Improve plot output

Changelog.md

@@ -1,3 +1,12 @@
+# 2.4.5
+
+## Common
+
+- Improve plots
+  - Sort minimum/typical/maximum labels
+  - Connect points with the same condition on the x-axis
+- Print when netlists do not need regeneration
+
 # 2.4.4
 
 ## Common

cace/__version__.py

@@ -11,7 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-__version__ = '2.4.4'
+__version__ = '2.4.5'
 
 if __name__ == '__main__':
     print(__version__, end='')

cace/common/cace_regenerate.py

@@ -437,6 +437,9 @@ def regenerate_rcx_netlist(datasheet, runtime_options):
         ):
             return False
 
+    else:
+        info('Not extracting netlist with parasitics from layout. Up to date.')
+
     return rcx_netlist
 
 
@@ -615,6 +618,9 @@ def regenerate_lvs_netlist(datasheet, runtime_options, pex=False):
         ):
             return False
 
+    else:
+        info('Not extracting LVS netlist from layout. Up to date.')
+
     return lvs_netlist
 
 
@@ -923,7 +929,7 @@ def regenerate_testbench(datasheet, runtime_options, testbenchpath, testbench):
         )
 
     if not need_testbench_netlist:
-        # Testbench exists and is up-to-date;  nothing to do
+        # Testbench exists and is up-to-date; nothing to do
         return 0
 
     if not os.path.isfile(source_file):
@@ -1108,6 +1114,8 @@ def regenerate_gds(datasheet, runtime_options):
 
         if mproc.returncode != 0:
             err(f'Magic process returned error code {mproc.returncode}.')
+    else:
+        info('Not regenerating GDSII from magic layout. Up to date.')
 
     if not os.path.isfile(gdspath):
         err(f'Could not generate gds layout: {gdspath}')

cace/parameter/parameter.py

@@ -15,6 +15,7 @@
 import os
 import re
 import sys
+import copy
 import traceback
 import subprocess
 from statistics import median, mean
@@ -176,10 +177,10 @@ def condition_gen(self):
         else:
             if 'minimum' in self.spec:
                 yield self.spec['minimum']
-            if 'maximum' in self.spec:
-                yield self.spec['maximum']
             if 'typical' in self.spec:
                 yield self.spec['typical']
+            if 'maximum' in self.spec:
+                yield self.spec['maximum']
 
 
 class Parameter(ABC, Thread):
@@ -1033,50 +1034,99 @@ def makeplot(
         if len(condition_sets) > 1:
             opacity = 0.5
 
-        # If a condition is used as x-axis, merge same entries TODO
+        # If the xvariable is a condition, remove it from the condition set
+        # since it is displayed on the xaxis anyways and merge the results
+
+        xvalues_list = []
+        yvalues_list = []
+        label_list = []
+
         if xvariable in conditions:
 
-            condition = conditions[xvariable]
-            # print(condition)
+            new_condition_sets = []
+            new_results_for_plot = []
+            hashes = []
 
-            for value in condition.values:
-                # print(value)
-                pass
+            # We only want ticks at certain locations
+            try:
+                ax.set_xticks(
+                    ticks=conditions[xvariable].values,
+                    labels=conditions[xvariable].values,
+                )
+            except:
+                ax.set_xticks(
+                    ticks=range(len(conditions[xvariable].values)),
+                    labels=conditions[xvariable].values,
+                )
+
+            # Get the result
+            for condition_set, results in zip(
+                condition_sets, results_for_plot
+            ):
 
-        # Get the result
-        for index, condition_set in enumerate(condition_sets):
+                # Create a deep copy of the condition set
+                condition_set = copy.deepcopy(condition_set)
 
-            # Get the results for this plot
-            collated_variable_values = results_for_plot[index]
+                # Remove the condition at the xaxis from the condition_set
+                condition_set.pop(xvariable)
+
+                # We also need to remove unique elements, or else no hash will match
+                condition_set.pop('N')
+                condition_set.pop('simpath')
+
+                cur_hash = hash(frozenset(condition_set.items()))
+
+                # Let' see if the condition set is not yet in the new condition sets
+                if not cur_hash in hashes:
+                    new_condition_sets.append(condition_set)
+                    new_results_for_plot.append(copy.deepcopy(results))
+                    hashes.append(cur_hash)
+
+                # If it is already, we need to extend the results
+                else:
+                    for index, this_hash in enumerate(hashes):
+                        # Found the condition set
+                        if cur_hash == this_hash:
+                            # Append to results
+                            for key in new_results_for_plot[index].keys():
+                                new_results_for_plot[index][key].extend(
+                                    list(results[key])
+                                )
+
+            condition_sets = new_condition_sets
+            results_for_plot = new_results_for_plot
+
+        # Generate the x and y values
+        for condition_set, results in zip(condition_sets, results_for_plot):
 
             xvalues = None
             if xvariable:
                 # Is the variable a simulation result?
-                if xvariable in collated_variable_values:
-                    xvalues = collated_variable_values[xvariable]
+                if xvariable in results:
+                    xvalues = results[xvariable]
                 # Else it may be a condition?
-                elif xvariable in condition_set:
-                    xvalues = condition_set[xvariable]
+                elif xvariable in conditions:
+                    xvalues = conditions[xvariable].values
                 else:
                     err(f'Unknown variable: {xvariable}')
                     return None
 
+            xvalues_list.append(xvalues)
+
             yvalues = []
             for yvariable in yvariables:
                 if yvariable:
                     # Is the variable a simulation result?
-                    if yvariable in collated_variable_values:
-                        yvalues.append(collated_variable_values[yvariable])
+                    if yvariable in results:
+                        yvalues.append(results[yvariable])
                     # Else it may be a condition?
                     elif yvariable in condition_set:
                         yvalues.append(condition_set[yvariable])
                     else:
                         err(f'Unknown variable: {yvariable}')
                         return None
 
-            marker = None
-            if not isinstance(xvalues, list) or len(xvalues) == 1:
-                marker = 'o'
+            yvalues_list.append(yvalues)
 
             # Get the label for the legend
             label = []
@@ -1089,6 +1139,16 @@ def makeplot(
                         )
             label = ', '.join(label)
 
+            label_list.append(label)
+
+        for xvalues, yvalues, label in zip(
+            xvalues_list, yvalues_list, label_list
+        ):
+
+            marker = None
+            if not isinstance(xvalues, list) or len(xvalues) == 1:
+                marker = 'o'
+
             for yvalue in yvalues:
                 self.plot(
                     xvalues,