Revert attribute changes by implementing @proprety and fix a couple o…

…f components

docs/modules/components.rst

@@ -713,22 +713,22 @@ equation, too. The Valve's dp_char parameter methods are the following.
         i = self.inl[0]
         o = self.outl[0]
         if self.is_variable(i.m, increment_filter):
-            self.jacobian[k, i.m.J_col()] = self.numeric_deriv(f, 'm', i)
+            self.jacobian[k, i.m.J_col] = self.numeric_deriv(f, 'm', i)
         if self.dp_char.param == 'v':
             if self.is_variable(i.p, increment_filter):
-                self.jacobian[k, i.p.J_col()] = self.numeric_deriv(
+                self.jacobian[k, i.p.J_col] = self.numeric_deriv(
                     self.dp_char_func, 'p', i
                 )
             if self.is_variable(i.h, increment_filter):
-                self.jacobian[k, i.h.J_col()] = self.numeric_deriv(
+                self.jacobian[k, i.h.J_col] = self.numeric_deriv(
                     self.dp_char_func, 'h', i
                 )
         else:
             if self.is_variable(i.p, increment_filter):
-                self.jacobian[k, i.p.J_col()] = 1
+                self.jacobian[k, i.p.J_col] = 1
 
         if self.is_variable(o.p):
-            self.jacobian[k, o.p.J_col()] = -1
+            self.jacobian[k, o.p.J_col] = -1
 
 For the Jacobian, the partial derivatives to all variables of the network
 are required. This means, that you have to calculate the partial derivatives

docs/modules/ude.rst

@@ -98,8 +98,8 @@ inside the Jacobian of the :code:`UserDefinedEquation` and returns it:
   connection required by the :code:`UserDefinedEquation`.
 - derivatives are referred to with the :code:`J_col` attribute of the
   variables of a :code:`Connection` object, i.e.
-  :code:`c.m.J_col()` for mass flow, :code:`c.p.J_col()` for pressure,
-  :code:`c.h.J_col()` for enthalpy, and :code:`c.fluid.J_col()[fluid_name]` for the
+  :code:`c.m.J_col` for mass flow, :code:`c.p.J_col` for pressure,
+  :code:`c.h.J_col` for enthalpy, and :code:`c.fluid.J_col[fluid_name]` for the
   derivative of the fluid composition towards a specific fluid
   :code:`fluid_name`.
 
@@ -116,9 +116,9 @@ derivatives to mass flow are not zero.
     ...     c1 = ude.conns[0]
     ...     c2 = ude.conns[1]
     ...     if c1.m.is_var:
-    ...         ude.jacobian[c1.m.J_col()] = 1
+    ...         ude.jacobian[c1.m.J_col] = 1
     ...     if c2.m.is_var:
-    ...         ude.jacobian[c2.m.J_col()] = -2 * ude.conns[1].m.val_SI
+    ...         ude.jacobian[c2.m.J_col] = -2 * ude.conns[1].m.val_SI
 
 Now we can create our instance of the :code:`UserDefinedEquation` and add it to
 the network. The class requires four mandatory arguments to be passed:
@@ -187,17 +187,17 @@ respectively to calculate the partial derivatives.
     ...     c1 = ude.conns[0]
     ...     c2 = ude.conns[1]
     ...     if c1.m.is_var:
-    ...         ude.jacobian[c1.m.J_col()] = 1 / ude.conns[0].m.val_SI
+    ...         ude.jacobian[c1.m.J_col] = 1 / ude.conns[0].m.val_SI
     ...     if c1.p.is_var:
-    ...         ude.jacobian[c1.p.J_col()] = - 2 / ude.conns[0].p.val_SI
+    ...         ude.jacobian[c1.p.J_col] = - 2 / ude.conns[0].p.val_SI
     ...     T = c2.calc_T()
     ...     if c2.p.is_var:
-    ...         ude.jacobian[c2.p.J_col()] = (
+    ...         ude.jacobian[c2.p.J_col] = (
     ...             dT_mix_dph(c2.p.val_SI, c2.h.val_SI, c2.fluid_data, c2.mixing_rule)
     ...             * 0.5 / (T ** 0.5)
     ...         )
     ...     if c2.h.is_var:
-    ...         ude.jacobian[c2.h.J_col()] = (
+    ...         ude.jacobian[c2.h.J_col] = (
     ...             dT_mix_pdh(c2.p.val_SI, c2.h.val_SI, c2.fluid_data, c2.mixing_rule)
     ...             * 0.5 / (T ** 0.5)
     ...         )
@@ -215,13 +215,13 @@ for the above derivatives would therefore look like this:
     ...     c1 = ude.conns[0]
     ...     c2 = ude.conns[1]
     ...     if c1.m.is_var:
-    ...         ude.jacobian[c1.m.J_col()] = ude.numeric_deriv('m', c1)
+    ...         ude.jacobian[c1.m.J_col] = ude.numeric_deriv('m', c1)
     ...     if c1.p.is_var:
-    ...         ude.jacobian[c1.p.J_col()] = ude.numeric_deriv('p', c1)
+    ...         ude.jacobian[c1.p.J_col] = ude.numeric_deriv('p', c1)
     ...     if c2.p.is_var:
-    ...         ude.jacobian[c2.p.J_col()] = ude.numeric_deriv('p', c2)
+    ...         ude.jacobian[c2.p.J_col] = ude.numeric_deriv('p', c2)
     ...     if c2.h.is_var:
-    ...         ude.jacobian[c2.h.J_col()] = ude.numeric_deriv('h', c2)
+    ...         ude.jacobian[c2.h.J_col] = ude.numeric_deriv('h', c2)
 
     >>> ude = UserDefinedEquation('ude numerical', my_ude, my_ude_deriv, [c1, c2])
     >>> nw.add_ude(ude)
@@ -279,11 +279,11 @@ instance must therefore be changed as below.
     ...     c1 = ude.conns[0]
     ...     c2 = ude.conns[1]
     ...     if c1.p.is_var:
-    ...         ude.jacobian[c1.p.J_col()] = dh_mix_dpQ(c1.p.val_SI, b, c1.fluid_data)
+    ...         ude.jacobian[c1.p.J_col] = dh_mix_dpQ(c1.p.val_SI, b, c1.fluid_data)
     ...     if c1.h.is_var:
-    ...         ude.jacobian[c1.h.J_col()] = -a
+    ...         ude.jacobian[c1.h.J_col] = -a
     ...     if c2.p.is_var:
-    ...         ude.jacobian[c2.p.J_col()] = a - 1
+    ...         ude.jacobian[c2.p.J_col] = a - 1
 
     >>> ude = UserDefinedEquation(
     ...     'my ude', my_ude, my_ude_deriv, [c1, c2], params={'a': 0.5, 'b': 1}

src/tespy/components/combustion/base.py

@@ -387,10 +387,10 @@ def mass_flow_deriv(self, k):
         inl, outl = self._get_combustion_connections()
         for i in inl:
             if i.m.is_var:
-                self.jacobian[k, i.m.J_col()] = 1
+                self.jacobian[k, i.m.J_col] = 1
 
         if outl[0].m.is_var:
-            self.jacobian[k, outl[0].m.J_col()] = -1
+            self.jacobian[k, outl[0].m.J_col] = -1
 
     def combustion_pressure_func(self):
         r"""
@@ -452,14 +452,14 @@ def combustion_pressure_deriv(self, k):
         """
         inl, outl = self._get_combustion_connections()
         if inl[0].p.is_var:
-            self.jacobian[k,  inl[0].p.J_col()] = 1
+            self.jacobian[k,  inl[0].p.J_col] = 1
         if outl[0].p.is_var:
-            self.jacobian[k,  outl[0].p.J_col()] = -1
+            self.jacobian[k,  outl[0].p.J_col] = -1
 
         if inl[1].p.is_var:
-            self.jacobian[k + 1,  inl[1].p.J_col()] = 1
+            self.jacobian[k + 1,  inl[1].p.J_col] = 1
         if outl[0].p.is_var:
-            self.jacobian[k + 1,  outl[0].p.J_col()] = -1
+            self.jacobian[k + 1,  outl[0].p.J_col] = -1
 
     def stoichiometry_func(self):
         r"""
@@ -779,11 +779,11 @@ def stoichiometry_deriv(self, increment_filter, k):
         for fluid, conn in itertools.product(self.fluid_eqs_list, conns):
             eq_num = self.fluid_eqs_list.index(fluid)
             if self.is_variable(conn.m, increment_filter):
-                self.jacobian[k + eq_num, conn.m.J_col()] = self.numeric_deriv(
+                self.jacobian[k + eq_num, conn.m.J_col] = self.numeric_deriv(
                     f, 'm', conn, fluid=fluid
                 )
             for fluid_name in conn.fluid.is_var:
-                self.jacobian[k + eq_num, conn.fluid.J_col()[fluid_name]] = self.numeric_deriv(
+                self.jacobian[k + eq_num, conn.fluid.J_col[fluid_name]] = self.numeric_deriv(
                     f, fluid_name, conn, fluid=fluid
                 )
 
@@ -883,14 +883,14 @@ def energy_balance_deriv(self, increment_filter, k):
         f = self.energy_balance_func
         for c in self.inl + self.outl:
             if self.is_variable(c.m, increment_filter):
-                self.jacobian[k, c.m.J_col()] = self.numeric_deriv(f, 'm', c)
+                self.jacobian[k, c.m.J_col] = self.numeric_deriv(f, 'm', c)
             if self.is_variable(c.p, increment_filter):
-                self.jacobian[k, c.p.J_col()] = self.numeric_deriv(f, 'p', c)
+                self.jacobian[k, c.p.J_col] = self.numeric_deriv(f, 'p', c)
             if self.is_variable(c.h, increment_filter):
                 if c == self.outl[0]:
-                    self.jacobian[k, c.h.J_col()] = -c.m.val_SI
+                    self.jacobian[k, c.h.J_col] = -c.m.val_SI
                 else:
-                    self.jacobian[k, c.h.J_col()] = c.m.val_SI
+                    self.jacobian[k, c.h.J_col] = c.m.val_SI
 
     def lambda_func(self):
         r"""
@@ -953,9 +953,9 @@ def lambda_deriv(self, increment_filter, k):
         f = self.lambda_func
         for conn in inl:
             if self.is_variable(conn.m, increment_filter):
-                self.jacobian[k, conn.m.J_col()] = self.numeric_deriv(f, 'm', conn)
+                self.jacobian[k, conn.m.J_col] = self.numeric_deriv(f, 'm', conn)
             for fluid in conn.fluid.is_var:
-                self.jacobian[k, conn.fluid.J_col()[fluid]] = self.numeric_deriv(f, fluid, conn)
+                self.jacobian[k, conn.fluid.J_col[fluid]] = self.numeric_deriv(f, fluid, conn)
 
     def calc_lambda(self):
         r"""
@@ -1057,18 +1057,18 @@ def ti_deriv(self, increment_filter, k):
                 deriv = 0
                 for f in self.fuel_list:
                     deriv -= i.fluid.val[f] * self.fuels[f]['LHV']
-                self.jacobian[k, i.m.J_col()] = deriv
+                self.jacobian[k, i.m.J_col] = deriv
             for f in (self.fuel_list & i.fluid.is_var):
-                self.jacobian[k, i.fluid.J_col()[f]] = -i.m.val_SI * self.fuels[f]['LHV']
+                self.jacobian[k, i.fluid.J_col[f]] = -i.m.val_SI * self.fuels[f]['LHV']
 
         o = outl[0]
         if o.m.is_var:
             deriv = 0
             for f in self.fuel_list:
                 deriv += o.fluid.val[f] * self.fuels[f]['LHV']
-            self.jacobian[k, o.m.J_col()] = deriv
+            self.jacobian[k, o.m.J_col] = deriv
         for f in (self.fuel_list & o.fluid.is_var):
-            self.jacobian[k, o.fluid.J_col()[f]] = o.m.val_SI * self.fuels[f]['LHV']
+            self.jacobian[k, o.fluid.J_col[f]] = o.m.val_SI * self.fuels[f]['LHV']
 
     def calc_ti(self):
         r"""
@@ -1162,14 +1162,14 @@ def bus_deriv(self, bus):
         f = self.calc_bus_value
         for c in self.inl + self.outl:
             if c.m.is_var:
-                if c.m.J_col() not in bus.jacobian:
-                    bus.jacobian[c.m.J_col()] = 0
-                bus.jacobian[c.m.J_col()] -= self.numeric_deriv(f, 'm', c, bus=bus)
+                if c.m.J_col not in bus.jacobian:
+                    bus.jacobian[c.m.J_col] = 0
+                bus.jacobian[c.m.J_col] -= self.numeric_deriv(f, 'm', c, bus=bus)
 
             for fluid in c.fluid.is_var:
-                if c.fluid.J_col()[fluid] not in bus.jacobian:
-                    bus.jacobian[c.fluid.J_col()[fluid]] = 0
-                bus.jacobian[c.fluid.J_col()[fluid]] -= self.numeric_deriv(f, fluid, c, bus=bus)
+                if c.fluid.J_col[fluid] not in bus.jacobian:
+                    bus.jacobian[c.fluid.J_col[fluid]] = 0
+                bus.jacobian[c.fluid.J_col[fluid]] -= self.numeric_deriv(f, fluid, c, bus=bus)
 
     def convergence_check(self):
         r"""

src/tespy/components/combustion/diabatic.py

@@ -271,9 +271,9 @@ def pr_deriv(self, increment_filter, k):
         i = self.inl[0]
         o = self.outl[0]
         if self.is_variable(i.p):
-            self.jacobian[k, i.p.J_col()] = self.pr.val
+            self.jacobian[k, i.p.J_col] = self.pr.val
         if self.is_variable(o.p):
-            self.jacobian[k, o.p.J_col()] = -1
+            self.jacobian[k, o.p.J_col] = -1
 
     def energy_balance_func(self):
         r"""