Merge pull request #62 from openworm/experimental

Select plots

Author: pgleeson

Tutorial/Source/HodgkinHuxley.py

@@ -14,7 +14,9 @@ def __init__(self, C_m=1, gmax_Na=120, gmax_K=36, gmax_L=0.3, E_Na=50,
                  E_K=-77, E_L=-54.387, t_0=0, t_n=450, delta_t=0.01,
                  I_inj_amplitude=0, I_inj_duration=0, I_inj_delay=0,
                  vc_delay=10, vc_duration=30, vc_condVoltage=-65,
-                 vc_testVoltage=10, vc_returnVoltage=-65, runMode='iclamp'):
+                 vc_testVoltage=10, vc_returnVoltage=-65, runMode='iclamp',
+                 injected_current_plot=True, gating_plot=True, cond_dens_plot=True,
+                 current_plot=True, memb_pot_plot=True):
 
         self.C_m  = C_m
         """ membrane capacitance, in uF/cm^2 """
@@ -73,6 +75,19 @@ def __init__(self, C_m=1, gmax_Na=120, gmax_K=36, gmax_L=0.3, E_Na=50,
         self.simpleSeriesResistance = 1e7
         """Current will be calculated by the difference in voltage between the target and parent, divided by this value, in mOhm"""
 
+        # plotting conditionals
+        self.injected_current_plot = injected_current_plot
+        self.gating_plot = gating_plot
+        self.cond_dens_plot = cond_dens_plot
+        self.current_plot = current_plot
+        self.memb_pot_plot = memb_pot_plot
+
+        self.num_plots = (int(self.injected_current_plot) +
+                          int(self.gating_plot) + int(self.cond_dens_plot) +
+                          int(self.current_plot) + int(self.memb_pot_plot))
+
+        self.plot_count = 0
+
     def alpha_m(self, V):
         """Channel gating kinetics. Functions of membrane voltage"""
         return 0.1*(V+40.0)/(1.0 - np.exp(-(V+40.0) / 10.0))
@@ -268,53 +283,79 @@ def Main(self, init_values=[-64.99584, 0.05296, 0.59590, 0.31773]):
                 plt.rcParams['figure.figsize'] = [10, 7]
 
             plt.close()
-            fig=plt.figure()
-            fig.canvas.header_visible = False
 
-            ax1 = plt.subplot(5,1,1)
+            fig=plt.figure(figsize=(7, self.num_plots * 2))
+            fig.canvas.header_visible = False
             plt.xlim([np.min(self.t),np.max(self.t)])  #for all subplots
-            plt.title('Simulation of Hodgkin Huxley model neuron')
 
-            if self.is_vclamp():
-                i_inj_values = [self.I_inj_vclamp(t,v) for t,v in zip(self.t,V)]
-            else:
-                i_inj_values = [self.I_inj(t) for t in self.t]
-
-            plt.plot(self.t, i_inj_values, 'k')
-            plt.ylabel('$I_{inj}$ ($\\mu{A}/cm^2$)')
-            if self.is_vclamp(): plt.ylim(-2000,3000)
-
-
-            plt.subplot(5,1,2, sharex = ax1)
-            plt.plot(self.t, m, 'r', label='m')
-            plt.plot(self.t, h, 'g', label='h')
-            plt.plot(self.t, n, 'b', label='n')
-            plt.ylabel('Gating Variable')
-            plt.legend()
-
-            plt.subplot(5,1,3, sharex = ax1)
-            plt.plot(self.t, gna, 'c', label='$g_{Na}$')
-            plt.plot(self.t, gk, 'y', label='$g_{K}$')
-            plt.ylabel('Cond. dens')
-            plt.legend()
-
-            plt.subplot(5,1,4, sharex = ax1)
-            plt.plot(self.t, ina, 'c', label='$I_{Na}$')
-            plt.plot(self.t, ik, 'y', label='$I_{K}$')
-            plt.plot(self.t, il, 'm', label='$I_{L}$')
-            plt.ylabel('Current')
-            plt.legend()
-
-            plt.subplot(5,1,5, sharex = ax1)
-            plt.plot(self.t, V, 'k')
-            plt.ylabel('V (mV)')
-            plt.xlabel('t (ms)')
-            if not self.is_vclamp(): plt.ylim(-85,60)
-            #plt.ylim(-1, 40)
+            if self.injected_current_plot:
+                ax1 = plt.subplot(self.num_plots,1,self.plot_count + 1)
+                plt.title('Simulation of Hodgkin Huxley model neuron')
+                if self.is_vclamp():
+                    i_inj_values = [self.I_inj_vclamp(t,v) for t,v in zip(self.t,V)]
+                else:
+                    i_inj_values = [self.I_inj(t) for t in self.t]
+
+                if self.is_vclamp(): plt.ylim(-2000,3000)
+
+                plt.plot(self.t, i_inj_values, 'k')
+                plt.ylabel('$I_{inj}$ ($\\mu{A}/cm^2$)')
+
+                self.plot_count += 1
+
+
+            if self.gating_plot:
+                try:
+                    plt.subplot(self.num_plots,1,self.plot_count+1, sharex = ax1)
+                except NameError:
+                    ax1 = plt.subplot(self.num_plots,1,self.plot_count + 1)
+                    plt.title('Simulation of Hodgkin Huxley model neuron')
+                plt.plot(self.t, m, 'r', label='m')
+                plt.plot(self.t, h, 'g', label='h')
+                plt.plot(self.t, n, 'b', label='n')
+                plt.ylabel('Gating Variable')
+                plt.legend()
+                self.plot_count += 1
+
+            if self.cond_dens_plot:
+                try:
+                    plt.subplot(self.num_plots,1,self.plot_count+1, sharex = ax1)
+                except NameError:
+                    ax1 = plt.subplot(self.num_plots,1,self.plot_count + 1)
+                    plt.title('Simulation of Hodgkin Huxley model neuron')
+                plt.plot(self.t, gna, 'c', label='$g_{Na}$')
+                plt.plot(self.t, gk, 'y', label='$g_{K}$')
+                plt.ylabel('Cond. dens ($mS/cm^2$)')
+                plt.legend()
+                self.plot_count += 1
+
+            if self.current_plot:
+                try:
+                    plt.subplot(self.num_plots,1,self.plot_count+1, sharex = ax1)
+                except NameError:
+                    ax1 = plt.subplot(self.num_plots,1,self.plot_count + 1)
+                    plt.title('Simulation of Hodgkin Huxley model neuron')
+                plt.plot(self.t, ina, 'c', label='$I_{Na}$')
+                plt.plot(self.t, ik, 'y', label='$I_{K}$')
+                plt.plot(self.t, il, 'm', label='$I_{L}$')
+                plt.ylabel('Current ($\\mu{A}/cm^2$)')
+                plt.legend()
+                self.plot_count += 1
+
+            if self.memb_pot_plot:
+                try:
+                    plt.subplot(self.num_plots,1,self.plot_count+1, sharex = ax1)
+                except NameError:
+                    ax1 = plt.subplot(self.num_plots,1,self.plot_count + 1)
+                    plt.title('Simulation of Hodgkin Huxley model neuron')
+                plt.plot(self.t, V, 'k')
+                plt.ylabel('$V_{m}$ (mV)')
+                plt.xlabel('t (ms)')
+                if not self.is_vclamp(): plt.ylim(-85,60)
+                #plt.ylim(-1, 40)
+                self.plot_count += 1
 
             plt.tight_layout()
-
-
             plt.show()
 
 if __name__ == '__main__':

notebooks/Python_HH_version/Python_Notebook_HH.ipynb

@@ -37,7 +37,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3785f140f0394fd186765d0525cd0f1a",
+       "model_id": "012cf4db99d64e63b2aba86949411ced",
        "version_major": 2,
        "version_minor": 0
       },
@@ -51,7 +51,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c5fc97be4b7f421fa15c5c60c6cb6b66",
+       "model_id": "76a0b6d59696460dbbf15f755c87eee9",
        "version_major": 2,
        "version_minor": 0
       },
@@ -90,7 +90,7 @@
     "\n",
     "3) The third plot shows the **conductance densities** for Sodium (**g<sub>Na</sub> = gmax<sub>Na</sub> m<sup>3</sup> h**) and Potassium (**g<sub>K</sub> = gmax<sub>K</sub> n<sup>4</sup>**). Note, the leak conductance density value, **g<sub>L</sub>** is not voltage dependent, and so does not vary with time. The maximum conductance densities for the 3 conductances can be changed or set to zero.\n",
     "\n",
-    "4) The fourth plot shows the influx (negative y-axis) and outflux (positive y-axis) of **ionic currents (I<sub>Na</sub> = gmax<sub>Na</sub> (V<sub>m</sub> - E<sub>Na</sub>), I<sub>K</sub> = gmax<sub>K</sub> (V<sub>m</sub> - E<sub>K</sub>) and I<sub>L</sub> = g<sub>L</sub> (V<sub>m</sub> - E<sub>L</sub>**) passing through each type of ion channel being modeled. The reversal potentials of each of these (**E<sub>X</sub>**) can be altered above. \n",
+    "4) The fourth plot shows the influx (negative y-axis) and outflux (positive y-axis) of **ionic currents (I<sub>Na</sub> = g<sub>Na</sub> (V<sub>m</sub> - E<sub>Na</sub>), I<sub>K</sub> = g<sub>K</sub> (V<sub>m</sub> - E<sub>K</sub>) and I<sub>L</sub> = g<sub>L</sub> (V<sub>m</sub> - E<sub>L</sub>**) passing through each type of ion channel being modeled. The reversal potentials of each of these (**E<sub>X</sub>**) can be altered above. \n",
     "\n",
     "5) The bottom plot shows the **neural membrane potential activity, V<sub>m</sub>**. The spikes here are called \"action potentials\". This variable is governed by the expression: **dV<sub>m</sub>/dt = (I<sub>inj</sub> - I<sub>Na</sub> - I<sub>K</sub> - I<sub>L</sub>)/C<sub>m</sub>**\n",
     "\n",
@@ -118,7 +118,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },

notebooks/Python_HH_version/ui_widget.py

@@ -204,8 +204,17 @@ def highlight_slider():
 #reset and defalult value buttons in single row
 button_row=ipywidgets.HBox([reset_button,showValue_togglebtn])
 
+#plot selectors
+header_plotting = ipywidgets.HTMLMath(value=r"<b> Select plots</b>")
+injected_current_plot_value = ipywidgets.Checkbox(value=True, description="Current injection", disabled=False,)
+gating_plot_value = ipywidgets.Checkbox(value=True, description="Gating variables", disabled=False,)
+cond_dens_plot_value = ipywidgets.Checkbox(value=True, description="Conductance densities", disabled=False,)
+current_plot_value = ipywidgets.Checkbox(value=True, description="Current densities", disabled=False,)
+memb_pot_plot_value = ipywidgets.Checkbox(value=True, description="Membrane potential", disabled=False,)
+plot_selection_row=ipywidgets.VBox([header_plotting, ipywidgets.HBox([injected_current_plot_value, gating_plot_value, cond_dens_plot_value]), ipywidgets.HBox([current_plot_value, memb_pot_plot_value])])
+
 #layout vertically all the widgets defined above
-modelInputs=ipywidgets.VBox([h1,h2,h3,h4,h5,h6,h7,h8,h9,h10,runMode_iclamp,runMode_vclamp,button_row,defalultValues])
+modelInputs=ipywidgets.VBox([h1,h2,h3,h4,h5,h6,h7,h8,h9,h10,runMode_iclamp,runMode_vclamp,button_row,defalultValues,plot_selection_row])
 
 # Main method to create interactive widget
 def launch_interactive_widget():
@@ -217,22 +226,39 @@ def launch_interactive_widget():
     HHmodel = SourceFileLoader("HodgkinHuxley.py","../../Tutorial/Source/HodgkinHuxley.py").load_module()
 
     #function to call python script as a module
-    def runHH(C_m, g_Na, g_K, g_L, E_Na, E_K, E_L, t_0, t_n, delta_t, I_inj_max, I_inj_width, I_inj_trans, vc_delay, vc_duration, vc_condVoltage, vc_testVoltage, vc_returnVoltage, runMode):
+    def runHH(C_m, g_Na, g_K, g_L, E_Na, E_K, E_L, t_0, t_n, delta_t,
+              I_inj_max, I_inj_width, I_inj_trans, vc_delay, vc_duration,
+              vc_condVoltage, vc_testVoltage, vc_returnVoltage, runMode,
+              injected_current_plot, gating_plot, cond_dens_plot, current_plot, memb_pot_plot):
 
         highlight_slider()
-        runner = HHmodel.HodgkinHuxley(C_m, g_Na, g_K, g_L, E_Na, E_K, E_L, t_0, t_n, delta_t, I_inj_max, I_inj_width, I_inj_trans, vc_delay, vc_duration, vc_condVoltage, vc_testVoltage, vc_returnVoltage, runMode)
+        runner = HHmodel.HodgkinHuxley(C_m, g_Na, g_K, g_L, E_Na, E_K, E_L,
+                                       t_0, t_n, delta_t, I_inj_max,
+                                       I_inj_width, I_inj_trans, vc_delay,
+                                       vc_duration, vc_condVoltage,
+                                       vc_testVoltage, vc_returnVoltage,
+                                       runMode,
+                                       injected_current_plot=injected_current_plot,
+                                       gating_plot=gating_plot,
+                                       cond_dens_plot=cond_dens_plot,
+                                       current_plot=current_plot,
+                                       memb_pot_plot=memb_pot_plot)
         # init_values are the steady state values for v,m,h,n at zero current injection
         runner.Main(init_values=[-63.8, 0.0609, 0.5538, 0.3361])
 
     #create plot area widget and interact with HHmodel
     wid_plotArea=ipywidgets.interactive_output(runHH,{'C_m':slider_capacitance,
-                                            'g_Na':slider_cond_Na, 'g_K':slider_cond_K, 'g_L':slider_cond_L,
-                                            'E_Na':slider_E_Na, 'E_K':slider_E_K, 'E_L':slider_E_L,
+                                            'g_Na':textBox_cond_Na, 'g_K':textBox_cond_K, 'g_L':textBox_cond_L,
+                                            'E_Na':textBox_E_Na, 'E_K':textBox_E_K, 'E_L':textBox_E_L,
                                             't_0':time_start, 't_n':time_end, 'delta_t':time_step,
-                                            'I_inj_max':slider_amplitude,'I_inj_width':slider_width,'I_inj_trans':slider_translation,
-                                            'vc_delay':slider_delay,'vc_duration':slider_duration,'vc_condVoltage':slider_condVoltage,
-                                            'vc_testVoltage':slider_testVoltage,'vc_returnVoltage':slider_returnVoltage,
-                                            'runMode':runMode_togglebtns})
+                                            'I_inj_max':textBox_amplitude,'I_inj_width':textBox_width,'I_inj_trans':textBox_translation,
+                                            'vc_delay':textBox_delay,'vc_duration':textBox_duration,'vc_condVoltage':textBox_condVoltage,
+                                            'vc_testVoltage':textBox_testVoltage,'vc_returnVoltage':textBox_returnVoltage,
+                                            'runMode':runMode_togglebtns, 'injected_current_plot': injected_current_plot_value,
+                                                      'gating_plot':gating_plot_value,
+                                                      'cond_dens_plot':cond_dens_plot_value,
+                                                      'current_plot':current_plot_value,
+                                                      'memb_pot_plot':memb_pot_plot_value})
 
     #display the widgets and plot area
     display(modelInputs,wid_plotArea)