "\n",
     "# We compile the r.h.s. of the dynamics into compiled functions as to later be able to evaluate their numerical values magnitude.\n",
-    "cf_f = hy.make_cfunc(f, vars=x)\n",
-    "cf_px = hy.make_cfunc([f[3]], vars=[x[0],x[1],x[2],x[4]]) # x,y,z,py"
+    "cf_f = hy.cfunc(f, vars=x)\n",
+    "cf_px = hy.cfunc([f[3]], vars=[x[0],x[1],x[2],x[4]]) # x,y,z,py"
    ]
   },
   {

doc/notebooks/arbitrary_precision.ipynb

@@ -634,7 +634,7 @@
     "sym_func = x**2-y**2\n",
     "\n",
     "# Compile it.\n",
-    "cf = hy.make_cfunc([sym_func], [x, y],\n",
+    "cf = hy.cfunc([sym_func], [x, y],\n",
     "                   fp_type=real, prec=prec)"
    ]
   },

doc/notebooks/compiled_functions.ipynb

@@ -57,7 +57,7 @@
    "id": "68861247-8ac9-42a3-bf58-b893282ccdf7",
    "metadata": {},
    "source": [
-    "We can now proceed to JIT compile ``sym_func`` via the ``make_cfunc()`` function. ``make_cfunc()`` takes two mandatory input arguments:\n",
+    "We can now proceed to JIT compile ``sym_func`` via the ``cfunc()`` function. ``cfunc()`` takes two mandatory input arguments:\n",
     "\n",
     "- the list of symbolic expressions representing the outputs of the vector function, and\n",
     "- the list of symbolic variables representing the inputs of the function.\n",
@@ -72,15 +72,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "cf = hy.make_cfunc([sym_func], [x, y])"
+    "cf = hy.cfunc([sym_func], [x, y])"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "8df3f92c-49b2-459d-bb0a-33e2e000812c",
    "metadata": {},
    "source": [
-    "The value returned by ``make_cfunc()`` is a callable function object which accepts as input a NumPy array representing the values to use in the evaluation of ``sym_func``:"
+    "The value returned by ``cfunc()`` is a callable function object which accepts as input a NumPy array representing the values to use in the evaluation of ``sym_func``:"
    ]
   },
   {
@@ -163,7 +163,7 @@
     }
    ],
    "source": [
-    "cf2 = hy.make_cfunc([sym_func], vars=[y,x])\n",
+    "cf2 = hy.cfunc([sym_func], vars=[y,x])\n",
     "\n",
     "# Evaluate for x=1 and y=5.\n",
     "cf2([5,1])"
@@ -205,9 +205,9 @@
    "id": "43f895e7-2da5-440d-84f8-1932d5a9d1fc",
    "metadata": {},
    "source": [
-    "``make_cfunc()`` accepts several additional keyword arguments, the most important of which is the boolean flag ``compact_mode`` (defaulting to ``False``). Similarly to the [adaptive Taylor integrators](<./Customising the adaptive integrator.ipynb>), you should enable ``compact_mode`` if you want to compile extremely large symbolic expressions that result in excessively long compilation times. The downside of ``compact_mode`` is a slight performance degradation due to the different code generation model adopted during the JIT compilation process.\n",
+    "``cfunc()`` accepts several additional keyword arguments, the most important of which is the boolean flag ``compact_mode`` (defaulting to ``False``). Similarly to the [adaptive Taylor integrators](<./Customising the adaptive integrator.ipynb>), you should enable ``compact_mode`` if you want to compile extremely large symbolic expressions that result in excessively long compilation times. The downside of ``compact_mode`` is a slight performance degradation due to the different code generation model adopted during the JIT compilation process.\n",
     "\n",
-    "The function object returned by ``make_cfunc()`` also accepts several optional keyword arguments. It is possible, for instance, to pass as ``outputs`` argument a pre-allocated NumPy array into which the result of the evaluation will be written. This is useful to avoid the overhead of allocating new memory for the return value, if such memory is already available:"
+    "The function object returned by ``cfunc()`` also accepts several optional keyword arguments. It is possible, for instance, to pass as ``outputs`` argument a pre-allocated NumPy array into which the result of the evaluation will be written. This is useful to avoid the overhead of allocating new memory for the return value, if such memory is already available:"
    ]
   },
   {
@@ -273,7 +273,7 @@
     "sym_func_par = hy.par[0]*x**2-hy.par[1]*y**2+hy.par[2]\n",
     "\n",
     "# Compile it.\n",
-    "cf_par = hy.make_cfunc([sym_func_par], [x, y])\n",
+    "cf_par = hy.cfunc([sym_func_par], [x, y])\n",
     "\n",
     "# Evaluate, specifying the parameter values\n",
     "cf_par([1,5], pars=[-1, -2, -3])"
@@ -315,7 +315,7 @@
     "sym_func_tm = x**2-y**2+hy.time\n",
     "\n",
     "# Compile it.\n",
-    "cf_tm = hy.make_cfunc([sym_func_tm], [x, y])\n",
+    "cf_tm = hy.cfunc([sym_func_tm], [x, y])\n",
     "\n",
     "# Evaluate for x=1, y=5 and time=6.\n",
     "cf_tm([1,5], time=6)"
@@ -538,7 +538,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "Ham_cf = hy.make_cfunc([Ham_sym], vars=[px,py,pz,x,y,z])"
+    "Ham_cf = hy.cfunc([Ham_sym], vars=[px,py,pz,x,y,z])"
    ]
   },
   {

doc/notebooks/computing_derivatives.ipynb

@@ -495,7 +495,7 @@
     }
    ],
    "source": [
-    "grad_diff_cf = hy.make_cfunc(grad_diff, sym_vars)\n",
+    "grad_diff_cf = hy.cfunc(grad_diff, sym_vars)\n",
     "grad_diff_cf.decomposition[8:-8]"
    ]
   },
@@ -582,7 +582,7 @@
     }
    ],
    "source": [
-    "grad_diff_tensors_cf = hy.make_cfunc(grad_diff_tensors, sym_vars)\n",
+    "grad_diff_tensors_cf = hy.cfunc(grad_diff_tensors, sym_vars)\n",
     "grad_diff_tensors_cf.decomposition[8:-8]"
    ]
   },
@@ -621,7 +621,7 @@
     "    grad_diff_tensors = [t[1] for t in hy.diff_tensors([sp_func],\n",
     "                                                       diff_args=hy.diff_args.vars,\n",
     "                                                       diff_order=1).get_derivatives(1)]\n",
-    "    grad_diff_tensors_cf = hy.make_cfunc(grad_diff_tensors, sym_vars)\n",
+    "    grad_diff_tensors_cf = hy.cfunc(grad_diff_tensors, sym_vars)\n",
     "    nops.append(len(grad_diff_tensors_cf.decomposition) - 2*nvars)\n",
     "\n",
     "%matplotlib inline\n",

doc/notebooks/elp2000.ipynb

@@ -230,7 +230,7 @@
     "                                                            thresh=thr)\n",
     "\n",
     "    # Compile the function for the evaluation of moon_x/y/z.\n",
-    "    moon_cf = hy.make_cfunc([moon_x, moon_y, moon_z], [tm], compact_mode=True)\n",
+    "    moon_cf = hy.cfunc([moon_x, moon_y, moon_z], [tm], compact_mode=True)\n",
     "    \n",
     "    # Run the evaluation.\n",
     "    elp2000_states = moon_cf(dates.reshape((1,-1)))\n",

doc/notebooks/ex_system_revisited.ipynb

@@ -499,7 +499,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "f_cf = hy.make_cfunc([x+y+1., x+y+z], [x, y, z])"
+    "f_cf = hy.cfunc([x+y+1., x+y+z], [x, y, z])"
    ]
   },
   {
@@ -608,7 +608,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "f_cf_fix = hy.make_cfunc([hy.fix(x+y)+1., hy.fix(x+y)+z],\n",
+    "f_cf_fix = hy.cfunc([hy.fix(x+y)+1., hy.fix(x+y)+z],\n",
     "                        [x, y, z])"
    ]
   },
@@ -711,7 +711,7 @@
     }
    ],
    "source": [
-    "g_cf = hy.make_cfunc([2.0 * (x + y + z)], [x, y, z])\n",
+    "g_cf = hy.cfunc([2.0 * (x + y + z)], [x, y, z])\n",
     "g_cf.decomposition"
    ]
   },
@@ -741,7 +741,7 @@
     }
    ],
    "source": [
-    "g_cf_fix = hy.make_cfunc([2.0 * hy.fix(x + y + z)],\n",
+    "g_cf_fix = hy.cfunc([2.0 * hy.fix(x + y + z)],\n",
     "                        [x, y, z])\n",
     "g_cf_fix.decomposition"
    ]

doc/notebooks/ffnn.ipynb

@@ -170,7 +170,7 @@
     }
    ],
    "source": [
-    "cf = hy.make_cfunc(ffnn, [x, y])\n",
+    "cf = hy.cfunc(ffnn, [x, y])\n",
     "print(cf)"
    ]
   },
@@ -251,7 +251,7 @@
     }
    ],
    "source": [
-    "cf = hy.make_cfunc(ffnn, [x, y])\n",
+    "cf = hy.cfunc(ffnn, [x, y])\n",
     "print(cf)"
    ]
   },

doc/notebooks/lagrangian_propagator.ipynb

@@ -214,7 +214,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "cf = hy.make_cfunc(pos_vel,\n",
+    "cf = hy.cfunc(pos_vel,\n",
     "                   # Specify the order in which the input\n",
     "                   # variables are passed to the compiled\n",
     "                   # function.\n",
@@ -373,7 +373,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "cf_stm = hy.make_cfunc(jac.flatten(),\n",
+    "cf_stm = hy.cfunc(jac.flatten(),\n",
     "                       # Specify the order in which the input\n",
     "                       # variables are passed to the compiled\n",
     "                       # function.\n",

doc/notebooks/projection.ipynb

@@ -113,7 +113,7 @@
    "source": [
     "# Define a compiled function for the computation\n",
     "# of the energy in an N-body system from its state vector.\n",
-    "en_cfunc = hy.make_cfunc([hy.model.nbody_energy(6, masses=masses, Gconst=G)],\n",
+    "en_cfunc = hy.cfunc([hy.model.nbody_energy(6, masses=masses, Gconst=G)],\n",
     "                         vars=state_vars)"
    ]
   },
@@ -511,7 +511,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "objfun_cfunc = hy.make_cfunc([dist2_ex] + grad_dist2_ex, vars=state_vars)"
+    "objfun_cfunc = hy.cfunc([dist2_ex] + grad_dist2_ex, vars=state_vars)"
    ]
   },
   {
@@ -561,7 +561,7 @@
     "\n",
     "# The compiled function for the computation of the gradient\n",
     "# of the constraint.\n",
-    "grad_cstr_cfunc = hy.make_cfunc(grad_cstr, vars=state_vars)"
+    "grad_cstr_cfunc = hy.cfunc(grad_cstr, vars=state_vars)"
    ]
   },
   {

doc/notebooks/single_precision.ipynb

@@ -113,7 +113,7 @@
    "source": [
     "x, v = hy.make_vars(\"x\", \"v\")\n",
     "\n",
-    "cf_en = hy.make_cfunc([en], fp_type=np.single,\n",
+    "cf_en = hy.cfunc([en], fp_type=np.single,\n",
     "                      vars=[x, v])"
    ]
   },

doc/notebooks/torch_and_heyoka.ipynb

@@ -181,7 +181,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model_heyoka_compiled=hk.make_cfunc(model_heyoka, [inp_1, inp_2, inp_3, inp_4])"
+    "model_heyoka_compiled=hk.cfunc(model_heyoka, [inp_1, inp_2, inp_3, inp_4])"
    ]
   },
   {

doc/notebooks/vsop2013.ipynb

@@ -248,7 +248,7 @@
     "                                                                 thresh=thr)[:3]\n",
     "\n",
     "    # Compile the function for the evaluation of venus_x/y/z.\n",
-    "    venus_cf = hy.make_cfunc([venus_x, venus_y, venus_z], [tm], compact_mode=True)\n",
+    "    venus_cf = hy.cfunc([venus_x, venus_y, venus_z], [tm], compact_mode=True)\n",
     "    \n",
     "    # Run the evaluation.\n",
     "    vsop_states = venus_cf(dates.reshape((1,-1)))\n",

heyoka/__init__.py

@@ -138,7 +138,7 @@ def taylor_add_jet(sys, order, **kwargs):
     return getattr(core, "_taylor_add_jet{}".format(fp_suffix))(sys, order, **kwargs)
 
 
-def make_cfunc(fn, vars, **kwargs):
+def cfunc(fn, vars, **kwargs):
     from . import core
 
     fp_type = kwargs.pop("fp_type", float)