diff --git a/hp4155/ADU for double gate devices-test/double_gate_ADU_interface.ipynb b/hp4155/ADU for double gate devices-test/double_gate_ADU_interface.ipynb
index faa36259919455bea1477b2715bf94ac309e80f5..22cd01eabc1c599f8abaedc2867943a5ab1ca695 100644
--- a/hp4155/ADU for double gate devices-test/double_gate_ADU_interface.ipynb
+++ b/hp4155/ADU for double gate devices-test/double_gate_ADU_interface.ipynb
@@ -9,7 +9,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "3cf0faebf88c45f49e903b375a7612b4",
+ "model_id": "9700a16182f3422a84b473ab09c2baff",
"version_major": 2,
"version_minor": 0
},
@@ -23,7 +23,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "e0c490c25be140d78098258f89fe2c7c",
+ "model_id": "350565f5a66047899593abab5701193e",
"version_major": 2,
"version_minor": 0
},
@@ -37,7 +37,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "c2beee9304734535a652146df18f2fb1",
+ "model_id": "e493c1fbe9174d9fa4e053e02fb8a0e1",
"version_major": 2,
"version_minor": 0
},
@@ -51,7 +51,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "17d7289741454a9f9f701f7b47ac38ce",
+ "model_id": "bb7847e53b864cbd9f4086ca613e5d13",
"version_major": 2,
"version_minor": 0
},
@@ -65,7 +65,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "55e13e645e6e429dade45a3f97226e0b",
+ "model_id": "40bc192190b5448caded4711d9d5e267",
"version_major": 2,
"version_minor": 0
},
@@ -84,7 +84,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "1a0e956e-9d25-481e-b1ec-b03babcb4c19",
+ "id": "40d5f341-bc61-4698-8bdd-f99e94f4e325",
"metadata": {},
"outputs": [],
"source": []
diff --git a/hp4155/ADU for double gate devices-test/lib/help.py b/hp4155/ADU for double gate devices-test/lib/help.py
index 0723fd389877aa224646d90084b8dee169fcd371..265b43fd3a81f8a615c5ae8fdabb20102dbe1227 100644
--- a/hp4155/ADU for double gate devices-test/lib/help.py
+++ b/hp4155/ADU for double gate devices-test/lib/help.py
@@ -204,7 +204,7 @@ def graph_tool(params,device):
device.delete_axis("Y2")
device.del_user_functions()
- device.error()
+ device.clear_error_stack()
# How to define user functions correctly and not multiple times
plot_list = [params["PLOT"]["x"],params["PLOT"]["y1"],params["PLOT"]["y2"]]
@@ -227,27 +227,76 @@ def graph_tool(params,device):
# Now send the parameters in the tool
device.display_variable('X',params["PLOT"]["x"])
- device.axis_scale('X',params["PLOT"]["x_scale"])
+ device.error_occured()
+ device.axis_scale('X','LIN')
+ device.error_occured()
device.display_variable_min_max('X','MIN',params["PLOT"]["x_min"])
+ device.error_occured()
device.display_variable_min_max('X','MAX',params["PLOT"]["x_max"])
+ device.error_occured()
if params["PLOT"]["y1_scale"]=='LOG':
device.display_variable('Y1',"A"+params["PLOT"]["y1"])
else:
device.display_variable('Y1',params["PLOT"]["y1"])
+ device.error_occured()
device.axis_scale('Y1',params["PLOT"]["y1_scale"])
+ device.error_occured()
device.display_variable_min_max('Y1','MIN',params["PLOT"]["y1_min"])
+ device.error_occured()
device.display_variable_min_max('Y1','MAX',params["PLOT"]["y1_max"])
+ device.error_occured()
if params["PLOT"]["y2"]!= "None":
if params["PLOT"]["y2_scale"]=='LOG':
device.display_variable('Y2',"A"+params["PLOT"]["y2"])
else:
device.display_variable('Y2',params["PLOT"]["y2"])
+ device.error_occured()
device.axis_scale('Y2',params["PLOT"]["y2_scale"])
+ device.error_occured()
device.display_variable_min_max('Y2','MIN',params["PLOT"]["y2_min"])
device.display_variable_min_max('Y2','MAX',params["PLOT"]["y2_max"])
+ device.error_occured()
+
+
+
+#plot software functions
+def values_to_plot(params,device):
+ #plotted variables as they are named in the tool
+ #return the plotted data for easier configuration
+ plotted_variables = {'X':device.get_axis_variable('X'),'Y1': device.get_axis_variable('Y1')}
+
+ if params["PLOT"]["y2"]!= "None":
+ plotted_variables['Y2']= device.get_axis_variable('Y2')
+
+ plot_values ={}
+ for axis,variable in plotted_variables.items():
+ plot_values.setdefault(axis,np.array(device.return_values(variable)))
+
+ return plot_values
+
+def set_axes_labels(plot_list):
+ axes_labels=[]
+
+ # define the axes labels similarly to the user functions
+ for element in plot_list:
+ if element != "None": #only the last one
+ if element.startswith("V"):
+ label = f"{element} (V)"
+ elif element.startswith('I') and element.endswith('mm'):
+ label = f"{element[:-2]} (uA/um)"
+ else: # regular I
+ label = f"{element} (A)"
+ axes_labels.append(label)
+
+ return axes_labels
+
+
+
+
+
diff --git a/hp4155/ADU for double gate devices-test/lib/measurements.py b/hp4155/ADU for double gate devices-test/lib/measurements.py
index 214ebdc82aba6540b06cbce0a41251657d0436f2..b32e48688f1f4aa8525852f748482f8d7856db90 100644
--- a/hp4155/ADU for double gate devices-test/lib/measurements.py
+++ b/hp4155/ADU for double gate devices-test/lib/measurements.py
@@ -26,99 +26,44 @@ def Transfer_VTG(device,params):
norm = normalization_factor(params["SAMPLE"]["width"])
points = params["VAR2"]["points"]
+ try:
+ device.setup_smu(params["MAP"]['TG'],params["SMU_T"])
+ device.setup_smu(params["MAP"]['D'],params["SMU_D"])
+ device.setup_smu(params["MAP"]['BG'],params["SMU_B"])
+ device.setup_smu(params["MAP"]['S'],params["SMU_S"])
- device.setup_smu(params["MAP"]['TG'],params["SMU_T"])
- device.setup_smu(params["MAP"]['D'],params["SMU_D"])
- device.setup_smu(params["MAP"]['BG'],params["SMU_B"])
- device.setup_smu(params["MAP"]['S'],params["SMU_S"])
-
- device.setup_var1(params["VAR1"])
- device.setup_var2(params["VAR2"])
-
- device.integration_time(params["INTEGRATION"])
-
- variables_list =["VTG","ITG","VDS","ID"]
- device.variables_to_save(variables_list)
-
- plotted_variables = graph_tool(params,device)
-
- device.single_measurement()
- while device.operation_completed()==False:
- pass
-
- device.autoscaling()
-
- values = dict([(variable,device.return_values(variable)) for variable in variables_list])
- df = get_dataframe_from_results(values)
+ device.setup_var1(params["VAR1"])
+ device.setup_var2(params["VAR2"])
+
+ device.integration_time(params["INTEGRATION"])
+
+ variables_list =["VTG","ITG","VDS","ID"]
+ device.variables_to_save(variables_list)
+
+ try:
+ plotted_variables = graph_tool(params,device)
+ except Exception as e:
+ error_box(e)
+ return
+
+
+ device.single_measurement()
+ while device.operation_completed()==False:
+ pass
+
+ device.autoscaling()
+ device.error_occured()
+
+ values = dict([(variable,device.return_values(variable)) for variable in variables_list])
+ df = get_dataframe_from_results(values)
+ except Exception as e:
+ error_box(e)
+ return
# Append the normalized current
df["IDmm/uA/um"]= (df["ID/A"].apply(lambda x: Decimal(str(x))*Decimal(str(norm)))).astype('float')
df["ITGmm/uA/um"]= (df["ITG/A"].apply(lambda x: Decimal(str(x))*Decimal(str(norm)))).astype('float')
- #plotted variables as they are named in the tool
- #return the plotted data for easier configuration
- plotted_variables = {'X':device.get_axis_variable('X'),'Y1': device.get_axis_variable('Y1')}
-
- if params["PLOT"]["y2"]!= "None":
- plotted_variables['Y2']= device.get_axis_variable('Y2')
-
- plot_values ={}
- for axis,variable in plotted_variables.items():
- plot_values.setdefault(axis,np.array(device.return_values(variable)))
-
- # Plot user specified results
- fig,ax1= plt.subplots(figsize=(10,6),layout='constrained')
-
- plot_list = [params["PLOT"]["x"],params["PLOT"]["y1"],params["PLOT"]["y2"]]
- scale_list = [params["PLOT"]["x_scale"],params["PLOT"]["y1_scale"],params["PLOT"]["y2_scale"]]
- axes_labels = []
-
- if scale_list[0] == 'LOG':
- ax1.set_xscale('log')
-
- if scale_list[1]=='LOG':
- ax1.set_yscale('log')
-
- # define the axes labels similarly to the user functions
- for element in plot_list:
- if element != "None": #only the last one
- if element.startswith("V"):
- label = f"{element} (V)"
- elif element.startswith('I') and element.endswith('mm'):
- label = f"{element[:-2]} (uA/(um))"
- else: # regular I
- label = f"{element} (A)"
- axes_labels.append(label)
-
- #now set the labels
- ax1.set_xlabel(axes_labels[0])
- ax1.set_ylabel(axes_labels[1])
-
- x = np.split(plot_values['X'],points)
- y1 = np.split(plot_values['Y1'],points)
- labels =np.mean(np.array_split(df["VDS/V"],points),axis = 1) # VDS values for labels
-
- for i in range(points):
- ax1.plot(x[i],y1[i],label = f"VDS:{round(labels[i],3)} (V)")
-
- #add the second axis if applicable
- if plot_list[2]!= "None":
- ax2 = ax1.twinx()
- y2 = np.split(plot_values['Y2'],points)
-
- if scale_list[2]=='LOG':
- ax1.set_yscale('log')
-
- ax2.set_ylabel(axes_labels[2])
- for i in range(points):
- ax2.plot(x[i],y2[i])
-
- # Adding title
- fig.suptitle('Transfer Curve', fontweight ="bold")
- fig.legend(loc='outside right upper')
-
- display(fig)
-
# Save the results
default_filename = f"{params['SAMPLE']['sample']}_{params['SAMPLE']['field']}_{params['SAMPLE']['device']}_TOP_GATE_U.txt"
@@ -166,81 +111,18 @@ def Transfer_VTG(device,params):
df.to_csv(file,sep=" ",mode='a')
- #save plot if checked
- if params["SAMPLE"]['save_fig'] == True:
- filename= os.path.splitext(file)[0]
- fig.savefig(filename+'.png')
-
-# Transfer only VBG
-def Transfer_VBG(device,params):
- device.setup_smu(params["MAP"]['TG'],params["SMU_T"])
- device.setup_smu(params["MAP"]['D'],params["SMU_D"])
- device.setup_smu(params["MAP"]['BG'],params["SMU_B"])
- device.setup_smu(params["MAP"]['S'],params["SMU_S"])
-
-
- device.setup_var1(params["VAR1"])
- device.setup_var2(params["VAR2"])
-
- device.integration_time(params["INTEGRATION"])
-
-
- variables_list =["VBG","IBG","VDS","ID"]
- device.variables_to_save(variables_list)
-
- plotted_variables = graph_tool(params,device)
-
- device.single_measurement()
- while device.operation_completed()==False:
- pass
- device.autoscaling()
-
- values = dict([(variable,device.return_values(variable)) for variable in variables_list])
- df = get_dataframe_from_results(values)
-
- # calculate normalization factor
- norm = normalization_factor(params["SAMPLE"]["width"])
- points = params["VAR2"]["points"]
-
- # Append the normalized current
- df["IDmm/uA/um"]= (df["ID/A"].apply(lambda x: Decimal(str(x))*Decimal(str(norm)))).astype('float')
- df["IBGmm/uA/um"]= (df["IBG/A"].apply(lambda x: Decimal(str(x))*Decimal(str(norm)))).astype('float')
-
- #plotted variables as they are named in the tool
- #return the plotted data for easier configuration
- plotted_variables = {'X':device.get_axis_variable('X'),'Y1': device.get_axis_variable('Y1')}
-
- if params["PLOT"]["y2"]!= "None":
- plotted_variables['Y2']= device.get_axis_variable('Y2')
+ plot_values = values_to_plot(params,device)
- plot_values ={}
- for axis,variable in plotted_variables.items():
- plot_values.setdefault(axis,np.array(device.return_values(variable)))
-
# Plot user specified results
fig,ax1= plt.subplots(figsize=(10,6),layout='constrained')
plot_list = [params["PLOT"]["x"],params["PLOT"]["y1"],params["PLOT"]["y2"]]
- scale_list = [params["PLOT"]["x_scale"],params["PLOT"]["y1_scale"],params["PLOT"]["y2_scale"]]
- axes_labels = []
-
- if scale_list[0] == 'LOG':
- ax1.set_xscale('log')
+ scale_list =['LIN',params["PLOT"]["y1_scale"],params["PLOT"]["y2_scale"]]
+ axes_labels = set_axes_labels(plot_list)
if scale_list[1]=='LOG':
ax1.set_yscale('log')
- # define the axes labels similarly to the user functions
- for element in plot_list:
- if element != "None": #only the last one
- if element.startswith("V"):
- label = f"{element} (V)"
- elif element.startswith('I') and element.endswith('mm'):
- label = f"{element[:-2]} (uA/(um))"
- else: # regular I
- label = f"{element} (A)"
- axes_labels.append(label)
-
#now set the labels
ax1.set_xlabel(axes_labels[0])
ax1.set_ylabel(axes_labels[1])
@@ -251,26 +133,86 @@ def Transfer_VBG(device,params):
for i in range(points):
ax1.plot(x[i],y1[i],label = f"VDS:{round(labels[i],3)} (V)")
+
+ # Adding title
+ fig.suptitle('Transfer Curve', fontweight ="bold")
+ fig.legend(loc='outside right upper')
+ display(fig)
+ #save plot if checked
+ if params["SAMPLE"]['save_fig'] == True:
+ filename= os.path.splitext(file)[0]
+ fig.savefig(filename+'_Y1.png')
+
#add the second axis if applicable
if plot_list[2]!= "None":
- ax2 = ax1.twinx()
+ fig,ax2=plt.subplots(figsize=(10,6),layout='constrained')
y2 = np.split(plot_values['Y2'],points)
if scale_list[2]=='LOG':
- ax1.set_yscale('log')
+ ax2.set_yscale('log')
+
+ ax2.set_xlabel(axes_labels[0])
ax2.set_ylabel(axes_labels[2])
for i in range(points):
- ax2.plot(x[i],y2[i])
+ ax2.plot(x[i],y2[i],label = f"VDS:{round(labels[i],3)} (V)")
+
+ # Adding title
+ fig.suptitle('Transfer Curve', fontweight ="bold")
+ fig.legend(loc='outside right upper')
+
+ display(fig)
+ #save plot if checked
+ if params["SAMPLE"]['save_fig'] == True:
+ filename= os.path.splitext(file)[0]
+ fig.savefig(filename+'_Y2.png')
- # Adding title
- fig.suptitle('Transfer Curve', fontweight ="bold")
- fig.legend(loc='outside right upper')
- display(fig)
+# Transfer only VBG
+def Transfer_VBG(device,params):
+ try:
+ device.setup_smu(params["MAP"]['TG'],params["SMU_T"])
+ device.setup_smu(params["MAP"]['D'],params["SMU_D"])
+ device.setup_smu(params["MAP"]['BG'],params["SMU_B"])
+ device.setup_smu(params["MAP"]['S'],params["SMU_S"])
+
+
+ device.setup_var1(params["VAR1"])
+ device.setup_var2(params["VAR2"])
+
+ device.integration_time(params["INTEGRATION"])
+
+
+ variables_list =["VBG","IBG","VDS","ID"]
+ device.variables_to_save(variables_list)
+ try:
+ plotted_variables = graph_tool(params,device)
+ except Exception as e:
+ error_box(e)
+ return
+
+ device.single_measurement()
+ while device.operation_completed()==False:
+ pass
+ device.autoscaling()
+ device.error_occured()
+
+ values = dict([(variable,device.return_values(variable)) for variable in variables_list])
+ df = get_dataframe_from_results(values)
+
+ except Exception as e:
+ error_box(e)
+ return
+ # calculate normalization factor
+ norm = normalization_factor(params["SAMPLE"]["width"])
+ points = params["VAR2"]["points"]
- # Save the results
+ # Append the normalized current
+ df["IDmm/uA/um"]= (df["ID/A"].apply(lambda x: Decimal(str(x))*Decimal(str(norm)))).astype('float')
+ df["IBGmm/uA/um"]= (df["IBG/A"].apply(lambda x: Decimal(str(x))*Decimal(str(norm)))).astype('float')
+
+ # Save the results
default_filename = f"{params['SAMPLE']['sample']}_{params['SAMPLE']['field']}_{params['SAMPLE']['device']}_BACK_GATE_U.txt"
root = tk.Tk()
@@ -321,88 +263,18 @@ def Transfer_VBG(device,params):
df.to_csv(file,sep=" ",mode='a')
- if params["SAMPLE"]["save_fig"] == True:
- filename= os.path.splitext(file)[0]
- fig.savefig(filename+'.png')
-
-def Transfer_BOTH(device,params):
- smu_t = device.smu_dict()
- smu_t.update(vname = 'VTG',iname='ITG',mode = 'V',func='VAR1')
-
- smu_b = device.smu_dict()
- smu_b.update(vname = 'VBG',iname='IBG',mode = 'V',func = 'VARD')
-
- device.setup_smu(params["MAP"]['TG'],params["SMU_T"])
- device.setup_smu(params["MAP"]['D'],params["SMU_D"])
- device.setup_smu(params["MAP"]['BG'],params["SMU_B"])
- device.setup_smu(params["MAP"]['S'],params["SMU_S"])
-
-
- device.setup_var1(params["VAR1"])
- device.setup_var2(params["VAR2"])
- device.setup_vard(params["VARD"])
-
- device.integration_time(params["INTEGRATION"])
-
- variables_list =["VBG","IBG","VDS","ID","VTG","ITG"]
- device.variables_to_save(variables_list)
-
- plotted_variables = graph_tool(params,device)
-
- device.single_measurement()
- while device.operation_completed()==False:
- pass
-
- device.autoscaling()
-
- values = dict([(variable,device.return_values(variable)) for variable in variables_list])
- df = get_dataframe_from_results(values)
-
- # calculate normalization factor
- norm = normalization_factor(params["SAMPLE"]["width"])
- points = params["VAR2"]["points"]
-
- # Append the normalized current
- df["IDmm/uA/um"]= (df["ID/A"].apply(lambda x: Decimal(str(x))*Decimal(str(norm)))).astype('float')
- df["IBGmm/uA/um"]= (df["IBG/A"].apply(lambda x: Decimal(str(x))*Decimal(str(norm)))).astype('float')
- df["ITGmm/uA/um"]= (df["ITG/A"].apply(lambda x: Decimal(str(x))*Decimal(str(norm)))).astype('float')
-
-
- #plotted variables as they are named in the tool
- #return the plotted data for easier configuration
- plotted_variables = {'X':device.get_axis_variable('X'),'Y1': device.get_axis_variable('Y1')}
-
- if params["PLOT"]["y2"]!= "None":
- plotted_variables['Y2']= device.get_axis_variable('Y2')
+ plot_values = values_to_plot(params,device)
- plot_values ={}
- for axis,variable in plotted_variables.items():
- plot_values.setdefault(axis,device.return_values(variable))
-
# Plot user specified results
fig,ax1= plt.subplots(figsize=(10,6),layout='constrained')
plot_list = [params["PLOT"]["x"],params["PLOT"]["y1"],params["PLOT"]["y2"]]
- scale_list = [params["PLOT"]["x_scale"],params["PLOT"]["y1_scale"],params["PLOT"]["y2_scale"]]
- axes_labels = []
-
- if scale_list[0] == 'LOG':
- ax1.set_xscale('log')
+ scale_list =['LIN',params["PLOT"]["y1_scale"],params["PLOT"]["y2_scale"]]
+ axes_labels = set_axes_labels(plot_list)
if scale_list[1]=='LOG':
ax1.set_yscale('log')
- # define the axes labels similarly to the user functions
- for element in plot_list:
- if element != "None": #only the last one
- if element.startswith("V"):
- label = f"{element} (V)"
- elif element.startswith('I') and element.endswith('mm'):
- label = f"{element[:-2]} (uA/(um))"
- else: # regular I
- label = f"{element} (A)"
- axes_labels.append(label)
-
#now set the labels
ax1.set_xlabel(axes_labels[0])
ax1.set_ylabel(axes_labels[1])
@@ -413,24 +285,94 @@ def Transfer_BOTH(device,params):
for i in range(points):
ax1.plot(x[i],y1[i],label = f"VDS:{round(labels[i],3)} (V)")
+
+ # Adding title
+ fig.suptitle('Transfer Curve', fontweight ="bold")
+ fig.legend(loc='outside right upper')
+ display(fig)
+ #save plot if checked
+ if params["SAMPLE"]['save_fig'] == True:
+ filename= os.path.splitext(file)[0]
+ fig.savefig(filename+'_Y1.png')
+
#add the second axis if applicable
if plot_list[2]!= "None":
- ax2 = ax1.twinx()
+ fig,ax2=plt.subplots(figsize=(10,6),layout='constrained')
y2 = np.split(plot_values['Y2'],points)
if scale_list[2]=='LOG':
- ax1.set_yscale('log')
+ ax2.set_yscale('log')
+
+ ax2.set_xlabel(axes_labels[0])
ax2.set_ylabel(axes_labels[2])
for i in range(points):
- ax2.plot(x[i],y2[i])
+ ax2.plot(x[i],y2[i],label = f"VDS:{round(labels[i],3)} (V)")
+
+ # Adding title
+ fig.suptitle('Transfer Curve', fontweight ="bold")
+ fig.legend(loc='outside right upper')
+
+ display(fig)
+ #save plot if checked
+ if params["SAMPLE"]['save_fig'] == True:
+ filename= os.path.splitext(file)[0]
+ fig.savefig(filename+'_Y2.png')
- # Adding title
- fig.suptitle('Transfer Curve', fontweight ="bold")
- fig.legend(loc='outside right upper')
+
+def Transfer_BOTH(device,params):
+ try:
+ smu_t = device.smu_dict()
+ smu_t.update(vname = 'VTG',iname='ITG',mode = 'V',func='VAR1')
+
+ smu_b = device.smu_dict()
+ smu_b.update(vname = 'VBG',iname='IBG',mode = 'V',func = 'VARD')
+
+ device.setup_smu(params["MAP"]['TG'],params["SMU_T"])
+ device.setup_smu(params["MAP"]['D'],params["SMU_D"])
+ device.setup_smu(params["MAP"]['BG'],params["SMU_B"])
+ device.setup_smu(params["MAP"]['S'],params["SMU_S"])
+
+
+ device.setup_var1(params["VAR1"])
+ device.setup_var2(params["VAR2"])
+ device.setup_vard(params["VARD"])
+
+ device.integration_time(params["INTEGRATION"])
+
+ variables_list =["VBG","IBG","VDS","ID","VTG","ITG"]
+ device.variables_to_save(variables_list)
+
+ try:
+ plotted_variables = graph_tool(params,device)
+ except Exception as e:
+ error_box(e)
+ return
+
+
+ device.single_measurement()
+ while device.operation_completed()==False:
+ pass
+
+ device.autoscaling()
+ device.error_occured()
+
+ values = dict([(variable,device.return_values(variable)) for variable in variables_list])
+ df = get_dataframe_from_results(values)
+ except Exception as e:
+ error_box(e)
+ return
+
+ # calculate normalization factor
+ norm = normalization_factor(params["SAMPLE"]["width"])
+ points = params["VAR2"]["points"]
+
+ # Append the normalized current
+ df["IDmm/uA/um"]= (df["ID/A"].apply(lambda x: Decimal(str(x))*Decimal(str(norm)))).astype('float')
+ df["IBGmm/uA/um"]= (df["IBG/A"].apply(lambda x: Decimal(str(x))*Decimal(str(norm)))).astype('float')
+ df["ITGmm/uA/um"]= (df["ITG/A"].apply(lambda x: Decimal(str(x))*Decimal(str(norm)))).astype('float')
- display(fig)
# Save the results
default_filename = f"{params['SAMPLE']['sample']}_{params['SAMPLE']['field']}_{params['SAMPLE']['device']}_BOTH_GATES_U.txt"
@@ -488,112 +430,109 @@ def Transfer_BOTH(device,params):
f.write("\nResults\n")
df.to_csv(file,sep=" ",mode='a')
- if params["SAMPLE"]["save_fig"] == True:
- filename= os.path.splitext(file)[0]
- fig.savefig(filename+'.png')
-
-# Output with VTG
-def Output_VTG(device,params):
- device.setup_smu(params['MAP']['TG'],params["SMU_T"])
- device.setup_smu(params['MAP']['D'],params["SMU_D"])
- device.setup_smu(params['MAP']['BG'],params["SMU_B"])
- device.setup_smu(params['MAP']['S'],params["SMU_S"])
-
- device.setup_var1(params["VAR1"])
- device.setup_var2(params["VAR2"])
-
- device.integration_time(params["INTEGRATION"])
-
- variables_list = ['VDS','ID','VTG','ITG']
- device.variables_to_save(variables_list)
-
- plotted_variables = graph_tool(params,device)
-
- device.single_measurement()
-
- while device.operation_completed()==False:
- pass
-
- device.autoscaling()
+ plot_values = values_to_plot(params,device)
- values = dict([(variable,device.return_values(variable)) for variable in variables_list])
- df = get_dataframe_from_results(values)
-
- # plot results
-
- # calculate normalization factor
- norm = normalization_factor(params['SAMPLE']["width"])
- points = params["VAR2"]["points"]
-
- # Append the normalized current
- df["IDmm/uA/um"]= (df["ID/A"].apply(lambda x: Decimal(str(x))*Decimal(str(norm)))).astype('float')
- df["ITGmm/uA/um"]= (df["ITG/A"].apply(lambda x: Decimal(str(x))*Decimal(str(norm)))).astype('float')
-
- #plotted variables as they are named in the tool
- #return the plotted data for easier configuration
- plotted_variables = {'X':device.get_axis_variable('X'),'Y1': device.get_axis_variable('Y1')}
-
- if params["PLOT"]["y2"]!= "None":
- plotted_variables['Y2']= device.get_axis_variable('Y2')
-
- plot_values ={}
- for axis,variable in plotted_variables.items():
- plot_values.setdefault(axis,np.array(device.return_values(variable)))
-
# Plot user specified results
fig,ax1= plt.subplots(figsize=(10,6),layout='constrained')
plot_list = [params["PLOT"]["x"],params["PLOT"]["y1"],params["PLOT"]["y2"]]
- scale_list = [params["PLOT"]["x_scale"],params["PLOT"]["y1_scale"],params["PLOT"]["y2_scale"]]
- axes_labels = []
-
- if scale_list[0] == 'LOG':
- ax1.set_xscale('log')
+ scale_list =['LIN',params["PLOT"]["y1_scale"],params["PLOT"]["y2_scale"]]
+ axes_labels = set_axes_labels(plot_list)
if scale_list[1]=='LOG':
ax1.set_yscale('log')
- # define the axes labels similarly to the user functions
- for element in plot_list:
- if element != "None": #only the last one
- if element.startswith("V"):
- label = f"{element} (V)"
- elif element.startswith('I') and element.endswith('mm'):
- label = f"{element[:-2]} (uA/(um))"
- else: # regular I
- label = f"{element} (A)"
- axes_labels.append(label)
-
#now set the labels
ax1.set_xlabel(axes_labels[0])
ax1.set_ylabel(axes_labels[1])
x = np.split(plot_values['X'],points)
y1 = np.split(plot_values['Y1'],points)
- labels =np.mean(np.array_split(df["VTG/V"],points),axis = 1) # VDS values for labels
+ labels =np.mean(np.array_split(df["VDS/V"],points),axis = 1) # VDS values for labels
for i in range(points):
- ax1.plot(x[i],y1[i],label = f"VTG:{round(labels[i],3)} (V)")
+ ax1.plot(x[i],y1[i],label = f"VDS:{round(labels[i],3)} (V)")
+
+ # Adding title
+ fig.suptitle('Transfer Curve', fontweight ="bold")
+ fig.legend(loc='outside right upper')
+ display(fig)
+ #save plot if checked
+ if params["SAMPLE"]['save_fig'] == True:
+ filename= os.path.splitext(file)[0]
+ fig.savefig(filename+'_Y1.png')
+
#add the second axis if applicable
if plot_list[2]!= "None":
- ax2 = ax1.twinx()
+ fig,ax2=plt.subplots(figsize=(10,6),layout='constrained')
y2 = np.split(plot_values['Y2'],points)
if scale_list[2]=='LOG':
- ax1.set_yscale('log')
+ ax2.set_yscale('log')
+
+ ax2.set_xlabel(axes_labels[0])
ax2.set_ylabel(axes_labels[2])
for i in range(points):
- ax2.plot(x[i],y2[i])
+ ax2.plot(x[i],y2[i],label = f"VDS:{round(labels[i],3)} (V)")
+
+ # Adding title
+ fig.suptitle('Transfer Curve', fontweight ="bold")
+ fig.legend(loc='outside right upper')
+
+ display(fig)
+ #save plot if checked
+ if params["SAMPLE"]['save_fig'] == True:
+ filename= os.path.splitext(file)[0]
+ fig.savefig(filename+'Y2.png')
- # Adding title
- fig.suptitle('Output Curve', fontweight ="bold")
- fig.legend(loc='outside right upper')
+# Output with VTG
+def Output_VTG(device,params):
+ try:
+ device.setup_smu(params['MAP']['TG'],params["SMU_T"])
+ device.setup_smu(params['MAP']['D'],params["SMU_D"])
+ device.setup_smu(params['MAP']['BG'],params["SMU_B"])
+ device.setup_smu(params['MAP']['S'],params["SMU_S"])
+
+ device.setup_var1(params["VAR1"])
+ device.setup_var2(params["VAR2"])
+
+ device.integration_time(params["INTEGRATION"])
+
+ variables_list = ['VDS','ID','VTG','ITG']
+ device.variables_to_save(variables_list)
+
+ try:
+ plotted_variables = graph_tool(params,device)
+ except Exception as e:
+ error_box(e)
+ return
+
+ device.single_measurement()
+
+ while device.operation_completed()==False:
+ pass
+
+ device.autoscaling()
+ device.error_occured()
+
+ values = dict([(variable,device.return_values(variable)) for variable in variables_list])
+ df = get_dataframe_from_results(values)
+ except Exception as e:
+ error_box(e)
+ return
- display(fig)
- #save Results
+ # plot results
+ # calculate normalization factor
+ norm = normalization_factor(params['SAMPLE']["width"])
+ points = params["VAR2"]["points"]
+
+ # Append the normalized current
+ df["IDmm/uA/um"]= (df["ID/A"].apply(lambda x: Decimal(str(x))*Decimal(str(norm)))).astype('float')
+ df["ITGmm/uA/um"]= (df["ITG/A"].apply(lambda x: Decimal(str(x))*Decimal(str(norm)))).astype('float')
+
# Save the results
default_filename = f"{params['SAMPLE']['sample']}_{params['SAMPLE']['field']}_{params['SAMPLE']['device']}_TOP_GATE_A.txt"
@@ -643,112 +582,113 @@ def Output_VTG(device,params):
f.write("\nResults\n")
df.to_csv(file,sep=" ",mode='a')
- if params["SAMPLE"]["save_fig"] == True:
- filename= os.path.splitext(file)[0]
- fig.savefig(filename+'.png')
-
-#Output VBG
-def Output_VBG(device,params):
- device.setup_smu(params["MAP"]['TG'],params["SMU_T"])
- device.setup_smu(params["MAP"]['D'],params["SMU_D"])
- device.setup_smu(params["MAP"]['BG'],params["SMU_B"])
- device.setup_smu(params["MAP"]['S'],params["SMU_S"])
-
-
- device.setup_var1(params["VAR1"])
- device.setup_var2(params["VAR2"])
-
- device.integration_time(params["INTEGRATION"])
-
- variables_list = ['VDS','ID','VBG','IBG']
- device.variables_to_save(variables_list)
-
- plotted_variables = graph_tool(params,device)
-
- device.single_measurement()
-
- while device.operation_completed()==False:
- pass
-
- device.autoscaling()
- values = dict([(variable,device.return_values(variable)) for variable in variables_list])
- df = get_dataframe_from_results(values)
-
- # plot results
-
- # calculate normalization factor
- norm = normalization_factor(params["SAMPLE"]["width"])
- points = params["VAR2"]["points"]
-
- # Append the normalized current
- df["IDmm/uA/um"]= (df["ID/A"].apply(lambda x: Decimal(str(x))*Decimal(str(norm)))).astype('float')
- df["IBGmm/uA/um"]= (df["IBG/A"].apply(lambda x: Decimal(str(x))*Decimal(str(norm)))).astype('float')
+ plot_values = values_to_plot(params,device)
- #plotted variables as they are named in the tool
- #return the plotted data for easier configuration
- plotted_variables = {'X':device.get_axis_variable('X'),'Y1': device.get_axis_variable('Y1')}
-
- if params["PLOT"]["y2"]!= "None":
- plotted_variables['Y2']= device.get_axis_variable('Y2')
-
- plot_values ={}
- for axis,variable in plotted_variables.items():
- plot_values.setdefault(axis,np.array(device.return_values(variable)))
-
# Plot user specified results
fig,ax1= plt.subplots(figsize=(10,6),layout='constrained')
plot_list = [params["PLOT"]["x"],params["PLOT"]["y1"],params["PLOT"]["y2"]]
- scale_list = [params["PLOT"]["x_scale"],params["PLOT"]["y1_scale"],params["PLOT"]["y2_scale"]]
- axes_labels = []
-
- if scale_list[0] == 'LOG':
- ax1.set_xscale('log')
+ scale_list =['LIN',params["PLOT"]["y1_scale"],params["PLOT"]["y2_scale"]]
+ axes_labels = set_axes_labels(plot_list)
if scale_list[1]=='LOG':
ax1.set_yscale('log')
- # define the axes labels similarly to the user functions
- for element in plot_list:
- if element != "None": #only the last one
- if element.startswith("V"):
- label = f"{element} (V)"
- elif element.startswith('I') and element.endswith('mm'):
- label = f"{element[:-2]} (uA/(um))"
- else: # regular I
- label = f"{element} (A)"
- axes_labels.append(label)
-
#now set the labels
ax1.set_xlabel(axes_labels[0])
ax1.set_ylabel(axes_labels[1])
x = np.split(plot_values['X'],points)
y1 = np.split(plot_values['Y1'],points)
- labels =np.mean(np.array_split(df["VBG/V"],points),axis = 1) # VDS values for labels
+ labels =np.mean(np.array_split(df["VTG/V"],points),axis = 1) # VDS values for labels
for i in range(points):
- ax1.plot(x[i],y1[i],label = f"VBG:{round(labels[i],3)} (V)")
+ ax1.plot(x[i],y1[i],label = f"VTG:{round(labels[i],3)} (V)")
+
+ # Adding title
+ fig.suptitle('Output Curve', fontweight ="bold")
+ fig.legend(loc='outside right upper')
+ display(fig)
+ #save plot if checked
+ if params["SAMPLE"]['save_fig'] == True:
+ filename= os.path.splitext(file)[0]
+ fig.savefig(filename+'_Y1.png')
+
#add the second axis if applicable
if plot_list[2]!= "None":
- ax2 = ax1.twinx()
+ fig,ax2=plt.subplots(figsize=(10,6),layout='constrained')
y2 = np.split(plot_values['Y2'],points)
- if scale_list[2]=='LOG':
- ax1.set_yscale('log')
-
- ax2.set_ylabel(axes_labels[2])
- for i in range(points):
- ax2.plot(x[i],y2[i])
+ if scale_list[2]=='LOG':
+ ax2.set_yscale('log')
+
+ ax2.set_xlabel(axes_labels[0])
+
+ ax2.set_ylabel(axes_labels[2])
+ for i in range(points):
+ ax2.plot(x[i],y2[i],label = f"VTG:{round(labels[i],3)} (V)")
+
+ # Adding title
+ fig.suptitle('Output Curve', fontweight ="bold")
+ fig.legend(loc='outside right upper')
+
+ display(fig)
+ #save plot if checked
+ if params["SAMPLE"]['save_fig'] == True:
+ filename= os.path.splitext(file)[0]
+ fig.savefig(filename+'Y2.png')
+
+
+#Output VBG
+def Output_VBG(device,params):
+ try:
+ device.setup_smu(params["MAP"]['TG'],params["SMU_T"])
+ device.setup_smu(params["MAP"]['D'],params["SMU_D"])
+ device.setup_smu(params["MAP"]['BG'],params["SMU_B"])
+ device.setup_smu(params["MAP"]['S'],params["SMU_S"])
+
+
+ device.setup_var1(params["VAR1"])
+ device.setup_var2(params["VAR2"])
+
+ device.integration_time(params["INTEGRATION"])
+
+ variables_list = ['VDS','ID','VBG','IBG']
+ device.variables_to_save(variables_list)
+
+ try:
+ plotted_variables = graph_tool(params,device)
+ except Exception as e:
+ error_box(e)
+ return
+
+
+ device.single_measurement()
+
+ while device.operation_completed()==False:
+ pass
+
+ device.autoscaling()
+ device.error_occured()
+
+ values = dict([(variable,device.return_values(variable)) for variable in variables_list])
+ df = get_dataframe_from_results(values)
+ except Exception as e:
+ error_box(e)
+ return
- # Adding title
- fig.suptitle('Output Curve', fontweight ="bold")
- fig.legend(loc='outside right upper')
+ # plot results
+
+ # calculate normalization factor
+ norm = normalization_factor(params["SAMPLE"]["width"])
+ points = params["VAR2"]["points"]
+
+ # Append the normalized current
+ df["IDmm/uA/um"]= (df["ID/A"].apply(lambda x: Decimal(str(x))*Decimal(str(norm)))).astype('float')
+ df["IBGmm/uA/um"]= (df["IBG/A"].apply(lambda x: Decimal(str(x))*Decimal(str(norm)))).astype('float')
- display(fig)
-
# Save the results
default_filename = f"{params['SAMPLE']['sample']}_{params['SAMPLE']['field']}_{params['SAMPLE']['device']}_BACK_GATE_A.txt"
@@ -797,123 +737,122 @@ def Output_VBG(device,params):
f.write("\nResults\n")
- df.to_csv(file,sep=" ",mode='a')
- if params["SAMPLE"]["save_fig"] == True:
- filename= os.path.splitext(file)[0]
- fig.savefig(filename+'.png')
-
-# Output both
-def Output_BOTH(device,params):
- device.setup_smu(params["MAP"]['TG'],params["SMU_T"])
- device.setup_smu(params["MAP"]['D'],params["SMU_D"])
- device.setup_smu(params["MAP"]['BG'],params["SMU_B"])
- device.setup_smu(params["MAP"]['S'],params["SMU_S"])
-
-
- device.setup_var1(params["VAR1"])
- device.setup_var2(params["VAR2"])
-
-
- device.integration_time(params["INTEGRATION"])
- plotted_variables = graph_tool(params,device)
-
-
- variables_list = ['VDS','ID','VBG','IBG','VTG','ITG']
- device.variables_to_save(variables_list)
- for i , value in enumerate(params["VAR3"]['values']):
- cons = device.cons_smu_dict()
- cons.update(comp = params['VAR3']['comp'],value = value)
- device.setup_cons_smu(params['MAP']['BG'],cons)
+ plot_values = values_to_plot(params,device)
- if i == 0:
- device.single_measurement()
- else:
- device.append_measurement()
-
- while device.operation_completed()==False:
- pass
- device.autoscaling()
-
- values = dict([(variable,device.return_values(variable)) for variable in variables_list])
- df = get_dataframe_from_results(values)
-
- # plot results
- points = params["VAR2"]['points']*params["VAR3"]["points"] # number of curves
-
- # calculate normalization factor
- norm = normalization_factor(params["SAMPLE"]["width"])
-
- # Append the normalized current
- df["IDmm/uA/um"]= (df["ID/A"].apply(lambda x: Decimal(str(x))*Decimal(str(norm)))).astype('float')
- df["IBGmm/uA/um"]= (df["IBG/A"].apply(lambda x: Decimal(str(x))*Decimal(str(norm)))).astype('float')
- df["ITGmm/uA/um"]= (df["ITG/A"].apply(lambda x: Decimal(str(x))*Decimal(str(norm)))).astype('float')
-
- #plotted variables as they are named in the tool
- #return the plotted data for easier configuration
- plotted_variables = {'X':device.get_axis_variable('X'),'Y1': device.get_axis_variable('Y1')}
-
- if params["PLOT"]["y2"]!= "None":
- plotted_variables['Y2']= device.get_axis_variable('Y2')
-
- plot_values ={}
- for axis,variable in plotted_variables.items():
- plot_values.setdefault(axis,np.array(device.return_values(variable)))
-
# Plot user specified results
fig,ax1= plt.subplots(figsize=(10,6),layout='constrained')
plot_list = [params["PLOT"]["x"],params["PLOT"]["y1"],params["PLOT"]["y2"]]
- scale_list = [params["PLOT"]["x_scale"],params["PLOT"]["y1_scale"],params["PLOT"]["y2_scale"]]
- axes_labels = []
-
- if scale_list[0] == 'LOG':
- ax1.set_xscale('log')
+ scale_list =['LIN',params["PLOT"]["y1_scale"],params["PLOT"]["y2_scale"]]
+ axes_labels = set_axes_labels(plot_list)
if scale_list[1]=='LOG':
ax1.set_yscale('log')
- # define the axes labels similarly to the user functions
- for element in plot_list:
- if element != "None": #only the last one
- if element.startswith("V"):
- label = f"{element} (V)"
- elif element.startswith('I') and element.endswith('mm'):
- label = f"{element[:-2]} (uA/(um))"
- else: # regular I
- label = f"{element} (A)"
- axes_labels.append(label)
-
#now set the labels
ax1.set_xlabel(axes_labels[0])
ax1.set_ylabel(axes_labels[1])
x = np.split(plot_values['X'],points)
y1 = np.split(plot_values['Y1'],points)
- labels_VBG =np.mean(np.array_split(df["VBG/V"],points),axis = 1) # VBG values for labels
- labels_VTG = np.mean(np.array_split(df["VTG/V"],points),axis = 1)
+ labels =np.mean(np.array_split(df["VBG/V"],points),axis = 1) # VDS values for labels
for i in range(points):
- ax1.plot(x[i],y1[i],label = f"VBG:{round(labels_VBG[i],3)} V , VTG:{round(labels_VTG[i],3)} V")
+ ax1.plot(x[i],y1[i],label = f"VBG:{round(labels[i],3)} (V)")
+
+ # Adding title
+ fig.suptitle('Output Curve', fontweight ="bold")
+ fig.legend(loc='outside right upper')
+ display(fig)
+ #save plot if checked
+ if params["SAMPLE"]['save_fig'] == True:
+ filename= os.path.splitext(file)[0]
+ fig.savefig(filename+'_Y1.png')
+
#add the second axis if applicable
if plot_list[2]!= "None":
- ax2 = ax1.twinx()
+ fig,ax2=plt.subplots(figsize=(10,6),layout='constrained')
y2 = np.split(plot_values['Y2'],points)
if scale_list[2]=='LOG':
- ax1.set_yscale('log')
+ ax2.set_yscale('log')
+
+ ax2.set_xlabel(axes_labels[0])
ax2.set_ylabel(axes_labels[2])
for i in range(points):
- ax2.plot(x[i],y2[i])
+ ax2.plot(x[i],y2[i],label = f"VBG:{round(labels[i],3)} (V)")
+
+ # Adding title
+ fig.suptitle('Output Curve', fontweight ="bold")
+ fig.legend(loc='outside right upper')
+
+ display(fig)
+ #save plot if checked
+ if params["SAMPLE"]['save_fig'] == True:
+ filename= os.path.splitext(file)[0]
+ fig.savefig(filename+'Y2.png')
+
+# Output both
+def Output_BOTH(device,params):
+ try:
+ device.setup_smu(params["MAP"]['TG'],params["SMU_T"])
+ device.setup_smu(params["MAP"]['D'],params["SMU_D"])
+ device.setup_smu(params["MAP"]['BG'],params["SMU_B"])
+ device.setup_smu(params["MAP"]['S'],params["SMU_S"])
+
+
+ device.setup_var1(params["VAR1"])
+ device.setup_var2(params["VAR2"])
+
+
+ device.integration_time(params["INTEGRATION"])
+ try:
+ plotted_variables = graph_tool(params,device)
+ except Exception as e:
+ error_box(e)
+ return
+
+
+
+ variables_list = ['VDS','ID','VBG','IBG','VTG','ITG']
+ device.variables_to_save(variables_list)
+
+ for i , value in enumerate(params["VAR3"]['values']):
+ cons = device.cons_smu_dict()
+ cons.update(comp = params['VAR3']['comp'],value = value)
+ device.setup_cons_smu(params['MAP']['BG'],cons)
+
+ if i == 0:
+ device.single_measurement()
+ else:
+ device.append_measurement()
+
+ while device.operation_completed()==False:
+ pass
+ device.autoscaling()
+ device.error_occured()
+
+ values = dict([(variable,device.return_values(variable)) for variable in variables_list])
+ df = get_dataframe_from_results(values)
+ except Exception as e:
+ error_box(e)
+ return
- # Adding title
- fig.suptitle('Output Curve', fontweight ="bold")
- fig.legend(loc='outside right upper')
+ # plot results
+ points = params["VAR2"]['points']*params["VAR3"]["points"] # number of curves
- display(fig)
- # Save the results
+ # calculate normalization factor
+ norm = normalization_factor(params["SAMPLE"]["width"])
+
+ # Append the normalized current
+ df["IDmm/uA/um"]= (df["ID/A"].apply(lambda x: Decimal(str(x))*Decimal(str(norm)))).astype('float')
+ df["IBGmm/uA/um"]= (df["IBG/A"].apply(lambda x: Decimal(str(x))*Decimal(str(norm)))).astype('float')
+ df["ITGmm/uA/um"]= (df["ITG/A"].apply(lambda x: Decimal(str(x))*Decimal(str(norm)))).astype('float')
+
+ # Save the results
default_filename = f"{params['SAMPLE']['sample']}_{params['SAMPLE']['field']}_{params['SAMPLE']['device']}_BOTH_GATES_A.txt"
root = tk.Tk()
@@ -967,100 +906,105 @@ def Output_BOTH(device,params):
f.write("\nResults\n")
df.to_csv(file,sep=" ",mode='a')
- if params["SAMPLE"]["save_fig"] == True:
- filename= os.path.splitext(file)[0]
- fig.savefig(filename+'.png')
-
-def Gatediode_VTG(device,params):
- device.setup_smu(params["MAP"]['TG'],params["SMU_T"])
- device.smu_disable(params["MAP"]['BG'])
- device.setup_smu(params["MAP"]['S'],params["SMU_S"])
-
- device.setup_var1(params["VAR1"])
-
- device.integration_time(params["INTEGRATION"])
-
- variables_list = ['VTG','ITG']
- device.variables_to_save(variables_list)
- plotted_variables = graph_tool(params,device)
-
- device.single_measurement()
-
- while device.operation_completed()==False:
- pass
- device.autoscaling()
-
- values = dict([(variable,device.return_values(variable)) for variable in variables_list])
- df = get_dataframe_from_results(values)
-
- # calculate normalization factor
- norm = normalization_factor(params['SAMPLE']["width"])
-
- # Append the normalized current
- df["ITGmm/uA/um"]= (df["ITG/A"].apply(lambda x: Decimal(str(x))*Decimal(str(norm)))).astype('float')
- #plotted variables as they are named in the tool
- #return the plotted data for easier configuration
- plotted_variables = {'X':device.get_axis_variable('X'),'Y1': device.get_axis_variable('Y1')}
+ plot_values = values_to_plot(params,device)
- if params["PLOT"]["y2"]!= "None":
- plotted_variables['Y2']= device.get_axis_variable('Y2')
-
- plot_values ={}
- for axis,variable in plotted_variables.items():
- plot_values.setdefault(axis,device.return_values(variable))
-
# Plot user specified results
fig,ax1= plt.subplots(figsize=(10,6),layout='constrained')
plot_list = [params["PLOT"]["x"],params["PLOT"]["y1"],params["PLOT"]["y2"]]
- scale_list = [params["PLOT"]["x_scale"],params["PLOT"]["y1_scale"],params["PLOT"]["y2_scale"]]
- axes_labels = []
-
- if scale_list[0] == 'LOG':
- ax1.set_xscale('log')
+ scale_list =['LIN',params["PLOT"]["y1_scale"],params["PLOT"]["y2_scale"]]
+ axes_labels = set_axes_labels(plot_list)
if scale_list[1]=='LOG':
ax1.set_yscale('log')
- # define the axes labels similarly to the user functions
- for element in plot_list:
- if element != "None": #only the last one
- if element.startswith("V"):
- label = f"{element} (V)"
- elif element.startswith('I') and element.endswith('mm'):
- label = f"{element[:-2]} (uA/(um))"
- else: # regular I
- label = f"{element} (A)"
- axes_labels.append(label)
-
#now set the labels
ax1.set_xlabel(axes_labels[0])
ax1.set_ylabel(axes_labels[1])
- x = plot_values['X']
- y1 = plot_values['Y1']
+ x = np.split(plot_values['X'],points)
+ y1 = np.split(plot_values['Y1'],points)
+ labels_VTG =np.mean(np.array_split(df["VTG/V"],points),axis = 1) # VTG values for labels
+ labels_VBG = np.mean(np.array_split(df["VBG/V"],points),axis = 1) # VBG values for labels
- ax1.plot(x,y1)
+ for i in range(points):
+ ax1.plot(x[i],y1[i],label = f"VTG:{round(labels_VTG[i],3)} (V) , VBG:{round(labels_VBG[i],3)} (V)")
+
+ # Adding title
+ fig.suptitle('Output Curve', fontweight ="bold")
+ fig.legend(loc='outside right upper')
+ display(fig)
+ #save plot if checked
+ if params["SAMPLE"]['save_fig'] == True:
+ filename= os.path.splitext(file)[0]
+ fig.savefig(filename+'_Y1.png')
+
#add the second axis if applicable
if plot_list[2]!= "None":
- ax2 = ax1.twinx()
- y2 = plot_values['Y2']
+ fig,ax2=plt.subplots(figsize=(10,6),layout='constrained')
+ y2 = np.split(plot_values['Y2'],points)
if scale_list[2]=='LOG':
- ax1.set_yscale('log')
+ ax2.set_yscale('log')
+
+ ax2.set_xlabel(axes_labels[0])
ax2.set_ylabel(axes_labels[2])
- ax2.plot(x,y2)
+ for i in range(points):
+ ax2.plot(x[i],y2[i],label = f"VTG:{round(labels_VTG[i],3)} (V) , VBG:{round(labels_VBG[i],3)} (V)")
+
+ # Adding title
+ fig.suptitle('Output Curve', fontweight ="bold")
+ fig.legend(loc='outside right upper')
+
+ display(fig)
+ #save plot if checked
+ if params["SAMPLE"]['save_fig'] == True:
+ filename= os.path.splitext(file)[0]
+ fig.savefig(filename+'_Y2.png')
+
- # Adding title
- fig.suptitle('Gatediode Curve', fontweight ="bold")
- fig.legend(loc='outside right upper')
+def Gatediode_VTG(device,params):
+ try:
+ device.setup_smu(params["MAP"]['TG'],params["SMU_T"])
+ device.smu_disable(params["MAP"]['BG'])
+ device.setup_smu(params["MAP"]['S'],params["SMU_S"])
+
+ device.setup_var1(params["VAR1"])
+
+ device.integration_time(params["INTEGRATION"])
+
+ variables_list = ['VTG','ITG']
+ device.variables_to_save(variables_list)
+ try:
+ plotted_variables = graph_tool(params,device)
+ except Exception as e:
+ error_box(e)
+ return
+
+
+ device.single_measurement()
+
+ while device.operation_completed()==False:
+ pass
+ device.autoscaling()
+ device.error_occured()
+
+ values = dict([(variable,device.return_values(variable)) for variable in variables_list])
+ df = get_dataframe_from_results(values)
+ except Exception as e:
+ error_box(e)
+ return
- display(fig)
+ # calculate normalization factor
+ norm = normalization_factor(params['SAMPLE']["width"])
- # Save the results
+ # Append the normalized current
+ df["ITGmm/uA/um"]= (df["ITG/A"].apply(lambda x: Decimal(str(x))*Decimal(str(norm)))).astype('float')
+
+ # Save the results
default_filename = f"{params['SAMPLE']['sample']}_{params['SAMPLE']['field']}_{params['SAMPLE']['device']}_TOP_GATE_D.txt"
root = tk.Tk()
@@ -1103,77 +1047,18 @@ def Gatediode_VTG(device,params):
f.write("\nResults\n")
df.to_csv(file,sep=" ",mode='a')
- if params["SAMPLE"]["save_fig"] == True:
- filename= os.path.splitext(file)[0]
- fig.savefig(filename+'.png')
-
-def Gatediode_VBG(device,params):
- device.setup_smu(params["MAP"]['BG'],params["SMU_B"])
- device.smu_disable(params["MAP"]['TG'])
- device.setup_smu(params["MAP"]['S'],params["SMU_S"])
-
- device.setup_var1(params["VAR1"])
-
- device.integration_time(params["INTEGRATION"])
-
- variables_list = ['VBG','IBG']
- device.variables_to_save(variables_list)
-
-
- plotted_variables = graph_tool(params,device)
-
- device.single_measurement()
- while device.operation_completed()==False:
- pass
-
- device.autoscaling()
-
- values = dict([(variable,device.return_values(variable)) for variable in variables_list])
- df = get_dataframe_from_results(values)
-
- # plot results
-
- # calculate normalization factor
- norm = normalization_factor(params["SAMPLE"]["width"])
-
- # Append the normalized current
- df["IBGmm/uA/um"]= (df["IBG/A"].apply(lambda x: Decimal(str(x))*Decimal(str(norm)))).astype('float')
-
- #plotted variables as they are named in the tool
- #return the plotted data for easier configuration
- plotted_variables = {'X':device.get_axis_variable('X'),'Y1': device.get_axis_variable('Y1')}
-
- if params["PLOT"]["y2"]!= "None":
- plotted_variables['Y2']= device.get_axis_variable('Y2')
-
- plot_values ={}
- for axis,variable in plotted_variables.items():
- plot_values.setdefault(axis,device.return_values(variable))
-
- # Plot user specified results
+ plot_values = values_to_plot(params,device)
+ # Plot user specified results
fig,ax1= plt.subplots(figsize=(10,6),layout='constrained')
plot_list = [params["PLOT"]["x"],params["PLOT"]["y1"],params["PLOT"]["y2"]]
- scale_list = [params["PLOT"]["x_scale"],params["PLOT"]["y1_scale"],params["PLOT"]["y2_scale"]]
- axes_labels = []
-
- if scale_list[0] == 'LOG':
- ax1.set_xscale('log')
+ scale_list =['LIN',params["PLOT"]["y1_scale"],params["PLOT"]["y2_scale"]]
+ axes_labels =set_axes_labels(plot_list)
if scale_list[1]=='LOG':
ax1.set_yscale('log')
- # define the axes labels similarly to the user functions
- for element in plot_list:
- if element != "None": #only the last one
- if element.startswith("V"):
- label = f"{element} (V)"
- elif element.startswith('I') and element.endswith('mm'):
- label = f"{element[:-2]} (uA/(um))"
- else: # regular I
- label = f"{element} (A)"
- axes_labels.append(label)
#now set the labels
ax1.set_xlabel(axes_labels[0])
@@ -1183,23 +1068,76 @@ def Gatediode_VBG(device,params):
y1 = plot_values['Y1']
ax1.plot(x,y1)
+ fig.suptitle('Gatediode Curve', fontweight ="bold")
+ fig.legend(loc='outside right upper')
+ display(fig)
+
+ if params["SAMPLE"]["save_fig"] == True:
+ filename= os.path.splitext(file)[0]
+ fig.savefig(filename+'_Y1.png')
+
#add the second axis if applicable
if plot_list[2]!= "None":
- ax2 = ax1.twinx()
+ fig,ax2=plt.subplots(figsize=(10,6),layout='constrained')
y2 = plot_values['Y2']
if scale_list[2]=='LOG':
- ax1.set_yscale('log')
-
+ ax2.set_yscale('log')
+
+ ax2.set_xlabel(axes_labels[0])
ax2.set_ylabel(axes_labels[2])
ax2.plot(x,y2)
- # Adding title
- fig.suptitle('Gatediode Curve', fontweight ="bold")
- fig.legend(loc='outside right upper')
+ fig.suptitle('Gatediode Curve', fontweight ="bold")
+ fig.legend(loc='outside right upper')
+ display(fig)
- display(fig)
+ if params["SAMPLE"]["save_fig"] == True:
+ filename= os.path.splitext(file)[0]
+ fig.savefig(filename+'_Y2.png')
+
+
+def Gatediode_VBG(device,params):
+ try:
+ device.setup_smu(params["MAP"]['BG'],params["SMU_B"])
+ device.smu_disable(params["MAP"]['TG'])
+ device.setup_smu(params["MAP"]['S'],params["SMU_S"])
+
+ device.setup_var1(params["VAR1"])
+
+ device.integration_time(params["INTEGRATION"])
+
+ variables_list = ['VBG','IBG']
+ device.variables_to_save(variables_list)
+
+ try:
+ plotted_variables = graph_tool(params,device)
+ except Exception as e:
+ error_box(e)
+ return
+
+
+ device.single_measurement()
+ while device.operation_completed()==False:
+ pass
+
+ device.autoscaling()
+ device.error_occured()
+
+ values = dict([(variable,device.return_values(variable)) for variable in variables_list])
+ df = get_dataframe_from_results(values)
+ except Exception as e:
+ error_box(e)
+ return
+
+ # plot results
+
+ # calculate normalization factor
+ norm = normalization_factor(params["SAMPLE"]["width"])
+
+ # Append the normalized current
+ df["IBGmm/uA/um"]= (df["IBG/A"].apply(lambda x: Decimal(str(x))*Decimal(str(norm)))).astype('float')
# Save the results
default_filename = f"{params['SAMPLE']['sample']}_{params['SAMPLE']['field']}_{params['SAMPLE']['device']}_BACK_GATE_D.txt"
@@ -1244,6 +1182,52 @@ def Gatediode_VBG(device,params):
f.write("\nResults\n")
df.to_csv(file,sep=" ",mode='a')
+
+ plot_values = values_to_plot(params,device)
+ # Plot user specified results
+ fig,ax1= plt.subplots(figsize=(10,6),layout='constrained')
+
+ plot_list = [params["PLOT"]["x"],params["PLOT"]["y1"],params["PLOT"]["y2"]]
+ scale_list =['LIN',params["PLOT"]["y1_scale"],params["PLOT"]["y2_scale"]]
+ axes_labels =set_axes_labels(plot_list)
+
+ if scale_list[1]=='LOG':
+ ax1.set_yscale('log')
+
+
+ #now set the labels
+ ax1.set_xlabel(axes_labels[0])
+ ax1.set_ylabel(axes_labels[1])
+
+ x = plot_values['X']
+ y1 = plot_values['Y1']
+
+ ax1.plot(x,y1)
+ fig.suptitle('Gatediode Curve', fontweight ="bold")
+ fig.legend(loc='outside right upper')
+ display(fig)
+
if params["SAMPLE"]["save_fig"] == True:
filename= os.path.splitext(file)[0]
- fig.savefig(filename+'.png')
\ No newline at end of file
+ fig.savefig(filename+'_Y1.png')
+
+
+ #add the second axis if applicable
+ if plot_list[2]!= "None":
+ fig,ax2=plt.subplots(figsize=(10,6),layout='constrained')
+ y2 = plot_values['Y2']
+
+ if scale_list[2]=='LOG':
+ ax2.set_yscale('log')
+
+ ax2.set_xlabel(axes_labels[0])
+ ax2.set_ylabel(axes_labels[2])
+ ax2.plot(x,y2)
+
+ fig.suptitle('Gatediode Curve', fontweight ="bold")
+ fig.legend(loc='outside right upper')
+ display(fig)
+
+ if params["SAMPLE"]["save_fig"] == True:
+ filename= os.path.splitext(file)[0]
+ fig.savefig(filename+'_Y2.png')
\ No newline at end of file
diff --git a/hp4155/hp4155a.py b/hp4155/hp4155a.py
index e925ca751e25e360b66f2eb04243035e3becc500..e6d32d16809b195bf4f3351501e7a277ed8239d1 100644
--- a/hp4155/hp4155a.py
+++ b/hp4155/hp4155a.py
@@ -45,7 +45,20 @@ class HP4155a(object):
self.inst.write(":PAGE:STR")
def error(self):
- return self.inst.query(":SYST:ERR?")
+ error = self.inst.query(":SYST:ERR?")
+ error_code = int(error.split(',',1)[0])
+ error_message = error.split(',',1)[1]
+ return error_code,error_message
+
+ def error_occured(self):
+ code,message = self.error()
+ if code != 0:
+ raise Exception(message)
+
+ def clear_error_stack(self):
+ code,_ = self.error()
+ while code != 0:
+ code,_ = self.error()
def operation_completed(self):
text = self.inst.query('*OPC?')