erp5_wendelin_drone: automatically fetch the amount of parameters

bt5/erp5_wendelin_drone/PathTemplateItem/portal_callables/DataAnalysisLine_recalculateScore.py

@@ -8,148 +8,64 @@ import re
 # Now, to save space, we just overwrite the old array with the new one, that contains the new scores
 # This new array will give us the final overview of the ranking of the parameters. This can be used by our genetic algorithm to decide when we can stop and which parameters worked the best.
 
-
-score_dtypes = {'name': 'S256', 'Param1': 'f8', 'Param2': 'f8', 
-          'distance_reciprocal': 'f8', 'ASML_reciprocal': 'f8', 
-          'ground_speed_reciprocal': 'f8', 'climb_rate_reciprocal': 'f8', 
-          'score_reciprocal': 'f8', 'score_cosine_row': 'f8', 
-          'score_cosine_column': 'f8'}
-
-new_score_dtypes= {'name': 'S256', 'Param1': 'f8', 'Param2': 'f8', 
-          'distance_reciprocal': 'f8', 'ASML_reciprocal': 'f8', 
-          'ground_speed_reciprocal': 'f8', 'climb_rate_reciprocal': 'f8', 
-          'score_reciprocal': 'f8', 'score_cosine_row': 'f8', 
-          'score_cosine_column': 'f8',
-          'iteration': 'i8'}
-          
-    
-plot_dtypes = {
-    'name': 'S256',
-    'Param1': 'f8',
-    'Param2': 'f8',
-    'timestamp': 'f8',
-    'distance_diff': 'f8',
-    'ASML_diff': 'f8',
-    'ground_speed_diff': 'f8',
-    'climb_rate_diff': 'f8',
-    'distance_reciprocal': 'f8',
-    'ASML_reciprocal': 'f8',
-    'ground_speed_reciprocal': 'f8',
-    'climb_rate_reciprocal': 'f8',
-    'score_reciprocal': 'f8',
-    'score_cosine_row': 'f8',
-    'score_cosine_column': 'f8',
-    'iteration': 'i8'
-}
-
-new_plot_dtypes = {
-    'Param1': 'f8',
-    'Param2': 'f8',
-    'timestamp': 'f8',
-    'distance_diff': 'f8',
-    'ASML_diff': 'f8',
-    'ground_speed_diff': 'f8',
-    'climb_rate_diff': 'f8',
-    'distance_reciprocal': 'f8',
-    'ASML_reciprocal': 'f8',
-    'ground_speed_reciprocal': 'f8',
-    'climb_rate_reciprocal': 'f8',
-    'score_reciprocal': 'f8',
-    'score_cosine_row': 'f8',
-    'score_cosine_column': 'f8',
-    'iteration': 'i8'
-}
-
-
-score_array = input_array_scores["Data Array"]
-new_score_array = out_array_scores["Data Array"]
-
-plot_array = input_array_plot["Data Array"]
-new_plot_array = out_array_plots["Data Array"]
-
-# Should only look at the newest few
+score_array = input_array_scores['Data Array']
 score_nparray = score_array.getArray()
 if score_nparray is None:
-  score_nparray = out_array_scores["Data Array"].initArray(shape=(0,), dtype=list(new_score_dtypes.items()))
+  return
 
+plot_array = input_array_plot['Data Array']
 plot_nparray = plot_array.getArray()
 if plot_nparray is None:
-  plot_nparray = out_array_plots["Data Array"].initArray(shape=(0,), dtype=list(new_plot_dtypes.items()))
-  
-old_score_df = pd.DataFrame.from_records(score_nparray[:].copy())
-old_plot_df = pd.DataFrame.from_records(plot_nparray[:].copy())
-
-progress_indicator = input_array_scores["Progress Indicator"]
-seen_sims = progress_indicator.getStringOffsetIndex()
-
-if seen_sims is None:
-  seen_sims = ""
-  
-sim_flight_names = list(old_score_df["name"])
-  
-# We will only continue if there is new data available.
-if len([x for x in sim_flight_names if x not in seen_sims]) == 0: 
-  return 
-
-
-
-
-
-
-
-
+  return
 
+new_score_dtypes = [(k, v[0]) for k, v in dict(score_nparray.dtype.fields).items()]
+new_score_dtypes.append(('iteration', 'i8'))
+new_score_array = out_array_scores['Data Array']
 new_score_nparray = new_score_array.getArray()
 if new_score_nparray is None:
-  new_score_nparray = out_array_scores["Data Array"].initArray(shape=(0,), dtype=list(new_score_dtypes.items()))
-
-
+  new_score_array.initArray(shape=(0,), dtype=new_score_dtypes)
 
+new_plot_dtypes = [(k, v[0]) for k, v in dict(plot_nparray.dtype.fields).items()]
+new_plot_dtypes.append(('iteration', 'i8'))
+new_plot_array = out_array_plots['Data Array']
 new_plot_nparray = new_plot_array.getArray()
 if new_plot_nparray is None:
-  new_plot_nparray = out_array_plots["Data Array"].initArray(shape=(0,), dtype=list(new_plot_dtypes.items()))
-
-
-
-
-new_score_df = pd.DataFrame.from_records(new_score_nparray[:].copy())
-
-new_plot_df = pd.DataFrame.from_records(new_plot_nparray[:].copy())
+  new_plot_array.initArray(shape=(0,), dtype=new_plot_dtypes)
 
+old_score_df = pd.DataFrame.from_records(score_nparray[:].copy())
+old_plot_df = pd.DataFrame.from_records(plot_nparray[:].copy())
 
+progress_indicator = input_array_scores['Progress Indicator']
+seen_sims = progress_indicator.getStringOffsetIndex()
 
+if seen_sims is None:
+  seen_sims = ""
 
+sim_flight_names = list(old_score_df['name'])
 
+# We will only continue if there is new data available.
+if len([x for x in sim_flight_names if x not in seen_sims]) == 0:
+  return
 
+new_score_df = pd.DataFrame.from_records(new_score_nparray[:].copy())
+new_plot_df = pd.DataFrame.from_records(new_plot_nparray[:].copy())
 
-new_score_iteration = new_score_df["iteration"].max()
+new_score_iteration = new_score_df['iteration'].max()
 if math.isnan(new_score_iteration):
   new_score_iteration = 0
 
-
-new_score_df = new_score_df.drop(["iteration"],axis=1)
-
-
+new_score_df = new_score_df.drop(['iteration'], axis=1)
 new_plot_iteration = new_plot_df["iteration"].max()
 if math.isnan(new_plot_iteration):
   new_plot_iteration = 0
 
-#new_plot_df = new_plot_df.drop(columns=["iteration"])
-
-
-answer_scores = pd.concat([old_score_df, new_score_df]).drop_duplicates(subset=['Param1','Param2'], keep='last')
-answer_plots = pd.concat([old_plot_df, new_plot_df]).drop_duplicates(subset=['Param1','Param2'], keep='last')
-
-answer_scores["score_reciprocal"] = answer_scores["score_reciprocal"].astype('float64')
-
-
-
-answer_scores = answer_scores.nlargest(5, "score_reciprocal")
-answer_plots = old_plot_df[old_plot_df["name"].isin(list(answer_scores["name"]))]
-
-
-
+param_subset = [k for k in old_score_df.dtypes.keys().values if not 'reciprocal' in k and not 'score' in k and k != 'name']
+answer_scores = pd.concat([old_score_df, new_score_df]).drop_duplicates(subset=param_subset, keep='last')
+answer_plots = pd.concat([old_plot_df, new_plot_df]).drop_duplicates(subset=param_subset, keep='last')
 
+answer_scores['score_reciprocal'] = answer_scores['score_reciprocal'].astype('float64')
+answer_scores = answer_scores.nlargest(5, 'score_reciprocal')
+answer_plots = old_plot_df[old_plot_df['name'].isin(list(answer_scores['name']))]
 
 new_score_iteration = new_score_iteration + 1
 new_plot_iteration = new_plot_iteration + 1
@@ -158,24 +74,14 @@ answer_scores["iteration"] = new_score_iteration
 answer_plots["iteration"] = new_plot_iteration
 
 # We will remove all the data from the data arrays before we append the new data. Essentially we will be left with only the best few iterations
-new_score_nparray = out_array_scores["Data Array"].initArray(shape=(0,), dtype=list(new_score_dtypes.items()))
-
-new_plot_nparray = out_array_plots["Data Array"].initArray(shape=(0,), dtype=list(new_plot_dtypes.items()))
+new_score_nparray = new_score_array.initArray(shape=(0,), dtype=new_score_dtypes)
+new_plot_nparray = new_plot_array.initArray(shape=(0,), dtype=new_plot_dtypes)
 
 new_score_nparray.append(answer_scores.to_records(index = False))
 
 
-
-
-
-
-
-
-
-
-
 # Group the DataFrame by the 'name' column
-grouped = answer_plots.groupby(['Param1', 'Param2'])
+grouped = answer_plots.groupby(param_subset)
 
 # Initialize a list to store the resulting DataFrames
 resulting_dfs = []
@@ -209,5 +115,5 @@ for df in resulting_dfs:
 
 
 progress_indicator.setStringOffsetIndex(sim_flight_names)
-    
+
 return