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+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

application functions

+
+ + + +
+ + + + +
+ + + +
+ + + +
+

source

+
+

run_wind_turbine

+
+
 run_wind_turbine (target, drive, filepath)
+
+

controller trained on steady dataset applied to steady dataset

+

run_wind_turbine(‘steady’, “testfiles\”, “ga–1362.401-s003-4x3-m005-WT0538-bddf277b0f729cc630efacf91b9f494f.properties”)

+

power_control result = 1172.9

+

controller trained on steady dataset applied to variable dataset

+

run_wind_turbine(‘variable’, “testfiles\”, “ga–1362.401-s003-4x3-m005-WT0538-bddf277b0f729cc630efacf91b9f494f.properties”)

+

power_control result = 739.6

+

controller trained on variable dataset applied to variable dataset

+

run_wind_turbine(‘variable’, “testfiles\”, “ga–2629.009-s001-5x5-m002-WT0416-31ecb19201d49e8c6f9dd1e172bd6944.properties”)

+

power_control result = 741.7

+

controller trained on variable dataset applied to steady dataset

+

run_wind_turbine(‘steady’, “testfiles\”, “ga–2629.009-s001-5x5-m002-WT0416-31ecb19201d49e8c6f9dd1e172bd6944.properties”)

+

power_control result = 1171.5

+
+
# run_wind_turbine('steady', "testfiles\\", "ga--1362.401-s003-4x3-m005-WT0538-bddf277b0f729cc630efacf91b9f494f.properties")
+# run_wind_turbine('variable', "testfiles\\", "ga--1362.401-s003-4x3-m005-WT0538-bddf277b0f729cc630efacf91b9f494f.properties")
+# run_wind_turbine('variable', "testfiles\\", "ga--2629.009-s001-5x5-m002-WT0416-31ecb19201d49e8c6f9dd1e172bd6944.properties")
+# run_wind_turbine('steady', "testfiles\\", "ga--2629.009-s001-5x5-m002-WT0416-31ecb19201d49e8c6f9dd1e172bd6944.properties")
+
+
2024-10-12 16:23:04.245655 26796 Start
+Score=-1172.912 power=1172.912
+{'start': 1100, 'end': 2100, 'average yaw error_baseline_logs': 6.907099999999999, 'angle covered_trad_baseline_logs': 27.30000000000004, 'yaw count_trad_baseline_logs': 5, 'time_yawing_trad_baseline_logs': 0.43004300430043}
+{'start': 1100, 'end': 2100, 'average yaw error_baseline_simu': 6.5231, 'angle covered_trad_baseline_simu': 53.46000000000012, 'yaw count_trad_baseline_simu': 15, 'time_yawing_trad_baseline_simu': 2.0502050205020503}
+{'start_index': 1100, 'stop_index': 2100, 'power_trad': 1168.471147532752, 'power_no_loss': 1203.9984307393847, 'power_control': 1172.9115903340005, 'average yaw error': 6.141389999999999, 'average reward': -1172.9115903340007, 'angle covered': 78.0, 'yaw count': 10, 'time_yawing': 2.6026026026026026}
+average_yaw_error_decrease_base=11.09
+average_yaw_error_decrease_simu=5.85
+energy_gain = 0.38
+net_energy_gain = 0.35
+2024-10-12 16:23:16.296789 26796 End
+Elapsed time: 12.05
+2024-10-12 16:23:16.296789 26796 Start
+Score=-739.604 power=739.604
+{'start': 1100, 'end': 2100, 'average yaw error_baseline_logs': 7.5581999999999985, 'angle covered_trad_baseline_logs': 97.31000000000012, 'yaw count_trad_baseline_logs': 14, 'time_yawing_trad_baseline_logs': 1.5801580158015802}
+{'start': 1100, 'end': 2100, 'average yaw error_baseline_simu': 7.0147549, 'angle covered_trad_baseline_simu': 112.13400000000004, 'yaw count_trad_baseline_simu': 27, 'time_yawing_trad_baseline_simu': 4.160416041604161}
+{'start_index': 1100, 'stop_index': 2100, 'power_trad': 736.0266006459701, 'power_no_loss': 757.8501598912032, 'power_control': 739.6036472129515, 'average yaw error': 6.040095999999999, 'average reward': -739.6036472129531, 'angle covered': 141.0, 'yaw count': 22, 'time_yawing': 4.704704704704705}
+average_yaw_error_decrease_base=20.09
+average_yaw_error_decrease_simu=13.89
+energy_gain = 0.49
+net_energy_gain = 0.42
+2024-10-12 16:23:28.205166 26796 End
+Elapsed time: 11.91
+2024-10-12 16:23:28.205166 26796 Start
+Score=-741.680 power=741.680
+{'start': 1100, 'end': 2100, 'average yaw error_baseline_logs': 7.5581999999999985, 'angle covered_trad_baseline_logs': 97.31000000000012, 'yaw count_trad_baseline_logs': 14, 'time_yawing_trad_baseline_logs': 1.5801580158015802}
+{'start': 1100, 'end': 2100, 'average yaw error_baseline_simu': 7.0147549, 'angle covered_trad_baseline_simu': 112.13400000000004, 'yaw count_trad_baseline_simu': 27, 'time_yawing_trad_baseline_simu': 4.160416041604161}
+{'start_index': 1100, 'stop_index': 2100, 'power_trad': 736.0266006459701, 'power_no_loss': 757.8501598912032, 'power_control': 741.6798023000322, 'average yaw error': 5.56032, 'average reward': -741.679802300033, 'angle covered': 201.0, 'yaw count': 42, 'time_yawing': 6.706706706706707}
+average_yaw_error_decrease_base=26.43
+average_yaw_error_decrease_simu=20.73
+energy_gain = 0.77
+net_energy_gain = 0.57
+2024-10-12 16:23:40.128764 26796 End
+Elapsed time: 11.92
+2024-10-12 16:23:40.128764 26796 Start
+Score=-1171.489 power=1171.489
+{'start': 1100, 'end': 2100, 'average yaw error_baseline_logs': 6.907099999999999, 'angle covered_trad_baseline_logs': 27.30000000000004, 'yaw count_trad_baseline_logs': 5, 'time_yawing_trad_baseline_logs': 0.43004300430043}
+{'start': 1100, 'end': 2100, 'average yaw error_baseline_simu': 6.5231, 'angle covered_trad_baseline_simu': 53.46000000000012, 'yaw count_trad_baseline_simu': 15, 'time_yawing_trad_baseline_simu': 2.0502050205020503}
+{'start_index': 1100, 'stop_index': 2100, 'power_trad': 1168.471147532752, 'power_no_loss': 1203.9984307393847, 'power_control': 1171.4890528211347, 'average yaw error': 6.269654, 'average reward': -1171.4890528211342, 'angle covered': 186.0, 'yaw count': 44, 'time_yawing': 6.206206206206206}
+average_yaw_error_decrease_base=9.23
+average_yaw_error_decrease_simu=3.89
+energy_gain = 0.26
+net_energy_gain = 0.07
+2024-10-12 16:23:52.161731 26796 End
+Elapsed time: 12.03
+
+
+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/arc.html b/arc.html new file mode 100644 index 00000000..16adb40f --- /dev/null +++ b/arc.html @@ -0,0 +1,1112 @@ + + + + + + + + + +ARC environment classes – pct + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

ARC environment classes

+
+ + + +
+ + + + +
+ + + +
+ + + +
+

ARC Fitness

+
+

Factory

+
+

source

+
+

ARCFitnessFactory

+
+
 ARCFitnessFactory ()
+
+

Initialize self. See help(type(self)) for accurate signature.

+
+
+
+

BaseARCFitness

+
+

source

+
+

BaseARCFitness

+
+
 BaseARCFitness ()
+
+

Base class of an ARCFitness. This class is not used directly by developers, but defines the functionality common to all.

+
+
+
+

SumSquareOfDiff

+
+

source

+
+

SumSquareOfDiff

+
+
 SumSquareOfDiff ()
+
+

A function that sums the square of the diffrences of two arrays.

+
+
+
+

AverageMaxOfDiff

+
+

source

+
+

AverageMaxOfDiff

+
+
 AverageMaxOfDiff ()
+
+

A function that sums the square of the diffrences of two arrays.

+
+
+
+

Euclidean

+
+

source

+
+

Euclidean

+
+
 Euclidean ()
+
+

A function that sums the square of the diffrences of two arrays.

+
+
# import random
+# random.seed(1)
+np.random.seed(1)
+# Create a 2-dimensional 3 by 3 numpy array of random floats from 0 to 9
+random_array = np.random.uniform(4, 5, (3, 3))
+print(random_array)
+array_of_fours = np.full((3, 3), 4)
+print(array_of_fours)
+euc = Euclidean()
+metric = euc(array_of_fours, random_array,  ['cells'])
+print(metric)
+np.random.seed(2)
+random_array = np.random.uniform(4.49, 4.5, (3, 3))
+print(random_array)
+metric = euc(array_of_fours, random_array,  ['cells'])
+print(metric)
+
+
[[4.417022   4.72032449 4.00011437]
+ [4.30233257 4.14675589 4.09233859]
+ [4.18626021 4.34556073 4.39676747]]
+[[4 4 4]
+ [4 4 4]
+ [4 4 4]]
+1.0610244787055576
+[[4.49435995 4.49025926 4.49549662]
+ [4.49435322 4.49420368 4.49330335]
+ [4.49204649 4.49619271 4.49299655]]
+0.49619270966350637
+
+
+
+
+
+
+

ARCDataProcessor

+
+

source

+
+

ARCDataProcessor

+
+
 ARCDataProcessor (config_dict, arc_dict)
+
+

Initialize self. See help(type(self)) for accurate signature.

+
+
# Example usage:
+print('Example using dims only')
+config_dict = {
+    'control_set': ['dims'],
+    'input_set': ['env'],
+    'dataset': 'train',
+    'index': 0
+}
+
+arc_dict = {
+    'test': [{'input': [[7, 0, 7], [7, 0, 7], [7, 7, 0]]}],
+    'train': [
+        {'input': [[0, 7, 7], [7, 7, 7], [0, 7, 7]], 'output': [[0, 0, 0, 0, 7, 7, 0, 7, 7], [0, 0, 0, 7, 7, 7, 7, 7, 7], [0, 0, 0, 0, 7, 7, 0, 7, 7], [0, 7, 7, 0, 7, 7, 0, 7, 7], [7, 7, 7, 7, 7, 7, 7, 7, 7], [0, 7, 7, 0, 7, 7, 0, 7, 7], [0, 0, 0, 0, 7, 7, 0, 7, 7], [0, 0, 0, 7, 7, 7, 7, 7, 7], [0, 0, 0, 0, 7, 7, 0, 7, 7]]}
+        # Add more entries as needed
+    ]
+}
+
+gp = ARCDataProcessor(config_dict, arc_dict)
+info = gp.create_info()
+print(info)
+ins = gp.get_env_inputs_names()
+print('names', ins)
+inds = gp.get_env_inputs_indexes()
+print('indexes', inds)
+
+state, info = gp.get_state()
+print(info)
+print('fitness', gp.fitness_function(), state)
+print()
+
+for i in range(-2,-4,-1):
+    actions = [i]
+    gp.apply_actions(actions)
+    state, info = gp.get_state()
+    print(info)
+    # print(len(values))
+    print('fitness', gp.fitness_function(), state)
+    print()
+
+
Example using dims only
+{'num_actions': 1, 'grid_shape': 'equal', 'dims': 1}
+names ['IWE']
+indexes [0]
+{'num_actions': 1, 'grid_shape': 'equal', 'dims': 1}
+fitness 72 {'inputs': {'dims': {'env': (3,)}}}
+
+{'num_actions': 1, 'grid_shape': 'equal', 'dims': 1}
+fitness 128 {'inputs': {'dims': {'env': (1,)}}}
+
+{'num_actions': 1, 'grid_shape': 'equal', 'dims': 1}
+fitness 128 {'inputs': {'dims': {'env': (1,)}}}
+
+
+
+
+
# Example usage:
+print('Example using env inputs only')
+config_dict = {
+    'control_set': ['cells'],
+    'input_set': ['env'],
+    'dataset': 'train',
+    'index': 0
+}
+
+gp = ARCDataProcessor(config_dict, arc_dict)
+info = gp.create_info()
+print(info)
+ins = gp.get_env_inputs_names()
+print('names', ins)
+inds = gp.get_env_inputs_indexes()
+print('indexes', inds)
+
+state, info = gp.get_state()
+print(info)
+print('fitness', gp.fitness_function(), state)
+print()
+
+for i in range(-2,-4,-1):
+    actions = [i for j in range(info['num_actions'])]
+    print(actions)
+    gp.apply_actions(actions)
+    state, info = gp.get_state()
+    print(info)
+    # print(len(values))
+    print('fitness', gp.fitness_function(), state)
+    print()
+
+
Example using env inputs only
+{'num_actions': 9, 'env': (3, 3)}
+names ['IE001', 'IE002', 'IE003', 'IE004', 'IE005', 'IE006', 'IE007', 'IE008', 'IE009']
+indexes [0, 1, 2, 3, 4, 5, 6, 7, 8]
+{'num_actions': 9, 'env': (3, 3)}
+
+
+
+
---------------------------------------------------------------------------
+ValueError                                Traceback (most recent call last)
+Cell In[13], line 21
+     19 state, info = gp.get_state()
+     20 print(info)
+---> 21 print('fitness', gp.fitness_function(), state)
+     22 print()
+     24 for i in range(-2,-4,-1):
+
+Cell In[11], line 225, in ARCDataProcessor.fitness_function(self)
+    223 output_array = self.get_output(self.dataset)
+    224 env_array = self.env
+--> 225 return self.fitness_function_arrays(output_array, env_array)
+
+Cell In[11], line 196, in ARCDataProcessor.fitness_function_arrays(self, output_array, env_array)
+    193 def fitness_function_arrays(self, output_array, env_array):
+--> 196     return self.fitness_function_type(output_array = output_array, env_array = env_array, control_set = self.control_set)
+
+Cell In[7], line 12, in SumSquareOfDiff.__call__(self, output_array, env_array, control_set)
+     10 element_metric = 0
+     11 if 'cells' in control_set:
+---> 12     diff = env_array[:output_array.shape[0], :output_array.shape[1]] - output_array
+     13     diff = np.where(np.isnan(diff), 0, diff)
+     14     diff = np.where(np.isnan(env_array[:output_array.shape[0], :output_array.shape[1]]), 10, diff)
+
+ValueError: operands could not be broadcast together with shapes (3,3) (9,9)
+
+
+
+
+
# Example usage:
+print('Example using cells only')
+config_dict = {
+    'control_set': ['cells'],
+    'input_set': ['env','inputs'],
+    # 'input_set': ['env','inputs', 'outputs'],
+    'dataset': 'train',
+    'index': 0
+}
+
+gp = ARCDataProcessor(config_dict, arc_dict)
+info = gp.create_info()
+print(info)
+
+ins = gp.get_env_inputs_names()
+print('names', ins)
+inds = gp.get_env_inputs_indexes()
+print('indexes', inds)
+
+state, info = gp.get_state()
+print(info)
+print('fitness', gp.fitness_function(), state)
+print()
+
+
Example using cells only
+{'num_actions': 9, 'env': (3, 3), 'inputs': (3, 3)}
+names ['IE001', 'IE002', 'IE003', 'IE004', 'IE005', 'IE006', 'IE007', 'IE008', 'IE009', 'II001', 'II002', 'II003', 'II004', 'II005', 'II006', 'II007', 'II008', 'II009']
+indexes [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]
+{'num_actions': 9, 'env': (3, 3), 'inputs': (3, 3)}
+fitness 2143.0 {'inputs': {'cells': {'env': array([[0., 7., 7.],
+       [7., 7., 7.],
+       [0., 7., 7.]], dtype=float32), 'inputs': array([[0, 7, 7],
+       [7, 7, 7],
+       [0, 7, 7]])}}}
+
+
+
+
+
+
+

ARCEnv

+
+

source

+
+

ARCEnv

+
+
 ARCEnv (namespace='')
+
+

*The main OpenAI Gym class.

+

It encapsulates an environment with arbitrary behind-the-scenes dynamics. An environment can be partially or fully observed.

+

The main API methods that users of this class need to know are:

+
    +
  • :meth:step - Takes a step in the environment using an action returning the next observation, reward, if the environment terminated and observation information.
    • +
  • :meth:reset - Resets the environment to an initial state, returning the initial observation and observation information.
    • +
  • :meth:render - Renders the environment observation with modes depending on the output
    • +
  • :meth:close - Closes the environment, important for rendering where pygame is imported
    • +
+

And set the following attributes:

+
    +
  • :attr:action_space - The Space object corresponding to valid actions
    • +
  • :attr:observation_space - The Space object corresponding to valid observations
    • +
  • :attr:reward_range - A tuple corresponding to the minimum and maximum possible rewards
    • +
  • :attr:spec - An environment spec that contains the information used to initialise the environment from gym.make
    • +
  • :attr:metadata - The metadata of the environment, i.e. render modes
    • +
  • :attr:np_random - The random number generator for the environment
    • +
+

Note: a default reward range set to :math:(-\infty,+\infty) already exists. Set it if you want a narrower range.*

+
+
+

Example

+
+
import pygame
+
+# Example usage:
+if os.name == 'nt': 
+    props = {'dir': 'C:\\packages\\arc-prize-2024', 'file_prefix':'arc-agi_training_', 'code':'007bbfb7', 'control_set': ['dims'], 'input_set': ['env'],'dataset': 'train'}
+    file_name = os.path.join(props['dir'], props['file_prefix']) + 'challenges.json' 
+    challenges_manager = ChallengesDataManager(file_name)
+    data = challenges_manager.get_data_for_key(props['code'])
+    print(data)
+    # arc_dict={}
+    # arc_dict['data'] = data
+    arc_env = ARCEnv()
+    arc_env.initialise(props, data)
+    arc_env.render()
+    #    print(state, fitness, done)
+    # print(arc_env.dimensions)
+    for i in range(6):
+        state, fitness, done, info = arc_env.step([1])
+        # print(state, fitness, done)
+        print(state, fitness, done, info)
+        arc_env.render()
+        sleep(1)
+
+
{'test': [{'input': [[7, 0, 7], [7, 0, 7], [7, 7, 0]]}], 'train': [{'input': [[0, 7, 7], [7, 7, 7], [0, 7, 7]], 'output': [[0, 0, 0, 0, 7, 7, 0, 7, 7], [0, 0, 0, 7, 7, 7, 7, 7, 7], [0, 0, 0, 0, 7, 7, 0, 7, 7], [0, 7, 7, 0, 7, 7, 0, 7, 7], [7, 7, 7, 7, 7, 7, 7, 7, 7], [0, 7, 7, 0, 7, 7, 0, 7, 7], [0, 0, 0, 0, 7, 7, 0, 7, 7], [0, 0, 0, 7, 7, 7, 7, 7, 7], [0, 0, 0, 0, 7, 7, 0, 7, 7]]}, {'input': [[4, 0, 4], [0, 0, 0], [0, 4, 0]], 'output': [[4, 0, 4, 0, 0, 0, 4, 0, 4], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 0, 0, 0, 0, 0, 4, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 4, 0, 4, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 4, 0, 0, 0, 0]]}, {'input': [[0, 0, 0], [0, 0, 2], [2, 0, 2]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 2], [0, 0, 0, 0, 0, 0, 2, 0, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 2], [2, 0, 2, 0, 0, 0, 2, 0, 2]]}, {'input': [[6, 6, 0], [6, 0, 0], [0, 6, 6]], 'output': [[6, 6, 0, 6, 6, 0, 0, 0, 0], [6, 0, 0, 6, 0, 0, 0, 0, 0], [0, 6, 6, 0, 6, 6, 0, 0, 0], [6, 6, 0, 0, 0, 0, 0, 0, 0], [6, 0, 0, 0, 0, 0, 0, 0, 0], [0, 6, 6, 0, 0, 0, 0, 0, 0], [0, 0, 0, 6, 6, 0, 6, 6, 0], [0, 0, 0, 6, 0, 0, 6, 0, 0], [0, 0, 0, 0, 6, 6, 0, 6, 6]]}, {'input': [[2, 2, 2], [0, 0, 0], [0, 2, 2]], 'output': [[2, 2, 2, 2, 2, 2, 2, 2, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 2, 0, 2, 2, 0, 2, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 2, 2, 2, 2, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 2, 2, 0, 2, 2]]}]}
+{'inputs': {'dims': {'env': (4,)}}} 50 True {'num_actions': 1, 'grid_shape': 'equal', 'dims': 1}
+{'inputs': {'dims': {'env': (5,)}}} 32 True {'num_actions': 1, 'grid_shape': 'equal', 'dims': 1}
+{'inputs': {'dims': {'env': (6,)}}} 18 True {'num_actions': 1, 'grid_shape': 'equal', 'dims': 1}
+{'inputs': {'dims': {'env': (7,)}}} 8 True {'num_actions': 1, 'grid_shape': 'equal', 'dims': 1}
+{'inputs': {'dims': {'env': (8,)}}} 2 True {'num_actions': 1, 'grid_shape': 'equal', 'dims': 1}
+{'inputs': {'dims': {'env': (9,)}}} 0 True {'num_actions': 1, 'grid_shape': 'equal', 'dims': 1}
+
+
+
+
sleep(2)
+arc_env.close()
+
+ + +
+
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/architectures.html b/architectures.html new file mode 100644 index 00000000..4b396929 --- /dev/null +++ b/architectures.html @@ -0,0 +1,913 @@ + + + + + + + + + + +Architectures – pct + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Architectures

+
+ +
+
+ Classes to create hierachy architectures from array configurations. +
+
+ + +
+ + + + +
+ + + +
+ + + +
+
#sys.path.append("..")
+
+
+

source

+
+

BaseArchitecture

+
+
 BaseArchitecture (name=None, config=None, env=None, inputs=None,
+                   history=None, error_collector=None, namespace=None)
+
+

Base class of an array architecture. This class is not used direclty by developers, but defines the functionality common to all.

+
+

source

+
+
+

ProportionalArchitecture

+
+
 ProportionalArchitecture (name='proportional', config=None, env=None,
+                           input_indexes=None, history=False,
+                           error_collector=None, namespace=None, **cargs)
+
+

Proportional Architecture

+
+

source

+
+
+

DynamicArchitecture

+
+
 DynamicArchitecture (*args, **kwargs)
+
+

Dynamic Architecture

+
+

source

+
+
+

run_from_properties_file

+
+
 run_from_properties_file (root_dir='.', path='.', file=None, nevals=None,
+                           runs=500, history=True, verbose=False,
+                           test=False, move=None, draw=False,
+                           plots_figsize=(15, 4), render=True,
+                           layout=None, early_termination=False,
+                           plots=None, seed=None, print_properties=False,
+                           figsize=(12, 12), summary=False,
+                           hpct_verbose=False, error_collector_type=None,
+                           error_response_type=None, draw_file=None,
+                           funcdata=False, interactive_mode=False)
+
+
+

source

+
+
+

load_properties

+
+
 load_properties (root_dir=None, file_path=None, file_name=None,
+                  nevals=None, seed=None, print_properties=False,
+                  gens=None, pop_size=None, evolve=False)
+
+
+

source

+
+
+

setup_environment

+
+
 setup_environment (properties, render=False, seed=None,
+                    early_termination=None, error_collector_type=None,
+                    error_response_type=None)
+
+
+

source

+
+
+

create_hierarchy

+
+
 create_hierarchy (env, error_collector, properties, history=False,
+                   suffixes=False)
+
+
+
from pct.environments import VelocityModel
+from pct.structure import ArchitectureStructure
+from pct.functions import IndexedParameter
+from pct.hierarchy import PCTHierarchy
+from pct.structure import LevelKey
+from pct.nodes import PCTNode
+
+
+
env = VelocityModel(name='VModel')
+namespace=env.namespace
+inputs=[2]
+structure = ArchitectureStructure()
+config = {'parameters': {}, 'level0': [[[1.0864011617580416, -1.0342161642584196], [-8.899524671308557, -8.976856229389936]], [-0.7295091920311653, -4.460573287694404], [0, 0], [[-4.146713118740296, 1.2794655139677662]]]}
+with warnings.catch_warnings():
+    warnings.simplefilter("ignore")
+    pa = DynamicArchitecture(structure=structure, config=config, env=env, input_indexes=inputs, namespace=namespace) #, error_collector=te)
+    pa()
+#hpct = pa.get_hierarchy()
+#hpct.summary()
+#move={'VModel': [-0.2, -0.3],'Input0': [-0.3, 0],'OL0C0ws': [-0.4, 0],'OL0C1ws': [0.6, 0]}
+#hpct.draw(move=move, with_edge_labels=True, figsize=(14,14))
+
+
+
from pct.environments import Pendulum
+from pct.functions import Constant
+
+
+
pen = Pendulum(name='Pendulum',render=True, seed=1)
+namespace = pen.namespace
+#print(namespace)
+inputs=[2, 3]
+config = {'level0': [[[0, 0, 1], [0, 0, 1]], [74.7727669099358, 37.42447782017047, 70.45900090605967], [[1, 0, 1]]], 'level1': [[[1, 0, 1], [1, 1, 1]], [0.028281504070566288, 0.29618653732851286], [[-49.05302284318027, 46.949638698585005], [-96.63198831250754, -29.78373183094591], [-3.8249493797145107, -60.95121501461629]]], 'level2': [[[1, 0], [0, 1]], [0.633350421196448, 0.5607290603484817], [[41.217164164869104, -76.70635343790484], [83.21744682425535, -96.9311066757899]], [1, 2]], 'parameters': {}}
+for key in config.keys():
+    print(key, config[key])
+pa = ProportionalArchitecture(config=config, env=pen, input_indexes=inputs, namespace=namespace)
+pa()
+hpct = pa.get_hierarchy()
+hpct.draw(move={'Pendulum': [-0.2, -0.3],'Input0': [-0.3, 0], 'Input1': [0.3, 0]})
+
+
level0 [[[0, 0, 1], [0, 0, 1]], [74.7727669099358, 37.42447782017047, 70.45900090605967], [[1, 0, 1]]]
+level1 [[[1, 0, 1], [1, 1, 1]], [0.028281504070566288, 0.29618653732851286], [[-49.05302284318027, 46.949638698585005], [-96.63198831250754, -29.78373183094591], [-3.8249493797145107, -60.95121501461629]]]
+level2 [[[1, 0], [0, 1]], [0.633350421196448, 0.5607290603484817], [[41.217164164869104, -76.70635343790484], [83.21744682425535, -96.9311066757899]], [1, 2]]
+parameters {}
+
+
+
+
pen = Pendulum(name='Pendulum',render=True, seed=1)
+namespace = pen.namespace
+inputs=[2, 3]
+config = {'level0': [[[0], [0]], [60.92750637861888], [[1]]], 'level1': [[[1], [1]], [0.8221926728404025, 0.5936199577845901], [[2.941087338758976, -68.59700733589955]], [1, 2]], 'parameters': {}}
+for key in config.keys():
+    print(key, config[key])
+pa = ProportionalArchitecture(config=config, env=pen, input_indexes=inputs, namespace = namespace)
+pa()
+hpct = pa.get_hierarchy()
+hpct.draw(move={'Pendulum': [-0.2, -0.3],'Input0': [-0.3, 0], 'Input1': [0.3, 0]})
+
+
level0 [[[0], [0]], [60.92750637861888], [[1]]]
+level1 [[[1], [1]], [0.8221926728404025, 0.5936199577845901], [[2.941087338758976, -68.59700733589955]], [1, 2]]
+parameters {}
+
+
+
+
pen = Pendulum(name='Pendulum',render=True, seed=1)
+namespace = pen.namespace
+inputs=[2]
+config = {'level0': [[[1]], [62.82423385532463], [[0]], [1]], 'parameters': {}}
+for key in config.keys():
+    print(key, config[key])
+    
+pa = ProportionalArchitecture(config=config, env=pen, input_indexes=inputs, namespace = namespace)
+pa()
+hpct = pa.get_hierarchy()
+hpct.draw(move={'Pendulum': [-0.2, -0.3],'Input0': [-0.3, 0]})
+
+
level0 [[[1]], [62.82423385532463], [[0]], [1]]
+parameters {}
+
+
+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/environment_processing.html b/environment_processing.html new file mode 100644 index 00000000..20a6bbc8 --- /dev/null +++ b/environment_processing.html @@ -0,0 +1,861 @@ + + + + + + + + + + +Environment processing – pct + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Environment processing

+
+ +
+
+ Bespoke processing for each environment. +
+
+ + +
+ + + + +
+ + + +
+ + + +
+

Wind Turbine results

+
+

source

+
+

wind_turbine_results

+
+
 wind_turbine_results (environment_properties=None, experiment=None,
+                       root=None, wt='WindTurbine', verbose=None,
+                       early=None, min=None, comparisons=False,
+                       comparisons_print_plots=False, property_dir=None,
+                       property_file=None, plots=None,
+                       log_testing_to_experiment=False)
+
+
+
+
+

EnvironmentProcessingFactory

+
+

source

+
+

EnvironmentProcessingFactory

+
+
 EnvironmentProcessingFactory ()
+
+

Initialize self. See help(type(self)) for accurate signature.

+
+
+
+

BaseEnvironmentProcessing

+
+

source

+
+

BaseEnvironmentProcessing

+
+
 BaseEnvironmentProcessing ()
+
+

Base class of environment processing.

+
+
+
+

WindTurbineEnvironmentProcessing

+
+

source

+
+

WindTurbineEnvironmentProcessing

+
+
 WindTurbineEnvironmentProcessing ()
+
+

WindTurbine environment processing.

+
+

source

+
+
+

DummyEnvironmentProcessing

+
+
 DummyEnvironmentProcessing ()
+
+

Dummy environment processing.

+
+
+
+

ARCEnvironmentProcessing

+
+

source

+
+

ARCEnvironmentProcessing

+
+
 ARCEnvironmentProcessing ()
+
+

ARC environment processing.

+
+
+
+

ARC Test

+
+
env_name = 'ARC'
+args={'file': 'ARC0701-FitnessError-CurrentError-Mode00', 'seed':1, 'drive':'G:\\My Drive\\', 'max': False, 'verbosed': {'display_env': True, 'hpct_verbose': False}}
+env_proc = EnvironmentProcessingFactory.createEnvironmentProcessing(f'{env_name}EnvironmentProcessing')
+env_proc.set_properties(args=args)
+ws=env_proc.get_workspace()
+name = env_proc.get_experiment_name()
+print(ws, name)
+
+
arc-challenge 0701-01
+
+
+
+
#env_proc.results(filepath='G:\\My Drive\\data\\ga\\ARC\\FitnessError-MovingSumError-Mode07\\ga-000.000-s001-1x1-m007-ARC0009-1e1867dd3cfb6967b085ac003458aa46.properties')
+# env_proc.results(filepath='testfiles\\data\\ga\\ga-000.000-s001-1x1-m007-ARC0010-9ddcf52416e60d65f19007957d07262d-consolidated.properties')
+
+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/environments.html b/environments.html new file mode 100644 index 00000000..4e1b9fd1 --- /dev/null +++ b/environments.html @@ -0,0 +1,1106 @@ + + + + + + + + + + +Environments – pct + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Environments

+
+ +
+
+ Classes to create simulated environment functions. +
+
+ + +
+ + + + +
+ + + +
+ + + +
+

source

+
+

EnvironmentFactory

+
+
 EnvironmentFactory ()
+
+

Initialize self. See help(type(self)) for accurate signature.

+
+

source

+
+
+

ControlEnvironment

+
+
 ControlEnvironment (name=None, value=None, links=None, new_name=True,
+                     namespace=None)
+
+

Abstract ControlEnvironment

+
+

source

+
+
+

OpenAIGym

+
+
 OpenAIGym (env_name=None, render=False, render_mode='rgb_array',
+            video_wrap=False, value=0, name='gym', seed=None, links=None,
+            new_name=True, early_termination=False, namespace=None,
+            **cargs)
+
+

A function that creates and runs an environment from OpenAI Gym. Parameter: The environment name. Flag to display environment. Links: Link to the action function.

+
+

source

+
+
+

CartPoleV1

+
+
 CartPoleV1 (render=False, render_mode='rgb_array', video_wrap=False,
+             value=0, name='CartPoleV1', seed=None, links=None,
+             new_name=True, namespace=None, **cargs)
+
+

A function that creates an runs the CartPole-v1 environment from OpenAI Gym. Parameter: The environment name. Flag to display environment. Links: Link to the action function.

+
+

source

+
+
+

CartPoleDV1

+
+
 CartPoleDV1 (render=False, render_mode='rgb_array', video_wrap=False,
+              value=0, name='CartPoleD-v1', seed=None, links=None,
+              new_name=True, namespace=None, **cargs)
+
+

A function that creates an runs the CartPole-v1 environment from OpenAI Gym. Parameter: The environment name. Flag to display environment. Links: Link to the action function.

+
+

source

+
+
+

Pendulum

+
+
 Pendulum (render=False, render_mode='rgb_array', video_wrap=False,
+           value=0, name='Pendulum', seed=None, links=None, new_name=True,
+           namespace=None, **cargs)
+
+

A function that creates an runs the Pendulum-v1 environment from OpenAI Gym. Parameter: The environment name. Flag to display environment. Links: Link to the action function.

+
+
class Pendulum_1(OpenAIGym):
+    "A function that creates an runs the Pendulum-v1 environment from OpenAI Gym. Parameter: The environment name. Flag to display environment. Links: Link to the action function."
+    # from obs[0], indices
+    # 0 cos(theta) - +1 is up, -1 is down, 0 is left and right
+    # 1 sin(theta) - +1 is left, -1 is right, 0 is up and down
+    # 2 theta dot - +dot is anti-clockwise, -dot is clockwise
+    # 3 theta dot - normalised to +/- 1    
+    # 4 theta +1/-1 (added here) 1 is pointing upwards, + is anti-clockwise, - is clockwise
+    # reward - -(theta^2 + 0.1*theta_dt^2 + 0.001*action^2)
+
+    def __init__(self, render=False, render_mode="rgb_array", video_wrap=False, value=0, name="Pendulum_1", 
+                 seed=None, links=None, new_name=True, namespace=None,**cargs):        
+        super().__init__('Pendulum-v1', render=render, render_mode=render_mode, video_wrap=video_wrap, value=value, name=name, seed=seed, 
+                         links=links, new_name=new_name, namespace=namespace, **cargs)
+        
+    def process_hierarchy_values(self):
+        self.hierarchy_values = self.links[0].get_value()
+   
+    def process_actions(self):
+        pass
+    
+    def apply_actions_get_obs(self):
+        return self.env.step([self.hierarchy_values])
+        
+    def parse_obs(self):    
+        self.value = self.obs[0]
+        self.reward = -self.obs[1]
+        self.done = self.obs[2]
+        self.info = self.obs[3]
+
+    def process_values(self):
+        vel = self.obs[0][2]/8.0
+        self.value = np.append(self.value, vel)
+        x = math.copysign(math.acos(self.obs[0][0]), self.obs[0][1])/math.pi
+        #theta = 100 - (10 * math.copysign(1-abs(x), x))
+        theta = 100 - (10 * x)
+        self.value = np.append(self.value, theta)
+        
+    class Factory:
+        def create(self, seed=None): return Pendulum_1(seed=seed)
+    class FactoryWithNamespace:
+        def create(self, namespace=None, seed=None): return Pendulum_1(namespace=namespace, seed=seed)
+
+
+

source

+
+
+

MountainCarV0

+
+
 MountainCarV0 (render=False, render_mode='rgb_array', video_wrap=False,
+                value=0, name='MountainCarV0', seed=None, links=None,
+                new_name=True, namespace=None, **cargs)
+
+

A function that creates and runs the MountainCar-v0 environment from OpenAI Gym. Parameter: The environment name. Flag to display environment. Links: Link to the action function.

+
+

source

+
+
+

MountainCarContinuousV0

+
+
 MountainCarContinuousV0 (render=False, render_mode='rgb_array',
+                          video_wrap=False, value=0,
+                          name='MountainCarContinuousV0', seed=None,
+                          links=None, new_name=True,
+                          early_termination=True, namespace=None, **cargs)
+
+

A function that creates and runs the MountainCarContinuous-v0 environment from OpenAI Gym. Parameter: The environment name. Flag to display environment. Links: Link to the action function.

+
+

source

+
+
+

WindTurbine

+
+
 WindTurbine (value=0, name='WindTurbine', links=None, new_name=True,
+              namespace=None, seed=None, **cargs)
+
+

A function that creates and runs the YawEnv environment for a wind turbine. Indexes 0 - action, 1 - yaw error, 2 - wind direction, 3 - wind speed (ignore 0).

+
+

source

+
+
+

VelocityModel

+
+
 VelocityModel (mass=50, value=0, name='VelocityModel', links=None,
+                num_links=1, new_name=True, indexes=0, namespace=None,
+                **cargs)
+
+

A simple model of a moving object of a particular mass. Parameters: The environment name, mass. Links: Link to the action function.

+
+

source

+
+
+

DummyModel

+
+
 DummyModel (name='World', value=0, links=None, new_name=True,
+             namespace=None, seed=None, **cargs)
+
+

Base class of a PCT function. This class is not used directly by developers, but defines the functionality common to all.

+
+

source

+
+
+

WebotsWrestler

+
+
 WebotsWrestler (render=False, value=0, name='WebotsWrestler', seed=None,
+                 links=None, new_name=True, early_termination=True,
+                 namespace=None)
+
+

A function that creates and runs a Webots Wrestler robot.

+
+

source

+
+
+

WebotsWrestlerSupervisor

+
+
 WebotsWrestlerSupervisor (render=False, value=0,
+                           name='WebotsWrestlerSupervisor', seed=None,
+                           links=None, new_name=True,
+                           early_termination=True, namespace=None)
+
+

A function that creates and runs a Webots Wrestler robot.

+
+

source

+
+
+

Bridge

+
+
 Bridge (render=False, value=0, name='Bridge', seed=None, links=None,
+         new_name=True, early_termination=True, namespace=None)
+
+

An environment function with sensors set by external system.

+
+

source

+
+
+

MicroGrid

+
+
 MicroGrid (value=0, name='MicroGrid', links=None, new_name=True,
+            namespace=None, seed=None, **cargs)
+
+

A function that creates and runs the microgrid environment for an energy management system.
‘Deep reinforcement learning for energy management in a microgrid with flexible demand.’
Taha Abdelhalim Nakabi, Pekka Toivanen.
https://doi.org/10.1016/j.segan.2020.100413

Inputs - st = [SoCt, BSCt, Cbt, Tt, Gt, Put, Lb,t, t].
0 - ISC - the average SoC (state-of-charge) of the TCLs,
1 - IL - the current load value of the daily consumption pattern.
2 - IPC - the pricing counter,
3 - IBS - the battery SoC,
4 - IEG - the energy generation,
5 - IT - the temparature,
6 - IEP - the electricty prices,
7 - ITS - the time step,
Actions:
0 - TCL action, Atcl,
1 - price action, Ap,
2 - energy deficiency action, Ad,
3 - energy excess action, Ae

+
+
from pct.microgrid import MicroGridEnv0Plus
+
+env = MicroGrid(seed=1)
+env.summary()
+
+
MicroGrid MicroGrid | 0
+
+
+
+
+

ARC

+
+

source

+
+
+

ARC

+
+
 ARC (value:float=0, name:str='ARC', links:Optional[List]=None,
+      new_name:bool=True, render:bool=False, seed:int=None,
+      namespace:Optional[str]=None, **cargs:dict)
+
+

A function that creates and runs an ARC environment from a file given the rask code.

+
+
env = ARC()
+env.add_link(Constant(1))
+# env.add_link(Constant(0))
+properties = { 'dir': 'C:\\packages\\arc-prize-2024', 'file_prefix':'arc-agi_training_', 'code':'007bbfb7', 'dataset': 'train', 'control_set': ['dims'], 'input_set': ['env']}
+# file_name = os.path.join(properties['dir'], properties['file_prefix']) + 'challenges.json' 
+# challenges_manager = ChallengesDataManager(file_name)
+# data = challenges_manager.get_data_for_key(properties['code'])
+# properties['data']=data
+
+# env.set_properties(properties)
+# env.set_render(True)
+# env.reset()
+# print(env.env.info)
+# for i in range(6):
+#     state = env()
+#     env.summary()    
+#     # print()
+# print(env.get_config())
+# print()
+# print(env.output_string())    
+# print()
+# print(state)
+
+# env.close()
+
+
+
env.close()
+
+
+
+

OpenAI Gym

+

An example showing how to use an OpenAI Gym function. And how to have another function which accesses one of the values of the gym environment.

+
+
from pct.functions import Constant
+from pct.functions import IndexedParameter
+from pct.putils import FunctionsList
+from pct.functions import Proportional
+
+
+
render=False 
+print(render)
+acrobot = OpenAIGym("Acrobot-v1", render=render, seed=1)
+namespace=acrobot.namespace
+acrobot.add_link(Constant(1, namespace=namespace))
+#acrobot.get_config()
+
+
False
+
+
+
+
#acrobot()
+#print(acrobot.reward)
+#print(getattr(acrobot, "reward"))
+
+
+
acrobot.output_string()
+
+
'0.000'
+
+
+
+
#acrobot.value[0]
+
+

The IndexedParameter type retrieves a value from a linked function based upon an index.

+
+
cos_angle1 = IndexedParameter(0, name="cos_angle1", namespace=namespace)
+cos_angle1.add_link(acrobot)
+print(cos_angle1.get_config())
+#cos_angle1()
+
+
{'type': 'IndexedParameter', 'name': 'cos_angle1', 'value': 0, 'links': {0: 'gym'}, 'index': 0}
+
+
+
+
acrobot.close()
+
+
+
pen = Pendulum(render=True, namespace=namespace, seed=1)
+pen.add_link(Constant([1], namespace=namespace))
+print(pen.get_config())
+#pen.run(steps=10, verbose=True)
+# why TypeError: size must be two numbers?
+
+
{'type': 'Pendulum', 'name': 'Pendulum', 'value': 0, 'links': {0: 'constant1'}, 'env_name': 'Pendulum-v1', 'reward': 0, 'done': False, 'info': {}}
+
+
+
+
pen.close()
+
+
+
+

Velocity Model

+
+
vm = VelocityModel(name='VelocityModel', namespace=namespace)
+vm.add_link(Constant(1, name='mycon'))
+print(vm.summary())
+config = vm.get_config()
+print(config)
+assert config == {'type': 'VelocityModel', 'name': 'VelocityModel', 'value': 0, 'links': {0: 'mycon'}, 'mass': 50}
+vm.run(steps=10, verbose=True)
+
+
VelocityModel VelocityModel | 0 | links  mycon 
+None
+{'type': 'VelocityModel', 'name': 'VelocityModel', 'value': 0, 'links': {0: 'mycon'}, 'mass': 50}
+0.020 0.040 0.060 0.080 0.100 0.120 0.140 0.160 0.180 0.200
+
+
+
0.19999999999999998
+
+
+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/errors.html b/errors.html new file mode 100644 index 00000000..044f3963 --- /dev/null +++ b/errors.html @@ -0,0 +1,1086 @@ + + + + + + + + + + +Errors – pct + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Errors

+
+ +
+
+ Types of error response of a percectual control hierarchy. +
+
+ + +
+ + + + +
+ + + +
+ + + +
+

BaseErrorType

+
+

source

+
+

BaseErrorType

+
+
 BaseErrorType (flip_error_response=False)
+
+

Base class of a type error response. This class is not used direclty by developers, but defines the interface common to all.

+
+
+
+

RootSumSquaredError

+
+

source

+
+

RootSumSquaredError

+
+
 RootSumSquaredError (flip_error_response=False)
+
+

The square root of the sum of the square of the errors.

+
+
+
+

RootMeanSquareError

+
+

source

+
+

RootMeanSquareError

+
+
 RootMeanSquareError (flip_error_response=False)
+
+

The square root of the mean of the sum of the square of the errors.

+
+
+
+

SummedError

+
+

source

+
+

SummedError

+
+
 SummedError (flip_error_response=False)
+
+

Sum of all errors.

+
+
+
+

SummedError

+
+

source

+
+

CurrentError

+
+
 CurrentError (flip_error_response=False)
+
+

The current error, rather than a function of the historical values.

+
+
+
+

CurrentError

+
+

source

+
+

SmoothError

+
+
 SmoothError (flip_error_response=False)
+
+

The exponential smoothed value of the error.

+
+
+
+

MovingSumError

+
+

source

+
+

MovingSumError

+
+
 MovingSumError (flip_error_response=False)
+
+

The moving sum of the error.

+
+
+
+

MovingAverageError

+
+

source

+
+

MovingAverageError

+
+
 MovingAverageError (flip_error_response=False)
+
+

The moving average of the error.

+
+
+
+

BaseErrorCollector

+
+

source

+
+

BaseErrorCollector

+
+
 BaseErrorCollector (limit, error_response, min=True)
+
+

Base class of an error collector. This class is not used direclty by developers, but defines the interface common to all.

+
+
+
+

TotalError

+
+

source

+
+

TotalError

+
+
 TotalError (limit=None, error_response=None, min=None, **cargs)
+
+

A class to collect all the errors of the control system run.

+
+
+
+

TopError

+
+

source

+
+

TopError

+
+
 TopError (limit=None, error_response=None, min=None, **cargs)
+
+

A class to collect all the errors of the top-level nodes.

+
+
+
+

InputsError

+
+

source

+
+

InputsError

+
+
 InputsError (limit=None, error_response=None, min=None, **cargs)
+
+

A class to collect the values of the input values.

+
+
+
+

ReferencedInputsError

+
+

source

+
+

ReferencedInputsError

+
+
 ReferencedInputsError (limit=None, error_response=None, min=None,
+                        **cargs)
+
+

A class to collect the values of the input values subtracted from reference values.

+
+
+
+

RewardError

+
+

source

+
+

RewardError

+
+
 RewardError (limit=None, error_response=None, min=None, **cargs)
+
+

A class that collects the reward value of the control system run.

+
+
+
+

FitnessError

+
+

source

+
+

FitnessError

+
+
 FitnessError (limit=None, error_response=None, min=None, **cargs)
+
+

A class that collects the fitness value of the control system run.

+
+
+
+

ErrorResponseFactory

+
+

source

+
+

ErrorResponseFactory

+
+
 ErrorResponseFactory ()
+
+

Initialize self. See help(type(self)) for accurate signature.

+
+
+
+

ErrorCollectorFactory

+
+

source

+
+

ErrorCollectorFactory

+
+
 ErrorCollectorFactory ()
+
+

Initialize self. See help(type(self)) for accurate signature.

+
+
rms = RootMeanSquareError()
+for i in range(10):
+    rms(i)
+er = rms.get_error_response()
+print(er)
+assert er == 5.338539126015656
+
+
5.338539126015656
+
+
+
+
er = RootSumSquaredError()
+te = TotalError(error_response=er, limit=250,min=True)   
+te.add_error_data([1, 2])
+print(te)
+
+
TotalError limit:250, limit_exceeded:False, : RootSumSquaredError error_response:2.23606797749979
+
+
+
+
# hpct = PCTHierarchy(1,1,error_collector=te)
+# hpct.run(steps=5, verbose=True)
+
+
+
err=te.error()
+print(err)
+
+
2.23606797749979
+
+
+
+
et = ErrorResponseFactory.createErrorResponse('RootSumSquaredError')   
+et(102)
+print(et.get_error_response())
+
+ec = ErrorCollectorFactory.createErrorCollector('TotalError')   
+ec.set_limit(100)
+ec.set_error_response(et)
+print(ec.error())
+
+
102.0
+102.0
+
+
+
+
ec = BaseErrorCollector.collector( 'RootMeanSquareError','InputsError', 10, flip_error_response=True, min=False)
+
+
+
ec.add_error_data([1])
+print(ec.error())
+print(ec)
+
+
-1.0
+InputsError limit:10, limit_exceeded:False, : RootMeanSquareError error_response:-1.0
+
+
+
+
ec.reset()
+
+
+
print(ec)
+
+
InputsError limit:10, limit_exceeded:False, : RootMeanSquareError error_response:None
+
+
+ + +
+
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/examples.html b/examples.html new file mode 100644 index 00000000..f48c966e --- /dev/null +++ b/examples.html @@ -0,0 +1,974 @@ + + + + + + + + + + +Examples – pct + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Examples

+
+ +
+
+ Examples of the PCT library in use. +
+
+ + +
+ + + + +
+ + + +
+ + + +
+
+
+
The autoreload extension is already loaded. To reload it, use:
+  %reload_ext autoreload
+
+
+
+
import gym, warnings
+render=False
+runs=1
+
+
+
render=True
+runs=2000
+
+
+

Cartpole

+

Cartpole is an Open AI gym environment for the inverted pendulum problem. The goal is to keep the pole balanced, by moving the cart left or right.

+

The environment provides observations (perceptions) for the state of the cart and pole.

+

0 - Cart Position
+1 - Cart Velocity
+2 - Pole Angle
+3 - Pole Angular Velocity

+

It takes one value, of 0 or 1, for applying a force to the left or right, respectively.

+

The PCT solution is a four-level hierarchy for controlling the perceptions at goal values. Only one goal reference is manually set, the highest level which is the pole angle of 0.

+

This example shows how a perceptual control hierarchy can be implemented with this library.

+
+
import matplotlib.pyplot as plt
+import numpy as np
+from pct.hierarchy import PCTHierarchy
+from pct.putils import FunctionsList
+from pct.environments import CartPoleV1
+from pct.functions import IndexedParameter
+from pct.functions import Integration
+from pct.functions import GreaterThan
+from pct.functions import PassOn
+
+

Create a hierarchy of 4 levels each with one node.

+
+
cartpole_hierarchy = PCTHierarchy(levels=4, cols=1, name="cartpoleh", build=False)
+namespace=cartpole_hierarchy.namespace
+cartpole_hierarchy.get_node(0, 0).name = 'cart_velocity_node'
+cartpole_hierarchy.get_node(1, 0).name = 'cart_position_node'
+cartpole_hierarchy.get_node(2, 0).name = 'pole_velocity_node'
+cartpole_hierarchy.get_node(3, 0).name = 'pole_angle_node'
+#FunctionsList.getInstance().report()
+#cartpole_hierarchy.summary(build=True)
+
+

Create the Cartpole gym environment function. This will apply the “action” output from the hierarchy and provide the new observations.

+
+
cartpole = CartPoleV1(name="CartPole-v1", render=render, namespace=namespace, seed=1)
+
+

Create functions for each of the observation parameters of the Cartpole environment. Insert them into the hierarchy at the desired places.

+
+
cartpole_hierarchy.insert_function(level=0, col=0, collection="perception", function=IndexedParameter(index=1, name="cart_velocity", links=[cartpole], namespace=namespace))
+cartpole_hierarchy.insert_function(level=1, col=0, collection="perception", function=IndexedParameter(index=0, name="cart_position", links=[cartpole], namespace=namespace))
+cartpole_hierarchy.insert_function(level=2, col=0, collection="perception", function=IndexedParameter(index=3, name="pole_velocity", links=[cartpole], namespace=namespace))
+cartpole_hierarchy.insert_function(level=3, col=0, collection="perception", function=IndexedParameter(index=2, name="pole_angle", links=[cartpole], namespace=namespace))
+
+

Link the references to the outputs of the level up.

+
+
cartpole_hierarchy.insert_function(level=0, col=0, collection="reference", function=PassOn(name="cart_velocity_reference", links=['proportional1'], namespace=namespace))
+cartpole_hierarchy.insert_function(level=1, col=0, collection="reference", function=PassOn(name="cart_position_reference", links=['proportional2'], namespace=namespace))
+cartpole_hierarchy.insert_function(level=2, col=0, collection="reference", function=PassOn(name="pole_velocity_reference", links=['proportional3'], namespace=namespace))
+
+

Set the highest level reference.

+
+
top = cartpole_hierarchy.get_function(level=3, col=0, collection="reference")
+top.set_name("pole_angle_reference")
+top.set_value(0)
+
+

Link the output of the hierarchy back to the Cartpole environment.

+
+
cartpole_hierarchy.summary(build=True)
+
+
**************************
+cartpoleh PCTHierarchy [1, 1, 1, 1] 0a8fcbad-d648-11ee-ae91-8cf8c5b8669b
+--------------------------
+PRE: None
+Level 0 Cols 1
+cart_velocity_node PCTNode 0a8fcbad-d648-11ee-ae91-8cf8c5b8669b
+----------------------------
+REF: cart_velocity_reference PassOn | 0 | links  proportional1 
+PER: cart_velocity IndexedParameter | index 1 | 0 | links  CartPole-v1 
+COM: subtract Subtract | 0 | links  cart_velocity_reference cart_velocity 
+OUT: proportional Proportional | gain 1 | 0 | links  subtract 
+----------------------------
+Level 1 Cols 1
+cart_position_node PCTNode 0a8fcbad-d648-11ee-ae91-8cf8c5b8669b
+----------------------------
+REF: cart_position_reference PassOn | 0 | links  proportional2 
+PER: cart_position IndexedParameter | index 0 | 0 | links  CartPole-v1 
+COM: subtract1 Subtract | 0 | links  cart_position_reference cart_position 
+OUT: proportional1 Proportional | gain 1 | 0 | links  subtract1 
+----------------------------
+Level 2 Cols 1
+pole_velocity_node PCTNode 0a8fcbad-d648-11ee-ae91-8cf8c5b8669b
+----------------------------
+REF: pole_velocity_reference PassOn | 0 | links  proportional3 
+PER: pole_velocity IndexedParameter | index 3 | 0 | links  CartPole-v1 
+COM: subtract2 Subtract | 0 | links  pole_velocity_reference pole_velocity 
+OUT: proportional2 Proportional | gain 1 | 0 | links  subtract2 
+----------------------------
+Level 3 Cols 1
+pole_angle_node PCTNode 0a8fcbad-d648-11ee-ae91-8cf8c5b8669b
+----------------------------
+REF: pole_angle_reference Constant | 0 
+PER: pole_angle IndexedParameter | index 2 | 0 | links  CartPole-v1 
+COM: subtract3 Subtract | 0 | links  pole_angle_reference pole_angle 
+OUT: proportional3 Proportional | gain 1 | 0 | links  subtract3 
+----------------------------
+POST: None
+**************************
+
+
+
+
cartpole_hierarchy.insert_function(level=0, col=0, collection="output", function=Integration(gain=-0.05, slow=4, name="force", links='subtract', namespace=namespace))
+
+

Set the names and gains of the output functions. This also shows another way of getting a function, by name.

+
+
FunctionsList.getInstance().get_function(namespace=namespace, name="proportional3").set_name("pole_angle_output")
+FunctionsList.getInstance().get_function(namespace=namespace, name="pole_angle_output").set_property('gain', 3.5)
+
+FunctionsList.getInstance().get_function(namespace=namespace, name="proportional2").set_name("pole_velocity_output")
+FunctionsList.getInstance().get_function(namespace=namespace, name="pole_velocity_output").set_property('gain', 0.5)
+
+FunctionsList.getInstance().get_function(namespace=namespace, name="proportional1").set_name("cart_position_output")
+FunctionsList.getInstance().get_function(namespace=namespace, name="cart_position_output").set_property('gain', 2)
+
+

Add a post function to convert the output to 1 or 0 as required by the Cartpole environment.

+
+
greaterthan = GreaterThan(threshold=0, upper=1, lower=0, links='force', namespace=namespace)
+cartpole_hierarchy.add_postprocessor(greaterthan)
+
+

Add the cartpole function as one that is executed before the actual hierarchy.

+
+
cartpole_hierarchy.add_preprocessor(cartpole)
+
+

Set the output of the hierachy as the action input to the Cartpole environment.

+
+
#link = cartpole_hierarchy.get_output_function()
+cartpole.add_link(greaterthan)
+
+

Sit back and observe the brilliance of your efforts.

+
+
cartpole_hierarchy.set_order("Down")
+
+
+
cartpole_hierarchy.summary()
+
+
**************************
+cartpoleh PCTHierarchy [1, 1, 1, 1] 0a8fcbad-d648-11ee-ae91-8cf8c5b8669b
+--------------------------
+PRE: CartPole-v1 CartPoleV1 | 0 | links  greaterthan 
+Level 3 Cols 1
+pole_angle_node PCTNode 0a8fcbad-d648-11ee-ae91-8cf8c5b8669b
+----------------------------
+REF: pole_angle_reference Constant | 0 
+PER: pole_angle IndexedParameter | index 2 | 0 | links  CartPole-v1 
+COM: subtract3 Subtract | 0 | links  pole_angle_reference pole_angle 
+OUT: pole_angle_output Proportional | gain 3.5 | 0 | links  subtract3 
+----------------------------
+Level 2 Cols 1
+pole_velocity_node PCTNode 0a8fcbad-d648-11ee-ae91-8cf8c5b8669b
+----------------------------
+REF: pole_velocity_reference PassOn | 0 | links  pole_angle_output 
+PER: pole_velocity IndexedParameter | index 3 | 0 | links  CartPole-v1 
+COM: subtract2 Subtract | 0 | links  pole_velocity_reference pole_velocity 
+OUT: pole_velocity_output Proportional | gain 0.5 | 0 | links  subtract2 
+----------------------------
+Level 1 Cols 1
+cart_position_node PCTNode 0a8fcbad-d648-11ee-ae91-8cf8c5b8669b
+----------------------------
+REF: cart_position_reference PassOn | 0 | links  pole_velocity_output 
+PER: cart_position IndexedParameter | index 0 | 0 | links  CartPole-v1 
+COM: subtract1 Subtract | 0 | links  cart_position_reference cart_position 
+OUT: cart_position_output Proportional | gain 2 | 0 | links  subtract1 
+----------------------------
+Level 0 Cols 1
+cart_velocity_node PCTNode 0a8fcbad-d648-11ee-ae91-8cf8c5b8669b
+----------------------------
+REF: cart_velocity_reference PassOn | 0 | links  cart_position_output 
+PER: cart_velocity IndexedParameter | index 1 | 0 | links  CartPole-v1 
+COM: subtract Subtract | 0 | links  cart_velocity_reference cart_velocity 
+OUT: force Integration | gain -0.05 slow 4  | 0 | links  subtract 
+----------------------------
+POST: greaterthan GreaterThan | threshold 0 upper 1 lower 0  | 0 | links  force 
+**************************
+
+
+
+
cartpole_hierarchy.draw(font_size=10, figsize=(8,12), move={'CartPole-v1': [-0.075, 0]}, node_size=1000, node_color='red')
+
+
+
cartpole_hierarchy.save("cartpole.json")
+
+
+
import networkx as nx
+gr = cartpole_hierarchy.graph()
+with warnings.catch_warnings():
+    warnings.simplefilter("ignore")
+    print(nx.info(gr))
+    print(gr.nodes())
+
+
DiGraph with 18 nodes and 21 edges
+['greaterthan', 'force', 'CartPole-v1', 'cart_velocity_reference', 'cart_position_output', 'subtract', 'cart_velocity', 'cart_position_reference', 'pole_velocity_output', 'subtract1', 'cart_position', 'pole_velocity_reference', 'pole_angle_output', 'subtract2', 'pole_velocity', 'pole_angle_reference', 'subtract3', 'pole_angle']
+
+
+

Run the hierarchy for 500 steps.

+
+
cartpole_hierarchy.run(1,verbose=False)
+
+
1
+
+
+
+
cartpole_hierarchy.run(runs,verbose=False)
+
+
1
+
+
+
+
cartpole.close()
+
+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/functions.html b/functions.html new file mode 100644 index 00000000..1308b398 --- /dev/null +++ b/functions.html @@ -0,0 +1,1526 @@ + + + + + + + + + + +Functions – pct + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Functions

+
+ +
+
+ Functions that form the elements of a perceptual control node (system). +
+
+ + +
+ + + + +
+ + + +
+ + + +
+

Overview

+

Each function outputs the result of applying the function logic and may be a scalar or array, depending on the input. The inputs are supplied as links to another function, from where the values are read. Unless the function is a simple case, such as a ‘Constant’.

+

Parameters are supplied as arguments to the constructor of the class.

+

All functions include the methods defined by the BaseFunction class.

+
+

source

+
+

HPCTFUNCTION

+
+
 HPCTFUNCTION (value, names=None, module=None, qualname=None, type=None,
+               start=1)
+
+

Types of control functions in a node.

+
+
+
+

Functions

+
+

BaseFunction

+
+

source

+
+

BaseFunction

+
+
 BaseFunction (name=None, value=None, links=None, new_name=True,
+               namespace=None)
+
+

Base class of a PCT function. This class is not used directly by developers, but defines the functionality common to all.

+
+

source

+
+
+

FunctionFactory

+
+
 FunctionFactory ()
+
+

Initialize self. See help(type(self)) for accurate signature.

+
+
#show_doc(BaseFunction.summary)
+
+
+
#show_doc(BaseFunction.get_config)
+
+
+
+
+

Subtract

+
+

source

+
+

Subtract

+
+
 Subtract (value=0, name='subtract', links=None, new_name=True,
+           namespace=None, **cargs)
+
+

A function that subtracts one value from another. Parameter: None. Links: Two links required to each the values to be subtracted.

+
+

source

+
+
+

Proportional

+
+
 Proportional (gain=1, value=0, name='proportional', links=None,
+               new_name=True, namespace=None, **cargs)
+
+

A proportion of the input value as defined by the gain parameter. Parameters: The gain value. Links: One.

+
+

source

+
+
+

Variable

+
+
 Variable (value=0, name='variable', links=None, new_name=True,
+           namespace=None, **cargs)
+
+

A function that returns a variable value. Parameter: The variable value. Links: None

+
+

source

+
+
+

PassOn

+
+
 PassOn (value=0, name='variable', links=None, new_name=True,
+         namespace=None, **cargs)
+
+

A function that passes on a variable value from a linked function. Parameter: None. Links: One

+
+

source

+
+
+

GreaterThan

+
+
 GreaterThan (threshold=0, upper=1, lower=0, value=0, name='greaterthan',
+              links=None, new_name=True, namespace=None, **cargs)
+
+

One of two supplied values is returned if the input is greater than supplied threshold.
Parameters: The threshold and upper and lower value. Links: One

+
+

source

+
+
+

Constant

+
+
 Constant (value=0, name='constant', new_name=True, namespace=None,
+           **cargs)
+
+

A function that returns a constant value. Parameter: The constant value. Links: None

+
+

source

+
+
+

Step

+
+
 Step (upper=None, lower=None, delay=None, period=None, value=0,
+       name='step', new_name=True, namespace=None, **cargs)
+
+

A function that returns an alternating signal. Parameter: The upper and lower values, and a delay value. Links: None

+
+

source

+
+
+

Integration

+
+
 Integration (gain=1, slow=2, value=0, name='integration', links=None,
+              new_name=True, namespace=None, **cargs)
+
+

A leaky integrating function. Equivalent of a exponential smoothing function, of the amplified input. Parameters: The gain and slow values. Links: One.

+
+

source

+
+
+

IntegrationDual

+
+
 IntegrationDual (gain=1, slow=2, value=0, name='integration', links=None,
+                  new_name=True, namespace=None, **cargs)
+
+

A leaky integrating function, applying one signal to another. Equivalent of a exponential smoothing function, of the amplified input. Parameters: The gain and slow values. Links: Two.

+
+

source

+
+
+

Sigmoid

+
+
 Sigmoid (range=2, slope=10, value=0, name='sigmoid', links=None,
+          new_name=True, namespace=None, **cargs)
+
+

A sigmoid function. Similar to a proportional function, but kept within a limit (+/- half the range). Parameters: The range and slope values. Links: One.

+
+

source

+
+
+

WeightedSum

+
+
 WeightedSum (weights=[0], value=0, name='weighted_sum', links=None,
+              new_name=True, usenumpy=False, namespace=None, **cargs)
+
+

A function that combines a set of inputs by multiplying each by a weight and then adding them up. Parameter: The weights array. Links: Links to all the input functions.

+
+

source

+
+
+

SmoothWeightedSum

+
+
 SmoothWeightedSum (weights=[0], smooth_factor=0.0, value=0,
+                    name='smooth_weighted_sum', links=None, new_name=True,
+                    usenumpy=False, namespace=None, **cargs)
+
+

A function that combines a set of inputs by multiplying each by a weight and then adding them up. And then smooths the result. Parameter: The weights array. Links: Links to all the input functions.

+
+

source

+
+
+

IndexedParameter

+
+
 IndexedParameter (index=None, value=0, name='indexed_parameter',
+                   links=None, new_name=True, namespace=None, **cargs)
+
+

A function that returns a parameter from a linked function, indexed by number. Parameter: The index. Links: One.

+
+
+
+

SigmoidWeightedSum

+
+

source

+
+

SigmoidWeightedSum

+
+
 SigmoidWeightedSum (weights=[0], range=2.0, slope=10.0, value=0,
+                     name='sigmoid_weighted_sum', links=None,
+                     new_name=True, usenumpy=False, namespace=None,
+                     **cargs)
+
+

A function that combines a set of inputs by multiplying each by a weight and then adding them up. And then limits the output by squashing with a sigmoid function. Parameter: The weights array. Links: Links to all the input functions.

+
+
+
+

SigmoidSmoothWeightedSum

+
+

source

+
+

SigmoidSmoothWeightedSum

+
+
 SigmoidSmoothWeightedSum (weights=[0], smooth_factor=0.0, range=2.0,
+                           slope=10.0, value=0,
+                           name='sigmoid_smooth_weighted_sum', links=None,
+                           new_name=True, usenumpy=False, namespace=None,
+                           **cargs)
+
+

A function that combines a set of inputs by multiplying each by a weight and then adding them up. It then smooths the result and then limits the output by squashing with a sigmoid function. Parameter: The weights array. Links: Links to all the input functions.

+
+

source

+
+
+

Derivative

+
+
 Derivative (history_length=1, value=0, name='derivative', links=None,
+             new_name=True, usenumpy=False, namespace=None, **cargs)
+
+

A function that provides the difference to previous values of the input signal. Parameter: The weights array. Links: Links to all the input functions.

+
+

source

+
+
+

DerivativeWeightedSum

+
+
 DerivativeWeightedSum (weights=[0], history_length=1, value=0,
+                        name='derivative_weighted_sum', links=None,
+                        new_name=True, usenumpy=False, namespace=None,
+                        **cargs)
+
+

A function that combines a set of inputs by multiplying each by a weight and then adding them up. And then takes the difference of with a past value. Parameter: The weights array. Links: Links to all the input functions.

+
+
+
+
+

Usage

+
+

Creating Functions

+

Standard class constructor. Different ways to create a function with the standard constructor.

+
+
prop = Proportional()
+print(prop.get_config())
+prop = Proportional("myprop", 10)
+print(prop.get_config())
+prop = Proportional(gain=10)
+print(prop.get_config())
+
+
{'type': 'Proportional', 'name': 'proportional', 'value': 0, 'links': {}, 'gain': 1}
+{'type': 'Proportional', 'name': 'proportional', 'value': 10, 'links': {}, 'gain': 'myprop'}
+{'type': 'Proportional', 'name': 'proportional', 'value': 0, 'links': {}, 'gain': 10}
+
+
+

Configuration class constructor. Create the function by passing a configuration structure to the constructor.

+
+
prop = Proportional(**{'name': 'myprop', 'value': 5, 'gain': 20})
+print(prop.get_config())
+
+
{'type': 'Proportional', 'name': 'myprop', 'value': 5, 'links': {}, 'gain': 20}
+
+
+

Configuration class method. Create the function by passing a configuration structure to a class method.

+
+
config = {'name': 'myprop', 'value': -0.5, 'gain': 21}
+prop = Proportional.from_config(config)
+print(prop.get_config())
+
+
{'type': 'Proportional', 'name': 'myprop', 'value': -0.5, 'links': {}, 'gain': 21}
+
+
+
+
prop = Proportional()
+print(prop.get_config())
+prop1 = Proportional.from_config(prop.get_config())
+print(prop1.get_config())
+assert prop.get_config() == prop1.get_config()
+
+
{'type': 'Proportional', 'name': 'proportional', 'value': 0, 'links': {}, 'gain': 1}
+{'type': 'Proportional', 'name': 'proportional', 'value': 0, 'links': {}, 'gain': 1}
+
+
+

An example showing creating a WeightedSum function.

+
+
wts=[1,1,1]
+ws = WeightedSum(weights=wts)
+ns = ws.namespace
+ws.add_link(Constant(10, namespace=ns))
+ws.add_link(Constant(5, namespace=ns))
+ws.add_link(Constant(20, namespace=ns))
+assert ws() == 35
+config = ws.get_config()
+
+
+
#ws1 = WeightedSum.from_config(config, namespace=ns)
+ws1 = WeightedSum.from_config(config, new_name= 'weighted_sum1', namespace=ns)
+ws1.get_config()
+
+
{'type': 'WeightedSum',
+ 'name': 'weighted_sum1',
+ 'value': 35,
+ 'links': {0: 'constant', 1: 'constant1', 2: 'constant2'},
+ 'weights': [1, 1, 1]}
+
+
+
+
scons = Constant(2, name='scons')
+sig = Sigmoid()
+sig.add_link(scons)
+sig()
+
+
0.9999092042625952
+
+
+
+
cons = Constant([2,2], name='cons')
+print(cons.output_string()+ "")
+
+
[2, 2]
+
+
+
+
+

Viewing Functions

+

View the details of the function with the “summary”, which prints the name, type, parameters, value and links (if any).

+
+
prop.summary()
+
+
proportional Proportional | gain 1 | 0
+
+
+

As already seen the function details can be seen by retrieving the configuration.

+
+
print(prop.get_config())
+
+
{'type': 'Proportional', 'name': 'proportional', 'value': 0, 'links': {}, 'gain': 1}
+
+
+

Or you can print the function.

+
+
print(prop)
+
+
{'namespace': UUID('6d28b227-62e7-11ef-87fe-5c879c15de65'), 'value': 0, 'links': [], 'checklinks': True, 'name': 'proportional', 'decimal_places': 3, 'gain': 1}
+
+
+

Set the decimal places for output display.

+
+
print(prop.output_string())
+prop.set_decimal_places(2)
+print(prop.output_string())
+
+
0.000
+0.00
+
+
+

You can also view a function graphically as a network of connected nodes.

+
+
sub = Subtract(links=[Constant(1, name='cons'), Proportional(10, name='prop')], name='sub')
+print(sub.value)
+g = sub.graph()
+print(g)
+sub.draw(node_size=2000)
+
+
0
+DiGraph with 3 nodes and 2 edges
+
+
+
+
+

+
+
+
+
+
+
+

Save and Load

+

Save a function to file.

+
+
import json
+
+
+
print(ws.get_config())
+ws.save("ws.json")
+
+
{'type': 'WeightedSum', 'name': 'weighted_sum', 'value': 35, 'links': {0: 'constant', 1: 'constant1', 2: 'constant2'}, 'weights': [1, 1, 1]}
+
+
+

Create a function from file.

+
+
wss = WeightedSum.load("ws.json", new_name='weighted_sum1', namespace=ns)
+print(ws.get_config())
+print(wss.get_config())
+assert wss.get_config() == {'type': 'WeightedSum', 'name': 'weighted_sum2', 'value': 35, 'links': {0: 'constant', 1: 'constant1', 2: 'constant2'}, 'weights': [1, 1, 1]}
+
+
{'type': 'WeightedSum', 'name': 'weighted_sum', 'value': 35, 'links': {0: 'constant', 1: 'constant1', 2: 'constant2'}, 'weights': [1, 1, 1]}
+{'type': 'WeightedSum', 'name': 'weighted_sum2', 'value': 35, 'links': {0: 'constant', 1: 'constant1', 2: 'constant2'}, 'weights': [1, 1, 1]}
+
+
+
+ +
+

Running a Fucntion

+

A function can simply be run by calling it, without any parameters. It will use whatever input was set by the links. It returns the result of the function. In this example it will be 5 * 3 / 10, that is, input * gain / slow.

+
+
out = sub()
+print(out)
+
+
9
+
+
+

A function can be also run in a loop with the run() method and provided the loop count.

+
+
integrator = Integration(gain=3, slow=10)
+integrator.add_link(sub)
+o = integrator.run(steps=10, verbose=True)
+
+
2.700 5.130 7.317 9.285 11.057 12.651 14.086 15.377 16.540 17.586
+
+
+
+
integrator(verbose=True)
+
+
18.527
+
+
+
18.527113905569998
+
+
+
+
output = integrator()
+print(output)
+#assert output == 1.5
+npt.assert_almost_equal(output, 19.374402515013)
+
+
19.374402515013
+
+
+
+
print(integrator.get_config())
+
+
{'type': 'Integration', 'name': 'integration', 'value': 19.374402515013, 'links': {0: 'subtract'}, 'gain': 3, 'slow': 10}
+
+
+

An example showing creating and running a sigmoid WeightedSum function.

+
+
wts=[0.01,0.01,0.01]
+sgws = SigmoidWeightedSum(weights=wts, range=1.0, slope=5.0)
+ns = sgws.namespace
+sgws.add_link(Constant(10, namespace=ns))
+sgws.add_link(Constant(5, namespace=ns))
+sgws.add_link(Constant(20, namespace=ns))
+sgws.summary()
+print(sgws.get_parameters_list())
+
+out = sgws()
+print(out)
+assert out == 0.3519528019683106
+
+
sigmoid_weighted_sum SigmoidWeightedSum | weights [0.01, 0.01, 0.01] range 1.00  slope 5.00 | 0 | links  constant constant1 constant2 
+[[0.01, 0.01, 0.01], 1.0, 5.0]
+0.3519528019683106
+
+
+

An example showing creating and running a sigmoid smooth WeightedSum function.

+
+
wts=[0.01,0.01,0.01]
+sgsmws = SigmoidSmoothWeightedSum(weights=wts, smooth_factor=0.9)
+ns = sgsmws.namespace
+sgsmws.add_link(Constant(10, namespace=ns))
+sgsmws.add_link(Constant(5, namespace=ns))
+sgsmws.add_link(Constant(20, namespace=ns))
+sgsmws.summary()
+print(sgsmws.get_parameters_list())
+print(sgsmws.get_graph_name())
+labels = {}
+sgsmws.get_weights_labels_funcdata(labels)
+print('wts labels',labels)
+for _ in range(5):
+    # print(sgsmsm())
+    out = sgsmws()
+    print(out)
+
+npt.assert_almost_equal(out, 0.34373448930708195)
+
+
sigmoid_smooth_weighted_sum SigmoidSmoothWeightedSum | weights [0.01, 0.01, 0.01] smooth 0.90 range 2.00  slope 10.00 | 0 | links  constant constant1 constant2 
+[[0.01, 0.01, 0.01], 0.9, 2.0, 10.0]
+sigmoid_smooth_weighted_sum
+0.90:2.00|10.00
+wts labels {('sigmoid_smooth_weighted_sum\n0.90:2.00|10.00', 'constant\n10.00'): '0.01', ('sigmoid_smooth_weighted_sum\n0.90:2.00|10.00', 'constant1\n5.00'): '0.01', ('sigmoid_smooth_weighted_sum\n0.90:2.00|10.00', 'constant2\n20.00'): '0.01'}
+0.08727737447415773
+0.16473508145615878
+0.23277835967904958
+0.2921474527779875
+0.34373448930708195
+
+
+

An example showing creating and running a smoothed WeightedSum function.

+
+
wts=[1.0,1.0,1.0]
+wts=[0.01,0.01,0.01]
+smws = SmoothWeightedSum(weights=wts, smooth_factor=0.9)
+ns = smws.namespace
+smws.add_link(Constant(10, namespace=ns))
+smws.add_link(Constant(5, namespace=ns))
+smws.add_link(Constant(20, namespace=ns))
+smws.summary()
+print(smws.get_parameters_list())
+print(smws.get_graph_name())
+for _ in range(5):
+    out = smws()
+    print(out)
+
+npt.assert_almost_equal(out, 0.1433285)
+
+
smooth_weighted_sum SmoothWeightedSum | weights [0.01, 0.01, 0.01] smooth 0.90 | 0 | links  constant constant1 constant2 
+[[0.01, 0.01, 0.01], 0.9]
+smooth_weighted_sum
+0.90
+0.034999999999999996
+0.0665
+0.09485
+0.120365
+0.1433285
+
+
+

An example showing creating and running a derivative function.

+
+
dv = Derivative(history_length=5)
+ns = dv.namespace
+cons = Constant(10, namespace=ns)
+dv.add_link(cons)
+dv.summary()
+print(dv.get_config())
+print(dv.get_parameters_list())
+print(dv.get_graph_name())
+labels = {}
+dv.get_weights_labels(labels)
+print(labels)
+for i in range(20):
+    cons.set_value(i*i)
+    out = dv()
+    print(out, end=" ")
+
+print()
+assert out == -136
+print(dv.history)
+assert dv.history == [225, 256, 289, 324, 361]
+
+
derivative Derivative | history_length 5  | 0 | links  constant 
+{'type': 'Derivative', 'name': 'derivative', 'value': 0, 'links': {0: 'constant'}, 'history_length': 5}
+[5]
+derivative
+5.00
+{}
+0 -1 -4 -9 -16 -24 -32 -40 -48 -56 -64 -72 -80 -88 -96 -104 -112 -120 -128 -136 
+[225, 256, 289, 324, 361]
+
+
+

An example showing creating a derivative WeightedSum function.

+
+
wts=[0.01,0.01,0.01]
+dvws = DerivativeWeightedSum(weights=wts, history_length=5)
+ns = dvws.namespace
+cons = Constant(10, namespace=ns)
+dvws.add_link(cons)
+
+#dvws.add_link(Constant(10, namespace=ns))
+dvws.add_link(Constant(5, namespace=ns))
+dvws.add_link(Constant(20, namespace=ns))
+dvws.summary()
+print(dvws.get_config())
+print(dvws.get_parameters_list())
+print(dvws.get_graph_name())
+labels = {}
+dvws.get_weights_labels(labels)
+print(labels)
+for i in range(20):
+    cons.set_value(i*i)
+    out = dvws()
+    print(out, end=" ")
+
+print()
+npt.assert_almost_equal(out, -1.3599999999999999)
+print(dvws.history)
+assert dvws.history == [2.5, 2.81, 3.14, 3.49, 3.86]
+
+
derivative_weighted_sum DerivativeWeightedSum | weights [0.01, 0.01, 0.01] history_length 5 | 0 | links  constant constant1 constant2 
+{'type': 'DerivativeWeightedSum', 'name': 'derivative_weighted_sum', 'value': 0, 'links': {0: 'constant', 1: 'constant1', 2: 'constant2'}, 'weights': [0.01, 0.01, 0.01], 'history_length': 5}
+[[0.01, 0.01, 0.01], 5]
+derivative_weighted_sum
+5
+{('derivative_weighted_sum', 'constant'): '0.01', ('derivative_weighted_sum', 'constant1'): '0.01', ('derivative_weighted_sum', 'constant2'): '0.01'}
+0.0 -0.010000000000000009 -0.040000000000000036 -0.09000000000000002 -0.16000000000000003 -0.24 -0.31999999999999995 -0.39999999999999997 -0.4800000000000001 -0.56 -0.64 -0.72 -0.7999999999999998 -0.8799999999999999 -0.96 -1.04 -1.12 -1.2000000000000002 -1.2800000000000002 -1.3599999999999999 
+[2.5, 2.81, 3.14, 3.49, 3.86]
+
+
+
+
wts=[0.01,0.01,0.01]
+dvws = DerivativeWeightedSum(weights=wts, history_length=0)
+ns = dvws.namespace
+cons = Constant(10, namespace=ns)
+dvws.add_link(cons)
+dvws.add_link(Constant(5, namespace=ns))
+dvws.add_link(Constant(20, namespace=ns))
+dvws.summary()
+print(dvws.get_config())
+print(dvws.get_parameters_list())
+print(dvws.get_graph_name())
+labels = {}
+dvws.get_weights_labels(labels)
+print(labels)
+for i in range(20):
+    cons.set_value(i*i)
+    out = dvws()
+    print(out, end=" ")
+
+print()
+npt.assert_almost_equal(out, 3.86)
+
+
derivative_weighted_sum DerivativeWeightedSum | weights [0.01, 0.01, 0.01] history_length 0 | 0 | links  constant constant1 constant2 
+{'type': 'DerivativeWeightedSum', 'name': 'derivative_weighted_sum', 'value': 0, 'links': {0: 'constant', 1: 'constant1', 2: 'constant2'}, 'weights': [0.01, 0.01, 0.01], 'history_length': 0}
+[[0.01, 0.01, 0.01], 0]
+derivative_weighted_sum
+0
+{('derivative_weighted_sum', 'constant'): '0.01', ('derivative_weighted_sum', 'constant1'): '0.01', ('derivative_weighted_sum', 'constant2'): '0.01'}
+0.25 0.26 0.29000000000000004 0.34 0.41000000000000003 0.5 0.61 0.74 0.8900000000000001 1.06 1.25 1.46 1.69 1.94 2.21 2.5 2.81 3.14 3.49 3.86
+
+
+
+
+
+

Examples

+
+

Configuration

+

Create a function from the configuration of another.

+
+
integrator = Integration(3, 10)
+ns=integrator.namespace
+cons = Constant(5, namespace=ns)
+integrator.add_link(cons)
+config = integrator.get_config()
+inte = Integration.from_config(config, new_name='integration1', namespace=ns)
+print(inte())
+target = {'type': 'Integration', 'name': 'integration1', 'value': 1.5, 'links': {0: 'constant'}, 'gain': 3, 'slow': 10}
+print(target)
+assert inte.get_config() == target
+
+
1.5
+{'type': 'Integration', 'name': 'integration1', 'value': 1.5, 'links': {0: 'constant'}, 'gain': 3, 'slow': 10}
+
+
+ + +
+
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/helpers.html b/helpers.html new file mode 100644 index 00000000..0797c089 --- /dev/null +++ b/helpers.html @@ -0,0 +1,1567 @@ + + + + + + + + + +Helper classes – pct + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Helper classes

+
+ + + +
+ + + + +
+ + + +
+ + + +
+

ListChecker

+
+

source

+
+

ListChecker

+
+
 ListChecker ()
+
+

Initialize self. See help(type(self)) for accurate signature.

+
+
+

check_float_list_close_to_zero

+
+
# @staticmethod
+# def check_float_list_close_to_zero(float_list, rel_tol=1e-9, abs_tol=0.0, gradient_abs_tol=0.0):
+#     """
+#     Checks if the values in the float list are close to zero within the specified tolerance
+#     and if the gradient (difference between consecutive values) is close to zero within the specified gradient tolerance.
+
+#     Returns:
+#         bool: True if all values are close to zero within the specified tolerance and the gradient of all consecutive values is close to zero within the specified gradient tolerance.
+#     """
+#     if not float_list:
+#         return True
+    
+#     values_close_to_zero = all(
+#         math.isclose(value, 0, rel_tol=rel_tol, abs_tol=abs_tol)
+#         for value in float_list
+#     )
+    
+#     if len(float_list) == 1:
+#         return values_close_to_zero
+    
+#     gradients_close_to_zero = all(
+#         math.isclose(float_list[i] - float_list[i - 1], 0, rel_tol=0, abs_tol=gradient_abs_tol)
+#         for i in range(1, len(float_list))
+#     )
+    
+#     return values_close_to_zero and gradients_close_to_zero
+
+
+
+

check_float_list_close_to_zero

+
+
# @staticmethod
+# def check_float_list_close_to_zero(float_list, rel_tol=1e-9, abs_tol=0.0, gradient_abs_tol=0.0):
+#     """
+#     Checks if the values in the float list are close to zero within the specified tolerance
+#     and if the gradient (difference between consecutive values) is close to zero within the specified gradient tolerance.
+
+#     Returns:
+#         bool: True if all values are close to zero within the specified tolerance and the gradient of all consecutive values is close to zero within the specified gradient tolerance.
+#     """
+#     if not float_list:
+#         return True
+    
+#     values_close_to_zero = all(
+#         math.isclose(value, 0, rel_tol=rel_tol, abs_tol=abs_tol)
+#         for value in float_list
+#     )
+    
+#     if len(float_list) == 1:
+#         return values_close_to_zero
+    
+#     gradients_close_to_zero = all(
+#         math.isclose(float_list[i] - float_list[i - 1], 0, rel_tol=0, abs_tol=gradient_abs_tol)
+#         for i in range(1, len(float_list))
+#     )
+    
+#     return values_close_to_zero and gradients_close_to_zero
+
+
+
+

check_float_list_close_to_zero

+
+
# @staticmethod
+# def check_float_list_close_to_zero(float_list, rel_tol=1e-9, abs_tol=0.0, gradient_abs_tol=0.0):
+#     """
+#     Checks if the values in the float list are close to zero within the specified tolerance
+#     and if the gradient (difference between consecutive values) is close to zero within the specified gradient tolerance.
+
+#     Returns:
+#         bool: True if all values are close to zero within the specified tolerance and the gradient of all consecutive values is close to zero within the specified gradient tolerance.
+#     """
+#     if not float_list:
+#         return True
+    
+#     values_close_to_zero = all(
+#         math.isclose(value, 0, rel_tol=rel_tol, abs_tol=abs_tol)
+#         for value in float_list
+#     )
+    
+#     if len(float_list) == 1:
+#         return values_close_to_zero
+    
+#     gradients_close_to_zero = all(
+#         math.isclose(float_list[i] - float_list[i - 1], 0, rel_tol=0, abs_tol=gradient_abs_tol)
+#         for i in range(1, len(float_list))
+#     )
+    
+#     return values_close_to_zero and gradients_close_to_zero
+
+
+
+

check_float_list_close_to_zero

+
+
# @staticmethod
+# def check_float_list_close_to_zero(float_list, rel_tol=1e-9, abs_tol=0.0, gradient_abs_tol=0.0):
+#     """
+#     Checks if the values in the float list are close to zero within the specified tolerance
+#     and if the gradient (difference between consecutive values) is close to zero within the specified gradient tolerance.
+
+#     Returns:
+#         bool: True if all values are close to zero within the specified tolerance and the gradient of all consecutive values is close to zero within the specified gradient tolerance.
+#     """
+#     if not float_list:
+#         return True
+    
+#     values_close_to_zero = all(
+#         math.isclose(value, 0, rel_tol=rel_tol, abs_tol=abs_tol)
+#         for value in float_list
+#     )
+    
+#     if len(float_list) == 1:
+#         return values_close_to_zero
+    
+#     gradients_close_to_zero = all(
+#         math.isclose(float_list[i] - float_list[i - 1], 0, rel_tol=0, abs_tol=gradient_abs_tol)
+#         for i in range(1, len(float_list))
+#     )
+    
+#     return values_close_to_zero and gradients_close_to_zero
+
+
+
# Example usage:
+float_list = [1.00000001, 1.00000002, 1.00000003]
+int_list = [1, 1, 1]
+
+print(ListChecker.check_list_unchanged(float_list))  # Should print: True (depending on the tolerance)
+print(ListChecker.check_integer_list_unchanged(int_list))  # Should print: True
+
+
(False, {'gradient_range': 2.220446049250313e-16, 'mean': 1.0000000199999999, 'std_dev': 8.164965850304249e-09})
+True
+
+
+
+
+
+

ARC Data

+
+

JSONDataManager

+
+

source

+
+

JSONDataManager

+
+
 JSONDataManager (path:str, show_timing:bool=False)
+
+

Initialize self. See help(type(self)) for accurate signature.

+
+
# #| export
+# class JSONDataManager:
+#     def __init__(self, path: str, show_timing: bool = False):
+#         self.data = self.load_json(path)
+#         self.show_timing = show_timing
+    
+#     @JSONDataManager.timing_decorator
+#     def load_json(self, path: str) -> Dict:
+#         with open(path, 'r') as file:
+#             return json.load(file)
+    
+#     def timing_decorator(method):
+#         def timed_method(self, *args, **kwargs):
+#             start_time = time.time()
+#             result = method(self, *args, **kwargs)
+#             end_time = time.time()
+#             if self.show_timing:
+#                 print(f"Execution time of {method.__name__}: {end_time - start_time:.4f} seconds")
+#             return result
+#         return timed_method
+
+#     def reload_data(self, path: str):
+#         self.data = self.load_json(path)
+
+
+
# import json
+# import time
+# from typing import Dict
+
+# def timing_decorator(method):
+#     def timed_method(self, *args, **kwargs):
+#         start_time = time.time()
+#         result = method(self, *args, **kwargs)
+#         end_time = time.time()
+#         if self.show_timing:
+#             print(f"Execution time of {method.__name__}: {end_time - start_time:.4f} seconds")
+#         return result
+#     return timed_method
+
+# class JSONDataManager:
+#     def __init__(self, path: str, show_timing: bool = False):
+#         self.data = self.load_json(path)
+#         self.show_timing = show_timing
+    
+#     @timing_decorator
+#     def load_json(self, path: str) -> Dict:
+#         with open(path, 'r') as file:
+#             return json.load(file)
+
+#     def reload_data(self, path: str):
+#         self.data = self.load_json(path)
+
+
+
+
+

ChallengesDataManager

+
+

source

+
+

ChallengesDataManager

+
+
 ChallengesDataManager (*args, **kwargs)
+
+
+
+
+

SolutionsDataManager

+
+

source

+
+

SolutionsDataManager

+
+
 SolutionsDataManager (*args, **kwargs)
+
+
+
# Example usage
+start_time = time.time()
+challenges_manager = ChallengesDataManager("C:\\packages\\arc-prize-2024\\arc-agi_training_challenges.json")
+end_time = time.time()
+print(f"Execution time of challenges load: {end_time - start_time:.4f} seconds")
+
+start_time = time.time()
+solutions_manager = SolutionsDataManager("C:\\packages\\arc-prize-2024\\arc-agi_training_solutions.json")
+end_time = time.time()
+print(f"Execution time of solutions load: {end_time - start_time:.4f} seconds")
+
+print(challenges_manager.get_all_keys())
+print(challenges_manager.count_all_keys())
+print('get_keys_with_equal_size_input_output')
+gkweqsio = challenges_manager.get_keys_with_equal_size_input_output()
+print(gkweqsio[0])
+print(gkweqsio[1])
+print('get_keys_with_equal_size_input_output_sorted')
+gkweqsios = challenges_manager.get_keys_with_equal_size_input_output_sorted()
+print(gkweqsios)
+# print(gkweqsios[0])
+# print(gkweqsios[1])
+
+print('get_keys_with_inconsistent_input_output_sizes')
+gkwiios = challenges_manager.get_keys_with_inconsistent_input_output_sizes()
+print(gkwiios[0])
+print(gkwiios[1])
+print('get_keys_with_variable_input_sizes')
+gkwvts = challenges_manager.get_keys_with_variable_input_sizes()
+print(gkwvts[0])
+print(gkwvts[1])
+print('get_input_array_histogram')
+hist = challenges_manager.get_input_array_histogram()
+print(hist)
+count = 0
+for key, value in hist.items():
+    count += value
+print(count)
+print('get_data_for_key',challenges_manager.get_data_for_key('007bbfb7'))
+print(challenges_manager.get_arrays_for_key('007bbfb7', 'train'))
+print(challenges_manager.get_arrays_for_key('007bbfb7', 'test'))
+
+print('get_largest_array_size', challenges_manager.get_largest_array_size())
+print('analyze_arrays', challenges_manager.analyze_arrays())
+
+
+print('get_all_keys', solutions_manager.get_all_keys())
+print(solutions_manager.count_all_keys())
+get_data_for_key = solutions_manager.get_data_for_key('007bbfb7')
+print('get_data_for_key', get_data_for_key)
+print(solutions_manager.get_arrays_for_key('007bbfb7', 'output'))
+
+
Execution time of challenges load: 0.1250 seconds
+Execution time of solutions load: 0.0120 seconds
+['007bbfb7', '00d62c1b', '017c7c7b', '025d127b', '045e512c', '0520fde7', '05269061', '05f2a901', '06df4c85', '08ed6ac7', '09629e4f', '0962bcdd', '0a938d79', '0b148d64', '0ca9ddb6', '0d3d703e', '0dfd9992', '0e206a2e', '10fcaaa3', '11852cab', '1190e5a7', '137eaa0f', '150deff5', '178fcbfb', '1a07d186', '1b2d62fb', '1b60fb0c', '1bfc4729', '1c786137', '1caeab9d', '1cf80156', '1e0a9b12', '1e32b0e9', '1f0c79e5', '1f642eb9', '1f85a75f', '1f876c06', '1fad071e', '2013d3e2', '2204b7a8', '22168020', '22233c11', '2281f1f4', '228f6490', '22eb0ac0', '234bbc79', '23581191', '239be575', '23b5c85d', '253bf280', '25d487eb', '25d8a9c8', '25ff71a9', '264363fd', '272f95fa', '27a28665', '28bf18c6', '28e73c20', '29623171', '29c11459', '29ec7d0e', '2bcee788', '2bee17df', '2c608aff', '2dc579da', '2dd70a9a', '2dee498d', '31aa019c', '321b1fc6', '32597951', '3345333e', '3428a4f5', '3618c87e', '3631a71a', '363442ee', '36d67576', '36fdfd69', '3906de3d', '39a8645d', '39e1d7f9', '3aa6fb7a', '3ac3eb23', '3af2c5a8', '3bd67248', '3bdb4ada', '3befdf3e', '3c9b0459', '3de23699', '3e980e27', '3eda0437', '3f7978a0', '40853293', '4093f84a', '41e4d17e', '4258a5f9', '4290ef0e', '42a50994', '4347f46a', '444801d8', '445eab21', '447fd412', '44d8ac46', '44f52bb0', '4522001f', '4612dd53', '46442a0e', '469497ad', '46f33fce', '47c1f68c', '484b58aa', '48d8fb45', '4938f0c2', '496994bd', '49d1d64f', '4be741c5', '4c4377d9', '4c5c2cf0', '50846271', '508bd3b6', '50cb2852', '5117e062', '5168d44c', '539a4f51', '53b68214', '543a7ed5', '54d82841', '54d9e175', '5521c0d9', '5582e5ca', '5614dbcf', '56dc2b01', '56ff96f3', '57aa92db', '5ad4f10b', '5bd6f4ac', '5c0a986e', '5c2c9af4', '5daaa586', '60b61512', '6150a2bd', '623ea044', '62c24649', '63613498', '6430c8c4', '6455b5f5', '662c240a', '67385a82', '673ef223', '6773b310', '67a3c6ac', '67a423a3', '67e8384a', '681b3aeb', '6855a6e4', '68b16354', '694f12f3', '6a1e5592', '6aa20dc0', '6b9890af', '6c434453', '6cdd2623', '6cf79266', '6d0160f0', '6d0aefbc', '6d58a25d', '6d75e8bb', '6e02f1e3', '6e19193c', '6e82a1ae', '6ecd11f4', '6f8cd79b', '6fa7a44f', '72322fa7', '72ca375d', '73251a56', '7447852a', '7468f01a', '746b3537', '74dd1130', '75b8110e', '760b3cac', '776ffc46', '77fdfe62', '780d0b14', '7837ac64', '794b24be', '7b6016b9', '7b7f7511', '7c008303', '7ddcd7ec', '7df24a62', '7e0986d6', '7f4411dc', '7fe24cdd', '80af3007', '810b9b61', '82819916', '83302e8f', '834ec97d', '8403a5d5', '846bdb03', '855e0971', '85c4e7cd', '868de0fa', '8731374e', '88a10436', '88a62173', '890034e9', '8a004b2b', '8be77c9e', '8d5021e8', '8d510a79', '8e1813be', '8e5a5113', '8eb1be9a', '8efcae92', '8f2ea7aa', '90c28cc7', '90f3ed37', '913fb3ed', '91413438', '91714a58', '9172f3a0', '928ad970', '93b581b8', '941d9a10', '94f9d214', '952a094c', '9565186b', '95990924', '963e52fc', '97999447', '97a05b5b', '98cf29f8', '995c5fa3', '99b1bc43', '99fa7670', '9aec4887', '9af7a82c', '9d9215db', '9dfd6313', '9ecd008a', '9edfc990', '9f236235', 'a1570a43', 'a2fd1cf0', 'a3325580', 'a3df8b1e', 'a416b8f3', 'a48eeaf7', 'a5313dff', 'a5f85a15', 'a61ba2ce', 'a61f2674', 'a64e4611', 'a65b410d', 'a68b268e', 'a699fb00', 'a740d043', 'a78176bb', 'a79310a0', 'a85d4709', 'a87f7484', 'a8c38be5', 'a8d7556c', 'a9f96cdd', 'aabf363d', 'aba27056', 'ac0a08a4', 'ae3edfdc', 'ae4f1146', 'aedd82e4', 'af902bf9', 'b0c4d837', 'b190f7f5', 'b1948b0a', 'b230c067', 'b27ca6d3', 'b2862040', 'b527c5c6', 'b548a754', 'b60334d2', 'b6afb2da', 'b7249182', 'b775ac94', 'b782dc8a', 'b8825c91', 'b8cdaf2b', 'b91ae062', 'b94a9452', 'b9b7f026', 'ba26e723', 'ba97ae07', 'bb43febb', 'bbc9ae5d', 'bc1d5164', 'bd4472b8', 'bda2d7a6', 'bdad9b1f', 'be94b721', 'beb8660c', 'c0f76784', 'c1d99e64', 'c3e719e8', 'c3f564a4', 'c444b776', 'c59eb873', 'c8cbb738', 'c8f0f002', 'c909285e', 'c9e6f938', 'c9f8e694', 'caa06a1f', 'cbded52d', 'cce03e0d', 'cdecee7f', 'ce22a75a', 'ce4f8723', 'ce602527', 'ce9e57f2', 'cf98881b', 'd037b0a7', 'd06dbe63', 'd07ae81c', 'd0f5fe59', 'd10ecb37', 'd13f3404', 'd22278a0', 'd23f8c26', 'd2abd087', 'd364b489', 'd406998b', 'd43fd935', 'd4469b4b', 'd4a91cb9', 'd4f3cd78', 'd511f180', 'd5d6de2d', 'd631b094', 'd687bc17', 'd6ad076f', 'd89b689b', 'd8c310e9', 'd90796e8', 'd9f24cd1', 'd9fac9be', 'dae9d2b5', 'db3e9e38', 'db93a21d', 'dbc1a6ce', 'dc0a314f', 'dc1df850', 'dc433765', 'ddf7fa4f', 'de1cd16c', 'ded97339', 'e179c5f4', 'e21d9049', 'e26a3af2', 'e3497940', 'e40b9e2f', 'e48d4e1a', 'e5062a87', 'e509e548', 'e50d258f', 'e6721834', 'e73095fd', 'e76a88a6', 'e8593010', 'e8dc4411', 'e9614598', 'e98196ab', 'e9afcf9a', 'ea32f347', 'ea786f4a', 'eb281b96', 'eb5a1d5d', 'ec883f72', 'ecdecbb3', 'ed36ccf7', 'ef135b50', 'f15e1fac', 'f1cefba8', 'f25fbde4', 'f25ffba3', 'f2829549', 'f35d900a', 'f5b8619d', 'f76d97a5', 'f8a8fe49', 'f8b3ba0a', 'f8c80d96', 'f8ff0b80', 'f9012d9b', 'fafffa47', 'fcb5c309', 'fcc82909', 'feca6190', 'ff28f65a', 'ff805c23']
+400
+get_keys_with_equal_size_input_output
+['00d62c1b', '045e512c', '05269061', '06df4c85', '08ed6ac7', '09629e4f', '0962bcdd', '0ca9ddb6', '0d3d703e', '0dfd9992', '11852cab', '1b60fb0c', '1bfc4729', '1e0a9b12', '1e32b0e9', '1f0c79e5', '1f642eb9', '1f876c06', '2204b7a8', '22168020', '22233c11', '2281f1f4', '228f6490', '22eb0ac0', '23581191', '25d8a9c8', '25ff71a9', '264363fd', '28e73c20', '29623171', '29ec7d0e', '2bcee788', '2bee17df', '2dd70a9a', '31aa019c', '321b1fc6', '32597951', '3345333e', '3618c87e', '3631a71a', '36d67576', '3906de3d', '39e1d7f9', '3aa6fb7a', '3bd67248', '3befdf3e', '3c9b0459', '3e980e27', '4093f84a', '41e4d17e', '4258a5f9', '444801d8', '44d8ac46', '484b58aa', '4c5c2cf0', '508bd3b6', '543a7ed5', '5521c0d9', '5582e5ca', '5c0a986e', '5c2c9af4', '60b61512', '6150a2bd', '623ea044', '63613498', '67a3c6ac', '67a423a3', '6855a6e4', '68b16354', '694f12f3', '6c434453', '6cf79266', '6d0160f0', '6d58a25d', '6e02f1e3', '6e19193c', '6e82a1ae', '73251a56', '74dd1130', '776ffc46', '794b24be', '7ddcd7ec', '7df24a62', '7f4411dc', '810b9b61', '83302e8f', '834ec97d', '8403a5d5', '85c4e7cd', '868de0fa', '890034e9', '8d510a79', '8f2ea7aa', '913fb3ed', '91714a58', '93b581b8', '941d9a10', '952a094c', '9565186b', '95990924', '9d9215db', '9dfd6313', '9edfc990', 'a48eeaf7', 'a5313dff', 'a5f85a15', 'a61f2674', 'a64e4611', 'a699fb00', 'a78176bb', 'a79310a0', 'a85d4709', 'a8d7556c', 'aabf363d', 'aba27056', 'ae3edfdc', 'af902bf9', 'b230c067', 'b527c5c6', 'b60334d2', 'b6afb2da', 'b775ac94', 'b8825c91', 'b8cdaf2b', 'bb43febb', 'bda2d7a6', 'bdad9b1f', 'c0f76784', 'c3f564a4', 'c9f8e694', 'caa06a1f', 'cbded52d', 'ce22a75a', 'd037b0a7', 'd06dbe63', 'd22278a0', 'd23f8c26', 'd2abd087', 'd364b489', 'd43fd935', 'd4f3cd78', 'd511f180', 'd5d6de2d', 'd6ad076f', 'd89b689b', 'd9f24cd1', 'db93a21d', 'ddf7fa4f', 'ded97339', 'e40b9e2f', 'e48d4e1a', 'e5062a87', 'e76a88a6', 'e8593010', 'e9614598', 'ea32f347', 'ea786f4a', 'ec883f72', 'ed36ccf7', 'ef135b50', 'f76d97a5', 'f8a8fe49', 'f8c80d96', 'fcc82909']
+164
+get_keys_with_equal_size_input_output_sorted
+[('0d3d703e', 3), ('25d8a9c8', 3), ('25ff71a9', 3), ('3c9b0459', 3), ('5582e5ca', 3), ('6150a2bd', 3), ('6e02f1e3', 3), ('74dd1130', 3), ('794b24be', 3), ('834ec97d', 3), ('9565186b', 3), ('9dfd6313', 3), ('a5f85a15', 3), ('a85d4709', 3), ('b8cdaf2b', 3), ('d037b0a7', 3), ('d23f8c26', 3), ('ea786f4a', 3), ('ed36ccf7', 3), ('f76d97a5', 3), ('1e0a9b12', 4), ('67a3c6ac', 4), ('67a423a3', 4), ('3618c87e', 5), ('68b16354', 5), ('a699fb00', 5), ('a79310a0', 5), ('d511f180', 5), ('00d62c1b', 6), ('28e73c20', 6), ('93b581b8', 6), ('bda2d7a6', 6), ('bdad9b1f', 6), ('ec883f72', 6), ('05269061', 7), ('3aa6fb7a', 7), ('aabf363d', 7), ('aba27056', 7), ('a5313dff', 8), ('cbded52d', 8), ('08ed6ac7', 9), ('0ca9ddb6', 9), ('1f0c79e5', 9), ('23581191', 9), ('4258a5f9', 9), ('60b61512', 9), ('8f2ea7aa', 9), ('a61f2674', 9), ('b60334d2', 9), ('ce22a75a', 9), ('11852cab', 10), ('1b60fb0c', 10), ('1bfc4729', 10), ('1f642eb9', 10), ('1f876c06', 10), ('2204b7a8', 10), ('22168020', 10), ('22233c11', 10), ('2281f1f4', 10), ('228f6490', 10), ('22eb0ac0', 10), ('2bcee788', 10), ('31aa019c', 10), ('321b1fc6', 10), ('3906de3d', 10), ('3befdf3e', 10), ('444801d8', 10), ('5c0a986e', 10), ('63613498', 10), ('694f12f3', 10), ('6c434453', 10), ('6e19193c', 10), ('6e82a1ae', 10), ('7ddcd7ec', 10), ('8403a5d5', 10), ('868de0fa', 10), ('8d510a79', 10), ('941d9a10', 10), ('952a094c', 10), ('95990924', 10), ('a48eeaf7', 10), ('a78176bb', 10), ('af902bf9', 10), ('b230c067', 10), ('b527c5c6', 10), ('b6afb2da', 10), ('bb43febb', 10), ('d22278a0', 10), ('d2abd087', 10), ('d364b489', 10), ('d43fd935', 10), ('d4f3cd78', 10), ('d5d6de2d', 10), ('d6ad076f', 10), ('d89b689b', 10), ('d9f24cd1', 10), ('db93a21d', 10), ('ddf7fa4f', 10), ('ded97339', 10), ('e40b9e2f', 10), ('e48d4e1a', 10), ('e5062a87', 10), ('e76a88a6', 10), ('e8593010', 10), ('e9614598', 10), ('ea32f347', 10), ('ef135b50', 10), ('f8c80d96', 10), ('fcc82909', 10), ('09629e4f', 11), ('29623171', 11), ('6d0160f0', 11), ('caa06a1f', 11), ('0962bcdd', 12), ('2bee17df', 12), ('44d8ac46', 12), ('508bd3b6', 12), ('85c4e7cd', 12), ('913fb3ed', 12), ('c0f76784', 12), ('c9f8e694', 12), ('36d67576', 13), ('3e980e27', 13), ('7f4411dc', 13), ('9edfc990', 13), ('d06dbe63', 13), ('4093f84a', 14), ('4c5c2cf0', 14), ('3bd67248', 15), ('41e4d17e', 15), ('543a7ed5', 15), ('5521c0d9', 15), ('623ea044', 15), ('6855a6e4', 15), ('810b9b61', 15), ('ae3edfdc', 15), ('f8a8fe49', 15), ('3345333e', 16), ('91714a58', 16), ('b8825c91', 16), ('c3f564a4', 16), ('1e32b0e9', 17), ('32597951', 17), ('29ec7d0e', 18), ('a8d7556c', 18), ('9d9215db', 19), ('06df4c85', 20), ('2dd70a9a', 20), ('6cf79266', 20), ('6d58a25d', 20), ('776ffc46', 20), ('045e512c', 21), ('0dfd9992', 21), ('73251a56', 21), ('890034e9', 21), ('5c2c9af4', 23), ('7df24a62', 23), ('39e1d7f9', 24), ('83302e8f', 24), ('484b58aa', 29), ('264363fd', 30), ('3631a71a', 30), ('a64e4611', 30), ('b775ac94', 30)]
+get_keys_with_inconsistent_input_output_sizes
+['007bbfb7', '017c7c7b', '3af2c5a8', '4522001f', '46f33fce', '4c4377d9', '539a4f51', '62c24649', '67e8384a', '6fa7a44f', '7fe24cdd', '8be77c9e', '8d5021e8', '9172f3a0', 'c3e719e8', 'cce03e0d', 'd13f3404']
+17
+get_keys_with_variable_input_sizes
+['00d62c1b', '025d127b', '05f2a901', '06df4c85', '0a938d79', '0b148d64', '0e206a2e', '10fcaaa3', '1190e5a7', '150deff5', '178fcbfb', '1a07d186', '1c786137', '1caeab9d', '1cf80156', '1e0a9b12', '239be575', '23b5c85d', '253bf280', '25d487eb', '272f95fa', '28e73c20', '2bee17df', '2c608aff', '2dc579da', '2dd70a9a', '2dee498d', '36fdfd69', '39e1d7f9', '3bd67248', '3de23699', '3eda0437', '3f7978a0', '40853293', '4290ef0e', '42a50994', '4347f46a', '4612dd53', '46442a0e', '47c1f68c', '4938f0c2', '49d1d64f', '4be741c5', '4c5c2cf0', '50846271', '50cb2852', '5168d44c', '53b68214', '54d82841', '54d9e175', '56dc2b01', '56ff96f3', '57aa92db', '5ad4f10b', '5daaa586', '623ea044', '6455b5f5', '67385a82', '673ef223', '67a3c6ac', '67a423a3', '68b16354', '6aa20dc0', '6b9890af', '6cdd2623', '6d75e8bb', '6ecd11f4', '6f8cd79b', '72322fa7', '7468f01a', '746b3537', '77fdfe62', '780d0b14', '7837ac64', '7b6016b9', '7b7f7511', '7f4411dc', '810b9b61', '82819916', '83302e8f', '834ec97d', '855e0971', '85c4e7cd', '868de0fa', '8731374e', '88a10436', '8a004b2b', '8e1813be', '8eb1be9a', '913fb3ed', '928ad970', '95990924', '97999447', '97a05b5b', '98cf29f8', '99fa7670', '9aec4887', '9af7a82c', '9dfd6313', '9edfc990', '9f236235', 'a1570a43', 'a2fd1cf0', 'a416b8f3', 'a5313dff', 'a5f85a15', 'a65b410d', 'a699fb00', 'a740d043', 'a79310a0', 'a87f7484', 'a8c38be5', 'aba27056', 'aedd82e4', 'b0c4d837', 'b190f7f5', 'b1948b0a', 'b2862040', 'b527c5c6', 'b548a754', 'b7249182', 'b775ac94', 'b782dc8a', 'b8cdaf2b', 'b9b7f026', 'ba97ae07', 'bd4472b8', 'bda2d7a6', 'be94b721', 'beb8660c', 'c1d99e64', 'c59eb873', 'c8cbb738', 'c909285e', 'caa06a1f', 'ce602527', 'ce9e57f2', 'd07ae81c', 'd0f5fe59', 'd10ecb37', 'd22278a0', 'd23f8c26', 'd4a91cb9', 'd511f180', 'd5d6de2d', 'd687bc17', 'd90796e8', 'd9fac9be', 'db3e9e38', 'db93a21d', 'dbc1a6ce', 'dc1df850', 'dc433765', 'de1cd16c', 'e21d9049', 'e26a3af2', 'e509e548', 'e6721834', 'e73095fd', 'e8dc4411', 'ea786f4a', 'eb281b96', 'eb5a1d5d', 'ec883f72', 'ecdecbb3', 'f15e1fac', 'f1cefba8', 'f35d900a', 'f5b8619d', 'f76d97a5', 'f9012d9b', 'fcb5c309', 'ff28f65a']
+173
+get_input_array_histogram
+{5: 18, 3: 236, 2: 57, 4: 78, 6: 5, 8: 2, 7: 3, 10: 1}
+400
+get_data_for_key {'test': [{'input': [[7, 0, 7], [7, 0, 7], [7, 7, 0]]}], 'train': [{'input': [[0, 7, 7], [7, 7, 7], [0, 7, 7]], 'output': [[0, 0, 0, 0, 7, 7, 0, 7, 7], [0, 0, 0, 7, 7, 7, 7, 7, 7], [0, 0, 0, 0, 7, 7, 0, 7, 7], [0, 7, 7, 0, 7, 7, 0, 7, 7], [7, 7, 7, 7, 7, 7, 7, 7, 7], [0, 7, 7, 0, 7, 7, 0, 7, 7], [0, 0, 0, 0, 7, 7, 0, 7, 7], [0, 0, 0, 7, 7, 7, 7, 7, 7], [0, 0, 0, 0, 7, 7, 0, 7, 7]]}, {'input': [[4, 0, 4], [0, 0, 0], [0, 4, 0]], 'output': [[4, 0, 4, 0, 0, 0, 4, 0, 4], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 0, 0, 0, 0, 0, 4, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 4, 0, 4, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 4, 0, 0, 0, 0]]}, {'input': [[0, 0, 0], [0, 0, 2], [2, 0, 2]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 2], [0, 0, 0, 0, 0, 0, 2, 0, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 2], [2, 0, 2, 0, 0, 0, 2, 0, 2]]}, {'input': [[6, 6, 0], [6, 0, 0], [0, 6, 6]], 'output': [[6, 6, 0, 6, 6, 0, 0, 0, 0], [6, 0, 0, 6, 0, 0, 0, 0, 0], [0, 6, 6, 0, 6, 6, 0, 0, 0], [6, 6, 0, 0, 0, 0, 0, 0, 0], [6, 0, 0, 0, 0, 0, 0, 0, 0], [0, 6, 6, 0, 0, 0, 0, 0, 0], [0, 0, 0, 6, 6, 0, 6, 6, 0], [0, 0, 0, 6, 0, 0, 6, 0, 0], [0, 0, 0, 0, 6, 6, 0, 6, 6]]}, {'input': [[2, 2, 2], [0, 0, 0], [0, 2, 2]], 'output': [[2, 2, 2, 2, 2, 2, 2, 2, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 2, 0, 2, 2, 0, 2, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 2, 2, 2, 2, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 2, 2, 0, 2, 2]]}]}
+[]
+[]
+get_largest_array_size ('1f85a75f', 900)
+analyze_arrays {'analysis': {'equal_input_output': ['045e512c', '05269061', '08ed6ac7', '09629e4f', '0962bcdd', '0ca9ddb6', '0d3d703e', '0dfd9992', '11852cab', '1b60fb0c', '1bfc4729', '1e32b0e9', '1f0c79e5', '1f642eb9', '1f876c06', '2204b7a8', '22168020', '22233c11', '2281f1f4', '228f6490', '22eb0ac0', '23581191', '25d8a9c8', '25ff71a9', '264363fd', '29623171', '29c11459', '29ec7d0e', '2bcee788', '31aa019c', '321b1fc6', '32597951', '3345333e', '3618c87e', '3631a71a', '363442ee', '36d67576', '3906de3d', '3aa6fb7a', '3befdf3e', '3c9b0459', '3e980e27', '4093f84a', '41e4d17e', '4258a5f9', '444801d8', '447fd412', '44d8ac46', '484b58aa', '508bd3b6', '543a7ed5', '5521c0d9', '5582e5ca', '5c0a986e', '5c2c9af4', '60b61512', '6150a2bd', '63613498', '6855a6e4', '694f12f3', '6a1e5592', '6c434453', '6cf79266', '6d0160f0', '6d58a25d', '6e02f1e3', '6e19193c', '6e82a1ae', '73251a56', '74dd1130', '760b3cac', '776ffc46', '794b24be', '7ddcd7ec', '7df24a62', '8403a5d5', '890034e9', '8d510a79', '8e5a5113', '8f2ea7aa', '90f3ed37', '91714a58', '93b581b8', '941d9a10', '952a094c', '9565186b', '9d9215db', 'a48eeaf7', 'a61f2674', 'a64e4611', 'a78176bb', 'a85d4709', 'a8d7556c', 'a9f96cdd', 'aabf363d', 'ae3edfdc', 'af902bf9', 'b230c067', 'b60334d2', 'b6afb2da', 'b8825c91', 'bb43febb', 'bdad9b1f', 'c0f76784', 'c3f564a4', 'c9f8e694', 'cbded52d', 'ce22a75a', 'd037b0a7', 'd06dbe63', 'd2abd087', 'd364b489', 'd43fd935', 'd4f3cd78', 'd6ad076f', 'd89b689b', 'd8c310e9', 'd9f24cd1', 'ddf7fa4f', 'ded97339', 'e40b9e2f', 'e48d4e1a', 'e5062a87', 'e76a88a6', 'e8593010', 'e9614598', 'e9afcf9a', 'ea32f347', 'ed36ccf7', 'ef135b50', 'f25ffba3', 'f8a8fe49', 'f8c80d96', 'fcc82909'], 'consistent_but_different_sizes': ['007bbfb7', '017c7c7b', '0520fde7', '137eaa0f', '1b2d62fb', '1fad071e', '2013d3e2', '27a28665', '28bf18c6', '3428a4f5', '39a8645d', '3af2c5a8', '445eab21', '44f52bb0', '4522001f', '46f33fce', '48d8fb45', '4c4377d9', '5117e062', '539a4f51', '5614dbcf', '5bd6f4ac', '62c24649', '6430c8c4', '662c240a', '6773b310', '67e8384a', '681b3aeb', '6d0aefbc', '6fa7a44f', '75b8110e', '7c008303', '7fe24cdd', '80af3007', '88a62173', '8be77c9e', '8d5021e8', '9172f3a0', '94f9d214', '995c5fa3', '99b1bc43', '9ecd008a', 'a61ba2ce', 'a68b268e', 'ae4f1146', 'bc1d5164', 'c3e719e8', 'c9e6f938', 'cce03e0d', 'cdecee7f', 'ce4f8723', 'cf98881b', 'd13f3404', 'd4469b4b', 'dae9d2b5', 'dc0a314f', 'e3497940', 'e98196ab', 'f2829549', 'f8ff0b80', 'fafffa47', 'ff805c23'], 'variable_output_sizes': ['00d62c1b', '025d127b', '05f2a901', '06df4c85', '0a938d79', '0b148d64', '0e206a2e', '10fcaaa3', '1190e5a7', '150deff5', '178fcbfb', '1a07d186', '1c786137', '1caeab9d', '1cf80156', '1e0a9b12', '1f85a75f', '234bbc79', '239be575', '23b5c85d', '253bf280', '25d487eb', '272f95fa', '28e73c20', '2bee17df', '2c608aff', '2dc579da', '2dd70a9a', '2dee498d', '36fdfd69', '39e1d7f9', '3ac3eb23', '3bd67248', '3bdb4ada', '3de23699', '3eda0437', '3f7978a0', '40853293', '4290ef0e', '42a50994', '4347f46a', '4612dd53', '46442a0e', '469497ad', '47c1f68c', '4938f0c2', '496994bd', '49d1d64f', '4be741c5', '4c5c2cf0', '50846271', '50cb2852', '5168d44c', '53b68214', '54d82841', '54d9e175', '56dc2b01', '56ff96f3', '57aa92db', '5ad4f10b', '5daaa586', '623ea044', '6455b5f5', '67385a82', '673ef223', '67a3c6ac', '67a423a3', '68b16354', '6aa20dc0', '6b9890af', '6cdd2623', '6d75e8bb', '6ecd11f4', '6f8cd79b', '72322fa7', '72ca375d', '7447852a', '7468f01a', '746b3537', '77fdfe62', '780d0b14', '7837ac64', '7b6016b9', '7b7f7511', '7e0986d6', '7f4411dc', '810b9b61', '82819916', '83302e8f', '834ec97d', '846bdb03', '855e0971', '85c4e7cd', '868de0fa', '8731374e', '88a10436', '8a004b2b', '8e1813be', '8eb1be9a', '8efcae92', '90c28cc7', '913fb3ed', '91413438', '928ad970', '95990924', '963e52fc', '97999447', '97a05b5b', '98cf29f8', '99fa7670', '9aec4887', '9af7a82c', '9dfd6313', '9edfc990', '9f236235', 'a1570a43', 'a2fd1cf0', 'a3325580', 'a3df8b1e', 'a416b8f3', 'a5313dff', 'a5f85a15', 'a65b410d', 'a699fb00', 'a740d043', 'a79310a0', 'a87f7484', 'a8c38be5', 'aba27056', 'ac0a08a4', 'aedd82e4', 'b0c4d837', 'b190f7f5', 'b1948b0a', 'b27ca6d3', 'b2862040', 'b527c5c6', 'b548a754', 'b7249182', 'b775ac94', 'b782dc8a', 'b8cdaf2b', 'b91ae062', 'b94a9452', 'b9b7f026', 'ba26e723', 'ba97ae07', 'bbc9ae5d', 'bd4472b8', 'bda2d7a6', 'be94b721', 'beb8660c', 'c1d99e64', 'c444b776', 'c59eb873', 'c8cbb738', 'c8f0f002', 'c909285e', 'caa06a1f', 'ce602527', 'ce9e57f2', 'd07ae81c', 'd0f5fe59', 'd10ecb37', 'd22278a0', 'd23f8c26', 'd406998b', 'd4a91cb9', 'd511f180', 'd5d6de2d', 'd631b094', 'd687bc17', 'd90796e8', 'd9fac9be', 'db3e9e38', 'db93a21d', 'dbc1a6ce', 'dc1df850', 'dc433765', 'de1cd16c', 'e179c5f4', 'e21d9049', 'e26a3af2', 'e509e548', 'e50d258f', 'e6721834', 'e73095fd', 'e8dc4411', 'ea786f4a', 'eb281b96', 'eb5a1d5d', 'ec883f72', 'ecdecbb3', 'f15e1fac', 'f1cefba8', 'f25fbde4', 'f35d900a', 'f5b8619d', 'f76d97a5', 'f8b3ba0a', 'f9012d9b', 'fcb5c309', 'feca6190', 'ff28f65a']}, 'counts': {'equal_input_output': 134, 'consistent_but_different_sizes': 62, 'variable_output_sizes': 204}}
+get_all_keys ['007bbfb7', '00d62c1b', '017c7c7b', '025d127b', '045e512c', '0520fde7', '05269061', '05f2a901', '06df4c85', '08ed6ac7', '09629e4f', '0962bcdd', '0a938d79', '0b148d64', '0ca9ddb6', '0d3d703e', '0dfd9992', '0e206a2e', '10fcaaa3', '11852cab', '1190e5a7', '137eaa0f', '150deff5', '178fcbfb', '1a07d186', '1b2d62fb', '1b60fb0c', '1bfc4729', '1c786137', '1caeab9d', '1cf80156', '1e0a9b12', '1e32b0e9', '1f0c79e5', '1f642eb9', '1f85a75f', '1f876c06', '1fad071e', '2013d3e2', '2204b7a8', '22168020', '22233c11', '2281f1f4', '228f6490', '22eb0ac0', '234bbc79', '23581191', '239be575', '23b5c85d', '253bf280', '25d487eb', '25d8a9c8', '25ff71a9', '264363fd', '272f95fa', '27a28665', '28bf18c6', '28e73c20', '29623171', '29c11459', '29ec7d0e', '2bcee788', '2bee17df', '2c608aff', '2dc579da', '2dd70a9a', '2dee498d', '31aa019c', '321b1fc6', '32597951', '3345333e', '3428a4f5', '3618c87e', '3631a71a', '363442ee', '36d67576', '36fdfd69', '3906de3d', '39a8645d', '39e1d7f9', '3aa6fb7a', '3ac3eb23', '3af2c5a8', '3bd67248', '3bdb4ada', '3befdf3e', '3c9b0459', '3de23699', '3e980e27', '3eda0437', '3f7978a0', '40853293', '4093f84a', '41e4d17e', '4258a5f9', '4290ef0e', '42a50994', '4347f46a', '444801d8', '445eab21', '447fd412', '44d8ac46', '44f52bb0', '4522001f', '4612dd53', '46442a0e', '469497ad', '46f33fce', '47c1f68c', '484b58aa', '48d8fb45', '4938f0c2', '496994bd', '49d1d64f', '4be741c5', '4c4377d9', '4c5c2cf0', '50846271', '508bd3b6', '50cb2852', '5117e062', '5168d44c', '539a4f51', '53b68214', '543a7ed5', '54d82841', '54d9e175', '5521c0d9', '5582e5ca', '5614dbcf', '56dc2b01', '56ff96f3', '57aa92db', '5ad4f10b', '5bd6f4ac', '5c0a986e', '5c2c9af4', '5daaa586', '60b61512', '6150a2bd', '623ea044', '62c24649', '63613498', '6430c8c4', '6455b5f5', '662c240a', '67385a82', '673ef223', '6773b310', '67a3c6ac', '67a423a3', '67e8384a', '681b3aeb', '6855a6e4', '68b16354', '694f12f3', '6a1e5592', '6aa20dc0', '6b9890af', '6c434453', '6cdd2623', '6cf79266', '6d0160f0', '6d0aefbc', '6d58a25d', '6d75e8bb', '6e02f1e3', '6e19193c', '6e82a1ae', '6ecd11f4', '6f8cd79b', '6fa7a44f', '72322fa7', '72ca375d', '73251a56', '7447852a', '7468f01a', '746b3537', '74dd1130', '75b8110e', '760b3cac', '776ffc46', '77fdfe62', '780d0b14', '7837ac64', '794b24be', '7b6016b9', '7b7f7511', '7c008303', '7ddcd7ec', '7df24a62', '7e0986d6', '7f4411dc', '7fe24cdd', '80af3007', '810b9b61', '82819916', '83302e8f', '834ec97d', '8403a5d5', '846bdb03', '855e0971', '85c4e7cd', '868de0fa', '8731374e', '88a10436', '88a62173', '890034e9', '8a004b2b', '8be77c9e', '8d5021e8', '8d510a79', '8e1813be', '8e5a5113', '8eb1be9a', '8efcae92', '8f2ea7aa', '90c28cc7', '90f3ed37', '913fb3ed', '91413438', '91714a58', '9172f3a0', '928ad970', '93b581b8', '941d9a10', '94f9d214', '952a094c', '9565186b', '95990924', '963e52fc', '97999447', '97a05b5b', '98cf29f8', '995c5fa3', '99b1bc43', '99fa7670', '9aec4887', '9af7a82c', '9d9215db', '9dfd6313', '9ecd008a', '9edfc990', '9f236235', 'a1570a43', 'a2fd1cf0', 'a3325580', 'a3df8b1e', 'a416b8f3', 'a48eeaf7', 'a5313dff', 'a5f85a15', 'a61ba2ce', 'a61f2674', 'a64e4611', 'a65b410d', 'a68b268e', 'a699fb00', 'a740d043', 'a78176bb', 'a79310a0', 'a85d4709', 'a87f7484', 'a8c38be5', 'a8d7556c', 'a9f96cdd', 'aabf363d', 'aba27056', 'ac0a08a4', 'ae3edfdc', 'ae4f1146', 'aedd82e4', 'af902bf9', 'b0c4d837', 'b190f7f5', 'b1948b0a', 'b230c067', 'b27ca6d3', 'b2862040', 'b527c5c6', 'b548a754', 'b60334d2', 'b6afb2da', 'b7249182', 'b775ac94', 'b782dc8a', 'b8825c91', 'b8cdaf2b', 'b91ae062', 'b94a9452', 'b9b7f026', 'ba26e723', 'ba97ae07', 'bb43febb', 'bbc9ae5d', 'bc1d5164', 'bd4472b8', 'bda2d7a6', 'bdad9b1f', 'be94b721', 'beb8660c', 'c0f76784', 'c1d99e64', 'c3e719e8', 'c3f564a4', 'c444b776', 'c59eb873', 'c8cbb738', 'c8f0f002', 'c909285e', 'c9e6f938', 'c9f8e694', 'caa06a1f', 'cbded52d', 'cce03e0d', 'cdecee7f', 'ce22a75a', 'ce4f8723', 'ce602527', 'ce9e57f2', 'cf98881b', 'd037b0a7', 'd06dbe63', 'd07ae81c', 'd0f5fe59', 'd10ecb37', 'd13f3404', 'd22278a0', 'd23f8c26', 'd2abd087', 'd364b489', 'd406998b', 'd43fd935', 'd4469b4b', 'd4a91cb9', 'd4f3cd78', 'd511f180', 'd5d6de2d', 'd631b094', 'd687bc17', 'd6ad076f', 'd89b689b', 'd8c310e9', 'd90796e8', 'd9f24cd1', 'd9fac9be', 'dae9d2b5', 'db3e9e38', 'db93a21d', 'dbc1a6ce', 'dc0a314f', 'dc1df850', 'dc433765', 'ddf7fa4f', 'de1cd16c', 'ded97339', 'e179c5f4', 'e21d9049', 'e26a3af2', 'e3497940', 'e40b9e2f', 'e48d4e1a', 'e5062a87', 'e509e548', 'e50d258f', 'e6721834', 'e73095fd', 'e76a88a6', 'e8593010', 'e8dc4411', 'e9614598', 'e98196ab', 'e9afcf9a', 'ea32f347', 'ea786f4a', 'eb281b96', 'eb5a1d5d', 'ec883f72', 'ecdecbb3', 'ed36ccf7', 'ef135b50', 'f15e1fac', 'f1cefba8', 'f25fbde4', 'f25ffba3', 'f2829549', 'f35d900a', 'f5b8619d', 'f76d97a5', 'f8a8fe49', 'f8b3ba0a', 'f8c80d96', 'f8ff0b80', 'f9012d9b', 'fafffa47', 'fcb5c309', 'fcc82909', 'feca6190', 'ff28f65a', 'ff805c23']
+400
+get_data_for_key [[7, 0, 7, 0, 0, 0, 7, 0, 7], [7, 0, 7, 0, 0, 0, 7, 0, 7], [7, 7, 0, 0, 0, 0, 7, 7, 0], [7, 0, 7, 0, 0, 0, 7, 0, 7], [7, 0, 7, 0, 0, 0, 7, 0, 7], [7, 7, 0, 0, 0, 0, 7, 7, 0], [7, 0, 7, 7, 0, 7, 0, 0, 0], [7, 0, 7, 7, 0, 7, 0, 0, 0], [7, 7, 0, 7, 7, 0, 0, 0, 0]]
+[]
+
+
+
+
+
+

DataManagerSingleton

+
+

source

+
+

DataManagerSingleton

+
+
 DataManagerSingleton (folder:str, prefix:str, show_timing:bool=False)
+
+

Initialize self. See help(type(self)) for accurate signature.

+
+
# From: https://www.kaggle.com/code/allegich/arc-2024-show-all-400-training-tasks?scriptVersionId=190186517&cellId=12
+def plot_task(task, task_solutions, i, t):
+    """    Plots the first train and test pairs of a specified task,
+    using same color scheme as the ARC app    """    
+    
+    num_train = len(task['train'])
+    num_test  = len(task['test'])
+    
+    w=num_train+num_test
+    fig, axs  = plt.subplots(2, w, figsize=(3*w ,3*2))
+    plt.suptitle(f'Set #{i}, {t}:', fontsize=20, fontweight='bold', y=1)
+    #plt.subplots_adjust(hspace = 0.15)
+    #plt.subplots_adjust(wspace=20, hspace=20)
+    
+    for j in range(num_train):     
+        plot_one(axs[0, j], j,'train', 'input')
+        plot_one(axs[1, j], j,'train', 'output')        
+    
+    
+    plot_one(axs[0, j+1], 0, 'test', 'input')
+
+
+    answer = task_solutions
+    input_matrix = answer
+    
+    axs[1, j+1].imshow(input_matrix, cmap=cmap, norm=norm)
+    axs[1, j+1].grid(True, which = 'both',color = 'lightgrey', linewidth = 0.5)
+    axs[1, j+1].set_yticks([x-0.5 for x in range(1 + len(input_matrix))])
+    axs[1, j+1].set_xticks([x-0.5 for x in range(1 + len(input_matrix[0]))])     
+    axs[1, j+1].set_xticklabels([])
+    axs[1, j+1].set_yticklabels([])
+    axs[1, j+1].set_title('Test output')
+
+    axs[1, j+1] = plt.figure(1).add_subplot(111)
+    axs[1, j+1].set_xlim([0, num_train+1])
+    
+    for m in range(1, num_train):
+        axs[1, j+1].plot([m,m],[0,1],'--', linewidth=1, color = 'black')
+    
+    axs[1, j+1].plot([num_train,num_train],[0,1],'-', linewidth=3, color = 'black')
+
+    axs[1, j+1].axis("off")
+
+    fig.patch.set_linewidth(5)
+    fig.patch.set_edgecolor('black') 
+    fig.patch.set_facecolor('#dddddd')
+   
+    plt.tight_layout()
+    
+    print(f'#{i}, {t}') # for fast and convinience search
+    plt.show()  
+    
+    print()
+    print()
+    
+    
+def plot_one(ax, i, train_or_test, input_or_output):
+    input_matrix = task[train_or_test][i][input_or_output]
+    ax.imshow(input_matrix, cmap=cmap, norm=norm)
+    ax.grid(True, which = 'both',color = 'lightgrey', linewidth = 0.5)
+    
+    plt.setp(plt.gcf().get_axes(), xticklabels=[], yticklabels=[])
+    ax.set_xticks([x-0.5 for x in range(1 + len(input_matrix[0]))])     
+    ax.set_yticks([x-0.5 for x in range(1 + len(input_matrix))])
+    
+    ax.set_title(train_or_test + ' ' + input_or_output)
+
+
+
code = '00000001'
+data_mgr = DataManagerSingleton.get_instance(folder = 'c:/tmp/arc-prize-2024', prefix = 'arc-agi_simple_', show_timing=True)
+data = data_mgr.get_data_for_code(code)
+print(data)
+out = data_mgr.get_solutions_for_code(code)
+print(out)
+print(data_mgr.get_num_arrays_for_code(code))
+
+
Execution time of __init__: 0.0000 seconds
+Execution time of __init__: 0.0000 seconds
+{'test': [{'input': [[2]]}], 'train': [{'input': [[3]], 'output': [[4]]}, {'input': [[7]], 'output': [[8]]}, {'input': [[1]], 'output': [[2]]}]}
+[[3]]
+3
+
+
+
+
code = '00000003'
+data_mgr1 = DataManagerSingleton.get_instance()
+data = data_mgr1.get_data_for_code(code)
+print(data)
+out = data_mgr1.get_solutions_for_code(code)
+print(out)
+print(data_mgr1.get_num_arrays_for_code(code))
+
+
{'test': [{'input': [[2, 2], [2, 2]]}], 'train': [{'input': [[3, 3], [3, 3]], 'output': [[4, 4], [4, 4]]}, {'input': [[7, 7], [7, 7]], 'output': [[8, 8], [8, 8]]}, {'input': [[1, 1], [1, 1]], 'output': [[2, 2], [2, 2]]}]}
+[[3, 3], [3, 3]]
+3
+
+
+
+
# From: https://www.kaggle.com/code/allegich/arc-2024-show-all-400-training-tasks?scriptVersionId=190186517&cellId=11
+# 0:black, 1:blue, 2:red, 3:green, 4:yellow, # 5:gray, 6:magenta, 7:orange, 8:sky, 9:brown
+# rgb_values = [
+#    (0, 0, 0), (0, 116, 217), (255, 65, 54), (46, 204, 64), (255, 220, 0), (170, 170, 170), (240, 18, 190), (255, 133, 27), (127, 219, 255), (135, 12, 37)]
+import matplotlib.pyplot as plt
+from   matplotlib import colors
+
+cmap = colors.ListedColormap(
+    ['#000000', '#0074D9', '#FF4136', '#2ECC40', '#FFDC00',
+     '#AAAAAA', '#F012BE', '#FF851B', '#7FDBFF', '#870C25'])
+norm = colors.Normalize(vmin=0, vmax=9)
+
+plt.figure(figsize=(3, 1), dpi=150)
+plt.imshow([list(range(10))], cmap=cmap, norm=norm)
+plt.xticks(list(range(10)))
+plt.yticks([])
+plt.show()
+
+
+
+

+
+
+
+
+
+
code = '00000003'
+task = data_mgr.get_data_for_code(code)
+print(task)
+task_solution = data_mgr.get_solutions_for_code(code)
+print(task_solution)
+plot_task(task,  task_solution, 15, code)
+
+
{'test': [{'input': [[2, 2], [2, 2]]}], 'train': [{'input': [[3, 3], [3, 3]], 'output': [[4, 4], [4, 4]]}, {'input': [[7, 7], [7, 7]], 'output': [[8, 8], [8, 8]]}, {'input': [[1, 1], [1, 1]], 'output': [[2, 2], [2, 2]]}]}
+[[3, 3], [3, 3]]
+#15, 00000003
+
+
+
+
+

+
+
+
+
+

+
+
+
+
+
code = '00000007'
+task = data_mgr.get_data_for_code(code)
+print(task)
+task_solution = data_mgr.get_solutions_for_code(code)
+print(task_solution)
+plot_task(task,  task_solution, 15, code)
+
+
{'test': [{'input': [[2, 2, 2, 2, 2, 2, 2, 2, 2], [2, 2, 2, 2, 2, 2, 2, 2, 2], [2, 2, 2, 2, 2, 2, 2, 2, 2], [2, 2, 2, 2, 2, 2, 2, 2, 2], [2, 2, 2, 2, 2, 2, 2, 2, 2], [2, 2, 2, 2, 2, 2, 2, 2, 2], [2, 2, 2, 2, 2, 2, 2, 2, 2], [2, 2, 2, 2, 2, 2, 2, 2, 2], [2, 2, 2, 2, 2, 2, 2, 2, 2]]}], 'train': [{'input': [[3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3]], 'output': [[6, 6, 6, 6, 6, 6, 6, 6, 6], [6, 6, 6, 6, 6, 6, 6, 6, 6], [6, 6, 6, 6, 6, 6, 6, 6, 6], [6, 6, 6, 6, 6, 6, 6, 6, 6], [6, 6, 6, 6, 6, 6, 6, 6, 6], [6, 6, 6, 6, 6, 6, 6, 6, 6], [6, 6, 6, 6, 6, 6, 6, 6, 6], [6, 6, 6, 6, 6, 6, 6, 6, 6], [6, 6, 6, 6, 6, 6, 6, 6, 6]]}, {'input': [[6, 6, 6, 6, 6, 6, 6, 6, 6], [6, 6, 6, 6, 6, 6, 6, 6, 6], [6, 6, 6, 6, 6, 6, 6, 6, 6], [6, 6, 6, 6, 6, 6, 6, 6, 6], [6, 6, 6, 6, 6, 6, 6, 6, 6], [6, 6, 6, 6, 6, 6, 6, 6, 6], [6, 6, 6, 6, 6, 6, 6, 6, 6], [6, 6, 6, 6, 6, 6, 6, 6, 6], [6, 6, 6, 6, 6, 6, 6, 6, 6]], 'output': [[9, 9, 9, 9, 9, 9, 9, 9, 9], [9, 9, 9, 9, 9, 9, 9, 9, 9], [9, 9, 9, 9, 9, 9, 9, 9, 9], [9, 9, 9, 9, 9, 9, 9, 9, 9], [9, 9, 9, 9, 9, 9, 9, 9, 9], [9, 9, 9, 9, 9, 9, 9, 9, 9], [9, 9, 9, 9, 9, 9, 9, 9, 9], [9, 9, 9, 9, 9, 9, 9, 9, 9], [9, 9, 9, 9, 9, 9, 9, 9, 9]]}, {'input': [[1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1]], 'output': [[4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4]]}]}
+[5, 5, 5, 5, 5, 5, 5, 5, 5]
+
+
+
+
---------------------------------------------------------------------------
+TypeError                                 Traceback (most recent call last)
+Cell In[23], line 7
+      5 task_solution = data_mgr.get_solutions_for_code(code)
+      6 print(task_solution)
+----> 7 plot_task(task,  task_solution, 15, code)
+
+Cell In[17], line 26, in plot_task(task, task_solutions, i, t)
+     23 answer = task_solutions
+     24 input_matrix = answer
+---> 26 axs[1, j+1].imshow(input_matrix, cmap=cmap, norm=norm)
+     27 axs[1, j+1].grid(True, which = 'both',color = 'lightgrey', linewidth = 0.5)
+     28 axs[1, j+1].set_yticks([x-0.5 for x in range(1 + len(input_matrix))])
+
+File c:\Users\ruper\AppData\Local\Programs\Python\Python39\lib\site-packages\matplotlib\_api\deprecation.py:456, in make_keyword_only.<locals>.wrapper(*args, **kwargs)
+    450 if len(args) > name_idx:
+    451     warn_deprecated(
+    452         since, message="Passing the %(name)s %(obj_type)s "
+    453         "positionally is deprecated since Matplotlib %(since)s; the "
+    454         "parameter will become keyword-only %(removal)s.",
+    455         name=name, obj_type=f"parameter of {func.__name__}()")
+--> 456 return func(*args, **kwargs)
+
+File c:\Users\ruper\AppData\Local\Programs\Python\Python39\lib\site-packages\matplotlib\__init__.py:1412, in _preprocess_data.<locals>.inner(ax, data, *args, **kwargs)
+   1409 @functools.wraps(func)
+   1410 def inner(ax, *args, data=None, **kwargs):
+   1411     if data is None:
+-> 1412         return func(ax, *map(sanitize_sequence, args), **kwargs)
+   1414     bound = new_sig.bind(ax, *args, **kwargs)
+   1415     auto_label = (bound.arguments.get(label_namer)
+   1416                   or bound.kwargs.get(label_namer))
+
+File c:\Users\ruper\AppData\Local\Programs\Python\Python39\lib\site-packages\matplotlib\axes\_axes.py:5488, in Axes.imshow(self, X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, interpolation_stage, filternorm, filterrad, resample, url, **kwargs)
+   5481 self.set_aspect(aspect)
+   5482 im = mimage.AxesImage(self, cmap, norm, interpolation,
+   5483                       origin, extent, filternorm=filternorm,
+   5484                       filterrad=filterrad, resample=resample,
+   5485                       interpolation_stage=interpolation_stage,
+   5486                       **kwargs)
+-> 5488 im.set_data(X)
+   5489 im.set_alpha(alpha)
+   5490 if im.get_clip_path() is None:
+   5491     # image does not already have clipping set, clip to axes patch
+
+File c:\Users\ruper\AppData\Local\Programs\Python\Python39\lib\site-packages\matplotlib\image.py:715, in _ImageBase.set_data(self, A)
+    711     self._A = self._A[:, :, 0]
+    713 if not (self._A.ndim == 2
+    714         or self._A.ndim == 3 and self._A.shape[-1] in [3, 4]):
+--> 715     raise TypeError("Invalid shape {} for image data"
+    716                     .format(self._A.shape))
+    718 if self._A.ndim == 3:
+    719     # If the input data has values outside the valid range (after
+    720     # normalisation), we issue a warning and then clip X to the bounds
+    721     # - otherwise casting wraps extreme values, hiding outliers and
+    722     # making reliable interpretation impossible.
+    723     high = 255 if np.issubdtype(self._A.dtype, np.integer) else 1
+
+TypeError: Invalid shape (9,) for image data
+
+
+
+
+
+

+
+
+
+
+
+
code = '007bbfb7'
+data_mgr.reload_data(folder = 'c:/tmp/arc-prize-2024', prefix = 'arc-agi_training_')
+data = data_mgr.get_data_for_code(code)
+print(data)
+out = data_mgr.get_solutions_for_code(code)
+print(out)
+print(data_mgr.get_num_arrays_for_code(code))
+
+
Execution time of reload_data: 0.1050 seconds
+Execution time of reload_data: 0.0130 seconds
+{'test': [{'input': [[7, 0, 7], [7, 0, 7], [7, 7, 0]]}], 'train': [{'input': [[0, 7, 7], [7, 7, 7], [0, 7, 7]], 'output': [[0, 0, 0, 0, 7, 7, 0, 7, 7], [0, 0, 0, 7, 7, 7, 7, 7, 7], [0, 0, 0, 0, 7, 7, 0, 7, 7], [0, 7, 7, 0, 7, 7, 0, 7, 7], [7, 7, 7, 7, 7, 7, 7, 7, 7], [0, 7, 7, 0, 7, 7, 0, 7, 7], [0, 0, 0, 0, 7, 7, 0, 7, 7], [0, 0, 0, 7, 7, 7, 7, 7, 7], [0, 0, 0, 0, 7, 7, 0, 7, 7]]}, {'input': [[4, 0, 4], [0, 0, 0], [0, 4, 0]], 'output': [[4, 0, 4, 0, 0, 0, 4, 0, 4], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 0, 0, 0, 0, 0, 4, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 4, 0, 4, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 4, 0, 0, 0, 0]]}, {'input': [[0, 0, 0], [0, 0, 2], [2, 0, 2]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 2], [0, 0, 0, 0, 0, 0, 2, 0, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 2], [2, 0, 2, 0, 0, 0, 2, 0, 2]]}, {'input': [[6, 6, 0], [6, 0, 0], [0, 6, 6]], 'output': [[6, 6, 0, 6, 6, 0, 0, 0, 0], [6, 0, 0, 6, 0, 0, 0, 0, 0], [0, 6, 6, 0, 6, 6, 0, 0, 0], [6, 6, 0, 0, 0, 0, 0, 0, 0], [6, 0, 0, 0, 0, 0, 0, 0, 0], [0, 6, 6, 0, 0, 0, 0, 0, 0], [0, 0, 0, 6, 6, 0, 6, 6, 0], [0, 0, 0, 6, 0, 0, 6, 0, 0], [0, 0, 0, 0, 6, 6, 0, 6, 6]]}, {'input': [[2, 2, 2], [0, 0, 0], [0, 2, 2]], 'output': [[2, 2, 2, 2, 2, 2, 2, 2, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 2, 0, 2, 2, 0, 2, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 2, 2, 2, 2, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 2, 2, 0, 2, 2]]}]}
+[[7, 0, 7, 0, 0, 0, 7, 0, 7], [7, 0, 7, 0, 0, 0, 7, 0, 7], [7, 7, 0, 0, 0, 0, 7, 7, 0], [7, 0, 7, 0, 0, 0, 7, 0, 7], [7, 0, 7, 0, 0, 0, 7, 0, 7], [7, 7, 0, 0, 0, 0, 7, 7, 0], [7, 0, 7, 7, 0, 7, 0, 0, 0], [7, 0, 7, 7, 0, 7, 0, 0, 0], [7, 7, 0, 7, 7, 0, 0, 0, 0]]
+5
+
+
+
+
code = '0d3d703e'
+task = data_mgr.get_data_for_code(code)
+print(task)
+task_solution = data_mgr.get_solutions_for_code(code)
+print(task_solution)
+plot_task(task,  task_solution, 15, code)
+
+
{'test': [{'input': [[8, 1, 3], [8, 1, 3], [8, 1, 3]]}], 'train': [{'input': [[3, 1, 2], [3, 1, 2], [3, 1, 2]], 'output': [[4, 5, 6], [4, 5, 6], [4, 5, 6]]}, {'input': [[2, 3, 8], [2, 3, 8], [2, 3, 8]], 'output': [[6, 4, 9], [6, 4, 9], [6, 4, 9]]}, {'input': [[5, 8, 6], [5, 8, 6], [5, 8, 6]], 'output': [[1, 9, 2], [1, 9, 2], [1, 9, 2]]}, {'input': [[9, 4, 2], [9, 4, 2], [9, 4, 2]], 'output': [[8, 3, 6], [8, 3, 6], [8, 3, 6]]}]}
+[[9, 5, 4], [9, 5, 4], [9, 5, 4]]
+#15, 0d3d703e
+
+
+
+
+

+
+
+
+
+

+
+
+
+
+
code = '25d8a9c8'
+task = data_mgr.get_data_for_code(code)
+print(task)
+task_solution = data_mgr.get_solutions_for_code(code)
+print(task_solution)
+plot_task(task,  task_solution, 15, code)
+
+
{'test': [{'input': [[4, 4, 4], [3, 2, 3], [8, 8, 8]]}], 'train': [{'input': [[4, 4, 4], [2, 3, 2], [2, 3, 3]], 'output': [[5, 5, 5], [0, 0, 0], [0, 0, 0]]}, {'input': [[7, 3, 3], [6, 6, 6], [3, 7, 7]], 'output': [[0, 0, 0], [5, 5, 5], [0, 0, 0]]}, {'input': [[2, 9, 2], [4, 4, 4], [9, 9, 9]], 'output': [[0, 0, 0], [5, 5, 5], [5, 5, 5]]}, {'input': [[2, 2, 4], [2, 2, 4], [1, 1, 1]], 'output': [[0, 0, 0], [0, 0, 0], [5, 5, 5]]}]}
+[[5, 5, 5], [0, 0, 0], [5, 5, 5]]
+#15, 25d8a9c8
+
+
+
+
+

+
+
+
+
+

+
+
+
+
+
code = '25ff71a9'
+task = data_mgr.get_data_for_code(code)
+print(task)
+task_solution = data_mgr.get_solutions_for_code(code)
+print(task_solution)
+plot_task(task,  task_solution, 15, code)
+
+
{'test': [{'input': [[2, 0, 0], [2, 0, 0], [0, 0, 0]]}, {'input': [[0, 0, 0], [0, 1, 0], [0, 0, 0]]}], 'train': [{'input': [[1, 1, 1], [0, 0, 0], [0, 0, 0]], 'output': [[0, 0, 0], [1, 1, 1], [0, 0, 0]]}, {'input': [[0, 0, 0], [1, 1, 1], [0, 0, 0]], 'output': [[0, 0, 0], [0, 0, 0], [1, 1, 1]]}, {'input': [[0, 1, 0], [1, 1, 0], [0, 0, 0]], 'output': [[0, 0, 0], [0, 1, 0], [1, 1, 0]]}, {'input': [[0, 2, 2], [0, 0, 2], [0, 0, 0]], 'output': [[0, 0, 0], [0, 2, 2], [0, 0, 2]]}]}
+[[0, 0, 0], [2, 0, 0], [2, 0, 0]]
+#15, 25ff71a9
+
+
+
+
+

+
+
+
+
+

+
+
+
+
+
code = '3c9b0459'
+task = data_mgr.get_data_for_code(code)
+print(task)
+task_solution = data_mgr.get_solutions_for_code(code)
+print(task_solution)
+plot_task(task,  task_solution, 15, code)
+
+
{'test': [{'input': [[6, 4, 4], [6, 6, 4], [4, 6, 7]]}], 'train': [{'input': [[2, 2, 1], [2, 1, 2], [2, 8, 1]], 'output': [[1, 8, 2], [2, 1, 2], [1, 2, 2]]}, {'input': [[9, 2, 4], [2, 4, 4], [2, 9, 2]], 'output': [[2, 9, 2], [4, 4, 2], [4, 2, 9]]}, {'input': [[8, 8, 8], [5, 5, 8], [8, 5, 5]], 'output': [[5, 5, 8], [8, 5, 5], [8, 8, 8]]}, {'input': [[3, 2, 9], [9, 9, 9], [2, 3, 3]], 'output': [[3, 3, 2], [9, 9, 9], [9, 2, 3]]}]}
+[[7, 6, 4], [4, 6, 6], [4, 4, 6]]
+#15, 3c9b0459
+
+
+
+
+

+
+
+
+
+

+
+
+
+
+
code = '5582e5ca'
+task = data_mgr.get_data_for_code(code)
+print(task)
+task_solution = data_mgr.get_solutions_for_code(code)
+print(task_solution)
+plot_task(task,  task_solution, 15, code)
+
+
{'test': [{'input': [[8, 8, 6], [4, 6, 9], [8, 3, 0]]}], 'train': [{'input': [[4, 4, 8], [6, 4, 3], [6, 3, 0]], 'output': [[4, 4, 4], [4, 4, 4], [4, 4, 4]]}, {'input': [[6, 8, 9], [1, 8, 1], [9, 4, 9]], 'output': [[9, 9, 9], [9, 9, 9], [9, 9, 9]]}, {'input': [[4, 6, 9], [6, 4, 1], [8, 8, 6]], 'output': [[6, 6, 6], [6, 6, 6], [6, 6, 6]]}]}
+[[8, 8, 8], [8, 8, 8], [8, 8, 8]]
+#15, 5582e5ca
+
+
+
+
+

+
+
+
+
+

+
+
+
+
+
code = '6150a2bd'
+task = data_mgr.get_data_for_code(code)
+print(task)
+task_solution = data_mgr.get_solutions_for_code(code)
+print(task_solution)
+plot_task(task,  task_solution, 15, code)
+
+
{'test': [{'input': [[6, 3, 5], [6, 8, 0], [4, 0, 0]]}], 'train': [{'input': [[3, 3, 8], [3, 7, 0], [5, 0, 0]], 'output': [[0, 0, 5], [0, 7, 3], [8, 3, 3]]}, {'input': [[5, 5, 2], [1, 0, 0], [0, 0, 0]], 'output': [[0, 0, 0], [0, 0, 1], [2, 5, 5]]}]}
+[[0, 0, 4], [0, 8, 6], [5, 3, 6]]
+#15, 6150a2bd
+
+
+
+
+

+
+
+
+
+

+
+
+
+
+
code = '150deff5'
+task = data_mgr.get_data_for_code(code)
+print(task)
+task_solution = data_mgr.get_solutions_for_code(code)
+print(task_solution)
+plot_task(task,  task_solution, 15, code)
+
+
{'test': [{'input': [[0, 0, 5, 5, 0, 5, 5, 5, 0, 0, 0], [0, 0, 5, 5, 0, 0, 5, 0, 0, 0, 0], [0, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0], [0, 0, 0, 0, 5, 5, 5, 0, 0, 0, 0], [0, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 5, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 5, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}], 'train': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 5, 5, 0, 0, 0, 0, 0, 0, 0], [0, 0, 5, 5, 5, 5, 5, 0, 0, 0, 0], [0, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0], [0, 0, 0, 5, 5, 5, 5, 5, 0, 0, 0], [0, 0, 0, 5, 0, 0, 5, 5, 0, 0, 0], [0, 0, 0, 0, 0, 5, 5, 5, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 8, 2, 2, 2, 0, 0, 0, 0], [0, 0, 0, 2, 8, 8, 0, 0, 0, 0, 0], [0, 0, 0, 2, 8, 8, 8, 8, 0, 0, 0], [0, 0, 0, 2, 0, 0, 8, 8, 0, 0, 0], [0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 5, 5, 5, 5, 5, 5, 0, 0, 0], [0, 5, 5, 5, 5, 5, 5, 0, 0, 0], [0, 0, 0, 5, 0, 0, 5, 0, 0, 0], [0, 0, 0, 0, 5, 5, 5, 0, 0, 0], [0, 0, 0, 0, 5, 5, 5, 0, 0, 0], [0, 0, 0, 0, 5, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 8, 8, 2, 8, 8, 2, 0, 0, 0], [0, 8, 8, 2, 8, 8, 2, 0, 0, 0], [0, 0, 0, 2, 0, 0, 2, 0, 0, 0], [0, 0, 0, 0, 2, 8, 8, 0, 0, 0], [0, 0, 0, 0, 2, 8, 8, 0, 0, 0], [0, 0, 0, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 5, 5, 5, 5, 5, 0, 0, 0], [0, 0, 0, 0, 5, 5, 0, 0, 0], [0, 0, 0, 5, 0, 0, 0, 0, 0], [0, 0, 0, 5, 5, 5, 0, 0, 0], [0, 0, 0, 5, 5, 5, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 2, 2, 8, 8, 0, 0, 0], [0, 0, 0, 0, 8, 8, 0, 0, 0], [0, 0, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 2, 8, 8, 0, 0, 0], [0, 0, 0, 2, 8, 8, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]]}]}
+[[0, 0, 8, 8, 0, 2, 2, 2, 0, 0, 0], [0, 0, 8, 8, 0, 0, 2, 0, 0, 0, 0], [0, 2, 2, 2, 8, 8, 2, 0, 0, 0, 0], [0, 0, 0, 0, 8, 8, 2, 0, 0, 0, 0], [0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0], [0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
+#15, 150deff5
+
+
+
+
+

+
+
+
+
+

+
+
+
+
+
code = '0ca9ddb6'
+task = data_mgr.get_data_for_code(code)
+print(task)
+task_solution = data_mgr.get_solutions_for_code(code)
+print(task_solution)
+plot_task(task,  task_solution, 15, code)
+
+
{'test': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 8, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 6, 0, 0, 0, 0, 0, 2, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]]}], 'train': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 0, 4, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0], [0, 4, 0, 4, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 7, 0, 0], [0, 0, 0, 0, 0, 7, 1, 7, 0], [0, 0, 0, 0, 0, 0, 7, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 8, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 2, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 8, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 4, 0, 4, 0], [0, 0, 7, 0, 0, 0, 2, 0, 0], [0, 7, 1, 7, 0, 4, 0, 4, 0], [0, 0, 7, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 7, 0, 0], [4, 0, 4, 0, 0, 7, 1, 7, 0], [0, 2, 0, 0, 0, 0, 7, 0, 0], [4, 0, 4, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 6, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 0, 4, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0], [0, 4, 0, 4, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 6, 0, 0], [0, 0, 0, 7, 0, 0, 0, 0, 0], [0, 0, 7, 1, 7, 0, 0, 0, 0], [0, 0, 0, 7, 0, 0, 0, 0, 0]]}]}
+[[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 7, 0, 0], [0, 4, 0, 4, 0, 7, 1, 7, 0], [0, 0, 2, 0, 0, 0, 7, 0, 0], [0, 4, 0, 4, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 8, 0, 0, 0], [0, 0, 0, 0, 0, 0, 4, 0, 4], [0, 6, 0, 0, 0, 0, 0, 2, 0], [0, 0, 0, 0, 0, 0, 4, 0, 4]]
+#15, 0ca9ddb6
+
+
+
+
+

+
+
+
+
+

+
+
+
+
+
code = '00d62c1b'
+task = data_mgr.get_data_for_code(code)
+print(task)
+task_solution = data_mgr.get_solutions_for_code(code)
+print(task_solution)
+plot_task(task,  task_solution, 15, code)
+
+
{'test': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 3, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 0, 3, 3, 3, 3, 3, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 3, 0, 0], [0, 0, 0, 0, 0, 0, 3, 3, 0, 3, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0], [0, 0, 3, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 0, 3, 0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}], 'train': [{'input': [[0, 0, 0, 0, 0, 0], [0, 0, 3, 0, 0, 0], [0, 3, 0, 3, 0, 0], [0, 0, 3, 0, 3, 0], [0, 0, 0, 3, 0, 0], [0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0], [0, 0, 3, 0, 0, 0], [0, 3, 4, 3, 0, 0], [0, 0, 3, 4, 3, 0], [0, 0, 0, 3, 0, 0], [0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 0, 3, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 3, 0, 0, 0, 0], [0, 0, 3, 0, 0, 0, 3, 0, 0, 0], [0, 0, 0, 0, 0, 3, 0, 3, 0, 0], [0, 0, 0, 3, 0, 3, 3, 0, 0, 0], [0, 0, 3, 3, 3, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 0, 3, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 3, 0, 0, 0, 0], [0, 0, 3, 0, 0, 0, 3, 0, 0, 0], [0, 0, 0, 0, 0, 3, 4, 3, 0, 0], [0, 0, 0, 3, 0, 3, 3, 0, 0, 0], [0, 0, 3, 3, 3, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 3, 0, 0, 0, 0], [0, 0, 0, 0, 3, 0, 0, 0, 0, 0], [0, 3, 3, 0, 3, 3, 0, 3, 0, 0], [3, 0, 0, 3, 0, 0, 3, 0, 3, 0], [0, 0, 0, 3, 0, 0, 3, 3, 0, 0], [0, 0, 0, 3, 0, 0, 3, 0, 0, 0], [0, 0, 0, 3, 0, 0, 3, 0, 0, 0], [0, 0, 0, 0, 3, 3, 0, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 3, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 3, 0, 0, 0, 0], [0, 0, 0, 0, 3, 0, 0, 0, 0, 0], [0, 3, 3, 0, 3, 3, 0, 3, 0, 0], [3, 0, 0, 3, 4, 4, 3, 4, 3, 0], [0, 0, 0, 3, 4, 4, 3, 3, 0, 0], [0, 0, 0, 3, 4, 4, 3, 0, 0, 0], [0, 0, 0, 3, 4, 4, 3, 0, 0, 0], [0, 0, 0, 0, 3, 3, 0, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 3, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 3, 3, 3, 0, 0, 0, 0], [0, 0, 3, 0, 0, 3, 0, 0, 0, 0], [0, 0, 3, 0, 0, 3, 0, 3, 0, 0], [0, 0, 3, 3, 3, 3, 3, 3, 3, 0], [0, 0, 0, 3, 0, 0, 0, 0, 3, 0], [0, 0, 0, 3, 0, 0, 0, 3, 3, 0], [0, 0, 0, 3, 3, 0, 0, 3, 0, 3], [0, 0, 0, 3, 0, 3, 0, 0, 3, 0], [0, 0, 0, 0, 3, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 3, 3, 3, 0, 0, 0, 0], [0, 0, 3, 4, 4, 3, 0, 0, 0, 0], [0, 0, 3, 4, 4, 3, 0, 3, 0, 0], [0, 0, 3, 3, 3, 3, 3, 3, 3, 0], [0, 0, 0, 3, 0, 0, 0, 0, 3, 0], [0, 0, 0, 3, 0, 0, 0, 3, 3, 0], [0, 0, 0, 3, 3, 0, 0, 3, 4, 3], [0, 0, 0, 3, 4, 3, 0, 0, 3, 0], [0, 0, 0, 0, 3, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 3, 3, 3, 3, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0], [0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0], [0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0], [0, 0, 3, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 3, 0, 3, 0, 0], [0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 3, 0, 0, 3, 0, 0, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 3, 0, 0, 3, 3, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 3, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 3, 3, 3, 3, 4, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 3, 0, 0, 0, 0, 0, 0, 0, 3, 0], [0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 3, 0, 0, 0, 0], [0, 0, 0, 0, 3, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 3, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 3, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 3, 0, 0, 0, 0], [0, 0, 3, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 3, 0, 3, 0, 0], [0, 0, 0, 0, 0, 0, 3, 3, 4, 4, 3, 0, 0, 3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 3, 3, 0, 0, 3, 0, 0, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 3, 0, 0, 3, 3, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 4, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 3, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}]}
+[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 3, 4, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 0, 3, 3, 3, 3, 3, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 3, 4, 4, 4, 4, 3, 4, 4, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 4, 4, 4, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 3, 4, 4, 4, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 4, 4, 4, 3, 0, 0], [0, 0, 0, 0, 0, 0, 3, 3, 4, 3, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0], [0, 0, 3, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 3, 4, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 0, 3, 0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 3, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 3, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
+#15, 00d62c1b
+
+
+
+
+

+
+
+
+
+

+
+
+
+ + +
+
+
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/hierarchy.html b/hierarchy.html new file mode 100644 index 00000000..826a5c77 --- /dev/null +++ b/hierarchy.html @@ -0,0 +1,1628 @@ + + + + + + + + + +Hierarchy – pct + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Hierarchy

+
+ + + +
+ + + + +
+ + + +
+ + + +
+

source

+
+

FunctionsData

+
+
 FunctionsData ()
+
+

Data collected for a set of functions

+
+
+

Defining the Hierarchy

+

Defining the PCTHierarchy

+
+

source

+
+

PCTHierarchy

+
+
 PCTHierarchy (levels=0, cols=0, pre=None, post=None, name='pcthierarchy',
+               clear_names=True, links='single', history=False,
+               build=True, error_collector=None, namespace=None, **pargs)
+
+

A hierarchical perceptual control system, of PCTNodes.

+
+
# history=True
+# hierarchy, env = PCTHierarchy.load_from_file('testfiles/ARC/ga-000.000-s001-1x1-m007-ARC0010-9ddcf52416e60d65f19007957d07262d-consolidated.properties', min=True, render=True, history=history)
+# hierarchy.summary()
+
+# score, dfig, pfigs = PCTHierarchy.run_and_draw_hierarchy(hierarchy, env, draw_file=True, draw_figsize=(5,5), history = history, plots="scEdges,scZero", steps=50)#, draw_file='/tmp/tmp.png')
+# print('Test score =',score)
+# dfig
+
+
+
# for pfig in pfigs:
+#     display(pfig)
+
+
+
+
+

Creating a Hierarchy

+

Create a hierarchy by defining the number of rows (levels) and columns.

+
+
from pct.functions import Constant
+
+
+
pre=Constant(5, name='precon')
+namespace=pre.namespace
+post=Constant(10, name='postcon', namespace=namespace)
+hpct = PCTHierarchy(3,3, pre=[pre], post=[post], history=True, clear_names=False, links="dense", namespace=namespace)
+hpct.hierarchy
+
+
[[<pct.nodes.PCTNode>,
+  <pct.nodes.PCTNode>,
+  <pct.nodes.PCTNode>],
+ [<pct.nodes.PCTNode>,
+  <pct.nodes.PCTNode>,
+  <pct.nodes.PCTNode>],
+ [<pct.nodes.PCTNode>,
+  <pct.nodes.PCTNode>,
+  <pct.nodes.PCTNode>]]
+
+
+
+
print(hpct.get_summary())
+
+
**************************
+PRE: 5.000 
+L0C0 0.000 0.000 0.000 0.000 
+L0C1 0.000 0.000 0.000 0.000 
+L0C2 0.000 0.000 0.000 0.000 
+L1C0 0.000 0.000 0.000 0.000 
+L1C1 0.000 0.000 0.000 0.000 
+L1C2 0.000 0.000 0.000 0.000 
+L2C0 0.000 0.000 0.000 0.000 
+L2C1 0.000 0.000 0.000 0.000 
+L2C2 0.000 0.000 0.000 0.000 
+POST: 10.000 
+
+
+
+
+
print(hpct.get_parameters_list())
+
+
[[[[5]], [[10]]], [[[[1, 1, 1]], [[0]], [[1]]], [[[1, 1, 1]], [[0]], [[1]]], [[[1, 1, 1]], [[0]], [[1]]]], [[[[1, 1, 1]], [[1, 1, 1]], [[1]]], [[[1, 1, 1]], [[1, 1, 1]], [[1]]], [[[1, 1, 1]], [[1, 1, 1]], [[1]]]], [[[[0]], [[1, 1, 1]], [[1]]], [[[0]], [[1, 1, 1]], [[1]]], [[[0]], [[1, 1, 1]], [[1]]]]]
+
+
+
+
print(hpct.get_grid())
+
+
[3, 3, 3]
+
+
+
+
hpct.change_namespace()
+
+
+
hpct.summary()
+
+
**************************
+pcthierarchy PCTHierarchy [3, 3, 3] b5096973-bd48-11ef-a948-8cf8c5b8669e
+--------------------------
+PRE: precon Constant | 5 
+Level 0 Cols 3
+level0col0 PCTNode b5096973-bd48-11ef-a948-8cf8c5b8669e
+----------------------------
+REF: weighted_sum WeightedSum | weights [1, 1, 1] | 0 | links  proportional3 proportional4 proportional5 
+PER: variable Variable | 0 
+COM: subtract Subtract | 0 | links  weighted_sum variable 
+OUT: proportional Proportional | gain 1 | 0 | links  subtract 
+----------------------------
+level0col1 PCTNode b5096973-bd48-11ef-a948-8cf8c5b8669e
+----------------------------
+REF: weighted_sum1 WeightedSum | weights [1, 1, 1] | 0 | links  proportional3 proportional4 proportional5 
+PER: variable1 Variable | 0 
+COM: subtract1 Subtract | 0 | links  weighted_sum1 variable1 
+OUT: proportional1 Proportional | gain 1 | 0 | links  subtract1 
+----------------------------
+level0col2 PCTNode b5096973-bd48-11ef-a948-8cf8c5b8669e
+----------------------------
+REF: weighted_sum2 WeightedSum | weights [1, 1, 1] | 0 | links  proportional3 proportional4 proportional5 
+PER: variable2 Variable | 0 
+COM: subtract2 Subtract | 0 | links  weighted_sum2 variable2 
+OUT: proportional2 Proportional | gain 1 | 0 | links  subtract2 
+----------------------------
+Level 1 Cols 3
+level1col0 PCTNode b5096973-bd48-11ef-a948-8cf8c5b8669e
+----------------------------
+REF: weighted_sum4 WeightedSum | weights [1, 1, 1] | 0 | links  proportional6 proportional7 proportional8 
+PER: weighted_sum3 WeightedSum | weights [1, 1, 1] | 0 | links  variable variable1 variable2 
+COM: subtract3 Subtract | 0 | links  weighted_sum4 weighted_sum3 
+OUT: proportional3 Proportional | gain 1 | 0 | links  subtract3 
+----------------------------
+level1col1 PCTNode b5096973-bd48-11ef-a948-8cf8c5b8669e
+----------------------------
+REF: weighted_sum6 WeightedSum | weights [1, 1, 1] | 0 | links  proportional6 proportional7 proportional8 
+PER: weighted_sum5 WeightedSum | weights [1, 1, 1] | 0 | links  variable variable1 variable2 
+COM: subtract4 Subtract | 0 | links  weighted_sum6 weighted_sum5 
+OUT: proportional4 Proportional | gain 1 | 0 | links  subtract4 
+----------------------------
+level1col2 PCTNode b5096973-bd48-11ef-a948-8cf8c5b8669e
+----------------------------
+REF: weighted_sum8 WeightedSum | weights [1, 1, 1] | 0 | links  proportional6 proportional7 proportional8 
+PER: weighted_sum7 WeightedSum | weights [1, 1, 1] | 0 | links  variable variable1 variable2 
+COM: subtract5 Subtract | 0 | links  weighted_sum8 weighted_sum7 
+OUT: proportional5 Proportional | gain 1 | 0 | links  subtract5 
+----------------------------
+Level 2 Cols 3
+level2col0 PCTNode b5096973-bd48-11ef-a948-8cf8c5b8669e
+----------------------------
+REF: constant Constant | 0 
+PER: weighted_sum9 WeightedSum | weights [1, 1, 1] | 0 | links  weighted_sum3 weighted_sum5 weighted_sum7 
+COM: subtract6 Subtract | 0 | links  constant weighted_sum9 
+OUT: proportional6 Proportional | gain 1 | 0 | links  subtract6 
+----------------------------
+level2col1 PCTNode b5096973-bd48-11ef-a948-8cf8c5b8669e
+----------------------------
+REF: constant1 Constant | 0 
+PER: weighted_sum10 WeightedSum | weights [1, 1, 1] | 0 | links  weighted_sum3 weighted_sum5 weighted_sum7 
+COM: subtract7 Subtract | 0 | links  constant1 weighted_sum10 
+OUT: proportional7 Proportional | gain 1 | 0 | links  subtract7 
+----------------------------
+level2col2 PCTNode b5096973-bd48-11ef-a948-8cf8c5b8669e
+----------------------------
+REF: constant2 Constant | 0 
+PER: weighted_sum11 WeightedSum | weights [1, 1, 1] | 0 | links  weighted_sum3 weighted_sum5 weighted_sum7 
+COM: subtract8 Subtract | 0 | links  constant2 weighted_sum11 
+OUT: proportional8 Proportional | gain 1 | 0 | links  subtract8 
+----------------------------
+POST: postcon Constant | 10 
+**************************
+
+
+
+
#FunctionsList.getInstance().report()
+
+

Create a hierarchy from a configuration.

+
+
config = hpct.get_config()
+print(config)
+
+
{'type': 'PCTHierarchy', 'name': 'pcthierarchy', 'pre': {'pre0': {'type': 'Constant', 'name': 'precon', 'value': 5, 'links': {}}}, 'levels': {'level0': {'level': 0, 'nodes': {'col0': {'col': 0, 'node': {'type': 'PCTNode', 'name': 'level0col0', 'refcoll': {'0': {'type': 'WeightedSum', 'name': 'weighted_sum', 'value': 0, 'links': {0: 'proportional3', 1: 'proportional4', 2: 'proportional5'}, 'weights': [1, 1, 1]}}, 'percoll': {'0': {'type': 'Variable', 'name': 'variable', 'value': 0, 'links': {}}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract', 'value': 0, 'links': {0: 'weighted_sum', 1: 'variable'}}}, 'outcoll': {'0': {'type': 'Proportional', 'name': 'proportional', 'value': 0, 'links': {0: 'subtract'}, 'gain': 1}}}}, 'col1': {'col': 1, 'node': {'type': 'PCTNode', 'name': 'level0col1', 'refcoll': {'0': {'type': 'WeightedSum', 'name': 'weighted_sum1', 'value': 0, 'links': {0: 'proportional3', 1: 'proportional4', 2: 'proportional5'}, 'weights': [1, 1, 1]}}, 'percoll': {'0': {'type': 'Variable', 'name': 'variable1', 'value': 0, 'links': {}}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract1', 'value': 0, 'links': {0: 'weighted_sum1', 1: 'variable1'}}}, 'outcoll': {'0': {'type': 'Proportional', 'name': 'proportional1', 'value': 0, 'links': {0: 'subtract1'}, 'gain': 1}}}}, 'col2': {'col': 2, 'node': {'type': 'PCTNode', 'name': 'level0col2', 'refcoll': {'0': {'type': 'WeightedSum', 'name': 'weighted_sum2', 'value': 0, 'links': {0: 'proportional3', 1: 'proportional4', 2: 'proportional5'}, 'weights': [1, 1, 1]}}, 'percoll': {'0': {'type': 'Variable', 'name': 'variable2', 'value': 0, 'links': {}}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract2', 'value': 0, 'links': {0: 'weighted_sum2', 1: 'variable2'}}}, 'outcoll': {'0': {'type': 'Proportional', 'name': 'proportional2', 'value': 0, 'links': {0: 'subtract2'}, 'gain': 1}}}}}}, 'level1': {'level': 1, 'nodes': {'col0': {'col': 0, 'node': {'type': 'PCTNode', 'name': 'level1col0', 'refcoll': {'0': {'type': 'WeightedSum', 'name': 'weighted_sum4', 'value': 0, 'links': {0: 'proportional6', 1: 'proportional7', 2: 'proportional8'}, 'weights': [1, 1, 1]}}, 'percoll': {'0': {'type': 'WeightedSum', 'name': 'weighted_sum3', 'value': 0, 'links': {0: 'variable', 1: 'variable1', 2: 'variable2'}, 'weights': [1, 1, 1]}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract3', 'value': 0, 'links': {0: 'weighted_sum4', 1: 'weighted_sum3'}}}, 'outcoll': {'0': {'type': 'Proportional', 'name': 'proportional3', 'value': 0, 'links': {0: 'subtract3'}, 'gain': 1}}}}, 'col1': {'col': 1, 'node': {'type': 'PCTNode', 'name': 'level1col1', 'refcoll': {'0': {'type': 'WeightedSum', 'name': 'weighted_sum6', 'value': 0, 'links': {0: 'proportional6', 1: 'proportional7', 2: 'proportional8'}, 'weights': [1, 1, 1]}}, 'percoll': {'0': {'type': 'WeightedSum', 'name': 'weighted_sum5', 'value': 0, 'links': {0: 'variable', 1: 'variable1', 2: 'variable2'}, 'weights': [1, 1, 1]}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract4', 'value': 0, 'links': {0: 'weighted_sum6', 1: 'weighted_sum5'}}}, 'outcoll': {'0': {'type': 'Proportional', 'name': 'proportional4', 'value': 0, 'links': {0: 'subtract4'}, 'gain': 1}}}}, 'col2': {'col': 2, 'node': {'type': 'PCTNode', 'name': 'level1col2', 'refcoll': {'0': {'type': 'WeightedSum', 'name': 'weighted_sum8', 'value': 0, 'links': {0: 'proportional6', 1: 'proportional7', 2: 'proportional8'}, 'weights': [1, 1, 1]}}, 'percoll': {'0': {'type': 'WeightedSum', 'name': 'weighted_sum7', 'value': 0, 'links': {0: 'variable', 1: 'variable1', 2: 'variable2'}, 'weights': [1, 1, 1]}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract5', 'value': 0, 'links': {0: 'weighted_sum8', 1: 'weighted_sum7'}}}, 'outcoll': {'0': {'type': 'Proportional', 'name': 'proportional5', 'value': 0, 'links': {0: 'subtract5'}, 'gain': 1}}}}}}, 'level2': {'level': 2, 'nodes': {'col0': {'col': 0, 'node': {'type': 'PCTNode', 'name': 'level2col0', 'refcoll': {'0': {'type': 'Constant', 'name': 'constant', 'value': 0, 'links': {}}}, 'percoll': {'0': {'type': 'WeightedSum', 'name': 'weighted_sum9', 'value': 0, 'links': {0: 'weighted_sum3', 1: 'weighted_sum5', 2: 'weighted_sum7'}, 'weights': [1, 1, 1]}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract6', 'value': 0, 'links': {0: 'constant', 1: 'weighted_sum9'}}}, 'outcoll': {'0': {'type': 'Proportional', 'name': 'proportional6', 'value': 0, 'links': {0: 'subtract6'}, 'gain': 1}}}}, 'col1': {'col': 1, 'node': {'type': 'PCTNode', 'name': 'level2col1', 'refcoll': {'0': {'type': 'Constant', 'name': 'constant1', 'value': 0, 'links': {}}}, 'percoll': {'0': {'type': 'WeightedSum', 'name': 'weighted_sum10', 'value': 0, 'links': {0: 'weighted_sum3', 1: 'weighted_sum5', 2: 'weighted_sum7'}, 'weights': [1, 1, 1]}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract7', 'value': 0, 'links': {0: 'constant1', 1: 'weighted_sum10'}}}, 'outcoll': {'0': {'type': 'Proportional', 'name': 'proportional7', 'value': 0, 'links': {0: 'subtract7'}, 'gain': 1}}}}, 'col2': {'col': 2, 'node': {'type': 'PCTNode', 'name': 'level2col2', 'refcoll': {'0': {'type': 'Constant', 'name': 'constant2', 'value': 0, 'links': {}}}, 'percoll': {'0': {'type': 'WeightedSum', 'name': 'weighted_sum11', 'value': 0, 'links': {0: 'weighted_sum3', 1: 'weighted_sum5', 2: 'weighted_sum7'}, 'weights': [1, 1, 1]}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract8', 'value': 0, 'links': {0: 'constant2', 1: 'weighted_sum11'}}}, 'outcoll': {'0': {'type': 'Proportional', 'name': 'proportional8', 'value': 0, 'links': {0: 'subtract8'}, 'gain': 1}}}}}}}, 'post': {'post0': {'type': 'Constant', 'name': 'postcon', 'value': 10, 'links': {}}}}
+
+
+
+
# h = PCTHierarchy.from_config(config, namespace=namespace)
+h = PCTHierarchy.from_config(config)
+
+
+
assert h.get_config() == hpct.get_config()
+
+
+
+

Viewing a Hierarchy

+

The hierarchy details can be viewed as a summary.

+
+
hpct.get_node(2,0).get_function('reference').set_value(1)
+hpct.get_node(2,1).get_function('reference').set_value(1)
+hpct.get_node(2,2).get_function('reference').set_value(1)
+for level in range(3):
+    for col in range(3):
+        hpct.get_node(level,col).get_function('output').set_property('gain', 10)
+hpct.summary()
+
+
**************************
+pcthierarchy PCTHierarchy [3, 3, 3] b5096973-bd48-11ef-a948-8cf8c5b8669e
+--------------------------
+PRE: precon Constant | 5 
+Level 0 Cols 3
+level0col0 PCTNode b5096973-bd48-11ef-a948-8cf8c5b8669e
+----------------------------
+REF: weighted_sum WeightedSum | weights [1, 1, 1] | 0 | links  proportional3 proportional4 proportional5 
+PER: variable Variable | 0 
+COM: subtract Subtract | 0 | links  weighted_sum variable 
+OUT: proportional Proportional | gain 10 | 0 | links  subtract 
+----------------------------
+level0col1 PCTNode b5096973-bd48-11ef-a948-8cf8c5b8669e
+----------------------------
+REF: weighted_sum1 WeightedSum | weights [1, 1, 1] | 0 | links  proportional3 proportional4 proportional5 
+PER: variable1 Variable | 0 
+COM: subtract1 Subtract | 0 | links  weighted_sum1 variable1 
+OUT: proportional1 Proportional | gain 10 | 0 | links  subtract1 
+----------------------------
+level0col2 PCTNode b5096973-bd48-11ef-a948-8cf8c5b8669e
+----------------------------
+REF: weighted_sum2 WeightedSum | weights [1, 1, 1] | 0 | links  proportional3 proportional4 proportional5 
+PER: variable2 Variable | 0 
+COM: subtract2 Subtract | 0 | links  weighted_sum2 variable2 
+OUT: proportional2 Proportional | gain 10 | 0 | links  subtract2 
+----------------------------
+Level 1 Cols 3
+level1col0 PCTNode b5096973-bd48-11ef-a948-8cf8c5b8669e
+----------------------------
+REF: weighted_sum4 WeightedSum | weights [1, 1, 1] | 0 | links  proportional6 proportional7 proportional8 
+PER: weighted_sum3 WeightedSum | weights [1, 1, 1] | 0 | links  variable variable1 variable2 
+COM: subtract3 Subtract | 0 | links  weighted_sum4 weighted_sum3 
+OUT: proportional3 Proportional | gain 10 | 0 | links  subtract3 
+----------------------------
+level1col1 PCTNode b5096973-bd48-11ef-a948-8cf8c5b8669e
+----------------------------
+REF: weighted_sum6 WeightedSum | weights [1, 1, 1] | 0 | links  proportional6 proportional7 proportional8 
+PER: weighted_sum5 WeightedSum | weights [1, 1, 1] | 0 | links  variable variable1 variable2 
+COM: subtract4 Subtract | 0 | links  weighted_sum6 weighted_sum5 
+OUT: proportional4 Proportional | gain 10 | 0 | links  subtract4 
+----------------------------
+level1col2 PCTNode b5096973-bd48-11ef-a948-8cf8c5b8669e
+----------------------------
+REF: weighted_sum8 WeightedSum | weights [1, 1, 1] | 0 | links  proportional6 proportional7 proportional8 
+PER: weighted_sum7 WeightedSum | weights [1, 1, 1] | 0 | links  variable variable1 variable2 
+COM: subtract5 Subtract | 0 | links  weighted_sum8 weighted_sum7 
+OUT: proportional5 Proportional | gain 10 | 0 | links  subtract5 
+----------------------------
+Level 2 Cols 3
+level2col0 PCTNode b5096973-bd48-11ef-a948-8cf8c5b8669e
+----------------------------
+REF: constant Constant | 1 
+PER: weighted_sum9 WeightedSum | weights [1, 1, 1] | 0 | links  weighted_sum3 weighted_sum5 weighted_sum7 
+COM: subtract6 Subtract | 0 | links  constant weighted_sum9 
+OUT: proportional6 Proportional | gain 10 | 0 | links  subtract6 
+----------------------------
+level2col1 PCTNode b5096973-bd48-11ef-a948-8cf8c5b8669e
+----------------------------
+REF: constant1 Constant | 1 
+PER: weighted_sum10 WeightedSum | weights [1, 1, 1] | 0 | links  weighted_sum3 weighted_sum5 weighted_sum7 
+COM: subtract7 Subtract | 0 | links  constant1 weighted_sum10 
+OUT: proportional7 Proportional | gain 10 | 0 | links  subtract7 
+----------------------------
+level2col2 PCTNode b5096973-bd48-11ef-a948-8cf8c5b8669e
+----------------------------
+REF: constant2 Constant | 1 
+PER: weighted_sum11 WeightedSum | weights [1, 1, 1] | 0 | links  weighted_sum3 weighted_sum5 weighted_sum7 
+COM: subtract8 Subtract | 0 | links  constant2 weighted_sum11 
+OUT: proportional8 Proportional | gain 10 | 0 | links  subtract8 
+----------------------------
+POST: postcon Constant | 10 
+**************************
+
+
+

The hierarchy details can be viewed as a configuration. That configuration can be used to create a hierarchy, as shown above.

+
+
hpct.get_config()
+
+
{'type': 'PCTHierarchy',
+ 'name': 'pcthierarchy',
+ 'pre': {'pre0': {'type': 'Constant',
+   'name': 'precon',
+   'value': 5,
+   'links': {}}},
+ 'levels': {'level0': {'level': 0,
+   'nodes': {'col0': {'col': 0,
+     'node': {'type': 'PCTNode',
+      'name': 'level0col0',
+      'refcoll': {'0': {'type': 'WeightedSum',
+        'name': 'weighted_sum',
+        'value': 0,
+        'links': {0: 'proportional3', 1: 'proportional4', 2: 'proportional5'},
+        'weights': [1, 1, 1]}},
+      'percoll': {'0': {'type': 'Variable',
+        'name': 'variable',
+        'value': 0,
+        'links': {}}},
+      'comcoll': {'0': {'type': 'Subtract',
+        'name': 'subtract',
+        'value': 0,
+        'links': {0: 'weighted_sum', 1: 'variable'}}},
+      'outcoll': {'0': {'type': 'Proportional',
+        'name': 'proportional',
+        'value': 0,
+        'links': {0: 'subtract'},
+        'gain': 10}}}},
+    'col1': {'col': 1,
+     'node': {'type': 'PCTNode',
+      'name': 'level0col1',
+      'refcoll': {'0': {'type': 'WeightedSum',
+        'name': 'weighted_sum1',
+        'value': 0,
+        'links': {0: 'proportional3', 1: 'proportional4', 2: 'proportional5'},
+        'weights': [1, 1, 1]}},
+      'percoll': {'0': {'type': 'Variable',
+        'name': 'variable1',
+        'value': 0,
+        'links': {}}},
+      'comcoll': {'0': {'type': 'Subtract',
+        'name': 'subtract1',
+        'value': 0,
+        'links': {0: 'weighted_sum1', 1: 'variable1'}}},
+      'outcoll': {'0': {'type': 'Proportional',
+        'name': 'proportional1',
+        'value': 0,
+        'links': {0: 'subtract1'},
+        'gain': 10}}}},
+    'col2': {'col': 2,
+     'node': {'type': 'PCTNode',
+      'name': 'level0col2',
+      'refcoll': {'0': {'type': 'WeightedSum',
+        'name': 'weighted_sum2',
+        'value': 0,
+        'links': {0: 'proportional3', 1: 'proportional4', 2: 'proportional5'},
+        'weights': [1, 1, 1]}},
+      'percoll': {'0': {'type': 'Variable',
+        'name': 'variable2',
+        'value': 0,
+        'links': {}}},
+      'comcoll': {'0': {'type': 'Subtract',
+        'name': 'subtract2',
+        'value': 0,
+        'links': {0: 'weighted_sum2', 1: 'variable2'}}},
+      'outcoll': {'0': {'type': 'Proportional',
+        'name': 'proportional2',
+        'value': 0,
+        'links': {0: 'subtract2'},
+        'gain': 10}}}}}},
+  'level1': {'level': 1,
+   'nodes': {'col0': {'col': 0,
+     'node': {'type': 'PCTNode',
+      'name': 'level1col0',
+      'refcoll': {'0': {'type': 'WeightedSum',
+        'name': 'weighted_sum4',
+        'value': 0,
+        'links': {0: 'proportional6', 1: 'proportional7', 2: 'proportional8'},
+        'weights': [1, 1, 1]}},
+      'percoll': {'0': {'type': 'WeightedSum',
+        'name': 'weighted_sum3',
+        'value': 0,
+        'links': {0: 'variable', 1: 'variable1', 2: 'variable2'},
+        'weights': [1, 1, 1]}},
+      'comcoll': {'0': {'type': 'Subtract',
+        'name': 'subtract3',
+        'value': 0,
+        'links': {0: 'weighted_sum4', 1: 'weighted_sum3'}}},
+      'outcoll': {'0': {'type': 'Proportional',
+        'name': 'proportional3',
+        'value': 0,
+        'links': {0: 'subtract3'},
+        'gain': 10}}}},
+    'col1': {'col': 1,
+     'node': {'type': 'PCTNode',
+      'name': 'level1col1',
+      'refcoll': {'0': {'type': 'WeightedSum',
+        'name': 'weighted_sum6',
+        'value': 0,
+        'links': {0: 'proportional6', 1: 'proportional7', 2: 'proportional8'},
+        'weights': [1, 1, 1]}},
+      'percoll': {'0': {'type': 'WeightedSum',
+        'name': 'weighted_sum5',
+        'value': 0,
+        'links': {0: 'variable', 1: 'variable1', 2: 'variable2'},
+        'weights': [1, 1, 1]}},
+      'comcoll': {'0': {'type': 'Subtract',
+        'name': 'subtract4',
+        'value': 0,
+        'links': {0: 'weighted_sum6', 1: 'weighted_sum5'}}},
+      'outcoll': {'0': {'type': 'Proportional',
+        'name': 'proportional4',
+        'value': 0,
+        'links': {0: 'subtract4'},
+        'gain': 10}}}},
+    'col2': {'col': 2,
+     'node': {'type': 'PCTNode',
+      'name': 'level1col2',
+      'refcoll': {'0': {'type': 'WeightedSum',
+        'name': 'weighted_sum8',
+        'value': 0,
+        'links': {0: 'proportional6', 1: 'proportional7', 2: 'proportional8'},
+        'weights': [1, 1, 1]}},
+      'percoll': {'0': {'type': 'WeightedSum',
+        'name': 'weighted_sum7',
+        'value': 0,
+        'links': {0: 'variable', 1: 'variable1', 2: 'variable2'},
+        'weights': [1, 1, 1]}},
+      'comcoll': {'0': {'type': 'Subtract',
+        'name': 'subtract5',
+        'value': 0,
+        'links': {0: 'weighted_sum8', 1: 'weighted_sum7'}}},
+      'outcoll': {'0': {'type': 'Proportional',
+        'name': 'proportional5',
+        'value': 0,
+        'links': {0: 'subtract5'},
+        'gain': 10}}}}}},
+  'level2': {'level': 2,
+   'nodes': {'col0': {'col': 0,
+     'node': {'type': 'PCTNode',
+      'name': 'level2col0',
+      'refcoll': {'0': {'type': 'Constant',
+        'name': 'constant',
+        'value': 1,
+        'links': {}}},
+      'percoll': {'0': {'type': 'WeightedSum',
+        'name': 'weighted_sum9',
+        'value': 0,
+        'links': {0: 'weighted_sum3', 1: 'weighted_sum5', 2: 'weighted_sum7'},
+        'weights': [1, 1, 1]}},
+      'comcoll': {'0': {'type': 'Subtract',
+        'name': 'subtract6',
+        'value': 0,
+        'links': {0: 'constant', 1: 'weighted_sum9'}}},
+      'outcoll': {'0': {'type': 'Proportional',
+        'name': 'proportional6',
+        'value': 0,
+        'links': {0: 'subtract6'},
+        'gain': 10}}}},
+    'col1': {'col': 1,
+     'node': {'type': 'PCTNode',
+      'name': 'level2col1',
+      'refcoll': {'0': {'type': 'Constant',
+        'name': 'constant1',
+        'value': 1,
+        'links': {}}},
+      'percoll': {'0': {'type': 'WeightedSum',
+        'name': 'weighted_sum10',
+        'value': 0,
+        'links': {0: 'weighted_sum3', 1: 'weighted_sum5', 2: 'weighted_sum7'},
+        'weights': [1, 1, 1]}},
+      'comcoll': {'0': {'type': 'Subtract',
+        'name': 'subtract7',
+        'value': 0,
+        'links': {0: 'constant1', 1: 'weighted_sum10'}}},
+      'outcoll': {'0': {'type': 'Proportional',
+        'name': 'proportional7',
+        'value': 0,
+        'links': {0: 'subtract7'},
+        'gain': 10}}}},
+    'col2': {'col': 2,
+     'node': {'type': 'PCTNode',
+      'name': 'level2col2',
+      'refcoll': {'0': {'type': 'Constant',
+        'name': 'constant2',
+        'value': 1,
+        'links': {}}},
+      'percoll': {'0': {'type': 'WeightedSum',
+        'name': 'weighted_sum11',
+        'value': 0,
+        'links': {0: 'weighted_sum3', 1: 'weighted_sum5', 2: 'weighted_sum7'},
+        'weights': [1, 1, 1]}},
+      'comcoll': {'0': {'type': 'Subtract',
+        'name': 'subtract8',
+        'value': 0,
+        'links': {0: 'constant2', 1: 'weighted_sum11'}}},
+      'outcoll': {'0': {'type': 'Proportional',
+        'name': 'proportional8',
+        'value': 0,
+        'links': {0: 'subtract8'},
+        'gain': 10}}}}}}},
+ 'post': {'post0': {'type': 'Constant',
+   'name': 'postcon',
+   'value': 10,
+   'links': {}}}}
+
+
+

Get the output function, which will be the output function of the last node, or the last item of the post-processor functions, if present.

+
+
link = hpct.get_output_function()
+print(link.get_config())
+
+
{'type': 'Constant', 'name': 'postcon', 'value': 10, 'links': {}}
+
+
+

The hierarhcy can also be viewed graphically as a network of connected nodes.

+
+
import os
+
+
+
ahpct = PCTHierarchy(2,2, links="dense")
+
+test = 3
+if test==1:
+    g = ahpct.graph()
+    pos=graphviz_layout(g, prog='dot')
+    nx.draw(g, pos=pos, with_labels=True, font_size=12, font_weight='bold', node_color='red',  node_size=500)
+
+if test ==2:
+    g = ahpct.graph()
+    pos = nx.multipartite_layout(g, subset_key="layer", align='horizontal')
+    pos['constant1'][0]+=0.2
+    c = pos['constant1'][0]
+    print(c)
+    nx.draw(g, pos=pos, with_labels=True, font_weight='bold', node_color='red',  node_size=750, arrowsize=25)
+
+if test ==3:
+    if os.name=='nt':        
+        ahpct.draw(file="ahpct.png",  node_size=1500, figsize=(10,10))# with_labels=True, font_weight='bold', node_color='red',  node_size=500, arrowsize=25, align='vertical'
+
+
C:\Users\ryoung\AppData\Local\Temp\ipykernel_988\790717467.py:277: UserWarning:
+
+This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.
+
+
+
+
+
+

Running a hierarchy

+

The hierachy can be run once by calling itself. The verbose flag will print the computations to the screen.

+
+
hpct(verbose=True)
+
+
5.000 
+level0col0 0.000 0.000 0.000 0.000 
+level0col1 0.000 0.000 0.000 0.000 
+level0col2 0.000 0.000 0.000 0.000 
+level1col0 0.000 0.000 0.000 0.000 
+level1col1 0.000 0.000 0.000 0.000 
+level1col2 0.000 0.000 0.000 0.000 
+level2col0 1.000 0.000 1.000 10.000 
+level2col1 1.000 0.000 1.000 10.000 
+level2col2 1.000 0.000 1.000 10.000 
+10.000
+
+
+
10
+
+
+

A hierarchy can be executed with the “run()” method, providing the number of iterations to run.

+
+
hpct1 = PCTHierarchy(3,3, pre=[pre], post=[post], history=True, links="dense")
+namespace=hpct1.namespace
+hpct1.get_node(2,0).get_function('reference').set_value(1)
+hpct1.get_node(2,1).get_function('reference').set_value(1)
+hpct1.get_node(2,2).get_function('reference').set_value(1)
+for level in range(3):
+    for col in range(3):
+        hpct1.get_node(level,col).get_function('output').set_property('gain', 10)
+
+hpct1.run(10)
+
+
10
+
+
+
+
+

Viewing Data

+

If the hierarchy is created with the “history” flag equal to True, the data can be retrieved for each node. The node is accessed by specifying the row and column within the hierarchy.

+
+
print(hpct1.get_node(1,1).history.data)
+assert hpct1.get_node(1,1).history.data == {'refcoll': {'weighted_sum6': [0.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0]}, 'percoll': {'weighted_sum5': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, 'comcoll': {'subtract4': [0.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0]}, 'outcoll': {'proportional4': [0.0, 300.0, 300.0, 300.0, 300.0, 300.0, 300.0, 300.0, 300.0, 300.0]}}
+
+
{'refcoll': {'weighted_sum6': [0, 30, 30, 30, 30, 30, 30, 30, 30, 30]}, 'percoll': {'weighted_sum5': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}, 'comcoll': {'subtract4': [0, 30, 30, 30, 30, 30, 30, 30, 30, 30]}, 'outcoll': {'proportional4': [0, 300, 300, 300, 300, 300, 300, 300, 300, 300]}}
+
+
+
+
+

Save and Load

+

Save a hierarchy to file.

+
+
import json
+
+
+
hpct1.save("hpct.json")
+
+

Create a hierarchy from file.

+
+
#loaded = PCTHierarchy.load("hpct.json", clear=False, namespace=namespace)
+loaded = PCTHierarchy.load("hpct.json", clear=False)
+loaded.summary()
+
+
**************************
+pcthierarchy PCTHierarchy [3, 3, 3] b5b0dae1-bd48-11ef-831e-8cf8c5b8669e
+--------------------------
+PRE: precon Constant | 5 
+Level 0 Cols 3
+level0col0 PCTNode b5b0dae1-bd48-11ef-831e-8cf8c5b8669e
+----------------------------
+REF: weighted_sum WeightedSum | weights [1, 1, 1] | 900 | links  proportional3 proportional4 proportional5 
+PER: variable Variable | 0 
+COM: subtract Subtract | 900 | links  weighted_sum variable 
+OUT: proportional Proportional | gain 10 | 9000 | links  subtract 
+----------------------------
+level0col1 PCTNode b5b0dae1-bd48-11ef-831e-8cf8c5b8669e
+----------------------------
+REF: weighted_sum1 WeightedSum | weights [1, 1, 1] | 900 | links  proportional3 proportional4 proportional5 
+PER: variable1 Variable | 0 
+COM: subtract1 Subtract | 900 | links  weighted_sum1 variable1 
+OUT: proportional1 Proportional | gain 10 | 9000 | links  subtract1 
+----------------------------
+level0col2 PCTNode b5b0dae1-bd48-11ef-831e-8cf8c5b8669e
+----------------------------
+REF: weighted_sum2 WeightedSum | weights [1, 1, 1] | 900 | links  proportional3 proportional4 proportional5 
+PER: variable2 Variable | 0 
+COM: subtract2 Subtract | 900 | links  weighted_sum2 variable2 
+OUT: proportional2 Proportional | gain 10 | 9000 | links  subtract2 
+----------------------------
+Level 1 Cols 3
+level1col0 PCTNode b5b0dae1-bd48-11ef-831e-8cf8c5b8669e
+----------------------------
+REF: weighted_sum4 WeightedSum | weights [1, 1, 1] | 30 | links  proportional6 proportional7 proportional8 
+PER: weighted_sum3 WeightedSum | weights [1, 1, 1] | 0 | links  variable variable1 variable2 
+COM: subtract3 Subtract | 30 | links  weighted_sum4 weighted_sum3 
+OUT: proportional3 Proportional | gain 10 | 300 | links  subtract3 
+----------------------------
+level1col1 PCTNode b5b0dae1-bd48-11ef-831e-8cf8c5b8669e
+----------------------------
+REF: weighted_sum6 WeightedSum | weights [1, 1, 1] | 30 | links  proportional6 proportional7 proportional8 
+PER: weighted_sum5 WeightedSum | weights [1, 1, 1] | 0 | links  variable variable1 variable2 
+COM: subtract4 Subtract | 30 | links  weighted_sum6 weighted_sum5 
+OUT: proportional4 Proportional | gain 10 | 300 | links  subtract4 
+----------------------------
+level1col2 PCTNode b5b0dae1-bd48-11ef-831e-8cf8c5b8669e
+----------------------------
+REF: weighted_sum8 WeightedSum | weights [1, 1, 1] | 30 | links  proportional6 proportional7 proportional8 
+PER: weighted_sum7 WeightedSum | weights [1, 1, 1] | 0 | links  variable variable1 variable2 
+COM: subtract5 Subtract | 30 | links  weighted_sum8 weighted_sum7 
+OUT: proportional5 Proportional | gain 10 | 300 | links  subtract5 
+----------------------------
+Level 2 Cols 3
+level2col0 PCTNode b5b0dae1-bd48-11ef-831e-8cf8c5b8669e
+----------------------------
+REF: constant Constant | 1 
+PER: weighted_sum9 WeightedSum | weights [1, 1, 1] | 0 | links  weighted_sum3 weighted_sum5 weighted_sum7 
+COM: subtract6 Subtract | 1 | links  constant weighted_sum9 
+OUT: proportional6 Proportional | gain 10 | 10 | links  subtract6 
+----------------------------
+level2col1 PCTNode b5b0dae1-bd48-11ef-831e-8cf8c5b8669e
+----------------------------
+REF: constant1 Constant | 1 
+PER: weighted_sum10 WeightedSum | weights [1, 1, 1] | 0 | links  weighted_sum3 weighted_sum5 weighted_sum7 
+COM: subtract7 Subtract | 1 | links  constant1 weighted_sum10 
+OUT: proportional7 Proportional | gain 10 | 10 | links  subtract7 
+----------------------------
+level2col2 PCTNode b5b0dae1-bd48-11ef-831e-8cf8c5b8669e
+----------------------------
+REF: constant2 Constant | 1 
+PER: weighted_sum11 WeightedSum | weights [1, 1, 1] | 0 | links  weighted_sum3 weighted_sum5 weighted_sum7 
+COM: subtract8 Subtract | 1 | links  constant2 weighted_sum11 
+OUT: proportional8 Proportional | gain 10 | 10 | links  subtract8 
+----------------------------
+POST: postcon Constant | 10 
+**************************
+
+
+
+
import networkx as nx
+import matplotlib.pyplot as plt
+
+
+
# https://matplotlib.org/3.1.0/gallery/color/named_colors.html
+loaded.draw(with_edge_labels=True, color_mapping={'w':'aqua','c':'limegreen','s':'goldenrod', 'p':'red', 'v':'silver'})
+
+
Backend agg is non-interactive backend. Turning interactive mode off.
+
+
+
+
+

+
+
+
+
+
+
Examples
+

Build a hierarchy by adding nodes and functions manually.

+

Create an empty hierarchy.

+
+
myhpct = PCTHierarchy()
+namespace=myhpct.namespace
+myhpct.summary(build=False)
+
+
**************************
+pcthierarchy PCTHierarchy [] b61220c9-bd48-11ef-940c-8cf8c5b8669e
+--------------------------
+PRE: None
+POST: None
+**************************
+
+
+

Add a node. Then nodes at particular positions.

+
+
myhpct.add_node(PCTNode(namespace=namespace))
+myhpct.add_node(PCTNode(namespace=namespace), level=1)
+myhpct.add_node(PCTNode(namespace=namespace), level=0)
+myhpct.summary(build=False)
+
+
**************************
+pcthierarchy PCTHierarchy [2, 1] b61220c9-bd48-11ef-940c-8cf8c5b8669e
+--------------------------
+PRE: None
+Level 0 Cols 2
+pctnode2 PCTNode b61220c9-bd48-11ef-940c-8cf8c5b8669e
+----------------------------
+REF: constant2 Constant | 0 
+PER: variable2 Variable | 0 
+COM: subtract2 Subtract | 0 
+OUT: proportional2 Proportional | gain 1 | 0 
+----------------------------
+pctnode PCTNode b61220c9-bd48-11ef-940c-8cf8c5b8669e
+----------------------------
+REF: constant Constant | 0 
+PER: variable Variable | 0 
+COM: subtract Subtract | 0 
+OUT: proportional Proportional | gain 1 | 0 
+----------------------------
+Level 1 Cols 1
+pctnode1 PCTNode b61220c9-bd48-11ef-940c-8cf8c5b8669e
+----------------------------
+REF: constant1 Constant | 0 
+PER: variable1 Variable | 0 
+COM: subtract1 Subtract | 0 
+OUT: proportional1 Proportional | gain 1 | 0 
+----------------------------
+POST: None
+**************************
+
+
+

Replace functions at particular positions in the hierarchy.

+
+
myhpct.insert_function(level=0, col=0, collection="perception", function=Proportional(3, name="prop2", namespace=namespace))
+myhpct.insert_function(level=1, col=0, collection="perception", function=WeightedSum(weights=[1,1], name="wsum", namespace=namespace))
+myhpct.insert_function(level=0, col=1, collection="reference", function=Proportional(1, name="passthru", namespace=namespace))
+
+

Add pre and post processor functions.

+
+
myhpct.add_preprocessor(Constant(1, name="cons1", namespace=namespace))
+myhpct.add_preprocessor(Proportional(5, name="prop1", namespace=namespace))
+myhpct.add_postprocessor(Proportional(5, name="postprop1", namespace=namespace))
+myhpct.add_postprocessor(Proportional(5, name="postprop2", namespace=namespace))
+
+

Link the functions together.

+
+
myhpct.set_links("prop1", "cons1")
+myhpct.set_links("prop2", "prop1")
+myhpct.add_links("wsum", "prop2", "variable")
+myhpct.set_links("passthru", "proportional1")
+myhpct.set_links("postprop1", "proportional")
+myhpct.set_links("postprop2", "postprop1")
+
+
+
myhpct.get_node(0,0).get_function('reference').set_value(1)
+myhpct.get_node(1,0).get_function('reference').set_value(1)
+myhpct.get_node(0,0).get_function('output').set_property('gain', 10)
+myhpct.get_node(0,1).get_function('output').set_property('gain', 10)
+myhpct.get_node(1,0).get_function('output').set_property('gain', 10)
+myhpct.summary(build=True)
+
+
**************************
+pcthierarchy PCTHierarchy [2, 1] b61220c9-bd48-11ef-940c-8cf8c5b8669e
+--------------------------
+PRE: cons1 Constant | 1 
+prop1 Proportional | gain 5 | 0 | links  cons1 
+Level 0 Cols 2
+pctnode2 PCTNode b61220c9-bd48-11ef-940c-8cf8c5b8669e
+----------------------------
+REF: constant2 Constant | 1 
+PER: prop2 Proportional | gain 3 | 0 | links  prop1 
+COM: subtract2 Subtract | 0 | links  constant2 prop2 
+OUT: proportional2 Proportional | gain 10 | 0 | links  subtract2 
+----------------------------
+pctnode PCTNode b61220c9-bd48-11ef-940c-8cf8c5b8669e
+----------------------------
+REF: passthru Proportional | gain 1 | 0 | links  proportional1 
+PER: variable Variable | 0 
+COM: subtract Subtract | 0 | links  passthru variable 
+OUT: proportional Proportional | gain 10 | 0 | links  subtract 
+----------------------------
+Level 1 Cols 1
+pctnode1 PCTNode b61220c9-bd48-11ef-940c-8cf8c5b8669e
+----------------------------
+REF: constant1 Constant | 1 
+PER: wsum WeightedSum | weights [1, 1] | 0 | links  prop2 variable 
+COM: subtract1 Subtract | 0 | links  constant1 wsum 
+OUT: proportional1 Proportional | gain 10 | 0 | links  subtract1 
+----------------------------
+POST: postprop1 Proportional | gain 5 | 0 | links  proportional 
+postprop2 Proportional | gain 5 | 0 | links  postprop1 
+**************************
+
+
+
+
myhpctconfig = myhpct.get_config()
+#print(myhpctconfig)
+assert myhpctconfig == {'type': 'PCTHierarchy', 'name': 'pcthierarchy', 'pre': {'pre0': {'type': 'Constant', 'name': 'cons1', 'value': 1, 'links': {}}, 'pre1': {'type': 'Proportional', 'name': 'prop1', 'value': 0, 'links': {0: 'cons1'}, 'gain': 5}}, 'levels': {'level0': {'level': 0, 'nodes': {'col0': {'col': 0, 'node': {'type': 'PCTNode', 'name': 'pctnode2', 'refcoll': {'0': {'type': 'Constant', 'name': 'constant2', 'value': 1, 'links': {}}}, 'percoll': {'0': {'type': 'Proportional', 'name': 'prop2', 'value': 0, 'links': {0: 'prop1'}, 'gain': 3}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract2', 'value': 0, 'links': {0: 'constant2', 1: 'prop2'}}}, 'outcoll': {'0': {'type': 'Proportional', 'name': 'proportional2', 'value': 0, 'links': {0: 'subtract2'}, 'gain': 10}}}}, 'col1': {'col': 1, 'node': {'type': 'PCTNode', 'name': 'pctnode', 'refcoll': {'0': {'type': 'Proportional', 'name': 'passthru', 'value': 0, 'links': {0: 'proportional1'}, 'gain': 1}}, 'percoll': {'0': {'type': 'Variable', 'name': 'variable', 'value': 0, 'links': {}}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract', 'value': 0, 'links': {0: 'passthru', 1: 'variable'}}}, 'outcoll': {'0': {'type': 'Proportional', 'name': 'proportional', 'value': 0, 'links': {0: 'subtract'}, 'gain': 10}}}}}}, 'level1': {'level': 1, 'nodes': {'col0': {'col': 0, 'node': {'type': 'PCTNode', 'name': 'pctnode1', 'refcoll': {'0': {'type': 'Constant', 'name': 'constant1', 'value': 1, 'links': {}}}, 'percoll': {'0': {'type': 'WeightedSum', 'name': 'wsum', 'value': 0, 'links': {0: 'prop2', 1: 'variable'}, 'weights': [1.0, 1.0]}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract1', 'value': 0, 'links': {0: 'constant1', 1: 'wsum'}}}, 'outcoll': {'0': {'type': 'Proportional', 'name': 'proportional1', 'value': 0, 'links': {0: 'subtract1'}, 'gain': 10}}}}}}}, 'post': {'post0': {'type': 'Proportional', 'name': 'postprop1', 'value': 0, 'links': {0: 'proportional'}, 'gain': 5}, 'post1': {'type': 'Proportional', 'name': 'postprop2', 'value': 0, 'links': {0: 'postprop1'}, 'gain': 5}}}
+
+

Define the order in which the node will be processed.

+
+
myhpct.set_order(["pctnode2", "pctnode1", "pctnode"])
+
+

Run the hierarchy once.

+
+
out = myhpct(verbose=True)
+print(out)
+assert out == -35000
+
+
1.000 5.000 
+pctnode2 1.000 15.000 -14.000 -140.000 
+pctnode1 1.000 15.000 -14.000 -140.000 
+pctnode -140.000 0.000 -140.000 -1400.000 
+-7000.000 -35000.000 
+-35000
+
+
+
+
# config =  {'type': 'Individual', 'name': 'pcthierarchy', 'pre': {'pre0': {'type': 'CartPoleV1', 'name': 'CartPoleV1', 'value': [0.03498833197860944, 0.20994561633454428, 0.012668159509212712, -0.2705237130920193, 0.047656152654718356], 'links': {0: 'Action1'}, 'env_name': 'CartPole-v1', 'reward': 1.0, 'done': False, 'info': {}}, 'pre1': {'type': 'IndexedParameter', 'name': 'ICV', 'value': 0.20994561633454428, 'links': {0: 'CartPoleV1'}, 'index': 1}, 'pre2': {'type': 'IndexedParameter', 'name': 'ICP', 'value': 0.03498833197860944, 'links': {0: 'CartPoleV1'}, 'index': 0}, 'pre3': {'type': 'IndexedParameter', 'name': 'IPV', 'value': -0.2705237130920193, 'links': {0: 'CartPoleV1'}, 'index': 3}, 'pre4': {'type': 'IndexedParameter', 'name': 'IPA', 'value': 0.012668159509212712, 'links': {0: 'CartPoleV1'}, 'index': 2}}, 'levels': {'level0': {'level': 0, 'nodes': {'col0': {'col': 0, 'node': {'type': 'PCTNode', 'name': 'L0C0', 'refcoll': {'0': {'type': 'EAConstant', 'name': 'RL0C0', 'value': 0, 'links': {}}}, 'percoll': {'0': {'type': 'EAWeightedSum', 'name': 'PL0C0', 'value': -0.2705237130920193, 'links': {0: 'ICV', 1: 'ICP', 2: 'IPV', 3: 'IPA'}, 'weights': [0, 0, 1, 0]}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'CL0C0', 'value': 0.2705237130920193, 'links': {0: 'RL0C0', 1: 'PL0C0'}}}, 'outcoll': {'0': {'type': 'EAProportional', 'name': 'OL0C0', 'value': -0.05046166000036782, 'links': {0: 'CL0C0'}, 'gain': -0.1865332226280776}}}}}}}, 'post': {'post0': {'type': 'EAWeightedSum', 'name': 'Action1', 'value': -0.005282911840894066, 'links': {0: 'OL0C0'}, 'weights': [0.10469159835121472]}}}
+# ind = PCTHierarchy.from_config(config)
+
+
+
from pct.errors import RootSumSquaredError, TotalError
+
+er = RootSumSquaredError()
+te = TotalError(error_response=er, limit=250,min=True)   
+te.add_error_data([1, 2])
+print(te)
+
+
+hpct = PCTHierarchy(1,1,error_collector=te)
+hpct.run(steps=5, verbose=True)
+
+
+err=te.error()
+print(err)
+
+
TotalError limit:250, limit_exceeded:False, : RootSumSquaredError error_response:2.23606797749979
+[0] 
+level0col0 0.000 0.000 0.000 0.000 
+
+Current score=2.23606797749979
+[1] 
+level0col0 0.000 0.000 0.000 0.000 
+
+Current score=2.23606797749979
+[2] 
+level0col0 0.000 0.000 0.000 0.000 
+
+Current score=2.23606797749979
+[3] 
+level0col0 0.000 0.000 0.000 0.000 
+
+Current score=2.23606797749979
+[4] 
+level0col0 0.000 0.000 0.000 0.000 
+
+Current score=2.23606797749979
+2.23606797749979
+
+
+ + +
+
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/index.html b/index.html new file mode 100644 index 00000000..cdd9f36a --- /dev/null +++ b/index.html @@ -0,0 +1,853 @@ + + + + + + + + + + +Perceptual Control Theory – pct + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Perceptual Control Theory

+
+ +
+
+ A python library for creating perceptual control hierarchies. +
+
+ + +
+ + + + +
+ + + +
+ + + +

With this library you can create and run simple or complex hierarchies of perceptual control systems as well as make use of the power of the Python platform and its rich set of packages.

+

In the context of this library a single control system comprising a perceptual, reference, comparator and output function is called a Node. The functions therein can be configured by the user.

+

A hierarchy is defined by a collection of nodes.

+
+

Install

+

pip install pct

+
+
+

Import

+

Examples of importing the library functionality.

+

import pct as p

+

from pct.hierarchy import Hierarchy

+

from pct import *

+
+
+

How to use

+

Import modules from the PCT library.

+
+
from pct.nodes import PCTNode
+
+

For the purposes of this example define a world model. This would not be required if the real world is used, or a simulation such as OpenAI Gym.

+
+
def velocity_model(velocity,  force , mass):
+    velocity = velocity + force / mass
+    return velocity
+
+# World value
+mass = 50
+
+

Create a PCTNode, a control system unit comprising a reference, perception, comparator and output function. The default value for the reference is 1. With the history flag set, the data for each iteration is recorded for later plotting.

+
+
pctnode = PCTNode(history=True)
+
+

Call the node repeatedly to control the perception of velocity. With the verbose flag set, the control values are printed. In this case the printed values are the iteration number, the (velocity) reference, the perception, the error and the (force) output.

+
+
for i in range(40):
+    print(i, end=" ")
+    force = pctnode(verbose=True)
+    velocity = velocity_model(pctnode.get_perception_value(), force, mass)
+    pctnode.set_perception_value(velocity)
+
+
0 0.000 0.000 0.000 0.000 
+1 0.000 0.000 0.000 0.000 
+2 0.000 0.000 0.000 0.000 
+3 0.000 0.000 0.000 0.000 
+4 0.000 0.000 0.000 0.000 
+5 0.000 0.000 0.000 0.000 
+6 0.000 0.000 0.000 0.000 
+7 0.000 0.000 0.000 0.000 
+8 0.000 0.000 0.000 0.000 
+9 0.000 0.000 0.000 0.000 
+10 0.000 0.000 0.000 0.000 
+11 0.000 0.000 0.000 0.000 
+12 0.000 0.000 0.000 0.000 
+13 0.000 0.000 0.000 0.000 
+14 0.000 0.000 0.000 0.000 
+15 0.000 0.000 0.000 0.000 
+16 0.000 0.000 0.000 0.000 
+17 0.000 0.000 0.000 0.000 
+18 0.000 0.000 0.000 0.000 
+19 0.000 0.000 0.000 0.000 
+20 0.000 0.000 0.000 0.000 
+21 0.000 0.000 0.000 0.000 
+22 0.000 0.000 0.000 0.000 
+23 0.000 0.000 0.000 0.000 
+24 0.000 0.000 0.000 0.000 
+25 0.000 0.000 0.000 0.000 
+26 0.000 0.000 0.000 0.000 
+27 0.000 0.000 0.000 0.000 
+28 0.000 0.000 0.000 0.000 
+29 0.000 0.000 0.000 0.000 
+30 0.000 0.000 0.000 0.000 
+31 0.000 0.000 0.000 0.000 
+32 0.000 0.000 0.000 0.000 
+33 0.000 0.000 0.000 0.000 
+34 0.000 0.000 0.000 0.000 
+35 0.000 0.000 0.000 0.000 
+36 0.000 0.000 0.000 0.000 
+37 0.000 0.000 0.000 0.000 
+38 0.000 0.000 0.000 0.000 
+39 0.000 0.000 0.000 0.000
+
+
+

Using the plotly library plot the data. The graph shows the perception being controlled to match the reference value.

+
import plotly.graph_objects as go
+fig = go.Figure(layout_title_text="Velocity Goal")
+fig.add_trace(go.Scatter(y=pctnode.history.data['refcoll']['constant'], name="ref"))
+fig.add_trace(go.Scatter(y=pctnode.history.data['percoll']['variable'], name="perc"))
+

This following code is only for the purposes of displaying image of the graph generated by the above code.

+
+
from IPython.display import Image
+
+
+
Image(url='http://www.perceptualrobots.com/wp-content/uploads/2020/08/pct_node_plot.png')
+
+ +
+
+

This shows a very basic example of the use of the PCT library. For more advanced functionality see the API documentation at https://perceptualrobots.github.io/pct/.

+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/microgrid.html b/microgrid.html new file mode 100644 index 00000000..d6f9f05a --- /dev/null +++ b/microgrid.html @@ -0,0 +1,801 @@ + + + + + + + + + +MicroGrid – pct + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

MicroGrid

+
+ + + +
+ + + + +
+ + + +
+ + + +
+
from os import getcwd
+# getcwd()
+
+
+

source

+
+

MicroGridEnvPlus

+
+
 MicroGridEnvPlus (**kwargs)
+
+

*The main OpenAI Gym class.

+

It encapsulates an environment with arbitrary behind-the-scenes dynamics. An environment can be partially or fully observed.

+

The main API methods that users of this class need to know are:

+
    +
  • :meth:step - Takes a step in the environment using an action returning the next observation, reward, if the environment terminated and observation information.
    • +
  • :meth:reset - Resets the environment to an initial state, returning the initial observation and observation information.
    • +
  • :meth:render - Renders the environment observation with modes depending on the output
    • +
  • :meth:close - Closes the environment, important for rendering where pygame is imported
    • +
+

And set the following attributes:

+
    +
  • :attr:action_space - The Space object corresponding to valid actions
    • +
  • :attr:observation_space - The Space object corresponding to valid observations
    • +
  • :attr:reward_range - A tuple corresponding to the minimum and maximum possible rewards
    • +
  • :attr:spec - An environment spec that contains the information used to initialise the environment from gym.make
    • +
  • :attr:metadata - The metadata of the environment, i.e. render modes
    • +
  • :attr:np_random - The random number generator for the environment
    • +
+

Note: a default reward range set to :math:(-\infty,+\infty) already exists. Set it if you want a narrower range.*

+
+

source

+
+
+

MicroGridEnv0Plus

+
+
 MicroGridEnv0Plus (**kwargs)
+
+

*The main OpenAI Gym class.

+

It encapsulates an environment with arbitrary behind-the-scenes dynamics. An environment can be partially or fully observed.

+

The main API methods that users of this class need to know are:

+
    +
  • :meth:step - Takes a step in the environment using an action returning the next observation, reward, if the environment terminated and observation information.
    • +
  • :meth:reset - Resets the environment to an initial state, returning the initial observation and observation information.
    • +
  • :meth:render - Renders the environment observation with modes depending on the output
    • +
  • :meth:close - Closes the environment, important for rendering where pygame is imported
    • +
+

And set the following attributes:

+
    +
  • :attr:action_space - The Space object corresponding to valid actions
    • +
  • :attr:observation_space - The Space object corresponding to valid observations
    • +
  • :attr:reward_range - A tuple corresponding to the minimum and maximum possible rewards
    • +
  • :attr:spec - An environment spec that contains the information used to initialise the environment from gym.make
    • +
  • :attr:metadata - The metadata of the environment, i.e. render modes
    • +
  • :attr:np_random - The random number generator for the environment
    • +
+

Note: a default reward range set to :math:(-\infty,+\infty) already exists. Set it if you want a narrower range.*

+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/network.html b/network.html new file mode 100644 index 00000000..ba697ecb --- /dev/null +++ b/network.html @@ -0,0 +1,753 @@ + + + + + + + + + + +Network – pct + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Network

+
+ +
+
+ A utilities for network connectivity. +
+
+ + +
+ + + + +
+ + + +
+ + + +
+

source

+
+

Server

+
+
 Server (host='localhost', port=6666, buf_size=1024)
+
+

Initialize self. See help(type(self)) for accurate signature.

+
+

source

+
+
+

Client

+
+
 Client (host='localhost', port=6666, buf_size=1024)
+
+

Initialize self. See help(type(self)) for accurate signature.

+
+

source

+
+
+

ClientConnectionManager

+
+
 ClientConnectionManager (host='localhost', port=6666, buf_size=1024)
+
+

A utility for managing a client socket connection.

+
+

source

+
+
+

ServerConnectionManager

+
+
 ServerConnectionManager (host='localhost', port=6666, buf_size=1024)
+
+

A utility for managing a client socket connection.

+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/nodes.html b/nodes.html new file mode 100644 index 00000000..ab034a55 --- /dev/null +++ b/nodes.html @@ -0,0 +1,1060 @@ + + + + + + + + + + +Nodes – pct + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Nodes

+
+ +
+
+ A node is a single control unit representing a feedback control loop. +
+
+ + +
+ + + + +
+ + + +
+ + + +
+
+
+
The autoreload extension is already loaded. To reload it, use:
+  %reload_ext autoreload
+
+
+
+

Overview

+

A node comprises four functions, reference, perceptual, comparator and output. Executing the node will run each of the functions in the order indicated above and return the output value.

+

The functions can actually be a collection of functions, each executed in the order they are added. This allows a chain of functions in case pre-processing is required, or post-processing in the case of the output.

+
+

source

+
+

ControlUnitIndices

+
+
 ControlUnitIndices (value, names=None, module=None, qualname=None,
+                     type=None, start=1)
+
+

An enumeration.

+
+

source

+
+
+

PCTNode

+
+
 PCTNode (reference=None, perception=None, comparator=None, output=None,
+          default=True, name='pctnode', history=False, build_links=False,
+          mode=0, namespace=None, **pargs)
+
+

A single PCT controller.

+
+
#node = PCTNode()
+#node.summary()
+
+
+

source

+
+
+

PCTNodeData

+
+
 PCTNodeData (name='pctnodedata')
+
+

Data collected for a PCTNode

+
+
+
+

Creating a Node

+

A node can be created simply.

+
+
node = PCTNode()
+node.summary()
+
+
pctnode PCTNode fc50e711-62e3-11ef-a9a5-5c879c15de65
+----------------------------
+REF: constant Constant | 0 
+PER: variable Variable | 0 
+COM: subtract Subtract | 0 | links  constant variable 
+OUT: proportional Proportional | gain 1 | 0 | links  subtract 
+----------------------------
+
+
+

That creates a node with default functions. Those are, a constant of 1 for the reference, a variable, with initial value 0, for the perception and a proportional function for the output, with a gain of 10.

+

A node can also be created by providing a name, and setting the history to True. The latter means that the values of all the functions are recorded during execution, which is useful for plotting the data later, as can be seen below.

+
+
dynamic_module_import( 'pct.functions', 'Constant')
+
+
+
reference = Constant(1)
+namespace=reference.namespace
+
+
+
node = PCTNode(name="mypctnode", history=True, reference = reference, output=Proportional(10, namespace=namespace), namespace=namespace)
+node.summary()
+
+
mypctnode PCTNode fc6106b5-62e3-11ef-8e63-5c879c15de65
+----------------------------
+REF: constant Constant | 1 
+PER: variable Variable | 0 
+COM: subtract Subtract | 0 | links  constant variable 
+OUT: proportional Proportional | gain 10 | 0 | links  subtract 
+----------------------------
+
+
+

Another way of creating a node is by first declaring the functions you want and passing them into the constructor.

+
+
UniqueNamer.getInstance().clear()
+r = Variable(0, name="velocity_reference")
+p = Constant(10, name="constant_perception")
+o = Integration(10, 100, name="integrator")
+integratingnode = PCTNode(reference=r, perception=p, output=o, name="integratingnode", history=True)
+
+

Yet another way to create a node is from a text configuration.

+
+
config_node = PCTNode.from_config({ 'name': 'mypctnode', 
+    'refcoll': {'0': {'type': 'Proportional', 'name': 'proportional', 'value': 0, 'links': {}, 'gain': 10}}, 
+    'percoll': {'0': {'type': 'Variable', 'name': 'velocity', 'value': 0.2, 'links': {}}}, 
+    'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract', 'value': 1, 'links': {0: 'constant', 1: 'velocity'}}}, 
+    'outcoll': {'0': {'type': 'Proportional', 'name': 'proportional', 'value': 10, 'links': {0: 'subtract'}, 'gain': 10}}})
+# config_node = PCTNode.from_config({ 'name': 'mypctnode1', 
+#     'refcoll': {'0': {'type': 'Proportional', 'name': 'proportional', 'value': 0, 'links': {}, 'gain': 10}}, 
+#     'percoll': {'0': {'type': 'Variable', 'name': 'velocity', 'value': 0.2, 'links': {}}}, 
+#     'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract', 'value': 1, 'links': {0: 'constant', 1: 'velocity'}}}, 
+#     'outcoll': {'0': {'type': 'Proportional', 'name': 'proportional', 'value': 10, 'links': {0: 'subtract'}, 'gain': 10}}}, namespace=namespace)
+
+
+
+

Viewing Nodes

+

The details of a node can be viewed in a number of ways, which is useful for checking the configuration. The summary method prints to the screen. The get_config method returns a string in a JSON format.

+
+
integratingnode.summary()
+
+
integratingnode PCTNode fc7424bb-62e3-11ef-9101-5c879c15de65
+----------------------------
+REF: velocity_reference Variable | 0 
+PER: constant_perception Constant | 10 
+COM: subtract Subtract | 0 | links  velocity_reference constant_perception 
+OUT: integrator Integration | gain 10 slow 100  | 0 | links  subtract 
+----------------------------
+
+
+
+
#print(integratingnode.get_config())
+assert integratingnode.get_config() == {'type': 'PCTNode', 'name': 'integratingnode', 'refcoll': {'0': {'type': 'Variable', 'name': 'velocity_reference', 'value': 0, 'links': {}}}, 'percoll': {'0': {'type': 'Constant', 'name': 'constant_perception', 'value': 10, 'links': {}}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract', 'value': 0, 'links': {0: 'velocity_reference', 1: 'constant_perception'}}}, 'outcoll': {'0': {'type': 'Integration', 'name': 'integrator', 'value': 0, 'links': {0: 'subtract'}, 'gain': 10, 'slow': 100}}}
+integratingnode.get_config()
+
+
{'type': 'PCTNode',
+ 'name': 'integratingnode',
+ 'refcoll': {'0': {'type': 'Variable',
+   'name': 'velocity_reference',
+   'value': 0,
+   'links': {}}},
+ 'percoll': {'0': {'type': 'Constant',
+   'name': 'constant_perception',
+   'value': 10,
+   'links': {}}},
+ 'comcoll': {'0': {'type': 'Subtract',
+   'name': 'subtract',
+   'value': 0,
+   'links': {0: 'velocity_reference', 1: 'constant_perception'}}},
+ 'outcoll': {'0': {'type': 'Integration',
+   'name': 'integrator',
+   'value': 0,
+   'links': {0: 'subtract'},
+   'gain': 10,
+   'slow': 100}}}
+
+
+

A node can also be viewed graphically as a network of connected nodes.

+
+
import os
+
+
+
if os.name=='nt':
+    integratingnode.draw(node_size=2000, figsize=(8,4))
+
+
+
+

+
+
+
+
+
+
+

Running a Node

+

For the purposes of this example we first create a function which is a very basic model of the physical environment. It defines how the world behaves when we pass it the output of the control system.

+
+
def velocity_model(velocity,  force , mass):
+    velocity = velocity + force / mass
+    return velocity
+
+mass = 50
+force = 0
+
+

In the following cell we start with a velocity of zero. The node is run once (second line), the output of which is the force to apply in the world velocity_model. That returns the updated velocity which we pass back into the node to be used in the next iteration of the loop.

+
+
velocity=0
+force = node()
+velocity = velocity_model(velocity, force, mass)
+node.set_perception_value(velocity)
+print(force)
+assert force == 10
+
+
10
+
+
+

The node can be run in a loop as shown below. With verbose set to True the output of each loop will be printed to the screen.

+
+
pctnode = PCTNode(history=True)
+pctnode.set_function_name("perception", "velocity")
+pctnode.set_function_name("reference", "reference")
+
+for i in range(40):
+    print(i, end=" ")
+    force = pctnode(verbose=True)
+    vel = velocity_model(pctnode.get_perception_value(), force, mass)
+    pctnode.set_perception_value(vel)
+
+
0 0.000 0.000 0.000 0.000 
+1 0.000 0.000 0.000 0.000 
+2 0.000 0.000 0.000 0.000 
+3 0.000 0.000 0.000 0.000 
+4 0.000 0.000 0.000 0.000 
+5 0.000 0.000 0.000 0.000 
+6 0.000 0.000 0.000 0.000 
+7 0.000 0.000 0.000 0.000 
+8 0.000 0.000 0.000 0.000 
+9 0.000 0.000 0.000 0.000 
+10 0.000 0.000 0.000 0.000 
+11 0.000 0.000 0.000 0.000 
+12 0.000 0.000 0.000 0.000 
+13 0.000 0.000 0.000 0.000 
+14 0.000 0.000 0.000 0.000 
+15 0.000 0.000 0.000 0.000 
+16 0.000 0.000 0.000 0.000 
+17 0.000 0.000 0.000 0.000 
+18 0.000 0.000 0.000 0.000 
+19 0.000 0.000 0.000 0.000 
+20 0.000 0.000 0.000 0.000 
+21 0.000 0.000 0.000 0.000 
+22 0.000 0.000 0.000 0.000 
+23 0.000 0.000 0.000 0.000 
+24 0.000 0.000 0.000 0.000 
+25 0.000 0.000 0.000 0.000 
+26 0.000 0.000 0.000 0.000 
+27 0.000 0.000 0.000 0.000 
+28 0.000 0.000 0.000 0.000 
+29 0.000 0.000 0.000 0.000 
+30 0.000 0.000 0.000 0.000 
+31 0.000 0.000 0.000 0.000 
+32 0.000 0.000 0.000 0.000 
+33 0.000 0.000 0.000 0.000 
+34 0.000 0.000 0.000 0.000 
+35 0.000 0.000 0.000 0.000 
+36 0.000 0.000 0.000 0.000 
+37 0.000 0.000 0.000 0.000 
+38 0.000 0.000 0.000 0.000 
+39 0.000 0.000 0.000 0.000
+
+
+
+
+

Save and Load

+

Save a node to file.

+
+
import json
+
+
+
integratingnode.save("inode.json")
+
+

Create a node from file.

+
+
nnode = PCTNode.load("inode.json")
+nnode.summary()
+print(nnode.get_config())
+
+
integratingnode PCTNode fcd83c93-62e3-11ef-9c4b-5c879c15de65
+----------------------------
+REF: velocity_reference Variable | 0 
+PER: constant_perception Constant | 10 
+COM: subtract Subtract | 0 | links  velocity_reference constant_perception 
+OUT: integrator Integration | gain 10 slow 100  | 0 | links  subtract 
+----------------------------
+{'type': 'PCTNode', 'name': 'integratingnode', 'refcoll': {'0': {'type': 'Variable', 'name': 'velocity_reference', 'value': 0, 'links': {}}}, 'percoll': {'0': {'type': 'Constant', 'name': 'constant_perception', 'value': 10, 'links': {}}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract', 'value': 0, 'links': {0: 'velocity_reference', 1: 'constant_perception'}}}, 'outcoll': {'0': {'type': 'Integration', 'name': 'integrator', 'value': 0, 'links': {0: 'subtract'}, 'gain': 10, 'slow': 100}}}
+
+
+
+
print(nnode.get_summary())
+
+
0.000 10.000 0.000 0.000
+
+
+
+
+

Plotting the Data

+

As the history of the variable pctnode was set to True the data is available for analysis. It can be plotted with python libraries such as matplotlib or plotly. Here is an example with the latter.

+

The graph shows the changing perception values as it is controlled to match the reference value.

+
import plotly.graph_objects as go
+fig = go.Figure(layout_title_text="Velocity Goal")
+fig.add_trace(go.Scatter(y=pctnode.history.data['refcoll']['reference'], name="ref"))
+fig.add_trace(go.Scatter(y=pctnode.history.data['percoll']['velocity'], name="perc"))
+

This following code is only for the purposes of displaying image of the graph generated by the above code.

+
+
from IPython.display import Image
+
+
+
Image(url='http://www.perceptualrobots.com/wp-content/uploads/2020/08/pct_node_plot.png')
+
+ +
+
+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/pct_examples.html b/pct_examples.html new file mode 100644 index 00000000..b3b39fe0 --- /dev/null +++ b/pct_examples.html @@ -0,0 +1,775 @@ + + + + + + + + + + +PCT Examples – pct + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

PCT Examples

+
+ +
+
+ Class to run hierarchy configurations. +
+
+ + +
+ + + + +
+ + + +
+ + + +
+
+
+
The autoreload extension is already loaded. To reload it, use:
+  %reload_ext autoreload
+
+
+
+

PCTExamples Class

+

The PCTExamples class is designed to load a PCTHierarchy configuration file and provide various methods to interact with the hierarchy.

+
+

source

+
+

PCTExamples

+
+
 PCTExamples (config_file, min=True, early_termination=False,
+              history=False)
+
+

PCTExamples class provides methods to load a PCT hierarchy from a configuration file, summarize the hierarchy, get the configuration, draw the hierarchy, run the hierarchy for a specified number of steps, and close the environment. Attributes: hierarchy (PCTHierarchy): The loaded PCT hierarchy. env (Environment): The environment associated with the PCT hierarchy.

+
+
# pct.draw( figsize=(10,10), font_size=6, funcdata=True, with_edge_labels=True, node_size=200)
+
+ + +
+
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/plot.html b/plot.html new file mode 100644 index 00000000..df0c1cf2 --- /dev/null +++ b/plot.html @@ -0,0 +1,813 @@ + + + + + + + + + +plotarrays classes – pct + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

plotarrays classes

+
+ + + +
+ + + + +
+ + + +
+ + + +
+

source

+
+

PlotARCArrays

+
+
 PlotARCArrays ()
+
+

Initialize self. See help(type(self)) for accurate signature.

+
+
pas = PlotARCArrays()
+image_filename = pas.to_image('/tmp/ARC', [[1,1,1],[1,1,1],[1,1,1]], [[2,2,2],[2,2,2],[2,2,2]], [[2.6,3,3],[3,3,3],[3,3,3]], 'train', '00000001')
+print(image_filename)
+
+
/tmp/ARC/00000001-a86e2e2e-6fc6-4ef2-9aec-04edbc75fac9.png
+
+
+
+
+

+
+
+
+
+
+
dataset = 'train'
+code = '001'
+pas = PlotARCArrays()
+arrays = [
+{'array' : [[1,1,1],[1,1,1],[1,1,1]], 'title' : 'input'},
+{'array' : [[2,2,2],[2,2,2],[2,2,2]], 'title' : 'output'},
+{'array' : [[3,3,3],[3,3,3],[3,3,3]], 'title' : 'env'}
+]
+f = pas.plot_arrays(arrays, dataset, code)
+
+
+
+

+
+
+
+
+
+
dataset = 'train'
+w=3
+fig, axs  = plt.subplots(1, w, figsize=(3*w ,3))
+code = '001'
+plt.suptitle(f'{dataset} {code}', fontsize=20, fontweight='bold', y=1)
+
+
+pas = PlotARCArrays()
+input_matrix = [[3,3,3],[3,3,3],[3,3,3]]
+pas.plot_array(axs[0], input_matrix, 'input')
+input_matrix = [[3,3,3],[3,3,3],[3,3,3]]
+pas.plot_array(axs[1], input_matrix, 'output')
+input_matrix = [[3,3,3],[3,3,3],[3,3,3]]
+pas.plot_array(axs[2], input_matrix, 'env')
+
+
+
+

+
+
+
+
+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/plotting.html b/plotting.html new file mode 100644 index 00000000..f953a11e --- /dev/null +++ b/plotting.html @@ -0,0 +1,845 @@ + + + + + + + + + + +Plotting – pct + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Plotting

+
+ +
+
+ Plotting classes. +
+
+ + +
+ + + + +
+ + + +
+ + + +
+

source

+
+

SubPlotter

+
+
 SubPlotter (width, height, title, plotsconfig=[['title', 'xlabel',
+             'ylabel', 1, 0, 111, ['line1']]], linewidth=2,
+             legend_fontsize=8)
+
+

Initialize self. See help(type(self)) for accurate signature.

+
+

source

+
+
+

run_hpct_animation

+
+
 run_hpct_animation (hpct, counter=None, plotter=None, interval=1,
+                     verbose=False)
+
+
+
window = 20
+plotter = SubPlotter(12, 4, "MyData", [["Data0", "time", "y", 2, window, 121, ['line1','line2']], 
+                                       ["Data1", "time", "y", 2, window, 122, ['line1','line2']]])
+
+for x in  range(10):
+    y11 = random()-0.5
+    y12 = random()-0.5
+    plotter.add_data(0, x, [y11, y12])
+    y21 = random()-0.5
+    y22 = random()-0.5                
+    plotter.add_data(1, x, [y21, y22])
+                    
+plotter.draw()
+
+
+
+

+
+
+
+
+
+
window = 20
+plotter = SubPlotter(12, 8, "MyData", [["Data0", "time", "y", 2, window, 211, ['line1','line2']], 
+                                       ["Data1", "time", "y", 2, window, 212, ['line1','line2']]])
+
+for x in  range(10):
+    y11 = random()-0.5
+    y12 = random()-0.5
+    plotter.add_data(0, x, [y11, y12])
+    y21 = random()-0.5
+    y22 = random()-0.5                
+    plotter.add_data(1, x, [y21, y22])
+                    
+plotter.draw()
+
+
+
+

+
+
+
+
+
+
window = 20
+plotter = SubPlotter(12, 8, "MyData", [["Data0", "time", "y", 2, window, 221, ['line1','line2']], 
+                                       ["Data1", "time", "y", 2, window, 222, ['line1','line2']],
+                                       ["Data2", "time", "y", 2, window, 212, ['line1','line2']]])
+
+for x in  range(10):
+    y11 = random()-0.5
+    y12 = random()-0.5
+    plotter.add_data(0, x, [y11, y12])
+    y21 = random()-0.5
+    y22 = random()-0.5                
+    plotter.add_data(1, x, [y21, y22])
+    y21 = random()-0.5
+    y22 = random()-0.5                
+    plotter.add_data(2, x, [y21, y22])
+                    
+plotter.draw()
+
+
+
+

+
+
+
+
+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/putils.html b/putils.html new file mode 100644 index 00000000..20cd98cb --- /dev/null +++ b/putils.html @@ -0,0 +1,1274 @@ + + + + + + + + + +Utilities – pct + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Utilities

+
+ + + +
+ + + + +
+ + + +
+ + + +
+

SingletonObjects

+
+

source

+
+

SingletonObjects

+
+
 SingletonObjects ()
+
+

A utility for refrencing objects that should only be declared once.

+
+
+
+

UniqueNamer

+
+

source

+
+

UniqueNamer

+
+
 UniqueNamer ()
+
+

A utility for ensuring the names of functions are unique.

+
+
+
+

FunctionsList

+
+

source

+
+

FunctionsList

+
+
 FunctionsList ()
+
+

A utility for storing functions created, keyed on the function name.

+
+
+
+

Memory

+
+

source

+
+

Memory

+
+
 Memory ()
+
+

A utility for recording global values.

+
+
+
+

NumberStats

+
+

source

+
+

NumberStats

+
+
 NumberStats ()
+
+

A utility for calculating the statistice of a number.

+
+
# for x in range(10.0):
+#     NumberStats.getInstance().add(x)
+#     NumberStats.getInstance().add(-x)
+
+# NumberStats.getInstance().report()
+
+
+
from pct.functions import Proportional
+
+
+
prop = Proportional()
+
+
+
UniqueNamer.getInstance().report()
+
+
+
FunctionsList.getInstance().report()
+
+
--- functions report
+
+
+
+

source

+
+
+

dynamic_module_import

+
+
 dynamic_module_import (modulename, package=None)
+
+
+

source

+
+
+

dynamic_class_load

+
+
 dynamic_class_load (modulename, classname)
+
+
+
#import importlib
+#importlib.import_module('pct.functions', 'Constant')
+
+
+

source

+
+
+

get_drive

+
+
 get_drive ()
+
+
+

source

+
+
+

loadjson

+
+
 loadjson (file)
+
+
+
var = loadjson("ws.json")
+print(var)
+
+
{'type': 'WeightedSum', 'name': 'weighted_sum', 'value': 35, 'links': {'0': 'constant', '1': 'constant1', '2': 'constant2'}, 'weights': [1, 1, 1]}
+
+
+
+
+
+

Counter

+
+

source

+
+

Counter

+
+
 Counter (limit=1000, init=0, step=1, print=100, pause=False, display=10)
+
+

Initialize self. See help(type(self)) for accurate signature.

+
+
+
+

Functions

+
+

source

+
+

set_dirs

+
+
 set_dirs (dirs)
+
+
+

source

+
+
+

stringIntListToListOfInts

+
+
 stringIntListToListOfInts (strList, delimiter)
+
+
+

source

+
+
+

stringFloatListToListOfFloats

+
+
 stringFloatListToListOfFloats (strList, delimiter)
+
+
+

source

+
+
+

stringListToListOfStrings

+
+
 stringListToListOfStrings (strList, delimiter=',')
+
+
+

source

+
+
+

listNumsToString

+
+
 listNumsToString (list)
+
+
+

source

+
+
+

round_lists

+
+
 round_lists (alist, formatted, places)
+
+
+

source

+
+
+

floatListsToString

+
+
 floatListsToString (alist, places)
+
+
+

source

+
+
+

limit_large_float

+
+
 limit_large_float (val, limit=10000000)
+
+
+

source

+
+
+

sigmoid

+
+
 sigmoid (x, range, slope)
+
+
+

source

+
+
+

smooth

+
+
 smooth (new_val, old_val, smooth_factor)
+
+
+

source

+
+
+

sigmoid_array

+
+
 sigmoid_array (x, range, slope)
+
+
+

source

+
+
+

dot

+
+
 dot (inputs, weights)
+
+
+

source

+
+
+

list_of_ones

+
+
 list_of_ones (num)
+
+
+
print(list_of_ones(3))
+
+
[1, 1, 1]
+
+
+
+

source

+
+
+

limit_to_range

+
+
 limit_to_range (num, lower, upper)
+
+
+

source

+
+
+

wrap_env

+
+
 wrap_env (env)
+
+
+

source

+
+
+

show_video

+
+
 show_video ()
+
+
+
import matplotlib.pyplot as plt
+x = 2 #np.linspace(-5, 5, 11)
+#x = -10000001
+range=2
+scale=2
+y = sigmoid(x, range, scale)
+print(y)
+# plt.plot(x, y)
+# plt.grid()
+# plt.xlim(-6, 6)
+# plt.xlabel('x')
+# plt.title('expit(x)')
+# plt.show()
+
+
0.7615941559557646
+
+
+
+

source

+
+
+

is_in_notebooks

+
+
 is_in_notebooks ()
+
+
+

source

+
+
+

printtime

+
+
 printtime (msg)
+
+
+

source

+
+
+

clip_value

+
+
 clip_value (val, range)
+
+
+
+
+

get_abs_tol

+
+

source

+
+

get_abs_tol

+
+
 get_abs_tol (key)
+
+
+
+
+

get_rel_tol

+
+

source

+
+

get_rel_tol

+
+
 get_rel_tol (key)
+
+
+

source

+
+
+

map_to_int_even_range

+
+
 map_to_int_even_range (val=None, inrange=None, outrange=None)
+
+
+

source

+
+
+

map_to_int_odd_range

+
+
 map_to_int_odd_range (val=None, inrange=None, outrange=None)
+
+
+
limits = [-2, 2]
+mapped = [1, 5]
+vals = [-3.1, -2.1, -1.51, -1.5, -1.4, -0.9,-0.5, -0.1,  0, 0.1, 0.6, 1.1, 2.1, 210.1 ]
+for val in vals:
+    print(val, map_to_int_odd_range(val, limits, mapped))
+
+
-3.1 1
+-2.1 1
+-1.51 1
+-1.5 1
+-1.4 2
+-0.9 2
+-0.5 3
+-0.1 3
+0 3
+0.1 3
+0.6 4
+1.1 4
+2.1 5
+210.1 5
+
+
+
+
#vals = [0.6]
+
+vals = [-3.1, -2.1, -1.51, -1.5, -1.4, -0.9, -0.5, -0.1, 0, 0.1, 0.6, 1.1, 2.1, 210.1 ]
+limits = [-2, 2]
+mapped = [1, 4]
+for val in vals:
+    print(val, map_to_int_even_range(val, limits, mapped))
+
+
-3.1 1
+-2.1 1
+-1.51 1
+-1.5 1
+-1.4 1
+-0.9 2
+-0.5 2
+-0.1 2
+0 3
+0.1 3
+0.6 3
+1.1 4
+2.1 4
+210.1 4
+
+
+
+

source

+
+
+

Timer

+
+
 Timer ()
+
+

Initialize self. See help(type(self)) for accurate signature.

+
+

source

+
+
+

TimerError

+

A custom exception used to report errors in use of Timer class

+
+
timer = Timer()
+timer.start()
+time.sleep(1)
+timer.stop()
+timer.start()
+time.sleep(1)
+timer.stop()
+
+print(timer.mean())
+print(timer.total())
+print(timer.count())
+
+
1.0111035999999998
+2.0222071999999995
+2
+
+
+
+
+
+

PCTRunProperties

+
+

source

+
+

PCTRunProperties

+
+
 PCTRunProperties ()
+
+

Initialize self. See help(type(self)) for accurate signature.

+
+
property_file = 'ga-000.000-s001-1x1-m007-ARC0010-9ddcf52416e60d65f19007957d07262d-consolidated.properties'
+property_dir = ''
+print(property_dir, property_file)
+ep = PCTRunProperties.get_environment_properties(root='testfiles', env='ARC', property_dir=property_dir, property_file=property_file)
+print(ep)
+
+
 ga-000.000-s001-1x1-m007-ARC0010-9ddcf52416e60d65f19007957d07262d-consolidated.properties
+({'code': '007bbfb7', 'index': 0, 'dataset': 'train', 'control_set': ['dims'], 'input_set': ['env'], 'history': 5, 'initial': 100}, 'ARC')
+
+
+
+

source

+
+
+

get_ram_mb

+
+
 get_ram_mb ()
+
+ + +
+
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/robots.txt b/robots.txt new file mode 100644 index 00000000..2f5900c5 --- /dev/null +++ b/robots.txt @@ -0,0 +1 @@ +Sitemap: https://perceptualrobots.github.io/pct/sitemap.xml diff --git a/search.json b/search.json new file mode 100644 index 00000000..8459669a --- /dev/null +++ b/search.json @@ -0,0 +1,852 @@ +[ + { + "objectID": "webots.html", + "href": "webots.html", + "title": "Webots", + "section": "", + "text": "source\n\nWebotsHelper\n\n WebotsHelper (name=None, mode=1)\n\nInitialize self. See help(type(self)) for accurate signature.", + "crumbs": [ + "Webots" + ] + }, + { + "objectID": "helpers.html", + "href": "helpers.html", + "title": "Helper classes", + "section": "", + "text": "source\n\n\n\n ListChecker ()\n\nInitialize self. See help(type(self)) for accurate signature.\n\n\n\n\n# @staticmethod\n# def check_float_list_close_to_zero(float_list, rel_tol=1e-9, abs_tol=0.0, gradient_abs_tol=0.0):\n# \"\"\"\n# Checks if the values in the float list are close to zero within the specified tolerance\n# and if the gradient (difference between consecutive values) is close to zero within the specified gradient tolerance.\n\n# Returns:\n# bool: True if all values are close to zero within the specified tolerance and the gradient of all consecutive values is close to zero within the specified gradient tolerance.\n# \"\"\"\n# if not float_list:\n# return True\n \n# values_close_to_zero = all(\n# math.isclose(value, 0, rel_tol=rel_tol, abs_tol=abs_tol)\n# for value in float_list\n# )\n \n# if len(float_list) == 1:\n# return values_close_to_zero\n \n# gradients_close_to_zero = all(\n# math.isclose(float_list[i] - float_list[i - 1], 0, rel_tol=0, abs_tol=gradient_abs_tol)\n# for i in range(1, len(float_list))\n# )\n \n# return values_close_to_zero and gradients_close_to_zero\n\n\n\n\n\n# @staticmethod\n# def check_float_list_close_to_zero(float_list, rel_tol=1e-9, abs_tol=0.0, gradient_abs_tol=0.0):\n# \"\"\"\n# Checks if the values in the float list are close to zero within the specified tolerance\n# and if the gradient (difference between consecutive values) is close to zero within the specified gradient tolerance.\n\n# Returns:\n# bool: True if all values are close to zero within the specified tolerance and the gradient of all consecutive values is close to zero within the specified gradient tolerance.\n# \"\"\"\n# if not float_list:\n# return True\n \n# values_close_to_zero = all(\n# math.isclose(value, 0, rel_tol=rel_tol, abs_tol=abs_tol)\n# for value in float_list\n# )\n \n# if len(float_list) == 1:\n# return values_close_to_zero\n \n# gradients_close_to_zero = all(\n# math.isclose(float_list[i] - float_list[i - 1], 0, rel_tol=0, abs_tol=gradient_abs_tol)\n# for i in range(1, len(float_list))\n# )\n \n# return values_close_to_zero and gradients_close_to_zero\n\n\n\n\n\n# @staticmethod\n# def check_float_list_close_to_zero(float_list, rel_tol=1e-9, abs_tol=0.0, gradient_abs_tol=0.0):\n# \"\"\"\n# Checks if the values in the float list are close to zero within the specified tolerance\n# and if the gradient (difference between consecutive values) is close to zero within the specified gradient tolerance.\n\n# Returns:\n# bool: True if all values are close to zero within the specified tolerance and the gradient of all consecutive values is close to zero within the specified gradient tolerance.\n# \"\"\"\n# if not float_list:\n# return True\n \n# values_close_to_zero = all(\n# math.isclose(value, 0, rel_tol=rel_tol, abs_tol=abs_tol)\n# for value in float_list\n# )\n \n# if len(float_list) == 1:\n# return values_close_to_zero\n \n# gradients_close_to_zero = all(\n# math.isclose(float_list[i] - float_list[i - 1], 0, rel_tol=0, abs_tol=gradient_abs_tol)\n# for i in range(1, len(float_list))\n# )\n \n# return values_close_to_zero and gradients_close_to_zero\n\n\n\n\n\n# @staticmethod\n# def check_float_list_close_to_zero(float_list, rel_tol=1e-9, abs_tol=0.0, gradient_abs_tol=0.0):\n# \"\"\"\n# Checks if the values in the float list are close to zero within the specified tolerance\n# and if the gradient (difference between consecutive values) is close to zero within the specified gradient tolerance.\n\n# Returns:\n# bool: True if all values are close to zero within the specified tolerance and the gradient of all consecutive values is close to zero within the specified gradient tolerance.\n# \"\"\"\n# if not float_list:\n# return True\n \n# values_close_to_zero = all(\n# math.isclose(value, 0, rel_tol=rel_tol, abs_tol=abs_tol)\n# for value in float_list\n# )\n \n# if len(float_list) == 1:\n# return values_close_to_zero\n \n# gradients_close_to_zero = all(\n# math.isclose(float_list[i] - float_list[i - 1], 0, rel_tol=0, abs_tol=gradient_abs_tol)\n# for i in range(1, len(float_list))\n# )\n \n# return values_close_to_zero and gradients_close_to_zero\n\n\n# Example usage:\nfloat_list = [1.00000001, 1.00000002, 1.00000003]\nint_list = [1, 1, 1]\n\nprint(ListChecker.check_list_unchanged(float_list)) # Should print: True (depending on the tolerance)\nprint(ListChecker.check_integer_list_unchanged(int_list)) # Should print: True\n\n(False, {'gradient_range': 2.220446049250313e-16, 'mean': 1.0000000199999999, 'std_dev': 8.164965850304249e-09})\nTrue", + "crumbs": [ + "Helper classes" + ] + }, + { + "objectID": "helpers.html#check_float_list_close_to_zero", + "href": "helpers.html#check_float_list_close_to_zero", + "title": "Helper classes", + "section": "", + "text": "# @staticmethod\n# def check_float_list_close_to_zero(float_list, rel_tol=1e-9, abs_tol=0.0, gradient_abs_tol=0.0):\n# \"\"\"\n# Checks if the values in the float list are close to zero within the specified tolerance\n# and if the gradient (difference between consecutive values) is close to zero within the specified gradient tolerance.\n\n# Returns:\n# bool: True if all values are close to zero within the specified tolerance and the gradient of all consecutive values is close to zero within the specified gradient tolerance.\n# \"\"\"\n# if not float_list:\n# return True\n \n# values_close_to_zero = all(\n# math.isclose(value, 0, rel_tol=rel_tol, abs_tol=abs_tol)\n# for value in float_list\n# )\n \n# if len(float_list) == 1:\n# return values_close_to_zero\n \n# gradients_close_to_zero = all(\n# math.isclose(float_list[i] - float_list[i - 1], 0, rel_tol=0, abs_tol=gradient_abs_tol)\n# for i in range(1, len(float_list))\n# )\n \n# return values_close_to_zero and gradients_close_to_zero", + "crumbs": [ + "Helper classes" + ] + }, + { + "objectID": "helpers.html#check_float_list_close_to_zero-1", + "href": "helpers.html#check_float_list_close_to_zero-1", + "title": "Helper classes", + "section": "", + "text": "# @staticmethod\n# def check_float_list_close_to_zero(float_list, rel_tol=1e-9, abs_tol=0.0, gradient_abs_tol=0.0):\n# \"\"\"\n# Checks if the values in the float list are close to zero within the specified tolerance\n# and if the gradient (difference between consecutive values) is close to zero within the specified gradient tolerance.\n\n# Returns:\n# bool: True if all values are close to zero within the specified tolerance and the gradient of all consecutive values is close to zero within the specified gradient tolerance.\n# \"\"\"\n# if not float_list:\n# return True\n \n# values_close_to_zero = all(\n# math.isclose(value, 0, rel_tol=rel_tol, abs_tol=abs_tol)\n# for value in float_list\n# )\n \n# if len(float_list) == 1:\n# return values_close_to_zero\n \n# gradients_close_to_zero = all(\n# math.isclose(float_list[i] - float_list[i - 1], 0, rel_tol=0, abs_tol=gradient_abs_tol)\n# for i in range(1, len(float_list))\n# )\n \n# return values_close_to_zero and gradients_close_to_zero", + "crumbs": [ + "Helper classes" + ] + }, + { + "objectID": "helpers.html#check_float_list_close_to_zero-2", + "href": "helpers.html#check_float_list_close_to_zero-2", + "title": "Helper classes", + "section": "", + "text": "# @staticmethod\n# def check_float_list_close_to_zero(float_list, rel_tol=1e-9, abs_tol=0.0, gradient_abs_tol=0.0):\n# \"\"\"\n# Checks if the values in the float list are close to zero within the specified tolerance\n# and if the gradient (difference between consecutive values) is close to zero within the specified gradient tolerance.\n\n# Returns:\n# bool: True if all values are close to zero within the specified tolerance and the gradient of all consecutive values is close to zero within the specified gradient tolerance.\n# \"\"\"\n# if not float_list:\n# return True\n \n# values_close_to_zero = all(\n# math.isclose(value, 0, rel_tol=rel_tol, abs_tol=abs_tol)\n# for value in float_list\n# )\n \n# if len(float_list) == 1:\n# return values_close_to_zero\n \n# gradients_close_to_zero = all(\n# math.isclose(float_list[i] - float_list[i - 1], 0, rel_tol=0, abs_tol=gradient_abs_tol)\n# for i in range(1, len(float_list))\n# )\n \n# return values_close_to_zero and gradients_close_to_zero", + "crumbs": [ + "Helper classes" + ] + }, + { + "objectID": "helpers.html#check_float_list_close_to_zero-3", + "href": "helpers.html#check_float_list_close_to_zero-3", + "title": "Helper classes", + "section": "", + "text": "# @staticmethod\n# def check_float_list_close_to_zero(float_list, rel_tol=1e-9, abs_tol=0.0, gradient_abs_tol=0.0):\n# \"\"\"\n# Checks if the values in the float list are close to zero within the specified tolerance\n# and if the gradient (difference between consecutive values) is close to zero within the specified gradient tolerance.\n\n# Returns:\n# bool: True if all values are close to zero within the specified tolerance and the gradient of all consecutive values is close to zero within the specified gradient tolerance.\n# \"\"\"\n# if not float_list:\n# return True\n \n# values_close_to_zero = all(\n# math.isclose(value, 0, rel_tol=rel_tol, abs_tol=abs_tol)\n# for value in float_list\n# )\n \n# if len(float_list) == 1:\n# return values_close_to_zero\n \n# gradients_close_to_zero = all(\n# math.isclose(float_list[i] - float_list[i - 1], 0, rel_tol=0, abs_tol=gradient_abs_tol)\n# for i in range(1, len(float_list))\n# )\n \n# return values_close_to_zero and gradients_close_to_zero\n\n\n# Example usage:\nfloat_list = [1.00000001, 1.00000002, 1.00000003]\nint_list = [1, 1, 1]\n\nprint(ListChecker.check_list_unchanged(float_list)) # Should print: True (depending on the tolerance)\nprint(ListChecker.check_integer_list_unchanged(int_list)) # Should print: True\n\n(False, {'gradient_range': 2.220446049250313e-16, 'mean': 1.0000000199999999, 'std_dev': 8.164965850304249e-09})\nTrue", + "crumbs": [ + "Helper classes" + ] + }, + { + "objectID": "helpers.html#jsondatamanager", + "href": "helpers.html#jsondatamanager", + "title": "Helper classes", + "section": "JSONDataManager", + "text": "JSONDataManager\n\nsource\n\nJSONDataManager\n\n JSONDataManager (path:str, show_timing:bool=False)\n\nInitialize self. See help(type(self)) for accurate signature.\n\n# #| export\n# class JSONDataManager:\n# def __init__(self, path: str, show_timing: bool = False):\n# self.data = self.load_json(path)\n# self.show_timing = show_timing\n \n# @JSONDataManager.timing_decorator\n# def load_json(self, path: str) -> Dict:\n# with open(path, 'r') as file:\n# return json.load(file)\n \n# def timing_decorator(method):\n# def timed_method(self, *args, **kwargs):\n# start_time = time.time()\n# result = method(self, *args, **kwargs)\n# end_time = time.time()\n# if self.show_timing:\n# print(f\"Execution time of {method.__name__}: {end_time - start_time:.4f} seconds\")\n# return result\n# return timed_method\n\n# def reload_data(self, path: str):\n# self.data = self.load_json(path)\n\n\n# import json\n# import time\n# from typing import Dict\n\n# def timing_decorator(method):\n# def timed_method(self, *args, **kwargs):\n# start_time = time.time()\n# result = method(self, *args, **kwargs)\n# end_time = time.time()\n# if self.show_timing:\n# print(f\"Execution time of {method.__name__}: {end_time - start_time:.4f} seconds\")\n# return result\n# return timed_method\n\n# class JSONDataManager:\n# def __init__(self, path: str, show_timing: bool = False):\n# self.data = self.load_json(path)\n# self.show_timing = show_timing\n \n# @timing_decorator\n# def load_json(self, path: str) -> Dict:\n# with open(path, 'r') as file:\n# return json.load(file)\n\n# def reload_data(self, path: str):\n# self.data = self.load_json(path)", + "crumbs": [ + "Helper classes" + ] + }, + { + "objectID": "helpers.html#challengesdatamanager", + "href": "helpers.html#challengesdatamanager", + "title": "Helper classes", + "section": "ChallengesDataManager", + "text": "ChallengesDataManager\n\nsource\n\nChallengesDataManager\n\n ChallengesDataManager (*args, **kwargs)", + "crumbs": [ + "Helper classes" + ] + }, + { + "objectID": "helpers.html#solutionsdatamanager", + "href": "helpers.html#solutionsdatamanager", + "title": "Helper classes", + "section": "SolutionsDataManager", + "text": "SolutionsDataManager\n\nsource\n\nSolutionsDataManager\n\n SolutionsDataManager (*args, **kwargs)\n\n\n# Example usage\nstart_time = time.time()\nchallenges_manager = ChallengesDataManager(\"C:\\\\packages\\\\arc-prize-2024\\\\arc-agi_training_challenges.json\")\nend_time = time.time()\nprint(f\"Execution time of challenges load: {end_time - start_time:.4f} seconds\")\n\nstart_time = time.time()\nsolutions_manager = SolutionsDataManager(\"C:\\\\packages\\\\arc-prize-2024\\\\arc-agi_training_solutions.json\")\nend_time = time.time()\nprint(f\"Execution time of solutions load: {end_time - start_time:.4f} seconds\")\n\nprint(challenges_manager.get_all_keys())\nprint(challenges_manager.count_all_keys())\nprint('get_keys_with_equal_size_input_output')\ngkweqsio = challenges_manager.get_keys_with_equal_size_input_output()\nprint(gkweqsio[0])\nprint(gkweqsio[1])\nprint('get_keys_with_equal_size_input_output_sorted')\ngkweqsios = challenges_manager.get_keys_with_equal_size_input_output_sorted()\nprint(gkweqsios)\n# print(gkweqsios[0])\n# print(gkweqsios[1])\n\nprint('get_keys_with_inconsistent_input_output_sizes')\ngkwiios = challenges_manager.get_keys_with_inconsistent_input_output_sizes()\nprint(gkwiios[0])\nprint(gkwiios[1])\nprint('get_keys_with_variable_input_sizes')\ngkwvts = challenges_manager.get_keys_with_variable_input_sizes()\nprint(gkwvts[0])\nprint(gkwvts[1])\nprint('get_input_array_histogram')\nhist = challenges_manager.get_input_array_histogram()\nprint(hist)\ncount = 0\nfor key, value in hist.items():\n count += value\nprint(count)\nprint('get_data_for_key',challenges_manager.get_data_for_key('007bbfb7'))\nprint(challenges_manager.get_arrays_for_key('007bbfb7', 'train'))\nprint(challenges_manager.get_arrays_for_key('007bbfb7', 'test'))\n\nprint('get_largest_array_size', challenges_manager.get_largest_array_size())\nprint('analyze_arrays', challenges_manager.analyze_arrays())\n\n\nprint('get_all_keys', solutions_manager.get_all_keys())\nprint(solutions_manager.count_all_keys())\nget_data_for_key = solutions_manager.get_data_for_key('007bbfb7')\nprint('get_data_for_key', get_data_for_key)\nprint(solutions_manager.get_arrays_for_key('007bbfb7', 'output'))\n\nExecution time of challenges load: 0.1250 seconds\nExecution time of solutions load: 0.0120 seconds\n['007bbfb7', '00d62c1b', '017c7c7b', '025d127b', '045e512c', '0520fde7', '05269061', '05f2a901', '06df4c85', '08ed6ac7', '09629e4f', '0962bcdd', '0a938d79', '0b148d64', '0ca9ddb6', '0d3d703e', '0dfd9992', '0e206a2e', '10fcaaa3', '11852cab', '1190e5a7', '137eaa0f', '150deff5', '178fcbfb', '1a07d186', '1b2d62fb', '1b60fb0c', '1bfc4729', '1c786137', '1caeab9d', '1cf80156', '1e0a9b12', '1e32b0e9', '1f0c79e5', '1f642eb9', '1f85a75f', '1f876c06', '1fad071e', '2013d3e2', '2204b7a8', '22168020', '22233c11', '2281f1f4', '228f6490', '22eb0ac0', '234bbc79', '23581191', '239be575', '23b5c85d', '253bf280', '25d487eb', '25d8a9c8', '25ff71a9', '264363fd', '272f95fa', '27a28665', '28bf18c6', '28e73c20', '29623171', '29c11459', '29ec7d0e', '2bcee788', '2bee17df', '2c608aff', '2dc579da', '2dd70a9a', '2dee498d', '31aa019c', '321b1fc6', '32597951', '3345333e', '3428a4f5', '3618c87e', '3631a71a', '363442ee', '36d67576', '36fdfd69', '3906de3d', '39a8645d', '39e1d7f9', '3aa6fb7a', '3ac3eb23', '3af2c5a8', '3bd67248', '3bdb4ada', '3befdf3e', '3c9b0459', '3de23699', '3e980e27', '3eda0437', '3f7978a0', '40853293', '4093f84a', '41e4d17e', '4258a5f9', '4290ef0e', '42a50994', '4347f46a', '444801d8', '445eab21', '447fd412', '44d8ac46', '44f52bb0', '4522001f', '4612dd53', '46442a0e', '469497ad', '46f33fce', '47c1f68c', '484b58aa', '48d8fb45', '4938f0c2', '496994bd', '49d1d64f', '4be741c5', '4c4377d9', '4c5c2cf0', '50846271', '508bd3b6', '50cb2852', '5117e062', '5168d44c', '539a4f51', '53b68214', '543a7ed5', '54d82841', '54d9e175', '5521c0d9', '5582e5ca', '5614dbcf', '56dc2b01', '56ff96f3', '57aa92db', '5ad4f10b', '5bd6f4ac', '5c0a986e', '5c2c9af4', '5daaa586', '60b61512', '6150a2bd', '623ea044', '62c24649', '63613498', '6430c8c4', '6455b5f5', '662c240a', '67385a82', '673ef223', '6773b310', '67a3c6ac', '67a423a3', '67e8384a', '681b3aeb', '6855a6e4', '68b16354', '694f12f3', '6a1e5592', '6aa20dc0', '6b9890af', '6c434453', '6cdd2623', '6cf79266', '6d0160f0', '6d0aefbc', '6d58a25d', '6d75e8bb', '6e02f1e3', '6e19193c', '6e82a1ae', '6ecd11f4', '6f8cd79b', '6fa7a44f', '72322fa7', '72ca375d', '73251a56', '7447852a', '7468f01a', '746b3537', '74dd1130', '75b8110e', '760b3cac', '776ffc46', '77fdfe62', '780d0b14', '7837ac64', '794b24be', '7b6016b9', '7b7f7511', '7c008303', '7ddcd7ec', '7df24a62', '7e0986d6', '7f4411dc', '7fe24cdd', '80af3007', '810b9b61', '82819916', '83302e8f', '834ec97d', '8403a5d5', '846bdb03', '855e0971', '85c4e7cd', '868de0fa', '8731374e', '88a10436', '88a62173', '890034e9', '8a004b2b', '8be77c9e', '8d5021e8', '8d510a79', '8e1813be', '8e5a5113', '8eb1be9a', '8efcae92', '8f2ea7aa', '90c28cc7', '90f3ed37', '913fb3ed', '91413438', '91714a58', '9172f3a0', '928ad970', '93b581b8', '941d9a10', '94f9d214', '952a094c', '9565186b', '95990924', '963e52fc', '97999447', '97a05b5b', '98cf29f8', '995c5fa3', '99b1bc43', '99fa7670', '9aec4887', '9af7a82c', '9d9215db', '9dfd6313', '9ecd008a', '9edfc990', '9f236235', 'a1570a43', 'a2fd1cf0', 'a3325580', 'a3df8b1e', 'a416b8f3', 'a48eeaf7', 'a5313dff', 'a5f85a15', 'a61ba2ce', 'a61f2674', 'a64e4611', 'a65b410d', 'a68b268e', 'a699fb00', 'a740d043', 'a78176bb', 'a79310a0', 'a85d4709', 'a87f7484', 'a8c38be5', 'a8d7556c', 'a9f96cdd', 'aabf363d', 'aba27056', 'ac0a08a4', 'ae3edfdc', 'ae4f1146', 'aedd82e4', 'af902bf9', 'b0c4d837', 'b190f7f5', 'b1948b0a', 'b230c067', 'b27ca6d3', 'b2862040', 'b527c5c6', 'b548a754', 'b60334d2', 'b6afb2da', 'b7249182', 'b775ac94', 'b782dc8a', 'b8825c91', 'b8cdaf2b', 'b91ae062', 'b94a9452', 'b9b7f026', 'ba26e723', 'ba97ae07', 'bb43febb', 'bbc9ae5d', 'bc1d5164', 'bd4472b8', 'bda2d7a6', 'bdad9b1f', 'be94b721', 'beb8660c', 'c0f76784', 'c1d99e64', 'c3e719e8', 'c3f564a4', 'c444b776', 'c59eb873', 'c8cbb738', 'c8f0f002', 'c909285e', 'c9e6f938', 'c9f8e694', 'caa06a1f', 'cbded52d', 'cce03e0d', 'cdecee7f', 'ce22a75a', 'ce4f8723', 'ce602527', 'ce9e57f2', 'cf98881b', 'd037b0a7', 'd06dbe63', 'd07ae81c', 'd0f5fe59', 'd10ecb37', 'd13f3404', 'd22278a0', 'd23f8c26', 'd2abd087', 'd364b489', 'd406998b', 'd43fd935', 'd4469b4b', 'd4a91cb9', 'd4f3cd78', 'd511f180', 'd5d6de2d', 'd631b094', 'd687bc17', 'd6ad076f', 'd89b689b', 'd8c310e9', 'd90796e8', 'd9f24cd1', 'd9fac9be', 'dae9d2b5', 'db3e9e38', 'db93a21d', 'dbc1a6ce', 'dc0a314f', 'dc1df850', 'dc433765', 'ddf7fa4f', 'de1cd16c', 'ded97339', 'e179c5f4', 'e21d9049', 'e26a3af2', 'e3497940', 'e40b9e2f', 'e48d4e1a', 'e5062a87', 'e509e548', 'e50d258f', 'e6721834', 'e73095fd', 'e76a88a6', 'e8593010', 'e8dc4411', 'e9614598', 'e98196ab', 'e9afcf9a', 'ea32f347', 'ea786f4a', 'eb281b96', 'eb5a1d5d', 'ec883f72', 'ecdecbb3', 'ed36ccf7', 'ef135b50', 'f15e1fac', 'f1cefba8', 'f25fbde4', 'f25ffba3', 'f2829549', 'f35d900a', 'f5b8619d', 'f76d97a5', 'f8a8fe49', 'f8b3ba0a', 'f8c80d96', 'f8ff0b80', 'f9012d9b', 'fafffa47', 'fcb5c309', 'fcc82909', 'feca6190', 'ff28f65a', 'ff805c23']\n400\nget_keys_with_equal_size_input_output\n['00d62c1b', '045e512c', '05269061', '06df4c85', '08ed6ac7', '09629e4f', '0962bcdd', '0ca9ddb6', '0d3d703e', '0dfd9992', '11852cab', '1b60fb0c', '1bfc4729', '1e0a9b12', '1e32b0e9', '1f0c79e5', '1f642eb9', '1f876c06', '2204b7a8', '22168020', '22233c11', '2281f1f4', '228f6490', '22eb0ac0', '23581191', '25d8a9c8', '25ff71a9', '264363fd', '28e73c20', '29623171', '29ec7d0e', '2bcee788', '2bee17df', '2dd70a9a', '31aa019c', '321b1fc6', '32597951', '3345333e', '3618c87e', '3631a71a', '36d67576', '3906de3d', '39e1d7f9', '3aa6fb7a', '3bd67248', '3befdf3e', '3c9b0459', '3e980e27', '4093f84a', '41e4d17e', '4258a5f9', '444801d8', '44d8ac46', '484b58aa', '4c5c2cf0', '508bd3b6', '543a7ed5', '5521c0d9', '5582e5ca', '5c0a986e', '5c2c9af4', '60b61512', '6150a2bd', '623ea044', '63613498', '67a3c6ac', '67a423a3', '6855a6e4', '68b16354', '694f12f3', '6c434453', '6cf79266', '6d0160f0', '6d58a25d', '6e02f1e3', '6e19193c', '6e82a1ae', '73251a56', '74dd1130', '776ffc46', '794b24be', '7ddcd7ec', '7df24a62', '7f4411dc', '810b9b61', '83302e8f', '834ec97d', '8403a5d5', '85c4e7cd', '868de0fa', '890034e9', '8d510a79', '8f2ea7aa', '913fb3ed', '91714a58', '93b581b8', '941d9a10', '952a094c', '9565186b', '95990924', '9d9215db', '9dfd6313', '9edfc990', 'a48eeaf7', 'a5313dff', 'a5f85a15', 'a61f2674', 'a64e4611', 'a699fb00', 'a78176bb', 'a79310a0', 'a85d4709', 'a8d7556c', 'aabf363d', 'aba27056', 'ae3edfdc', 'af902bf9', 'b230c067', 'b527c5c6', 'b60334d2', 'b6afb2da', 'b775ac94', 'b8825c91', 'b8cdaf2b', 'bb43febb', 'bda2d7a6', 'bdad9b1f', 'c0f76784', 'c3f564a4', 'c9f8e694', 'caa06a1f', 'cbded52d', 'ce22a75a', 'd037b0a7', 'd06dbe63', 'd22278a0', 'd23f8c26', 'd2abd087', 'd364b489', 'd43fd935', 'd4f3cd78', 'd511f180', 'd5d6de2d', 'd6ad076f', 'd89b689b', 'd9f24cd1', 'db93a21d', 'ddf7fa4f', 'ded97339', 'e40b9e2f', 'e48d4e1a', 'e5062a87', 'e76a88a6', 'e8593010', 'e9614598', 'ea32f347', 'ea786f4a', 'ec883f72', 'ed36ccf7', 'ef135b50', 'f76d97a5', 'f8a8fe49', 'f8c80d96', 'fcc82909']\n164\nget_keys_with_equal_size_input_output_sorted\n[('0d3d703e', 3), ('25d8a9c8', 3), ('25ff71a9', 3), ('3c9b0459', 3), ('5582e5ca', 3), ('6150a2bd', 3), ('6e02f1e3', 3), ('74dd1130', 3), ('794b24be', 3), ('834ec97d', 3), ('9565186b', 3), ('9dfd6313', 3), ('a5f85a15', 3), ('a85d4709', 3), ('b8cdaf2b', 3), ('d037b0a7', 3), ('d23f8c26', 3), ('ea786f4a', 3), ('ed36ccf7', 3), ('f76d97a5', 3), ('1e0a9b12', 4), ('67a3c6ac', 4), ('67a423a3', 4), ('3618c87e', 5), ('68b16354', 5), ('a699fb00', 5), ('a79310a0', 5), ('d511f180', 5), ('00d62c1b', 6), ('28e73c20', 6), ('93b581b8', 6), ('bda2d7a6', 6), ('bdad9b1f', 6), ('ec883f72', 6), ('05269061', 7), ('3aa6fb7a', 7), ('aabf363d', 7), ('aba27056', 7), ('a5313dff', 8), ('cbded52d', 8), ('08ed6ac7', 9), ('0ca9ddb6', 9), ('1f0c79e5', 9), ('23581191', 9), ('4258a5f9', 9), ('60b61512', 9), ('8f2ea7aa', 9), ('a61f2674', 9), ('b60334d2', 9), ('ce22a75a', 9), ('11852cab', 10), ('1b60fb0c', 10), ('1bfc4729', 10), ('1f642eb9', 10), ('1f876c06', 10), ('2204b7a8', 10), ('22168020', 10), ('22233c11', 10), ('2281f1f4', 10), ('228f6490', 10), ('22eb0ac0', 10), ('2bcee788', 10), ('31aa019c', 10), ('321b1fc6', 10), ('3906de3d', 10), ('3befdf3e', 10), ('444801d8', 10), ('5c0a986e', 10), ('63613498', 10), ('694f12f3', 10), ('6c434453', 10), ('6e19193c', 10), ('6e82a1ae', 10), ('7ddcd7ec', 10), ('8403a5d5', 10), ('868de0fa', 10), ('8d510a79', 10), ('941d9a10', 10), ('952a094c', 10), ('95990924', 10), ('a48eeaf7', 10), ('a78176bb', 10), ('af902bf9', 10), ('b230c067', 10), ('b527c5c6', 10), ('b6afb2da', 10), ('bb43febb', 10), ('d22278a0', 10), ('d2abd087', 10), ('d364b489', 10), ('d43fd935', 10), ('d4f3cd78', 10), ('d5d6de2d', 10), ('d6ad076f', 10), ('d89b689b', 10), ('d9f24cd1', 10), ('db93a21d', 10), ('ddf7fa4f', 10), ('ded97339', 10), ('e40b9e2f', 10), ('e48d4e1a', 10), ('e5062a87', 10), ('e76a88a6', 10), ('e8593010', 10), ('e9614598', 10), ('ea32f347', 10), ('ef135b50', 10), ('f8c80d96', 10), ('fcc82909', 10), ('09629e4f', 11), ('29623171', 11), ('6d0160f0', 11), ('caa06a1f', 11), ('0962bcdd', 12), ('2bee17df', 12), ('44d8ac46', 12), ('508bd3b6', 12), ('85c4e7cd', 12), ('913fb3ed', 12), ('c0f76784', 12), ('c9f8e694', 12), ('36d67576', 13), ('3e980e27', 13), ('7f4411dc', 13), ('9edfc990', 13), ('d06dbe63', 13), ('4093f84a', 14), ('4c5c2cf0', 14), ('3bd67248', 15), ('41e4d17e', 15), ('543a7ed5', 15), ('5521c0d9', 15), ('623ea044', 15), ('6855a6e4', 15), ('810b9b61', 15), ('ae3edfdc', 15), ('f8a8fe49', 15), ('3345333e', 16), ('91714a58', 16), ('b8825c91', 16), ('c3f564a4', 16), ('1e32b0e9', 17), ('32597951', 17), ('29ec7d0e', 18), ('a8d7556c', 18), ('9d9215db', 19), ('06df4c85', 20), ('2dd70a9a', 20), ('6cf79266', 20), ('6d58a25d', 20), ('776ffc46', 20), ('045e512c', 21), ('0dfd9992', 21), ('73251a56', 21), ('890034e9', 21), ('5c2c9af4', 23), ('7df24a62', 23), ('39e1d7f9', 24), ('83302e8f', 24), ('484b58aa', 29), ('264363fd', 30), ('3631a71a', 30), ('a64e4611', 30), ('b775ac94', 30)]\nget_keys_with_inconsistent_input_output_sizes\n['007bbfb7', '017c7c7b', '3af2c5a8', '4522001f', '46f33fce', '4c4377d9', '539a4f51', '62c24649', '67e8384a', '6fa7a44f', '7fe24cdd', '8be77c9e', '8d5021e8', '9172f3a0', 'c3e719e8', 'cce03e0d', 'd13f3404']\n17\nget_keys_with_variable_input_sizes\n['00d62c1b', '025d127b', '05f2a901', '06df4c85', '0a938d79', '0b148d64', '0e206a2e', '10fcaaa3', '1190e5a7', '150deff5', '178fcbfb', '1a07d186', '1c786137', '1caeab9d', '1cf80156', '1e0a9b12', '239be575', '23b5c85d', '253bf280', '25d487eb', '272f95fa', '28e73c20', '2bee17df', '2c608aff', '2dc579da', '2dd70a9a', '2dee498d', '36fdfd69', '39e1d7f9', '3bd67248', '3de23699', '3eda0437', '3f7978a0', '40853293', '4290ef0e', '42a50994', '4347f46a', '4612dd53', '46442a0e', '47c1f68c', '4938f0c2', '49d1d64f', '4be741c5', '4c5c2cf0', '50846271', '50cb2852', '5168d44c', '53b68214', '54d82841', '54d9e175', '56dc2b01', '56ff96f3', '57aa92db', '5ad4f10b', '5daaa586', '623ea044', '6455b5f5', '67385a82', '673ef223', '67a3c6ac', '67a423a3', '68b16354', '6aa20dc0', '6b9890af', '6cdd2623', '6d75e8bb', '6ecd11f4', '6f8cd79b', '72322fa7', '7468f01a', '746b3537', '77fdfe62', '780d0b14', '7837ac64', '7b6016b9', '7b7f7511', '7f4411dc', '810b9b61', '82819916', '83302e8f', '834ec97d', '855e0971', '85c4e7cd', '868de0fa', '8731374e', '88a10436', '8a004b2b', '8e1813be', '8eb1be9a', '913fb3ed', '928ad970', '95990924', '97999447', '97a05b5b', '98cf29f8', '99fa7670', '9aec4887', '9af7a82c', '9dfd6313', '9edfc990', '9f236235', 'a1570a43', 'a2fd1cf0', 'a416b8f3', 'a5313dff', 'a5f85a15', 'a65b410d', 'a699fb00', 'a740d043', 'a79310a0', 'a87f7484', 'a8c38be5', 'aba27056', 'aedd82e4', 'b0c4d837', 'b190f7f5', 'b1948b0a', 'b2862040', 'b527c5c6', 'b548a754', 'b7249182', 'b775ac94', 'b782dc8a', 'b8cdaf2b', 'b9b7f026', 'ba97ae07', 'bd4472b8', 'bda2d7a6', 'be94b721', 'beb8660c', 'c1d99e64', 'c59eb873', 'c8cbb738', 'c909285e', 'caa06a1f', 'ce602527', 'ce9e57f2', 'd07ae81c', 'd0f5fe59', 'd10ecb37', 'd22278a0', 'd23f8c26', 'd4a91cb9', 'd511f180', 'd5d6de2d', 'd687bc17', 'd90796e8', 'd9fac9be', 'db3e9e38', 'db93a21d', 'dbc1a6ce', 'dc1df850', 'dc433765', 'de1cd16c', 'e21d9049', 'e26a3af2', 'e509e548', 'e6721834', 'e73095fd', 'e8dc4411', 'ea786f4a', 'eb281b96', 'eb5a1d5d', 'ec883f72', 'ecdecbb3', 'f15e1fac', 'f1cefba8', 'f35d900a', 'f5b8619d', 'f76d97a5', 'f9012d9b', 'fcb5c309', 'ff28f65a']\n173\nget_input_array_histogram\n{5: 18, 3: 236, 2: 57, 4: 78, 6: 5, 8: 2, 7: 3, 10: 1}\n400\nget_data_for_key {'test': [{'input': [[7, 0, 7], [7, 0, 7], [7, 7, 0]]}], 'train': [{'input': [[0, 7, 7], [7, 7, 7], [0, 7, 7]], 'output': [[0, 0, 0, 0, 7, 7, 0, 7, 7], [0, 0, 0, 7, 7, 7, 7, 7, 7], [0, 0, 0, 0, 7, 7, 0, 7, 7], [0, 7, 7, 0, 7, 7, 0, 7, 7], [7, 7, 7, 7, 7, 7, 7, 7, 7], [0, 7, 7, 0, 7, 7, 0, 7, 7], [0, 0, 0, 0, 7, 7, 0, 7, 7], [0, 0, 0, 7, 7, 7, 7, 7, 7], [0, 0, 0, 0, 7, 7, 0, 7, 7]]}, {'input': [[4, 0, 4], [0, 0, 0], [0, 4, 0]], 'output': [[4, 0, 4, 0, 0, 0, 4, 0, 4], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 0, 0, 0, 0, 0, 4, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 4, 0, 4, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 4, 0, 0, 0, 0]]}, {'input': [[0, 0, 0], [0, 0, 2], [2, 0, 2]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 2], [0, 0, 0, 0, 0, 0, 2, 0, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 2], [2, 0, 2, 0, 0, 0, 2, 0, 2]]}, {'input': [[6, 6, 0], [6, 0, 0], [0, 6, 6]], 'output': [[6, 6, 0, 6, 6, 0, 0, 0, 0], [6, 0, 0, 6, 0, 0, 0, 0, 0], [0, 6, 6, 0, 6, 6, 0, 0, 0], [6, 6, 0, 0, 0, 0, 0, 0, 0], [6, 0, 0, 0, 0, 0, 0, 0, 0], [0, 6, 6, 0, 0, 0, 0, 0, 0], [0, 0, 0, 6, 6, 0, 6, 6, 0], [0, 0, 0, 6, 0, 0, 6, 0, 0], [0, 0, 0, 0, 6, 6, 0, 6, 6]]}, {'input': [[2, 2, 2], [0, 0, 0], [0, 2, 2]], 'output': [[2, 2, 2, 2, 2, 2, 2, 2, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 2, 0, 2, 2, 0, 2, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 2, 2, 2, 2, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 2, 2, 0, 2, 2]]}]}\n[]\n[]\nget_largest_array_size ('1f85a75f', 900)\nanalyze_arrays {'analysis': {'equal_input_output': ['045e512c', '05269061', '08ed6ac7', '09629e4f', '0962bcdd', '0ca9ddb6', '0d3d703e', '0dfd9992', '11852cab', '1b60fb0c', '1bfc4729', '1e32b0e9', '1f0c79e5', '1f642eb9', '1f876c06', '2204b7a8', '22168020', '22233c11', '2281f1f4', '228f6490', '22eb0ac0', '23581191', '25d8a9c8', '25ff71a9', '264363fd', '29623171', '29c11459', '29ec7d0e', '2bcee788', '31aa019c', '321b1fc6', '32597951', '3345333e', '3618c87e', '3631a71a', '363442ee', '36d67576', '3906de3d', '3aa6fb7a', '3befdf3e', '3c9b0459', '3e980e27', '4093f84a', '41e4d17e', '4258a5f9', '444801d8', '447fd412', '44d8ac46', '484b58aa', '508bd3b6', '543a7ed5', '5521c0d9', '5582e5ca', '5c0a986e', '5c2c9af4', '60b61512', '6150a2bd', '63613498', '6855a6e4', '694f12f3', '6a1e5592', '6c434453', '6cf79266', '6d0160f0', '6d58a25d', '6e02f1e3', '6e19193c', '6e82a1ae', '73251a56', '74dd1130', '760b3cac', '776ffc46', '794b24be', '7ddcd7ec', '7df24a62', '8403a5d5', '890034e9', '8d510a79', '8e5a5113', '8f2ea7aa', '90f3ed37', '91714a58', '93b581b8', '941d9a10', '952a094c', '9565186b', '9d9215db', 'a48eeaf7', 'a61f2674', 'a64e4611', 'a78176bb', 'a85d4709', 'a8d7556c', 'a9f96cdd', 'aabf363d', 'ae3edfdc', 'af902bf9', 'b230c067', 'b60334d2', 'b6afb2da', 'b8825c91', 'bb43febb', 'bdad9b1f', 'c0f76784', 'c3f564a4', 'c9f8e694', 'cbded52d', 'ce22a75a', 'd037b0a7', 'd06dbe63', 'd2abd087', 'd364b489', 'd43fd935', 'd4f3cd78', 'd6ad076f', 'd89b689b', 'd8c310e9', 'd9f24cd1', 'ddf7fa4f', 'ded97339', 'e40b9e2f', 'e48d4e1a', 'e5062a87', 'e76a88a6', 'e8593010', 'e9614598', 'e9afcf9a', 'ea32f347', 'ed36ccf7', 'ef135b50', 'f25ffba3', 'f8a8fe49', 'f8c80d96', 'fcc82909'], 'consistent_but_different_sizes': ['007bbfb7', '017c7c7b', '0520fde7', '137eaa0f', '1b2d62fb', '1fad071e', '2013d3e2', '27a28665', '28bf18c6', '3428a4f5', '39a8645d', '3af2c5a8', '445eab21', '44f52bb0', '4522001f', '46f33fce', '48d8fb45', '4c4377d9', '5117e062', '539a4f51', '5614dbcf', '5bd6f4ac', '62c24649', '6430c8c4', '662c240a', '6773b310', '67e8384a', '681b3aeb', '6d0aefbc', '6fa7a44f', '75b8110e', '7c008303', '7fe24cdd', '80af3007', '88a62173', '8be77c9e', '8d5021e8', '9172f3a0', '94f9d214', '995c5fa3', '99b1bc43', '9ecd008a', 'a61ba2ce', 'a68b268e', 'ae4f1146', 'bc1d5164', 'c3e719e8', 'c9e6f938', 'cce03e0d', 'cdecee7f', 'ce4f8723', 'cf98881b', 'd13f3404', 'd4469b4b', 'dae9d2b5', 'dc0a314f', 'e3497940', 'e98196ab', 'f2829549', 'f8ff0b80', 'fafffa47', 'ff805c23'], 'variable_output_sizes': ['00d62c1b', '025d127b', '05f2a901', '06df4c85', '0a938d79', '0b148d64', '0e206a2e', '10fcaaa3', '1190e5a7', '150deff5', '178fcbfb', '1a07d186', '1c786137', '1caeab9d', '1cf80156', '1e0a9b12', '1f85a75f', '234bbc79', '239be575', '23b5c85d', '253bf280', '25d487eb', '272f95fa', '28e73c20', '2bee17df', '2c608aff', '2dc579da', '2dd70a9a', '2dee498d', '36fdfd69', '39e1d7f9', '3ac3eb23', '3bd67248', '3bdb4ada', '3de23699', '3eda0437', '3f7978a0', '40853293', '4290ef0e', '42a50994', '4347f46a', '4612dd53', '46442a0e', '469497ad', '47c1f68c', '4938f0c2', '496994bd', '49d1d64f', '4be741c5', '4c5c2cf0', '50846271', '50cb2852', '5168d44c', '53b68214', '54d82841', '54d9e175', '56dc2b01', '56ff96f3', '57aa92db', '5ad4f10b', '5daaa586', '623ea044', '6455b5f5', '67385a82', '673ef223', '67a3c6ac', '67a423a3', '68b16354', '6aa20dc0', '6b9890af', '6cdd2623', '6d75e8bb', '6ecd11f4', '6f8cd79b', '72322fa7', '72ca375d', '7447852a', '7468f01a', '746b3537', '77fdfe62', '780d0b14', '7837ac64', '7b6016b9', '7b7f7511', '7e0986d6', '7f4411dc', '810b9b61', '82819916', '83302e8f', '834ec97d', '846bdb03', '855e0971', '85c4e7cd', '868de0fa', '8731374e', '88a10436', '8a004b2b', '8e1813be', '8eb1be9a', '8efcae92', '90c28cc7', '913fb3ed', '91413438', '928ad970', '95990924', '963e52fc', '97999447', '97a05b5b', '98cf29f8', '99fa7670', '9aec4887', '9af7a82c', '9dfd6313', '9edfc990', '9f236235', 'a1570a43', 'a2fd1cf0', 'a3325580', 'a3df8b1e', 'a416b8f3', 'a5313dff', 'a5f85a15', 'a65b410d', 'a699fb00', 'a740d043', 'a79310a0', 'a87f7484', 'a8c38be5', 'aba27056', 'ac0a08a4', 'aedd82e4', 'b0c4d837', 'b190f7f5', 'b1948b0a', 'b27ca6d3', 'b2862040', 'b527c5c6', 'b548a754', 'b7249182', 'b775ac94', 'b782dc8a', 'b8cdaf2b', 'b91ae062', 'b94a9452', 'b9b7f026', 'ba26e723', 'ba97ae07', 'bbc9ae5d', 'bd4472b8', 'bda2d7a6', 'be94b721', 'beb8660c', 'c1d99e64', 'c444b776', 'c59eb873', 'c8cbb738', 'c8f0f002', 'c909285e', 'caa06a1f', 'ce602527', 'ce9e57f2', 'd07ae81c', 'd0f5fe59', 'd10ecb37', 'd22278a0', 'd23f8c26', 'd406998b', 'd4a91cb9', 'd511f180', 'd5d6de2d', 'd631b094', 'd687bc17', 'd90796e8', 'd9fac9be', 'db3e9e38', 'db93a21d', 'dbc1a6ce', 'dc1df850', 'dc433765', 'de1cd16c', 'e179c5f4', 'e21d9049', 'e26a3af2', 'e509e548', 'e50d258f', 'e6721834', 'e73095fd', 'e8dc4411', 'ea786f4a', 'eb281b96', 'eb5a1d5d', 'ec883f72', 'ecdecbb3', 'f15e1fac', 'f1cefba8', 'f25fbde4', 'f35d900a', 'f5b8619d', 'f76d97a5', 'f8b3ba0a', 'f9012d9b', 'fcb5c309', 'feca6190', 'ff28f65a']}, 'counts': {'equal_input_output': 134, 'consistent_but_different_sizes': 62, 'variable_output_sizes': 204}}\nget_all_keys ['007bbfb7', '00d62c1b', '017c7c7b', '025d127b', '045e512c', '0520fde7', '05269061', '05f2a901', '06df4c85', '08ed6ac7', '09629e4f', '0962bcdd', '0a938d79', '0b148d64', '0ca9ddb6', '0d3d703e', '0dfd9992', '0e206a2e', '10fcaaa3', '11852cab', '1190e5a7', '137eaa0f', '150deff5', '178fcbfb', '1a07d186', '1b2d62fb', '1b60fb0c', '1bfc4729', '1c786137', '1caeab9d', '1cf80156', '1e0a9b12', '1e32b0e9', '1f0c79e5', '1f642eb9', '1f85a75f', '1f876c06', '1fad071e', '2013d3e2', '2204b7a8', '22168020', '22233c11', '2281f1f4', '228f6490', '22eb0ac0', '234bbc79', '23581191', '239be575', '23b5c85d', '253bf280', '25d487eb', '25d8a9c8', '25ff71a9', '264363fd', '272f95fa', '27a28665', '28bf18c6', '28e73c20', '29623171', '29c11459', '29ec7d0e', '2bcee788', '2bee17df', '2c608aff', '2dc579da', '2dd70a9a', '2dee498d', '31aa019c', '321b1fc6', '32597951', '3345333e', '3428a4f5', '3618c87e', '3631a71a', '363442ee', '36d67576', '36fdfd69', '3906de3d', '39a8645d', '39e1d7f9', '3aa6fb7a', '3ac3eb23', '3af2c5a8', '3bd67248', '3bdb4ada', '3befdf3e', '3c9b0459', '3de23699', '3e980e27', '3eda0437', '3f7978a0', '40853293', '4093f84a', '41e4d17e', '4258a5f9', '4290ef0e', '42a50994', '4347f46a', '444801d8', '445eab21', '447fd412', '44d8ac46', '44f52bb0', '4522001f', '4612dd53', '46442a0e', '469497ad', '46f33fce', '47c1f68c', '484b58aa', '48d8fb45', '4938f0c2', '496994bd', '49d1d64f', '4be741c5', '4c4377d9', '4c5c2cf0', '50846271', '508bd3b6', '50cb2852', '5117e062', '5168d44c', '539a4f51', '53b68214', '543a7ed5', '54d82841', '54d9e175', '5521c0d9', '5582e5ca', '5614dbcf', '56dc2b01', '56ff96f3', '57aa92db', '5ad4f10b', '5bd6f4ac', '5c0a986e', '5c2c9af4', '5daaa586', '60b61512', '6150a2bd', '623ea044', '62c24649', '63613498', '6430c8c4', '6455b5f5', '662c240a', '67385a82', '673ef223', '6773b310', '67a3c6ac', '67a423a3', '67e8384a', '681b3aeb', '6855a6e4', '68b16354', '694f12f3', '6a1e5592', '6aa20dc0', '6b9890af', '6c434453', '6cdd2623', '6cf79266', '6d0160f0', '6d0aefbc', '6d58a25d', '6d75e8bb', '6e02f1e3', '6e19193c', '6e82a1ae', '6ecd11f4', '6f8cd79b', '6fa7a44f', '72322fa7', '72ca375d', '73251a56', '7447852a', '7468f01a', '746b3537', '74dd1130', '75b8110e', '760b3cac', '776ffc46', '77fdfe62', '780d0b14', '7837ac64', '794b24be', '7b6016b9', '7b7f7511', '7c008303', '7ddcd7ec', '7df24a62', '7e0986d6', '7f4411dc', '7fe24cdd', '80af3007', '810b9b61', '82819916', '83302e8f', '834ec97d', '8403a5d5', '846bdb03', '855e0971', '85c4e7cd', '868de0fa', '8731374e', '88a10436', '88a62173', '890034e9', '8a004b2b', '8be77c9e', '8d5021e8', '8d510a79', '8e1813be', '8e5a5113', '8eb1be9a', '8efcae92', '8f2ea7aa', '90c28cc7', '90f3ed37', '913fb3ed', '91413438', '91714a58', '9172f3a0', '928ad970', '93b581b8', '941d9a10', '94f9d214', '952a094c', '9565186b', '95990924', '963e52fc', '97999447', '97a05b5b', '98cf29f8', '995c5fa3', '99b1bc43', '99fa7670', '9aec4887', '9af7a82c', '9d9215db', '9dfd6313', '9ecd008a', '9edfc990', '9f236235', 'a1570a43', 'a2fd1cf0', 'a3325580', 'a3df8b1e', 'a416b8f3', 'a48eeaf7', 'a5313dff', 'a5f85a15', 'a61ba2ce', 'a61f2674', 'a64e4611', 'a65b410d', 'a68b268e', 'a699fb00', 'a740d043', 'a78176bb', 'a79310a0', 'a85d4709', 'a87f7484', 'a8c38be5', 'a8d7556c', 'a9f96cdd', 'aabf363d', 'aba27056', 'ac0a08a4', 'ae3edfdc', 'ae4f1146', 'aedd82e4', 'af902bf9', 'b0c4d837', 'b190f7f5', 'b1948b0a', 'b230c067', 'b27ca6d3', 'b2862040', 'b527c5c6', 'b548a754', 'b60334d2', 'b6afb2da', 'b7249182', 'b775ac94', 'b782dc8a', 'b8825c91', 'b8cdaf2b', 'b91ae062', 'b94a9452', 'b9b7f026', 'ba26e723', 'ba97ae07', 'bb43febb', 'bbc9ae5d', 'bc1d5164', 'bd4472b8', 'bda2d7a6', 'bdad9b1f', 'be94b721', 'beb8660c', 'c0f76784', 'c1d99e64', 'c3e719e8', 'c3f564a4', 'c444b776', 'c59eb873', 'c8cbb738', 'c8f0f002', 'c909285e', 'c9e6f938', 'c9f8e694', 'caa06a1f', 'cbded52d', 'cce03e0d', 'cdecee7f', 'ce22a75a', 'ce4f8723', 'ce602527', 'ce9e57f2', 'cf98881b', 'd037b0a7', 'd06dbe63', 'd07ae81c', 'd0f5fe59', 'd10ecb37', 'd13f3404', 'd22278a0', 'd23f8c26', 'd2abd087', 'd364b489', 'd406998b', 'd43fd935', 'd4469b4b', 'd4a91cb9', 'd4f3cd78', 'd511f180', 'd5d6de2d', 'd631b094', 'd687bc17', 'd6ad076f', 'd89b689b', 'd8c310e9', 'd90796e8', 'd9f24cd1', 'd9fac9be', 'dae9d2b5', 'db3e9e38', 'db93a21d', 'dbc1a6ce', 'dc0a314f', 'dc1df850', 'dc433765', 'ddf7fa4f', 'de1cd16c', 'ded97339', 'e179c5f4', 'e21d9049', 'e26a3af2', 'e3497940', 'e40b9e2f', 'e48d4e1a', 'e5062a87', 'e509e548', 'e50d258f', 'e6721834', 'e73095fd', 'e76a88a6', 'e8593010', 'e8dc4411', 'e9614598', 'e98196ab', 'e9afcf9a', 'ea32f347', 'ea786f4a', 'eb281b96', 'eb5a1d5d', 'ec883f72', 'ecdecbb3', 'ed36ccf7', 'ef135b50', 'f15e1fac', 'f1cefba8', 'f25fbde4', 'f25ffba3', 'f2829549', 'f35d900a', 'f5b8619d', 'f76d97a5', 'f8a8fe49', 'f8b3ba0a', 'f8c80d96', 'f8ff0b80', 'f9012d9b', 'fafffa47', 'fcb5c309', 'fcc82909', 'feca6190', 'ff28f65a', 'ff805c23']\n400\nget_data_for_key [[7, 0, 7, 0, 0, 0, 7, 0, 7], [7, 0, 7, 0, 0, 0, 7, 0, 7], [7, 7, 0, 0, 0, 0, 7, 7, 0], [7, 0, 7, 0, 0, 0, 7, 0, 7], [7, 0, 7, 0, 0, 0, 7, 0, 7], [7, 7, 0, 0, 0, 0, 7, 7, 0], [7, 0, 7, 7, 0, 7, 0, 0, 0], [7, 0, 7, 7, 0, 7, 0, 0, 0], [7, 7, 0, 7, 7, 0, 0, 0, 0]]\n[]", + "crumbs": [ + "Helper classes" + ] + }, + { + "objectID": "helpers.html#datamanagersingleton", + "href": "helpers.html#datamanagersingleton", + "title": "Helper classes", + "section": "DataManagerSingleton", + "text": "DataManagerSingleton\n\nsource\n\nDataManagerSingleton\n\n DataManagerSingleton (folder:str, prefix:str, show_timing:bool=False)\n\nInitialize self. See help(type(self)) for accurate signature.\n\n# From: https://www.kaggle.com/code/allegich/arc-2024-show-all-400-training-tasks?scriptVersionId=190186517&cellId=12\ndef plot_task(task, task_solutions, i, t):\n \"\"\" Plots the first train and test pairs of a specified task,\n using same color scheme as the ARC app \"\"\" \n \n num_train = len(task['train'])\n num_test = len(task['test'])\n \n w=num_train+num_test\n fig, axs = plt.subplots(2, w, figsize=(3*w ,3*2))\n plt.suptitle(f'Set #{i}, {t}:', fontsize=20, fontweight='bold', y=1)\n #plt.subplots_adjust(hspace = 0.15)\n #plt.subplots_adjust(wspace=20, hspace=20)\n \n for j in range(num_train): \n plot_one(axs[0, j], j,'train', 'input')\n plot_one(axs[1, j], j,'train', 'output') \n \n \n plot_one(axs[0, j+1], 0, 'test', 'input')\n\n\n answer = task_solutions\n input_matrix = answer\n \n axs[1, j+1].imshow(input_matrix, cmap=cmap, norm=norm)\n axs[1, j+1].grid(True, which = 'both',color = 'lightgrey', linewidth = 0.5)\n axs[1, j+1].set_yticks([x-0.5 for x in range(1 + len(input_matrix))])\n axs[1, j+1].set_xticks([x-0.5 for x in range(1 + len(input_matrix[0]))]) \n axs[1, j+1].set_xticklabels([])\n axs[1, j+1].set_yticklabels([])\n axs[1, j+1].set_title('Test output')\n\n axs[1, j+1] = plt.figure(1).add_subplot(111)\n axs[1, j+1].set_xlim([0, num_train+1])\n \n for m in range(1, num_train):\n axs[1, j+1].plot([m,m],[0,1],'--', linewidth=1, color = 'black')\n \n axs[1, j+1].plot([num_train,num_train],[0,1],'-', linewidth=3, color = 'black')\n\n axs[1, j+1].axis(\"off\")\n\n fig.patch.set_linewidth(5)\n fig.patch.set_edgecolor('black') \n fig.patch.set_facecolor('#dddddd')\n \n plt.tight_layout()\n \n print(f'#{i}, {t}') # for fast and convinience search\n plt.show() \n \n print()\n print()\n \n \ndef plot_one(ax, i, train_or_test, input_or_output):\n input_matrix = task[train_or_test][i][input_or_output]\n ax.imshow(input_matrix, cmap=cmap, norm=norm)\n ax.grid(True, which = 'both',color = 'lightgrey', linewidth = 0.5)\n \n plt.setp(plt.gcf().get_axes(), xticklabels=[], yticklabels=[])\n ax.set_xticks([x-0.5 for x in range(1 + len(input_matrix[0]))]) \n ax.set_yticks([x-0.5 for x in range(1 + len(input_matrix))])\n \n ax.set_title(train_or_test + ' ' + input_or_output)\n\n\ncode = '00000001'\ndata_mgr = DataManagerSingleton.get_instance(folder = 'c:/tmp/arc-prize-2024', prefix = 'arc-agi_simple_', show_timing=True)\ndata = data_mgr.get_data_for_code(code)\nprint(data)\nout = data_mgr.get_solutions_for_code(code)\nprint(out)\nprint(data_mgr.get_num_arrays_for_code(code))\n\nExecution time of __init__: 0.0000 seconds\nExecution time of __init__: 0.0000 seconds\n{'test': [{'input': [[2]]}], 'train': [{'input': [[3]], 'output': [[4]]}, {'input': [[7]], 'output': [[8]]}, {'input': [[1]], 'output': [[2]]}]}\n[[3]]\n3\n\n\n\ncode = '00000003'\ndata_mgr1 = DataManagerSingleton.get_instance()\ndata = data_mgr1.get_data_for_code(code)\nprint(data)\nout = data_mgr1.get_solutions_for_code(code)\nprint(out)\nprint(data_mgr1.get_num_arrays_for_code(code))\n\n{'test': [{'input': [[2, 2], [2, 2]]}], 'train': [{'input': [[3, 3], [3, 3]], 'output': [[4, 4], [4, 4]]}, {'input': [[7, 7], [7, 7]], 'output': [[8, 8], [8, 8]]}, {'input': [[1, 1], [1, 1]], 'output': [[2, 2], [2, 2]]}]}\n[[3, 3], [3, 3]]\n3\n\n\n\n# From: https://www.kaggle.com/code/allegich/arc-2024-show-all-400-training-tasks?scriptVersionId=190186517&cellId=11\n# 0:black, 1:blue, 2:red, 3:green, 4:yellow, # 5:gray, 6:magenta, 7:orange, 8:sky, 9:brown\n# rgb_values = [\n# (0, 0, 0), (0, 116, 217), (255, 65, 54), (46, 204, 64), (255, 220, 0), (170, 170, 170), (240, 18, 190), (255, 133, 27), (127, 219, 255), (135, 12, 37)]\nimport matplotlib.pyplot as plt\nfrom matplotlib import colors\n\ncmap = colors.ListedColormap(\n ['#000000', '#0074D9', '#FF4136', '#2ECC40', '#FFDC00',\n '#AAAAAA', '#F012BE', '#FF851B', '#7FDBFF', '#870C25'])\nnorm = colors.Normalize(vmin=0, vmax=9)\n\nplt.figure(figsize=(3, 1), dpi=150)\nplt.imshow([list(range(10))], cmap=cmap, norm=norm)\nplt.xticks(list(range(10)))\nplt.yticks([])\nplt.show()\n\n\n\n\n\n\n\n\n\ncode = '00000003'\ntask = data_mgr.get_data_for_code(code)\nprint(task)\ntask_solution = data_mgr.get_solutions_for_code(code)\nprint(task_solution)\nplot_task(task, task_solution, 15, code)\n\n{'test': [{'input': [[2, 2], [2, 2]]}], 'train': [{'input': [[3, 3], [3, 3]], 'output': [[4, 4], [4, 4]]}, {'input': [[7, 7], [7, 7]], 'output': [[8, 8], [8, 8]]}, {'input': [[1, 1], [1, 1]], 'output': [[2, 2], [2, 2]]}]}\n[[3, 3], [3, 3]]\n#15, 00000003\n\n\n\n\n\n\n\n\n\n\n\n\n\n\ncode = '00000007'\ntask = data_mgr.get_data_for_code(code)\nprint(task)\ntask_solution = data_mgr.get_solutions_for_code(code)\nprint(task_solution)\nplot_task(task, task_solution, 15, code)\n\n{'test': [{'input': [[2, 2, 2, 2, 2, 2, 2, 2, 2], [2, 2, 2, 2, 2, 2, 2, 2, 2], [2, 2, 2, 2, 2, 2, 2, 2, 2], [2, 2, 2, 2, 2, 2, 2, 2, 2], [2, 2, 2, 2, 2, 2, 2, 2, 2], [2, 2, 2, 2, 2, 2, 2, 2, 2], [2, 2, 2, 2, 2, 2, 2, 2, 2], [2, 2, 2, 2, 2, 2, 2, 2, 2], [2, 2, 2, 2, 2, 2, 2, 2, 2]]}], 'train': [{'input': [[3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 3, 3, 3, 3]], 'output': [[6, 6, 6, 6, 6, 6, 6, 6, 6], [6, 6, 6, 6, 6, 6, 6, 6, 6], [6, 6, 6, 6, 6, 6, 6, 6, 6], [6, 6, 6, 6, 6, 6, 6, 6, 6], [6, 6, 6, 6, 6, 6, 6, 6, 6], [6, 6, 6, 6, 6, 6, 6, 6, 6], [6, 6, 6, 6, 6, 6, 6, 6, 6], [6, 6, 6, 6, 6, 6, 6, 6, 6], [6, 6, 6, 6, 6, 6, 6, 6, 6]]}, {'input': [[6, 6, 6, 6, 6, 6, 6, 6, 6], [6, 6, 6, 6, 6, 6, 6, 6, 6], [6, 6, 6, 6, 6, 6, 6, 6, 6], [6, 6, 6, 6, 6, 6, 6, 6, 6], [6, 6, 6, 6, 6, 6, 6, 6, 6], [6, 6, 6, 6, 6, 6, 6, 6, 6], [6, 6, 6, 6, 6, 6, 6, 6, 6], [6, 6, 6, 6, 6, 6, 6, 6, 6], [6, 6, 6, 6, 6, 6, 6, 6, 6]], 'output': [[9, 9, 9, 9, 9, 9, 9, 9, 9], [9, 9, 9, 9, 9, 9, 9, 9, 9], [9, 9, 9, 9, 9, 9, 9, 9, 9], [9, 9, 9, 9, 9, 9, 9, 9, 9], [9, 9, 9, 9, 9, 9, 9, 9, 9], [9, 9, 9, 9, 9, 9, 9, 9, 9], [9, 9, 9, 9, 9, 9, 9, 9, 9], [9, 9, 9, 9, 9, 9, 9, 9, 9], [9, 9, 9, 9, 9, 9, 9, 9, 9]]}, {'input': [[1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1]], 'output': [[4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4]]}]}\n[5, 5, 5, 5, 5, 5, 5, 5, 5]\n\n\n\n---------------------------------------------------------------------------\nTypeError Traceback (most recent call last)\nCell In[23], line 7\n 5 task_solution = data_mgr.get_solutions_for_code(code)\n 6 print(task_solution)\n----> 7 plot_task(task, task_solution, 15, code)\n\nCell In[17], line 26, in plot_task(task, task_solutions, i, t)\n 23 answer = task_solutions\n 24 input_matrix = answer\n---> 26 axs[1, j+1].imshow(input_matrix, cmap=cmap, norm=norm)\n 27 axs[1, j+1].grid(True, which = 'both',color = 'lightgrey', linewidth = 0.5)\n 28 axs[1, j+1].set_yticks([x-0.5 for x in range(1 + len(input_matrix))])\n\nFile c:\\Users\\ruper\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\matplotlib\\_api\\deprecation.py:456, in make_keyword_only.<locals>.wrapper(*args, **kwargs)\n 450 if len(args) > name_idx:\n 451 warn_deprecated(\n 452 since, message=\"Passing the %(name)s %(obj_type)s \"\n 453 \"positionally is deprecated since Matplotlib %(since)s; the \"\n 454 \"parameter will become keyword-only %(removal)s.\",\n 455 name=name, obj_type=f\"parameter of {func.__name__}()\")\n--> 456 return func(*args, **kwargs)\n\nFile c:\\Users\\ruper\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\matplotlib\\__init__.py:1412, in _preprocess_data.<locals>.inner(ax, data, *args, **kwargs)\n 1409 @functools.wraps(func)\n 1410 def inner(ax, *args, data=None, **kwargs):\n 1411 if data is None:\n-> 1412 return func(ax, *map(sanitize_sequence, args), **kwargs)\n 1414 bound = new_sig.bind(ax, *args, **kwargs)\n 1415 auto_label = (bound.arguments.get(label_namer)\n 1416 or bound.kwargs.get(label_namer))\n\nFile c:\\Users\\ruper\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\matplotlib\\axes\\_axes.py:5488, in Axes.imshow(self, X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, interpolation_stage, filternorm, filterrad, resample, url, **kwargs)\n 5481 self.set_aspect(aspect)\n 5482 im = mimage.AxesImage(self, cmap, norm, interpolation,\n 5483 origin, extent, filternorm=filternorm,\n 5484 filterrad=filterrad, resample=resample,\n 5485 interpolation_stage=interpolation_stage,\n 5486 **kwargs)\n-> 5488 im.set_data(X)\n 5489 im.set_alpha(alpha)\n 5490 if im.get_clip_path() is None:\n 5491 # image does not already have clipping set, clip to axes patch\n\nFile c:\\Users\\ruper\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\matplotlib\\image.py:715, in _ImageBase.set_data(self, A)\n 711 self._A = self._A[:, :, 0]\n 713 if not (self._A.ndim == 2\n 714 or self._A.ndim == 3 and self._A.shape[-1] in [3, 4]):\n--> 715 raise TypeError(\"Invalid shape {} for image data\"\n 716 .format(self._A.shape))\n 718 if self._A.ndim == 3:\n 719 # If the input data has values outside the valid range (after\n 720 # normalisation), we issue a warning and then clip X to the bounds\n 721 # - otherwise casting wraps extreme values, hiding outliers and\n 722 # making reliable interpretation impossible.\n 723 high = 255 if np.issubdtype(self._A.dtype, np.integer) else 1\n\nTypeError: Invalid shape (9,) for image data\n\n\n\n\n\n\n\n\n\n\n\ncode = '007bbfb7'\ndata_mgr.reload_data(folder = 'c:/tmp/arc-prize-2024', prefix = 'arc-agi_training_')\ndata = data_mgr.get_data_for_code(code)\nprint(data)\nout = data_mgr.get_solutions_for_code(code)\nprint(out)\nprint(data_mgr.get_num_arrays_for_code(code))\n\nExecution time of reload_data: 0.1050 seconds\nExecution time of reload_data: 0.0130 seconds\n{'test': [{'input': [[7, 0, 7], [7, 0, 7], [7, 7, 0]]}], 'train': [{'input': [[0, 7, 7], [7, 7, 7], [0, 7, 7]], 'output': [[0, 0, 0, 0, 7, 7, 0, 7, 7], [0, 0, 0, 7, 7, 7, 7, 7, 7], [0, 0, 0, 0, 7, 7, 0, 7, 7], [0, 7, 7, 0, 7, 7, 0, 7, 7], [7, 7, 7, 7, 7, 7, 7, 7, 7], [0, 7, 7, 0, 7, 7, 0, 7, 7], [0, 0, 0, 0, 7, 7, 0, 7, 7], [0, 0, 0, 7, 7, 7, 7, 7, 7], [0, 0, 0, 0, 7, 7, 0, 7, 7]]}, {'input': [[4, 0, 4], [0, 0, 0], [0, 4, 0]], 'output': [[4, 0, 4, 0, 0, 0, 4, 0, 4], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 0, 0, 0, 0, 0, 4, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 4, 0, 4, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 4, 0, 0, 0, 0]]}, {'input': [[0, 0, 0], [0, 0, 2], [2, 0, 2]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 2], [0, 0, 0, 0, 0, 0, 2, 0, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 2], [2, 0, 2, 0, 0, 0, 2, 0, 2]]}, {'input': [[6, 6, 0], [6, 0, 0], [0, 6, 6]], 'output': [[6, 6, 0, 6, 6, 0, 0, 0, 0], [6, 0, 0, 6, 0, 0, 0, 0, 0], [0, 6, 6, 0, 6, 6, 0, 0, 0], [6, 6, 0, 0, 0, 0, 0, 0, 0], [6, 0, 0, 0, 0, 0, 0, 0, 0], [0, 6, 6, 0, 0, 0, 0, 0, 0], [0, 0, 0, 6, 6, 0, 6, 6, 0], [0, 0, 0, 6, 0, 0, 6, 0, 0], [0, 0, 0, 0, 6, 6, 0, 6, 6]]}, {'input': [[2, 2, 2], [0, 0, 0], [0, 2, 2]], 'output': [[2, 2, 2, 2, 2, 2, 2, 2, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 2, 0, 2, 2, 0, 2, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 2, 2, 2, 2, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 2, 2, 0, 2, 2]]}]}\n[[7, 0, 7, 0, 0, 0, 7, 0, 7], [7, 0, 7, 0, 0, 0, 7, 0, 7], [7, 7, 0, 0, 0, 0, 7, 7, 0], [7, 0, 7, 0, 0, 0, 7, 0, 7], [7, 0, 7, 0, 0, 0, 7, 0, 7], [7, 7, 0, 0, 0, 0, 7, 7, 0], [7, 0, 7, 7, 0, 7, 0, 0, 0], [7, 0, 7, 7, 0, 7, 0, 0, 0], [7, 7, 0, 7, 7, 0, 0, 0, 0]]\n5\n\n\n\ncode = '0d3d703e'\ntask = data_mgr.get_data_for_code(code)\nprint(task)\ntask_solution = data_mgr.get_solutions_for_code(code)\nprint(task_solution)\nplot_task(task, task_solution, 15, code)\n\n{'test': [{'input': [[8, 1, 3], [8, 1, 3], [8, 1, 3]]}], 'train': [{'input': [[3, 1, 2], [3, 1, 2], [3, 1, 2]], 'output': [[4, 5, 6], [4, 5, 6], [4, 5, 6]]}, {'input': [[2, 3, 8], [2, 3, 8], [2, 3, 8]], 'output': [[6, 4, 9], [6, 4, 9], [6, 4, 9]]}, {'input': [[5, 8, 6], [5, 8, 6], [5, 8, 6]], 'output': [[1, 9, 2], [1, 9, 2], [1, 9, 2]]}, {'input': [[9, 4, 2], [9, 4, 2], [9, 4, 2]], 'output': [[8, 3, 6], [8, 3, 6], [8, 3, 6]]}]}\n[[9, 5, 4], [9, 5, 4], [9, 5, 4]]\n#15, 0d3d703e\n\n\n\n\n\n\n\n\n\n\n\n\n\n\ncode = '25d8a9c8'\ntask = data_mgr.get_data_for_code(code)\nprint(task)\ntask_solution = data_mgr.get_solutions_for_code(code)\nprint(task_solution)\nplot_task(task, task_solution, 15, code)\n\n{'test': [{'input': [[4, 4, 4], [3, 2, 3], [8, 8, 8]]}], 'train': [{'input': [[4, 4, 4], [2, 3, 2], [2, 3, 3]], 'output': [[5, 5, 5], [0, 0, 0], [0, 0, 0]]}, {'input': [[7, 3, 3], [6, 6, 6], [3, 7, 7]], 'output': [[0, 0, 0], [5, 5, 5], [0, 0, 0]]}, {'input': [[2, 9, 2], [4, 4, 4], [9, 9, 9]], 'output': [[0, 0, 0], [5, 5, 5], [5, 5, 5]]}, {'input': [[2, 2, 4], [2, 2, 4], [1, 1, 1]], 'output': [[0, 0, 0], [0, 0, 0], [5, 5, 5]]}]}\n[[5, 5, 5], [0, 0, 0], [5, 5, 5]]\n#15, 25d8a9c8\n\n\n\n\n\n\n\n\n\n\n\n\n\n\ncode = '25ff71a9'\ntask = data_mgr.get_data_for_code(code)\nprint(task)\ntask_solution = data_mgr.get_solutions_for_code(code)\nprint(task_solution)\nplot_task(task, task_solution, 15, code)\n\n{'test': [{'input': [[2, 0, 0], [2, 0, 0], [0, 0, 0]]}, {'input': [[0, 0, 0], [0, 1, 0], [0, 0, 0]]}], 'train': [{'input': [[1, 1, 1], [0, 0, 0], [0, 0, 0]], 'output': [[0, 0, 0], [1, 1, 1], [0, 0, 0]]}, {'input': [[0, 0, 0], [1, 1, 1], [0, 0, 0]], 'output': [[0, 0, 0], [0, 0, 0], [1, 1, 1]]}, {'input': [[0, 1, 0], [1, 1, 0], [0, 0, 0]], 'output': [[0, 0, 0], [0, 1, 0], [1, 1, 0]]}, {'input': [[0, 2, 2], [0, 0, 2], [0, 0, 0]], 'output': [[0, 0, 0], [0, 2, 2], [0, 0, 2]]}]}\n[[0, 0, 0], [2, 0, 0], [2, 0, 0]]\n#15, 25ff71a9\n\n\n\n\n\n\n\n\n\n\n\n\n\n\ncode = '3c9b0459'\ntask = data_mgr.get_data_for_code(code)\nprint(task)\ntask_solution = data_mgr.get_solutions_for_code(code)\nprint(task_solution)\nplot_task(task, task_solution, 15, code)\n\n{'test': [{'input': [[6, 4, 4], [6, 6, 4], [4, 6, 7]]}], 'train': [{'input': [[2, 2, 1], [2, 1, 2], [2, 8, 1]], 'output': [[1, 8, 2], [2, 1, 2], [1, 2, 2]]}, {'input': [[9, 2, 4], [2, 4, 4], [2, 9, 2]], 'output': [[2, 9, 2], [4, 4, 2], [4, 2, 9]]}, {'input': [[8, 8, 8], [5, 5, 8], [8, 5, 5]], 'output': [[5, 5, 8], [8, 5, 5], [8, 8, 8]]}, {'input': [[3, 2, 9], [9, 9, 9], [2, 3, 3]], 'output': [[3, 3, 2], [9, 9, 9], [9, 2, 3]]}]}\n[[7, 6, 4], [4, 6, 6], [4, 4, 6]]\n#15, 3c9b0459\n\n\n\n\n\n\n\n\n\n\n\n\n\n\ncode = '5582e5ca'\ntask = data_mgr.get_data_for_code(code)\nprint(task)\ntask_solution = data_mgr.get_solutions_for_code(code)\nprint(task_solution)\nplot_task(task, task_solution, 15, code)\n\n{'test': [{'input': [[8, 8, 6], [4, 6, 9], [8, 3, 0]]}], 'train': [{'input': [[4, 4, 8], [6, 4, 3], [6, 3, 0]], 'output': [[4, 4, 4], [4, 4, 4], [4, 4, 4]]}, {'input': [[6, 8, 9], [1, 8, 1], [9, 4, 9]], 'output': [[9, 9, 9], [9, 9, 9], [9, 9, 9]]}, {'input': [[4, 6, 9], [6, 4, 1], [8, 8, 6]], 'output': [[6, 6, 6], [6, 6, 6], [6, 6, 6]]}]}\n[[8, 8, 8], [8, 8, 8], [8, 8, 8]]\n#15, 5582e5ca\n\n\n\n\n\n\n\n\n\n\n\n\n\n\ncode = '6150a2bd'\ntask = data_mgr.get_data_for_code(code)\nprint(task)\ntask_solution = data_mgr.get_solutions_for_code(code)\nprint(task_solution)\nplot_task(task, task_solution, 15, code)\n\n{'test': [{'input': [[6, 3, 5], [6, 8, 0], [4, 0, 0]]}], 'train': [{'input': [[3, 3, 8], [3, 7, 0], [5, 0, 0]], 'output': [[0, 0, 5], [0, 7, 3], [8, 3, 3]]}, {'input': [[5, 5, 2], [1, 0, 0], [0, 0, 0]], 'output': [[0, 0, 0], [0, 0, 1], [2, 5, 5]]}]}\n[[0, 0, 4], [0, 8, 6], [5, 3, 6]]\n#15, 6150a2bd\n\n\n\n\n\n\n\n\n\n\n\n\n\n\ncode = '150deff5'\ntask = data_mgr.get_data_for_code(code)\nprint(task)\ntask_solution = data_mgr.get_solutions_for_code(code)\nprint(task_solution)\nplot_task(task, task_solution, 15, code)\n\n{'test': [{'input': [[0, 0, 5, 5, 0, 5, 5, 5, 0, 0, 0], [0, 0, 5, 5, 0, 0, 5, 0, 0, 0, 0], [0, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0], [0, 0, 0, 0, 5, 5, 5, 0, 0, 0, 0], [0, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 5, 0, 0, 0, 0], [0, 0, 0, 0, 0, 5, 5, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}], 'train': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 5, 5, 0, 0, 0, 0, 0, 0, 0], [0, 0, 5, 5, 5, 5, 5, 0, 0, 0, 0], [0, 0, 0, 5, 5, 5, 0, 0, 0, 0, 0], [0, 0, 0, 5, 5, 5, 5, 5, 0, 0, 0], [0, 0, 0, 5, 0, 0, 5, 5, 0, 0, 0], [0, 0, 0, 0, 0, 5, 5, 5, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 8, 0, 0, 0, 0, 0, 0, 0], [0, 0, 8, 8, 2, 2, 2, 0, 0, 0, 0], [0, 0, 0, 2, 8, 8, 0, 0, 0, 0, 0], [0, 0, 0, 2, 8, 8, 8, 8, 0, 0, 0], [0, 0, 0, 2, 0, 0, 8, 8, 0, 0, 0], [0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 5, 5, 5, 5, 5, 5, 0, 0, 0], [0, 5, 5, 5, 5, 5, 5, 0, 0, 0], [0, 0, 0, 5, 0, 0, 5, 0, 0, 0], [0, 0, 0, 0, 5, 5, 5, 0, 0, 0], [0, 0, 0, 0, 5, 5, 5, 0, 0, 0], [0, 0, 0, 0, 5, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 8, 8, 2, 8, 8, 2, 0, 0, 0], [0, 8, 8, 2, 8, 8, 2, 0, 0, 0], [0, 0, 0, 2, 0, 0, 2, 0, 0, 0], [0, 0, 0, 0, 2, 8, 8, 0, 0, 0], [0, 0, 0, 0, 2, 8, 8, 0, 0, 0], [0, 0, 0, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 5, 5, 5, 5, 5, 0, 0, 0], [0, 0, 0, 0, 5, 5, 0, 0, 0], [0, 0, 0, 5, 0, 0, 0, 0, 0], [0, 0, 0, 5, 5, 5, 0, 0, 0], [0, 0, 0, 5, 5, 5, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 2, 2, 8, 8, 0, 0, 0], [0, 0, 0, 0, 8, 8, 0, 0, 0], [0, 0, 0, 2, 0, 0, 0, 0, 0], [0, 0, 0, 2, 8, 8, 0, 0, 0], [0, 0, 0, 2, 8, 8, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]]}]}\n[[0, 0, 8, 8, 0, 2, 2, 2, 0, 0, 0], [0, 0, 8, 8, 0, 0, 2, 0, 0, 0, 0], [0, 2, 2, 2, 8, 8, 2, 0, 0, 0, 0], [0, 0, 0, 0, 8, 8, 2, 0, 0, 0, 0], [0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0], [0, 0, 0, 0, 0, 8, 8, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]\n#15, 150deff5\n\n\n\n\n\n\n\n\n\n\n\n\n\n\ncode = '0ca9ddb6'\ntask = data_mgr.get_data_for_code(code)\nprint(task)\ntask_solution = data_mgr.get_solutions_for_code(code)\nprint(task_solution)\nplot_task(task, task_solution, 15, code)\n\n{'test': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 8, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 6, 0, 0, 0, 0, 0, 2, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]]}], 'train': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 0, 4, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0], [0, 4, 0, 4, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 7, 0, 0], [0, 0, 0, 0, 0, 7, 1, 7, 0], [0, 0, 0, 0, 0, 0, 7, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 8, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 2, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 8, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 4, 0, 4, 0], [0, 0, 7, 0, 0, 0, 2, 0, 0], [0, 7, 1, 7, 0, 4, 0, 4, 0], [0, 0, 7, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 7, 0, 0], [4, 0, 4, 0, 0, 7, 1, 7, 0], [0, 2, 0, 0, 0, 0, 7, 0, 0], [4, 0, 4, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 6, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 0, 4, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0], [0, 4, 0, 4, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 6, 0, 0], [0, 0, 0, 7, 0, 0, 0, 0, 0], [0, 0, 7, 1, 7, 0, 0, 0, 0], [0, 0, 0, 7, 0, 0, 0, 0, 0]]}]}\n[[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 7, 0, 0], [0, 4, 0, 4, 0, 7, 1, 7, 0], [0, 0, 2, 0, 0, 0, 7, 0, 0], [0, 4, 0, 4, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 8, 0, 0, 0], [0, 0, 0, 0, 0, 0, 4, 0, 4], [0, 6, 0, 0, 0, 0, 0, 2, 0], [0, 0, 0, 0, 0, 0, 4, 0, 4]]\n#15, 0ca9ddb6\n\n\n\n\n\n\n\n\n\n\n\n\n\n\ncode = '00d62c1b'\ntask = data_mgr.get_data_for_code(code)\nprint(task)\ntask_solution = data_mgr.get_solutions_for_code(code)\nprint(task_solution)\nplot_task(task, task_solution, 15, code)\n\n{'test': [{'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 3, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 0, 3, 3, 3, 3, 3, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 3, 0, 0], [0, 0, 0, 0, 0, 0, 3, 3, 0, 3, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0], [0, 0, 3, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 0, 3, 0, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}], 'train': [{'input': [[0, 0, 0, 0, 0, 0], [0, 0, 3, 0, 0, 0], [0, 3, 0, 3, 0, 0], [0, 0, 3, 0, 3, 0], [0, 0, 0, 3, 0, 0], [0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0], [0, 0, 3, 0, 0, 0], [0, 3, 4, 3, 0, 0], [0, 0, 3, 4, 3, 0], [0, 0, 0, 3, 0, 0], [0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 0, 3, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 3, 0, 0, 0, 0], [0, 0, 3, 0, 0, 0, 3, 0, 0, 0], [0, 0, 0, 0, 0, 3, 0, 3, 0, 0], [0, 0, 0, 3, 0, 3, 3, 0, 0, 0], [0, 0, 3, 3, 3, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 0, 3, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 3, 0, 0, 0, 0], [0, 0, 3, 0, 0, 0, 3, 0, 0, 0], [0, 0, 0, 0, 0, 3, 4, 3, 0, 0], [0, 0, 0, 3, 0, 3, 3, 0, 0, 0], [0, 0, 3, 3, 3, 0, 0, 0, 0, 0], [0, 0, 0, 3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 3, 0, 0, 0, 0], [0, 0, 0, 0, 3, 0, 0, 0, 0, 0], [0, 3, 3, 0, 3, 3, 0, 3, 0, 0], [3, 0, 0, 3, 0, 0, 3, 0, 3, 0], [0, 0, 0, 3, 0, 0, 3, 3, 0, 0], [0, 0, 0, 3, 0, 0, 3, 0, 0, 0], [0, 0, 0, 3, 0, 0, 3, 0, 0, 0], [0, 0, 0, 0, 3, 3, 0, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 3, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 3, 0, 0, 0, 0], [0, 0, 0, 0, 3, 0, 0, 0, 0, 0], [0, 3, 3, 0, 3, 3, 0, 3, 0, 0], [3, 0, 0, 3, 4, 4, 3, 4, 3, 0], [0, 0, 0, 3, 4, 4, 3, 3, 0, 0], [0, 0, 0, 3, 4, 4, 3, 0, 0, 0], [0, 0, 0, 3, 4, 4, 3, 0, 0, 0], [0, 0, 0, 0, 3, 3, 0, 3, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 3, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}, {'input': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 3, 3, 3, 0, 0, 0, 0], [0, 0, 3, 0, 0, 3, 0, 0, 0, 0], [0, 0, 3, 0, 0, 3, 0, 3, 0, 0], [0, 0, 3, 3, 3, 3, 3, 3, 3, 0], [0, 0, 0, 3, 0, 0, 0, 0, 3, 0], [0, 0, 0, 3, 0, 0, 0, 3, 3, 0], [0, 0, 0, 3, 3, 0, 0, 3, 0, 3], [0, 0, 0, 3, 0, 3, 0, 0, 3, 0], [0, 0, 0, 0, 3, 0, 0, 0, 0, 0]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 3, 3, 3, 0, 0, 0, 0], [0, 0, 3, 4, 4, 3, 0, 0, 0, 0], [0, 0, 3, 4, 4, 3, 0, 3, 0, 0], [0, 0, 3, 3, 3, 3, 3, 3, 3, 0], [0, 0, 0, 3, 0, 0, 0, 0, 3, 0], [0, 0, 0, 3, 0, 0, 0, 3, 3, 0], [0, 0, 0, 3, 3, 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"text": "The autoreload extension is already loaded. To reload it, use:\n %reload_ext autoreload\n\n\n\n#sys.path.append(\"..\")\n\n\nsource\n\nLevelKey\n\n LevelKey (value, names=None, module=None, qualname=None, type=None,\n start=1)\n\nAn enumeration.\n\nsource\n\n\nArchitectureStructure\n\n ArchitectureStructure (references=None, config=None, attr_mut_pb=None,\n lower_float=None, upper_float=None,\n levels_limit=None, columns_limit=None, sigma=None,\n mu=None, alpha=None, modes=None, namespace=None,\n **cargs)\n\nArchitectureStructure\n\narch = ArchitectureStructure()\n\n\nconfig = arch.get_config()\nprint(arch.get_config())\ntarget = {'parameters': { 'modes' : {LevelKey.ZERO:3, LevelKey.N:3,LevelKey.TOP:4,LevelKey.ZEROTOP :4} }}\nassert config == target\n\n{'parameters': {'modes': {<LevelKey.ZERO: 'level0'>: 3, <LevelKey.N: 'leveln'>: 3, <LevelKey.TOP: 'leveltop'>: 4, <LevelKey.ZEROTOP: 'level0top'>: 4}}}", + "crumbs": [ + "Structure" + ] + }, + { + "objectID": "pct_examples.html", + "href": "pct_examples.html", + "title": "PCT Examples", + "section": "", + "text": "The autoreload extension is already loaded. To reload it, use:\n %reload_ext autoreload", + "crumbs": [ + "PCT Examples" + ] + }, + { + "objectID": "pct_examples.html#pctexamples-class", + "href": "pct_examples.html#pctexamples-class", + "title": "PCT Examples", + "section": "PCTExamples Class", + "text": "PCTExamples Class\nThe PCTExamples class is designed to load a PCTHierarchy configuration file and provide various methods to interact with the hierarchy.\n\nsource\n\nPCTExamples\n\n PCTExamples (config_file, min=True, early_termination=False,\n history=False)\n\nPCTExamples class provides methods to load a PCT hierarchy from a configuration file, summarize the hierarchy, get the configuration, draw the hierarchy, run the hierarchy for a specified number of steps, and close the environment. Attributes: hierarchy (PCTHierarchy): The loaded PCT hierarchy. env (Environment): The environment associated with the PCT hierarchy.\n\n# pct.draw( figsize=(10,10), font_size=6, funcdata=True, with_edge_labels=True, node_size=200)", + "crumbs": [ + "PCT Examples" + ] + }, + { + "objectID": "network.html", + "href": "network.html", + "title": "Network", + "section": "", + "text": "source\n\nServer\n\n Server (host='localhost', port=6666, buf_size=1024)\n\nInitialize self. See help(type(self)) for accurate signature.\n\nsource\n\n\nClient\n\n Client (host='localhost', port=6666, buf_size=1024)\n\nInitialize self. See help(type(self)) for accurate signature.\n\nsource\n\n\nClientConnectionManager\n\n ClientConnectionManager (host='localhost', port=6666, buf_size=1024)\n\nA utility for managing a client socket connection.\n\nsource\n\n\nServerConnectionManager\n\n ServerConnectionManager (host='localhost', port=6666, buf_size=1024)\n\nA utility for managing a client socket connection.", + "crumbs": [ + "Network" + ] + }, + { + "objectID": "microgrid.html", + "href": "microgrid.html", + "title": "MicroGrid", + "section": "", + "text": "from os import getcwd\n# getcwd()\n\n\nsource\n\nMicroGridEnvPlus\n\n MicroGridEnvPlus (**kwargs)\n\n*The main OpenAI Gym class.\nIt encapsulates an environment with arbitrary behind-the-scenes dynamics. An environment can be partially or fully observed.\nThe main API methods that users of this class need to know are:\n\n:meth:step - Takes a step in the environment using an action returning the next observation, reward, if the environment terminated and observation information.\n:meth:reset - Resets the environment to an initial state, returning the initial observation and observation information.\n:meth:render - Renders the environment observation with modes depending on the output\n:meth:close - Closes the environment, important for rendering where pygame is imported\n\nAnd set the following attributes:\n\n:attr:action_space - The Space object corresponding to valid actions\n:attr:observation_space - The Space object corresponding to valid observations\n:attr:reward_range - A tuple corresponding to the minimum and maximum possible rewards\n:attr:spec - An environment spec that contains the information used to initialise the environment from gym.make\n:attr:metadata - The metadata of the environment, i.e. render modes\n:attr:np_random - The random number generator for the environment\n\nNote: a default reward range set to :math:(-\\infty,+\\infty) already exists. Set it if you want a narrower range.*\n\nsource\n\n\nMicroGridEnv0Plus\n\n MicroGridEnv0Plus (**kwargs)\n\n*The main OpenAI Gym class.\nIt encapsulates an environment with arbitrary behind-the-scenes dynamics. An environment can be partially or fully observed.\nThe main API methods that users of this class need to know are:\n\n:meth:step - Takes a step in the environment using an action returning the next observation, reward, if the environment terminated and observation information.\n:meth:reset - Resets the environment to an initial state, returning the initial observation and observation information.\n:meth:render - Renders the environment observation with modes depending on the output\n:meth:close - Closes the environment, important for rendering where pygame is imported\n\nAnd set the following attributes:\n\n:attr:action_space - The Space object corresponding to valid actions\n:attr:observation_space - The Space object corresponding to valid observations\n:attr:reward_range - A tuple corresponding to the minimum and maximum possible rewards\n:attr:spec - An environment spec that contains the information used to initialise the environment from gym.make\n:attr:metadata - The metadata of the environment, i.e. render modes\n:attr:np_random - The random number generator for the environment\n\nNote: a default reward range set to :math:(-\\infty,+\\infty) already exists. Set it if you want a narrower range.*", + "crumbs": [ + "MicroGrid" + ] + }, + { + "objectID": "errors.html", + "href": "errors.html", + "title": "Errors", + "section": "", + "text": "source\n\n\n\n BaseErrorType (flip_error_response=False)\n\nBase class of a type error response. This class is not used direclty by developers, but defines the interface common to all.", + "crumbs": [ + "Errors" + ] + }, + { + "objectID": "errors.html#baseerrortype", + "href": "errors.html#baseerrortype", + "title": "Errors", + "section": "", + "text": "source\n\n\n\n BaseErrorType (flip_error_response=False)\n\nBase class of a type error response. This class is not used direclty by developers, but defines the interface common to all.", + "crumbs": [ + "Errors" + ] + }, + { + "objectID": "errors.html#rootsumsquarederror", + "href": "errors.html#rootsumsquarederror", + "title": "Errors", + "section": "RootSumSquaredError", + "text": "RootSumSquaredError\n\nsource\n\nRootSumSquaredError\n\n RootSumSquaredError (flip_error_response=False)\n\nThe square root of the sum of the square of the errors.", + "crumbs": [ + "Errors" + ] + }, + { + "objectID": "errors.html#rootmeansquareerror", + "href": "errors.html#rootmeansquareerror", + "title": "Errors", + "section": "RootMeanSquareError", + "text": "RootMeanSquareError\n\nsource\n\nRootMeanSquareError\n\n RootMeanSquareError (flip_error_response=False)\n\nThe square root of the mean of the sum of the square of the errors.", + "crumbs": [ + "Errors" + ] + }, + { + "objectID": "errors.html#summederror", + "href": "errors.html#summederror", + "title": "Errors", + "section": "SummedError", + "text": "SummedError\n\nsource\n\nSummedError\n\n SummedError (flip_error_response=False)\n\nSum of all errors.", + "crumbs": [ + "Errors" + ] + }, + { + "objectID": "errors.html#summederror-2", + "href": "errors.html#summederror-2", + "title": "Errors", + "section": "SummedError", + "text": "SummedError\n\nsource\n\nCurrentError\n\n CurrentError (flip_error_response=False)\n\nThe current error, rather than a function of the historical values.", + "crumbs": [ + "Errors" + ] + }, + { + "objectID": "errors.html#currenterror-1", + "href": "errors.html#currenterror-1", + "title": "Errors", + "section": "CurrentError", + "text": "CurrentError\n\nsource\n\nSmoothError\n\n SmoothError (flip_error_response=False)\n\nThe exponential smoothed value of the error.", + "crumbs": [ + "Errors" + ] + }, + { + "objectID": "errors.html#movingsumerror", + "href": "errors.html#movingsumerror", + "title": "Errors", + "section": "MovingSumError", + "text": "MovingSumError\n\nsource\n\nMovingSumError\n\n MovingSumError (flip_error_response=False)\n\nThe moving sum of the error.", + "crumbs": [ + "Errors" + ] + }, + { + "objectID": "errors.html#movingaverageerror", + "href": "errors.html#movingaverageerror", + "title": "Errors", + "section": "MovingAverageError", + "text": "MovingAverageError\n\nsource\n\nMovingAverageError\n\n MovingAverageError (flip_error_response=False)\n\nThe moving average of the error.", + "crumbs": [ + "Errors" + ] + }, + { + "objectID": "errors.html#baseerrorcollector", + "href": "errors.html#baseerrorcollector", + "title": "Errors", + "section": "BaseErrorCollector", + "text": "BaseErrorCollector\n\nsource\n\nBaseErrorCollector\n\n BaseErrorCollector (limit, error_response, min=True)\n\nBase class of an error collector. This class is not used direclty by developers, but defines the interface common to all.", + "crumbs": [ + "Errors" + ] + }, + { + "objectID": "errors.html#totalerror", + "href": "errors.html#totalerror", + "title": "Errors", + "section": "TotalError", + "text": "TotalError\n\nsource\n\nTotalError\n\n TotalError (limit=None, error_response=None, min=None, **cargs)\n\nA class to collect all the errors of the control system run.", + "crumbs": [ + "Errors" + ] + }, + { + "objectID": "errors.html#toperror", + "href": "errors.html#toperror", + "title": "Errors", + "section": "TopError", + "text": "TopError\n\nsource\n\nTopError\n\n TopError (limit=None, error_response=None, min=None, **cargs)\n\nA class to collect all the errors of the top-level nodes.", + "crumbs": [ + "Errors" + ] + }, + { + "objectID": "errors.html#inputserror", + "href": "errors.html#inputserror", + "title": "Errors", + "section": "InputsError", + "text": "InputsError\n\nsource\n\nInputsError\n\n InputsError (limit=None, error_response=None, min=None, **cargs)\n\nA class to collect the values of the input values.", + "crumbs": [ + "Errors" + ] + }, + { + "objectID": "errors.html#referencedinputserror", + "href": "errors.html#referencedinputserror", + "title": "Errors", + "section": "ReferencedInputsError", + "text": "ReferencedInputsError\n\nsource\n\nReferencedInputsError\n\n ReferencedInputsError (limit=None, error_response=None, min=None,\n **cargs)\n\nA class to collect the values of the input values subtracted from reference values.", + "crumbs": [ + "Errors" + ] + }, + { + "objectID": "errors.html#rewarderror", + "href": "errors.html#rewarderror", + "title": "Errors", + "section": "RewardError", + "text": "RewardError\n\nsource\n\nRewardError\n\n RewardError (limit=None, error_response=None, min=None, **cargs)\n\nA class that collects the reward value of the control system run.", + "crumbs": [ + "Errors" + ] + }, + { + "objectID": "errors.html#fitnesserror", + "href": "errors.html#fitnesserror", + "title": "Errors", + "section": "FitnessError", + "text": "FitnessError\n\nsource\n\nFitnessError\n\n FitnessError (limit=None, error_response=None, min=None, **cargs)\n\nA class that collects the fitness value of the control system run.", + "crumbs": [ + "Errors" + ] + }, + { + "objectID": "errors.html#errorresponsefactory", + "href": "errors.html#errorresponsefactory", + "title": "Errors", + "section": "ErrorResponseFactory", + "text": "ErrorResponseFactory\n\nsource\n\nErrorResponseFactory\n\n ErrorResponseFactory ()\n\nInitialize self. See help(type(self)) for accurate signature.", + "crumbs": [ + "Errors" + ] + }, + { + "objectID": "errors.html#errorcollectorfactory", + "href": "errors.html#errorcollectorfactory", + "title": "Errors", + "section": "ErrorCollectorFactory", + "text": "ErrorCollectorFactory\n\nsource\n\nErrorCollectorFactory\n\n ErrorCollectorFactory ()\n\nInitialize self. See help(type(self)) for accurate signature.\n\nrms = RootMeanSquareError()\nfor i in range(10):\n rms(i)\ner = rms.get_error_response()\nprint(er)\nassert er == 5.338539126015656\n\n5.338539126015656\n\n\n\ner = RootSumSquaredError()\nte = TotalError(error_response=er, limit=250,min=True) \nte.add_error_data([1, 2])\nprint(te)\n\nTotalError limit:250, limit_exceeded:False, : RootSumSquaredError error_response:2.23606797749979\n\n\n\n# hpct = PCTHierarchy(1,1,error_collector=te)\n# hpct.run(steps=5, verbose=True)\n\n\nerr=te.error()\nprint(err)\n\n2.23606797749979\n\n\n\net = ErrorResponseFactory.createErrorResponse('RootSumSquaredError') \net(102)\nprint(et.get_error_response())\n\nec = ErrorCollectorFactory.createErrorCollector('TotalError') \nec.set_limit(100)\nec.set_error_response(et)\nprint(ec.error())\n\n102.0\n102.0\n\n\n\nec = BaseErrorCollector.collector( 'RootMeanSquareError','InputsError', 10, flip_error_response=True, min=False)\n\n\nec.add_error_data([1])\nprint(ec.error())\nprint(ec)\n\n-1.0\nInputsError limit:10, limit_exceeded:False, : RootMeanSquareError error_response:-1.0\n\n\n\nec.reset()\n\n\nprint(ec)\n\nInputsError limit:10, limit_exceeded:False, : RootMeanSquareError error_response:None", + "crumbs": [ + "Errors" + ] + }, + { + "objectID": "nodes.html", + "href": "nodes.html", + "title": "Nodes", + "section": "", + "text": "The autoreload extension is already loaded. To reload it, use:\n %reload_ext autoreload", + "crumbs": [ + "Nodes" + ] + }, + { + "objectID": "nodes.html#overview", + "href": "nodes.html#overview", + "title": "Nodes", + "section": "Overview", + "text": "Overview\nA node comprises four functions, reference, perceptual, comparator and output. Executing the node will run each of the functions in the order indicated above and return the output value.\nThe functions can actually be a collection of functions, each executed in the order they are added. This allows a chain of functions in case pre-processing is required, or post-processing in the case of the output.\n\nsource\n\nControlUnitIndices\n\n ControlUnitIndices (value, names=None, module=None, qualname=None,\n type=None, start=1)\n\nAn enumeration.\n\nsource\n\n\nPCTNode\n\n PCTNode (reference=None, perception=None, comparator=None, output=None,\n default=True, name='pctnode', history=False, build_links=False,\n mode=0, namespace=None, **pargs)\n\nA single PCT controller.\n\n#node = PCTNode()\n#node.summary()\n\n\nsource\n\n\nPCTNodeData\n\n PCTNodeData (name='pctnodedata')\n\nData collected for a PCTNode", + "crumbs": [ + "Nodes" + ] + }, + { + "objectID": "nodes.html#creating-a-node", + "href": "nodes.html#creating-a-node", + "title": "Nodes", + "section": "Creating a Node", + "text": "Creating a Node\nA node can be created simply.\n\nnode = PCTNode()\nnode.summary()\n\npctnode PCTNode fc50e711-62e3-11ef-a9a5-5c879c15de65\n----------------------------\nREF: constant Constant | 0 \nPER: variable Variable | 0 \nCOM: subtract Subtract | 0 | links constant variable \nOUT: proportional Proportional | gain 1 | 0 | links subtract \n----------------------------\n\n\nThat creates a node with default functions. Those are, a constant of 1 for the reference, a variable, with initial value 0, for the perception and a proportional function for the output, with a gain of 10.\nA node can also be created by providing a name, and setting the history to True. The latter means that the values of all the functions are recorded during execution, which is useful for plotting the data later, as can be seen below.\n\ndynamic_module_import( 'pct.functions', 'Constant')\n\n\nreference = Constant(1)\nnamespace=reference.namespace\n\n\nnode = PCTNode(name=\"mypctnode\", history=True, reference = reference, output=Proportional(10, namespace=namespace), namespace=namespace)\nnode.summary()\n\nmypctnode PCTNode fc6106b5-62e3-11ef-8e63-5c879c15de65\n----------------------------\nREF: constant Constant | 1 \nPER: variable Variable | 0 \nCOM: subtract Subtract | 0 | links constant variable \nOUT: proportional Proportional | gain 10 | 0 | links subtract \n----------------------------\n\n\nAnother way of creating a node is by first declaring the functions you want and passing them into the constructor.\n\nUniqueNamer.getInstance().clear()\nr = Variable(0, name=\"velocity_reference\")\np = Constant(10, name=\"constant_perception\")\no = Integration(10, 100, name=\"integrator\")\nintegratingnode = PCTNode(reference=r, perception=p, output=o, name=\"integratingnode\", history=True)\n\nYet another way to create a node is from a text configuration.\n\nconfig_node = PCTNode.from_config({ 'name': 'mypctnode', \n 'refcoll': {'0': {'type': 'Proportional', 'name': 'proportional', 'value': 0, 'links': {}, 'gain': 10}}, \n 'percoll': {'0': {'type': 'Variable', 'name': 'velocity', 'value': 0.2, 'links': {}}}, \n 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract', 'value': 1, 'links': {0: 'constant', 1: 'velocity'}}}, \n 'outcoll': {'0': {'type': 'Proportional', 'name': 'proportional', 'value': 10, 'links': {0: 'subtract'}, 'gain': 10}}})\n# config_node = PCTNode.from_config({ 'name': 'mypctnode1', \n# 'refcoll': {'0': {'type': 'Proportional', 'name': 'proportional', 'value': 0, 'links': {}, 'gain': 10}}, \n# 'percoll': {'0': {'type': 'Variable', 'name': 'velocity', 'value': 0.2, 'links': {}}}, \n# 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract', 'value': 1, 'links': {0: 'constant', 1: 'velocity'}}}, \n# 'outcoll': {'0': {'type': 'Proportional', 'name': 'proportional', 'value': 10, 'links': {0: 'subtract'}, 'gain': 10}}}, namespace=namespace)", + "crumbs": [ + "Nodes" + ] + }, + { + "objectID": "nodes.html#viewing-nodes", + "href": "nodes.html#viewing-nodes", + "title": "Nodes", + "section": "Viewing Nodes", + "text": "Viewing Nodes\nThe details of a node can be viewed in a number of ways, which is useful for checking the configuration. The summary method prints to the screen. The get_config method returns a string in a JSON format.\n\nintegratingnode.summary()\n\nintegratingnode PCTNode fc7424bb-62e3-11ef-9101-5c879c15de65\n----------------------------\nREF: velocity_reference Variable | 0 \nPER: constant_perception Constant | 10 \nCOM: subtract Subtract | 0 | links velocity_reference constant_perception \nOUT: integrator Integration | gain 10 slow 100 | 0 | links subtract \n----------------------------\n\n\n\n#print(integratingnode.get_config())\nassert integratingnode.get_config() == {'type': 'PCTNode', 'name': 'integratingnode', 'refcoll': {'0': {'type': 'Variable', 'name': 'velocity_reference', 'value': 0, 'links': {}}}, 'percoll': {'0': {'type': 'Constant', 'name': 'constant_perception', 'value': 10, 'links': {}}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract', 'value': 0, 'links': {0: 'velocity_reference', 1: 'constant_perception'}}}, 'outcoll': {'0': {'type': 'Integration', 'name': 'integrator', 'value': 0, 'links': {0: 'subtract'}, 'gain': 10, 'slow': 100}}}\nintegratingnode.get_config()\n\n{'type': 'PCTNode',\n 'name': 'integratingnode',\n 'refcoll': {'0': {'type': 'Variable',\n 'name': 'velocity_reference',\n 'value': 0,\n 'links': {}}},\n 'percoll': {'0': {'type': 'Constant',\n 'name': 'constant_perception',\n 'value': 10,\n 'links': {}}},\n 'comcoll': {'0': {'type': 'Subtract',\n 'name': 'subtract',\n 'value': 0,\n 'links': {0: 'velocity_reference', 1: 'constant_perception'}}},\n 'outcoll': {'0': {'type': 'Integration',\n 'name': 'integrator',\n 'value': 0,\n 'links': {0: 'subtract'},\n 'gain': 10,\n 'slow': 100}}}\n\n\nA node can also be viewed graphically as a network of connected nodes.\n\nimport os\n\n\nif os.name=='nt':\n integratingnode.draw(node_size=2000, figsize=(8,4))", + "crumbs": [ + "Nodes" + ] + }, + { + "objectID": "nodes.html#running-a-node", + "href": "nodes.html#running-a-node", + "title": "Nodes", + "section": "Running a Node", + "text": "Running a Node\nFor the purposes of this example we first create a function which is a very basic model of the physical environment. It defines how the world behaves when we pass it the output of the control system.\n\ndef velocity_model(velocity, force , mass):\n velocity = velocity + force / mass\n return velocity\n\nmass = 50\nforce = 0\n\nIn the following cell we start with a velocity of zero. The node is run once (second line), the output of which is the force to apply in the world velocity_model. That returns the updated velocity which we pass back into the node to be used in the next iteration of the loop.\n\nvelocity=0\nforce = node()\nvelocity = velocity_model(velocity, force, mass)\nnode.set_perception_value(velocity)\nprint(force)\nassert force == 10\n\n10\n\n\nThe node can be run in a loop as shown below. With verbose set to True the output of each loop will be printed to the screen.\n\npctnode = PCTNode(history=True)\npctnode.set_function_name(\"perception\", \"velocity\")\npctnode.set_function_name(\"reference\", \"reference\")\n\nfor i in range(40):\n print(i, end=\" \")\n force = pctnode(verbose=True)\n vel = velocity_model(pctnode.get_perception_value(), force, mass)\n pctnode.set_perception_value(vel)\n\n0 0.000 0.000 0.000 0.000 \n1 0.000 0.000 0.000 0.000 \n2 0.000 0.000 0.000 0.000 \n3 0.000 0.000 0.000 0.000 \n4 0.000 0.000 0.000 0.000 \n5 0.000 0.000 0.000 0.000 \n6 0.000 0.000 0.000 0.000 \n7 0.000 0.000 0.000 0.000 \n8 0.000 0.000 0.000 0.000 \n9 0.000 0.000 0.000 0.000 \n10 0.000 0.000 0.000 0.000 \n11 0.000 0.000 0.000 0.000 \n12 0.000 0.000 0.000 0.000 \n13 0.000 0.000 0.000 0.000 \n14 0.000 0.000 0.000 0.000 \n15 0.000 0.000 0.000 0.000 \n16 0.000 0.000 0.000 0.000 \n17 0.000 0.000 0.000 0.000 \n18 0.000 0.000 0.000 0.000 \n19 0.000 0.000 0.000 0.000 \n20 0.000 0.000 0.000 0.000 \n21 0.000 0.000 0.000 0.000 \n22 0.000 0.000 0.000 0.000 \n23 0.000 0.000 0.000 0.000 \n24 0.000 0.000 0.000 0.000 \n25 0.000 0.000 0.000 0.000 \n26 0.000 0.000 0.000 0.000 \n27 0.000 0.000 0.000 0.000 \n28 0.000 0.000 0.000 0.000 \n29 0.000 0.000 0.000 0.000 \n30 0.000 0.000 0.000 0.000 \n31 0.000 0.000 0.000 0.000 \n32 0.000 0.000 0.000 0.000 \n33 0.000 0.000 0.000 0.000 \n34 0.000 0.000 0.000 0.000 \n35 0.000 0.000 0.000 0.000 \n36 0.000 0.000 0.000 0.000 \n37 0.000 0.000 0.000 0.000 \n38 0.000 0.000 0.000 0.000 \n39 0.000 0.000 0.000 0.000", + "crumbs": [ + "Nodes" + ] + }, + { + "objectID": "nodes.html#save-and-load", + "href": "nodes.html#save-and-load", + "title": "Nodes", + "section": "Save and Load", + "text": "Save and Load\nSave a node to file.\n\nimport json\n\n\nintegratingnode.save(\"inode.json\")\n\nCreate a node from file.\n\nnnode = PCTNode.load(\"inode.json\")\nnnode.summary()\nprint(nnode.get_config())\n\nintegratingnode PCTNode fcd83c93-62e3-11ef-9c4b-5c879c15de65\n----------------------------\nREF: velocity_reference Variable | 0 \nPER: constant_perception Constant | 10 \nCOM: subtract Subtract | 0 | links velocity_reference constant_perception \nOUT: integrator Integration | gain 10 slow 100 | 0 | links subtract \n----------------------------\n{'type': 'PCTNode', 'name': 'integratingnode', 'refcoll': {'0': {'type': 'Variable', 'name': 'velocity_reference', 'value': 0, 'links': {}}}, 'percoll': {'0': {'type': 'Constant', 'name': 'constant_perception', 'value': 10, 'links': {}}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract', 'value': 0, 'links': {0: 'velocity_reference', 1: 'constant_perception'}}}, 'outcoll': {'0': {'type': 'Integration', 'name': 'integrator', 'value': 0, 'links': {0: 'subtract'}, 'gain': 10, 'slow': 100}}}\n\n\n\nprint(nnode.get_summary())\n\n0.000 10.000 0.000 0.000", + "crumbs": [ + "Nodes" + ] + }, + { + "objectID": "nodes.html#plotting-the-data", + "href": "nodes.html#plotting-the-data", + "title": "Nodes", + "section": "Plotting the Data", + "text": "Plotting the Data\nAs the history of the variable pctnode was set to True the data is available for analysis. It can be plotted with python libraries such as matplotlib or plotly. Here is an example with the latter.\nThe graph shows the changing perception values as it is controlled to match the reference value.\nimport plotly.graph_objects as go\nfig = go.Figure(layout_title_text=\"Velocity Goal\")\nfig.add_trace(go.Scatter(y=pctnode.history.data['refcoll']['reference'], name=\"ref\"))\nfig.add_trace(go.Scatter(y=pctnode.history.data['percoll']['velocity'], name=\"perc\"))\nThis following code is only for the purposes of displaying image of the graph generated by the above code.\n\nfrom IPython.display import Image\n\n\nImage(url='http://www.perceptualrobots.com/wp-content/uploads/2020/08/pct_node_plot.png')", + "crumbs": [ + "Nodes" + ] + }, + { + "objectID": "index.html", + "href": "index.html", + "title": "Perceptual Control Theory", + "section": "", + "text": "With this library you can create and run simple or complex hierarchies of perceptual control systems as well as make use of the power of the Python platform and its rich set of packages.\nIn the context of this library a single control system comprising a perceptual, reference, comparator and output function is called a Node. The functions therein can be configured by the user.\nA hierarchy is defined by a collection of nodes.", + "crumbs": [ + "Perceptual Control Theory" + ] + }, + { + "objectID": "index.html#install", + "href": "index.html#install", + "title": "Perceptual Control Theory", + "section": "Install", + "text": "Install\npip install pct", + "crumbs": [ + "Perceptual Control Theory" + ] + }, + { + "objectID": "index.html#import", + "href": "index.html#import", + "title": "Perceptual Control Theory", + "section": "Import", + "text": "Import\nExamples of importing the library functionality.\nimport pct as p\nfrom pct.hierarchy import Hierarchy\nfrom pct import *", + "crumbs": [ + "Perceptual Control Theory" + ] + }, + { + "objectID": "index.html#how-to-use", + "href": "index.html#how-to-use", + "title": "Perceptual Control Theory", + "section": "How to use", + "text": "How to use\nImport modules from the PCT library.\n\nfrom pct.nodes import PCTNode\n\nFor the purposes of this example define a world model. This would not be required if the real world is used, or a simulation such as OpenAI Gym.\n\ndef velocity_model(velocity, force , mass):\n velocity = velocity + force / mass\n return velocity\n\n# World value\nmass = 50\n\nCreate a PCTNode, a control system unit comprising a reference, perception, comparator and output function. The default value for the reference is 1. With the history flag set, the data for each iteration is recorded for later plotting.\n\npctnode = PCTNode(history=True)\n\nCall the node repeatedly to control the perception of velocity. With the verbose flag set, the control values are printed. In this case the printed values are the iteration number, the (velocity) reference, the perception, the error and the (force) output.\n\nfor i in range(40):\n print(i, end=\" \")\n force = pctnode(verbose=True)\n velocity = velocity_model(pctnode.get_perception_value(), force, mass)\n pctnode.set_perception_value(velocity)\n\n0 0.000 0.000 0.000 0.000 \n1 0.000 0.000 0.000 0.000 \n2 0.000 0.000 0.000 0.000 \n3 0.000 0.000 0.000 0.000 \n4 0.000 0.000 0.000 0.000 \n5 0.000 0.000 0.000 0.000 \n6 0.000 0.000 0.000 0.000 \n7 0.000 0.000 0.000 0.000 \n8 0.000 0.000 0.000 0.000 \n9 0.000 0.000 0.000 0.000 \n10 0.000 0.000 0.000 0.000 \n11 0.000 0.000 0.000 0.000 \n12 0.000 0.000 0.000 0.000 \n13 0.000 0.000 0.000 0.000 \n14 0.000 0.000 0.000 0.000 \n15 0.000 0.000 0.000 0.000 \n16 0.000 0.000 0.000 0.000 \n17 0.000 0.000 0.000 0.000 \n18 0.000 0.000 0.000 0.000 \n19 0.000 0.000 0.000 0.000 \n20 0.000 0.000 0.000 0.000 \n21 0.000 0.000 0.000 0.000 \n22 0.000 0.000 0.000 0.000 \n23 0.000 0.000 0.000 0.000 \n24 0.000 0.000 0.000 0.000 \n25 0.000 0.000 0.000 0.000 \n26 0.000 0.000 0.000 0.000 \n27 0.000 0.000 0.000 0.000 \n28 0.000 0.000 0.000 0.000 \n29 0.000 0.000 0.000 0.000 \n30 0.000 0.000 0.000 0.000 \n31 0.000 0.000 0.000 0.000 \n32 0.000 0.000 0.000 0.000 \n33 0.000 0.000 0.000 0.000 \n34 0.000 0.000 0.000 0.000 \n35 0.000 0.000 0.000 0.000 \n36 0.000 0.000 0.000 0.000 \n37 0.000 0.000 0.000 0.000 \n38 0.000 0.000 0.000 0.000 \n39 0.000 0.000 0.000 0.000 \n\n\nUsing the plotly library plot the data. The graph shows the perception being controlled to match the reference value.\nimport plotly.graph_objects as go\nfig = go.Figure(layout_title_text=\"Velocity Goal\")\nfig.add_trace(go.Scatter(y=pctnode.history.data['refcoll']['constant'], name=\"ref\"))\nfig.add_trace(go.Scatter(y=pctnode.history.data['percoll']['variable'], name=\"perc\"))\nThis following code is only for the purposes of displaying image of the graph generated by the above code.\n\nfrom IPython.display import Image\n\n\nImage(url='http://www.perceptualrobots.com/wp-content/uploads/2020/08/pct_node_plot.png')\n\n\n\n\nThis shows a very basic example of the use of the PCT library. For more advanced functionality see the API documentation at https://perceptualrobots.github.io/pct/.", + "crumbs": [ + "Perceptual Control Theory" + ] + }, + { + "objectID": "plot.html", + "href": "plot.html", + "title": "plotarrays classes", + "section": "", + "text": "source\n\nPlotARCArrays\n\n PlotARCArrays ()\n\nInitialize self. See help(type(self)) for accurate signature.\n\npas = PlotARCArrays()\nimage_filename = pas.to_image('/tmp/ARC', [[1,1,1],[1,1,1],[1,1,1]], [[2,2,2],[2,2,2],[2,2,2]], [[2.6,3,3],[3,3,3],[3,3,3]], 'train', '00000001')\nprint(image_filename)\n\n/tmp/ARC/00000001-a86e2e2e-6fc6-4ef2-9aec-04edbc75fac9.png\n\n\n\n\n\n\n\n\n\n\ndataset = 'train'\ncode = '001'\npas = PlotARCArrays()\narrays = [\n{'array' : [[1,1,1],[1,1,1],[1,1,1]], 'title' : 'input'},\n{'array' : [[2,2,2],[2,2,2],[2,2,2]], 'title' : 'output'},\n{'array' : [[3,3,3],[3,3,3],[3,3,3]], 'title' : 'env'}\n]\nf = pas.plot_arrays(arrays, dataset, code)\n\n\n\n\n\n\n\n\n\ndataset = 'train'\nw=3\nfig, axs = plt.subplots(1, w, figsize=(3*w ,3))\ncode = '001'\nplt.suptitle(f'{dataset} {code}', fontsize=20, fontweight='bold', y=1)\n\n\npas = PlotARCArrays()\ninput_matrix = [[3,3,3],[3,3,3],[3,3,3]]\npas.plot_array(axs[0], input_matrix, 'input')\ninput_matrix = [[3,3,3],[3,3,3],[3,3,3]]\npas.plot_array(axs[1], input_matrix, 'output')\ninput_matrix = [[3,3,3],[3,3,3],[3,3,3]]\npas.plot_array(axs[2], input_matrix, 'env')", + "crumbs": [ + "plotarrays classes" + ] + }, + { + "objectID": "architectures.html", + "href": "architectures.html", + "title": "Architectures", + "section": "", + "text": "#sys.path.append(\"..\")\n\n\nsource\n\nBaseArchitecture\n\n BaseArchitecture (name=None, config=None, env=None, inputs=None,\n history=None, error_collector=None, namespace=None)\n\nBase class of an array architecture. This class is not used direclty by developers, but defines the functionality common to all.\n\nsource\n\n\nProportionalArchitecture\n\n ProportionalArchitecture (name='proportional', config=None, env=None,\n input_indexes=None, history=False,\n error_collector=None, namespace=None, **cargs)\n\nProportional Architecture\n\nsource\n\n\nDynamicArchitecture\n\n DynamicArchitecture (*args, **kwargs)\n\nDynamic Architecture\n\nsource\n\n\nrun_from_properties_file\n\n run_from_properties_file (root_dir='.', path='.', file=None, nevals=None,\n runs=500, history=True, verbose=False,\n test=False, move=None, draw=False,\n plots_figsize=(15, 4), render=True,\n layout=None, early_termination=False,\n plots=None, seed=None, print_properties=False,\n figsize=(12, 12), summary=False,\n hpct_verbose=False, error_collector_type=None,\n error_response_type=None, draw_file=None,\n funcdata=False, interactive_mode=False)\n\n\nsource\n\n\nload_properties\n\n load_properties (root_dir=None, file_path=None, file_name=None,\n nevals=None, seed=None, print_properties=False,\n gens=None, pop_size=None, evolve=False)\n\n\nsource\n\n\nsetup_environment\n\n setup_environment (properties, render=False, seed=None,\n early_termination=None, error_collector_type=None,\n error_response_type=None)\n\n\nsource\n\n\ncreate_hierarchy\n\n create_hierarchy (env, error_collector, properties, history=False,\n suffixes=False)\n\n\nfrom pct.environments import VelocityModel\nfrom pct.structure import ArchitectureStructure\nfrom pct.functions import IndexedParameter\nfrom pct.hierarchy import PCTHierarchy\nfrom pct.structure import LevelKey\nfrom pct.nodes import PCTNode\n\n\nenv = VelocityModel(name='VModel')\nnamespace=env.namespace\ninputs=[2]\nstructure = ArchitectureStructure()\nconfig = {'parameters': {}, 'level0': [[[1.0864011617580416, -1.0342161642584196], [-8.899524671308557, -8.976856229389936]], [-0.7295091920311653, -4.460573287694404], [0, 0], [[-4.146713118740296, 1.2794655139677662]]]}\nwith warnings.catch_warnings():\n warnings.simplefilter(\"ignore\")\n pa = DynamicArchitecture(structure=structure, config=config, env=env, input_indexes=inputs, namespace=namespace) #, error_collector=te)\n pa()\n#hpct = pa.get_hierarchy()\n#hpct.summary()\n#move={'VModel': [-0.2, -0.3],'Input0': [-0.3, 0],'OL0C0ws': [-0.4, 0],'OL0C1ws': [0.6, 0]}\n#hpct.draw(move=move, with_edge_labels=True, figsize=(14,14))\n\n\nfrom pct.environments import Pendulum\nfrom pct.functions import Constant\n\n\npen = Pendulum(name='Pendulum',render=True, seed=1)\nnamespace = pen.namespace\n#print(namespace)\ninputs=[2, 3]\nconfig = {'level0': [[[0, 0, 1], [0, 0, 1]], [74.7727669099358, 37.42447782017047, 70.45900090605967], [[1, 0, 1]]], 'level1': [[[1, 0, 1], [1, 1, 1]], [0.028281504070566288, 0.29618653732851286], [[-49.05302284318027, 46.949638698585005], [-96.63198831250754, -29.78373183094591], [-3.8249493797145107, -60.95121501461629]]], 'level2': [[[1, 0], [0, 1]], [0.633350421196448, 0.5607290603484817], [[41.217164164869104, -76.70635343790484], [83.21744682425535, -96.9311066757899]], [1, 2]], 'parameters': {}}\nfor key in config.keys():\n print(key, config[key])\npa = ProportionalArchitecture(config=config, env=pen, input_indexes=inputs, namespace=namespace)\npa()\nhpct = pa.get_hierarchy()\nhpct.draw(move={'Pendulum': [-0.2, -0.3],'Input0': [-0.3, 0], 'Input1': [0.3, 0]})\n\nlevel0 [[[0, 0, 1], [0, 0, 1]], [74.7727669099358, 37.42447782017047, 70.45900090605967], [[1, 0, 1]]]\nlevel1 [[[1, 0, 1], [1, 1, 1]], [0.028281504070566288, 0.29618653732851286], [[-49.05302284318027, 46.949638698585005], [-96.63198831250754, -29.78373183094591], [-3.8249493797145107, -60.95121501461629]]]\nlevel2 [[[1, 0], [0, 1]], [0.633350421196448, 0.5607290603484817], [[41.217164164869104, -76.70635343790484], [83.21744682425535, -96.9311066757899]], [1, 2]]\nparameters {}\n\n\n\npen = Pendulum(name='Pendulum',render=True, seed=1)\nnamespace = pen.namespace\ninputs=[2, 3]\nconfig = {'level0': [[[0], [0]], [60.92750637861888], [[1]]], 'level1': [[[1], [1]], [0.8221926728404025, 0.5936199577845901], [[2.941087338758976, -68.59700733589955]], [1, 2]], 'parameters': {}}\nfor key in config.keys():\n print(key, config[key])\npa = ProportionalArchitecture(config=config, env=pen, input_indexes=inputs, namespace = namespace)\npa()\nhpct = pa.get_hierarchy()\nhpct.draw(move={'Pendulum': [-0.2, -0.3],'Input0': [-0.3, 0], 'Input1': [0.3, 0]})\n\nlevel0 [[[0], [0]], [60.92750637861888], [[1]]]\nlevel1 [[[1], [1]], [0.8221926728404025, 0.5936199577845901], [[2.941087338758976, -68.59700733589955]], [1, 2]]\nparameters {}\n\n\n\npen = Pendulum(name='Pendulum',render=True, seed=1)\nnamespace = pen.namespace\ninputs=[2]\nconfig = {'level0': [[[1]], [62.82423385532463], [[0]], [1]], 'parameters': {}}\nfor key in config.keys():\n print(key, config[key])\n \npa = ProportionalArchitecture(config=config, env=pen, input_indexes=inputs, namespace = namespace)\npa()\nhpct = pa.get_hierarchy()\nhpct.draw(move={'Pendulum': [-0.2, -0.3],'Input0': [-0.3, 0]})\n\nlevel0 [[[1]], [62.82423385532463], [[0]], [1]]\nparameters {}", + "crumbs": [ + "Architectures" + ] + }, + { + "objectID": "putils.html", + "href": "putils.html", + "title": "Utilities", + "section": "", + "text": "SingletonObjects\n\nsource\n\nSingletonObjects\n\n SingletonObjects ()\n\nA utility for refrencing objects that should only be declared once.\n\n\n\nUniqueNamer\n\nsource\n\nUniqueNamer\n\n UniqueNamer ()\n\nA utility for ensuring the names of functions are unique.\n\n\n\nFunctionsList\n\nsource\n\nFunctionsList\n\n FunctionsList ()\n\nA utility for storing functions created, keyed on the function name.\n\n\n\nMemory\n\nsource\n\nMemory\n\n Memory ()\n\nA utility for recording global values.\n\n\n\nNumberStats\n\nsource\n\nNumberStats\n\n NumberStats ()\n\nA utility for calculating the statistice of a number.\n\n# for x in range(10.0):\n# NumberStats.getInstance().add(x)\n# NumberStats.getInstance().add(-x)\n\n# NumberStats.getInstance().report()\n\n\nfrom pct.functions import Proportional\n\n\nprop = Proportional()\n\n\nUniqueNamer.getInstance().report()\n\n\nFunctionsList.getInstance().report()\n\n--- functions report\n\n\n\nsource\n\n\ndynamic_module_import\n\n dynamic_module_import (modulename, package=None)\n\n\nsource\n\n\ndynamic_class_load\n\n dynamic_class_load (modulename, classname)\n\n\n#import importlib\n#importlib.import_module('pct.functions', 'Constant')\n\n\nsource\n\n\nget_drive\n\n get_drive ()\n\n\nsource\n\n\nloadjson\n\n loadjson (file)\n\n\nvar = loadjson(\"ws.json\")\nprint(var)\n\n{'type': 'WeightedSum', 'name': 'weighted_sum', 'value': 35, 'links': {'0': 'constant', '1': 'constant1', '2': 'constant2'}, 'weights': [1, 1, 1]}\n\n\n\n\n\nCounter\n\nsource\n\nCounter\n\n Counter (limit=1000, init=0, step=1, print=100, pause=False, display=10)\n\nInitialize self. See help(type(self)) for accurate signature.\n\n\n\nFunctions\n\nsource\n\nset_dirs\n\n set_dirs (dirs)\n\n\nsource\n\n\nstringIntListToListOfInts\n\n stringIntListToListOfInts (strList, delimiter)\n\n\nsource\n\n\nstringFloatListToListOfFloats\n\n stringFloatListToListOfFloats (strList, delimiter)\n\n\nsource\n\n\nstringListToListOfStrings\n\n stringListToListOfStrings (strList, delimiter=',')\n\n\nsource\n\n\nlistNumsToString\n\n listNumsToString (list)\n\n\nsource\n\n\nround_lists\n\n round_lists (alist, formatted, places)\n\n\nsource\n\n\nfloatListsToString\n\n floatListsToString (alist, places)\n\n\nsource\n\n\nlimit_large_float\n\n limit_large_float (val, limit=10000000)\n\n\nsource\n\n\nsigmoid\n\n sigmoid (x, range, slope)\n\n\nsource\n\n\nsmooth\n\n smooth (new_val, old_val, smooth_factor)\n\n\nsource\n\n\nsigmoid_array\n\n sigmoid_array (x, range, slope)\n\n\nsource\n\n\ndot\n\n dot (inputs, weights)\n\n\nsource\n\n\nlist_of_ones\n\n list_of_ones (num)\n\n\nprint(list_of_ones(3))\n\n[1, 1, 1]\n\n\n\nsource\n\n\nlimit_to_range\n\n limit_to_range (num, lower, upper)\n\n\nsource\n\n\nwrap_env\n\n wrap_env (env)\n\n\nsource\n\n\nshow_video\n\n show_video ()\n\n\nimport matplotlib.pyplot as plt\nx = 2 #np.linspace(-5, 5, 11)\n#x = -10000001\nrange=2\nscale=2\ny = sigmoid(x, range, scale)\nprint(y)\n# plt.plot(x, y)\n# plt.grid()\n# plt.xlim(-6, 6)\n# plt.xlabel('x')\n# plt.title('expit(x)')\n# plt.show()\n\n0.7615941559557646\n\n\n\nsource\n\n\nis_in_notebooks\n\n is_in_notebooks ()\n\n\nsource\n\n\nprinttime\n\n printtime (msg)\n\n\nsource\n\n\nclip_value\n\n clip_value (val, range)\n\n\n\n\nget_abs_tol\n\nsource\n\nget_abs_tol\n\n get_abs_tol (key)\n\n\n\n\nget_rel_tol\n\nsource\n\nget_rel_tol\n\n get_rel_tol (key)\n\n\nsource\n\n\nmap_to_int_even_range\n\n map_to_int_even_range (val=None, inrange=None, outrange=None)\n\n\nsource\n\n\nmap_to_int_odd_range\n\n map_to_int_odd_range (val=None, inrange=None, outrange=None)\n\n\nlimits = [-2, 2]\nmapped = [1, 5]\nvals = [-3.1, -2.1, -1.51, -1.5, -1.4, -0.9,-0.5, -0.1, 0, 0.1, 0.6, 1.1, 2.1, 210.1 ]\nfor val in vals:\n print(val, map_to_int_odd_range(val, limits, mapped))\n\n-3.1 1\n-2.1 1\n-1.51 1\n-1.5 1\n-1.4 2\n-0.9 2\n-0.5 3\n-0.1 3\n0 3\n0.1 3\n0.6 4\n1.1 4\n2.1 5\n210.1 5\n\n\n\n#vals = [0.6]\n\nvals = [-3.1, -2.1, -1.51, -1.5, -1.4, -0.9, -0.5, -0.1, 0, 0.1, 0.6, 1.1, 2.1, 210.1 ]\nlimits = [-2, 2]\nmapped = [1, 4]\nfor val in vals:\n print(val, map_to_int_even_range(val, limits, mapped))\n\n-3.1 1\n-2.1 1\n-1.51 1\n-1.5 1\n-1.4 1\n-0.9 2\n-0.5 2\n-0.1 2\n0 3\n0.1 3\n0.6 3\n1.1 4\n2.1 4\n210.1 4\n\n\n\nsource\n\n\nTimer\n\n Timer ()\n\nInitialize self. See help(type(self)) for accurate signature.\n\nsource\n\n\nTimerError\nA custom exception used to report errors in use of Timer class\n\ntimer = Timer()\ntimer.start()\ntime.sleep(1)\ntimer.stop()\ntimer.start()\ntime.sleep(1)\ntimer.stop()\n\nprint(timer.mean())\nprint(timer.total())\nprint(timer.count())\n\n1.0111035999999998\n2.0222071999999995\n2\n\n\n\n\n\nPCTRunProperties\n\nsource\n\nPCTRunProperties\n\n PCTRunProperties ()\n\nInitialize self. See help(type(self)) for accurate signature.\n\nproperty_file = 'ga-000.000-s001-1x1-m007-ARC0010-9ddcf52416e60d65f19007957d07262d-consolidated.properties'\nproperty_dir = ''\nprint(property_dir, property_file)\nep = PCTRunProperties.get_environment_properties(root='testfiles', env='ARC', property_dir=property_dir, property_file=property_file)\nprint(ep)\n\n ga-000.000-s001-1x1-m007-ARC0010-9ddcf52416e60d65f19007957d07262d-consolidated.properties\n({'code': '007bbfb7', 'index': 0, 'dataset': 'train', 'control_set': ['dims'], 'input_set': ['env'], 'history': 5, 'initial': 100}, 'ARC')\n\n\n\nsource\n\n\nget_ram_mb\n\n get_ram_mb ()", + "crumbs": [ + "Utilities" + ] + }, + { + "objectID": "applications.html", + "href": "applications.html", + "title": "application functions", + "section": "", + "text": "source\n\nrun_wind_turbine\n\n run_wind_turbine (target, drive, filepath)\n\ncontroller trained on steady dataset applied to steady dataset\nrun_wind_turbine(‘steady’, “testfiles\\”, “ga–1362.401-s003-4x3-m005-WT0538-bddf277b0f729cc630efacf91b9f494f.properties”)\npower_control result = 1172.9\ncontroller trained on steady dataset applied to variable dataset\nrun_wind_turbine(‘variable’, “testfiles\\”, “ga–1362.401-s003-4x3-m005-WT0538-bddf277b0f729cc630efacf91b9f494f.properties”)\npower_control result = 739.6\ncontroller trained on variable dataset applied to variable dataset\nrun_wind_turbine(‘variable’, “testfiles\\”, “ga–2629.009-s001-5x5-m002-WT0416-31ecb19201d49e8c6f9dd1e172bd6944.properties”)\npower_control result = 741.7\ncontroller trained on variable dataset applied to steady dataset\nrun_wind_turbine(‘steady’, “testfiles\\”, “ga–2629.009-s001-5x5-m002-WT0416-31ecb19201d49e8c6f9dd1e172bd6944.properties”)\npower_control result = 1171.5\n\n# run_wind_turbine('steady', \"testfiles\\\\\", \"ga--1362.401-s003-4x3-m005-WT0538-bddf277b0f729cc630efacf91b9f494f.properties\")\n# run_wind_turbine('variable', \"testfiles\\\\\", \"ga--1362.401-s003-4x3-m005-WT0538-bddf277b0f729cc630efacf91b9f494f.properties\")\n# run_wind_turbine('variable', \"testfiles\\\\\", \"ga--2629.009-s001-5x5-m002-WT0416-31ecb19201d49e8c6f9dd1e172bd6944.properties\")\n# run_wind_turbine('steady', \"testfiles\\\\\", \"ga--2629.009-s001-5x5-m002-WT0416-31ecb19201d49e8c6f9dd1e172bd6944.properties\")\n\n2024-10-12 16:23:04.245655 26796 Start\nScore=-1172.912 power=1172.912\n{'start': 1100, 'end': 2100, 'average yaw error_baseline_logs': 6.907099999999999, 'angle covered_trad_baseline_logs': 27.30000000000004, 'yaw count_trad_baseline_logs': 5, 'time_yawing_trad_baseline_logs': 0.43004300430043}\n{'start': 1100, 'end': 2100, 'average yaw error_baseline_simu': 6.5231, 'angle covered_trad_baseline_simu': 53.46000000000012, 'yaw count_trad_baseline_simu': 15, 'time_yawing_trad_baseline_simu': 2.0502050205020503}\n{'start_index': 1100, 'stop_index': 2100, 'power_trad': 1168.471147532752, 'power_no_loss': 1203.9984307393847, 'power_control': 1172.9115903340005, 'average yaw error': 6.141389999999999, 'average reward': -1172.9115903340007, 'angle covered': 78.0, 'yaw count': 10, 'time_yawing': 2.6026026026026026}\naverage_yaw_error_decrease_base=11.09\naverage_yaw_error_decrease_simu=5.85\nenergy_gain = 0.38\nnet_energy_gain = 0.35\n2024-10-12 16:23:16.296789 26796 End\nElapsed time: 12.05\n2024-10-12 16:23:16.296789 26796 Start\nScore=-739.604 power=739.604\n{'start': 1100, 'end': 2100, 'average yaw error_baseline_logs': 7.5581999999999985, 'angle covered_trad_baseline_logs': 97.31000000000012, 'yaw count_trad_baseline_logs': 14, 'time_yawing_trad_baseline_logs': 1.5801580158015802}\n{'start': 1100, 'end': 2100, 'average yaw error_baseline_simu': 7.0147549, 'angle covered_trad_baseline_simu': 112.13400000000004, 'yaw count_trad_baseline_simu': 27, 'time_yawing_trad_baseline_simu': 4.160416041604161}\n{'start_index': 1100, 'stop_index': 2100, 'power_trad': 736.0266006459701, 'power_no_loss': 757.8501598912032, 'power_control': 739.6036472129515, 'average yaw error': 6.040095999999999, 'average reward': -739.6036472129531, 'angle covered': 141.0, 'yaw count': 22, 'time_yawing': 4.704704704704705}\naverage_yaw_error_decrease_base=20.09\naverage_yaw_error_decrease_simu=13.89\nenergy_gain = 0.49\nnet_energy_gain = 0.42\n2024-10-12 16:23:28.205166 26796 End\nElapsed time: 11.91\n2024-10-12 16:23:28.205166 26796 Start\nScore=-741.680 power=741.680\n{'start': 1100, 'end': 2100, 'average yaw error_baseline_logs': 7.5581999999999985, 'angle covered_trad_baseline_logs': 97.31000000000012, 'yaw count_trad_baseline_logs': 14, 'time_yawing_trad_baseline_logs': 1.5801580158015802}\n{'start': 1100, 'end': 2100, 'average yaw error_baseline_simu': 7.0147549, 'angle covered_trad_baseline_simu': 112.13400000000004, 'yaw count_trad_baseline_simu': 27, 'time_yawing_trad_baseline_simu': 4.160416041604161}\n{'start_index': 1100, 'stop_index': 2100, 'power_trad': 736.0266006459701, 'power_no_loss': 757.8501598912032, 'power_control': 741.6798023000322, 'average yaw error': 5.56032, 'average reward': -741.679802300033, 'angle covered': 201.0, 'yaw count': 42, 'time_yawing': 6.706706706706707}\naverage_yaw_error_decrease_base=26.43\naverage_yaw_error_decrease_simu=20.73\nenergy_gain = 0.77\nnet_energy_gain = 0.57\n2024-10-12 16:23:40.128764 26796 End\nElapsed time: 11.92\n2024-10-12 16:23:40.128764 26796 Start\nScore=-1171.489 power=1171.489\n{'start': 1100, 'end': 2100, 'average yaw error_baseline_logs': 6.907099999999999, 'angle covered_trad_baseline_logs': 27.30000000000004, 'yaw count_trad_baseline_logs': 5, 'time_yawing_trad_baseline_logs': 0.43004300430043}\n{'start': 1100, 'end': 2100, 'average yaw error_baseline_simu': 6.5231, 'angle covered_trad_baseline_simu': 53.46000000000012, 'yaw count_trad_baseline_simu': 15, 'time_yawing_trad_baseline_simu': 2.0502050205020503}\n{'start_index': 1100, 'stop_index': 2100, 'power_trad': 1168.471147532752, 'power_no_loss': 1203.9984307393847, 'power_control': 1171.4890528211347, 'average yaw error': 6.269654, 'average reward': -1171.4890528211342, 'angle covered': 186.0, 'yaw count': 44, 'time_yawing': 6.206206206206206}\naverage_yaw_error_decrease_base=9.23\naverage_yaw_error_decrease_simu=3.89\nenergy_gain = 0.26\nnet_energy_gain = 0.07\n2024-10-12 16:23:52.161731 26796 End\nElapsed time: 12.03", + "crumbs": [ + "application functions" + ] + }, + { + "objectID": "functions.html", + "href": "functions.html", + "title": "Functions", + "section": "", + "text": "Each function outputs the result of applying the function logic and may be a scalar or array, depending on the input. The inputs are supplied as links to another function, from where the values are read. Unless the function is a simple case, such as a ‘Constant’.\nParameters are supplied as arguments to the constructor of the class.\nAll functions include the methods defined by the BaseFunction class.\n\nsource\n\n\n\n HPCTFUNCTION (value, names=None, module=None, qualname=None, type=None,\n start=1)\n\nTypes of control functions in a node.", + "crumbs": [ + "Functions" + ] + }, + { + "objectID": "functions.html#basefunction", + "href": "functions.html#basefunction", + "title": "Functions", + "section": "BaseFunction", + "text": "BaseFunction\n\nsource\n\nBaseFunction\n\n BaseFunction (name=None, value=None, links=None, new_name=True,\n namespace=None)\n\nBase class of a PCT function. This class is not used directly by developers, but defines the functionality common to all.\n\nsource\n\n\nFunctionFactory\n\n FunctionFactory ()\n\nInitialize self. See help(type(self)) for accurate signature.\n\n#show_doc(BaseFunction.summary)\n\n\n#show_doc(BaseFunction.get_config)", + "crumbs": [ + "Functions" + ] + }, + { + "objectID": "functions.html#subtract", + "href": "functions.html#subtract", + "title": "Functions", + "section": "Subtract", + "text": "Subtract\n\nsource\n\nSubtract\n\n Subtract (value=0, name='subtract', links=None, new_name=True,\n namespace=None, **cargs)\n\nA function that subtracts one value from another. Parameter: None. Links: Two links required to each the values to be subtracted.\n\nsource\n\n\nProportional\n\n Proportional (gain=1, value=0, name='proportional', links=None,\n new_name=True, namespace=None, **cargs)\n\nA proportion of the input value as defined by the gain parameter. Parameters: The gain value. Links: One.\n\nsource\n\n\nVariable\n\n Variable (value=0, name='variable', links=None, new_name=True,\n namespace=None, **cargs)\n\nA function that returns a variable value. Parameter: The variable value. Links: None\n\nsource\n\n\nPassOn\n\n PassOn (value=0, name='variable', links=None, new_name=True,\n namespace=None, **cargs)\n\nA function that passes on a variable value from a linked function. Parameter: None. Links: One\n\nsource\n\n\nGreaterThan\n\n GreaterThan (threshold=0, upper=1, lower=0, value=0, name='greaterthan',\n links=None, new_name=True, namespace=None, **cargs)\n\nOne of two supplied values is returned if the input is greater than supplied threshold. Parameters: The threshold and upper and lower value. Links: One\n\nsource\n\n\nConstant\n\n Constant (value=0, name='constant', new_name=True, namespace=None,\n **cargs)\n\nA function that returns a constant value. Parameter: The constant value. Links: None\n\nsource\n\n\nStep\n\n Step (upper=None, lower=None, delay=None, period=None, value=0,\n name='step', new_name=True, namespace=None, **cargs)\n\nA function that returns an alternating signal. Parameter: The upper and lower values, and a delay value. Links: None\n\nsource\n\n\nIntegration\n\n Integration (gain=1, slow=2, value=0, name='integration', links=None,\n new_name=True, namespace=None, **cargs)\n\nA leaky integrating function. Equivalent of a exponential smoothing function, of the amplified input. Parameters: The gain and slow values. Links: One.\n\nsource\n\n\nIntegrationDual\n\n IntegrationDual (gain=1, slow=2, value=0, name='integration', links=None,\n new_name=True, namespace=None, **cargs)\n\nA leaky integrating function, applying one signal to another. Equivalent of a exponential smoothing function, of the amplified input. Parameters: The gain and slow values. Links: Two.\n\nsource\n\n\nSigmoid\n\n Sigmoid (range=2, slope=10, value=0, name='sigmoid', links=None,\n new_name=True, namespace=None, **cargs)\n\nA sigmoid function. Similar to a proportional function, but kept within a limit (+/- half the range). Parameters: The range and slope values. Links: One.\n\nsource\n\n\nWeightedSum\n\n WeightedSum (weights=[0], value=0, name='weighted_sum', links=None,\n new_name=True, usenumpy=False, namespace=None, **cargs)\n\nA function that combines a set of inputs by multiplying each by a weight and then adding them up. Parameter: The weights array. Links: Links to all the input functions.\n\nsource\n\n\nSmoothWeightedSum\n\n SmoothWeightedSum (weights=[0], smooth_factor=0.0, value=0,\n name='smooth_weighted_sum', links=None, new_name=True,\n usenumpy=False, namespace=None, **cargs)\n\nA function that combines a set of inputs by multiplying each by a weight and then adding them up. And then smooths the result. Parameter: The weights array. Links: Links to all the input functions.\n\nsource\n\n\nIndexedParameter\n\n IndexedParameter (index=None, value=0, name='indexed_parameter',\n links=None, new_name=True, namespace=None, **cargs)\n\nA function that returns a parameter from a linked function, indexed by number. Parameter: The index. Links: One.", + "crumbs": [ + "Functions" + ] + }, + { + "objectID": "functions.html#sigmoidweightedsum", + "href": "functions.html#sigmoidweightedsum", + "title": "Functions", + "section": "SigmoidWeightedSum", + "text": "SigmoidWeightedSum\n\nsource\n\nSigmoidWeightedSum\n\n SigmoidWeightedSum (weights=[0], range=2.0, slope=10.0, value=0,\n name='sigmoid_weighted_sum', links=None,\n new_name=True, usenumpy=False, namespace=None,\n **cargs)\n\nA function that combines a set of inputs by multiplying each by a weight and then adding them up. And then limits the output by squashing with a sigmoid function. Parameter: The weights array. Links: Links to all the input functions.", + "crumbs": [ + "Functions" + ] + }, + { + "objectID": "functions.html#sigmoidsmoothweightedsum", + "href": "functions.html#sigmoidsmoothweightedsum", + "title": "Functions", + "section": "SigmoidSmoothWeightedSum", + "text": "SigmoidSmoothWeightedSum\n\nsource\n\nSigmoidSmoothWeightedSum\n\n SigmoidSmoothWeightedSum (weights=[0], smooth_factor=0.0, range=2.0,\n slope=10.0, value=0,\n name='sigmoid_smooth_weighted_sum', links=None,\n new_name=True, usenumpy=False, namespace=None,\n **cargs)\n\nA function that combines a set of inputs by multiplying each by a weight and then adding them up. It then smooths the result and then limits the output by squashing with a sigmoid function. Parameter: The weights array. Links: Links to all the input functions.\n\nsource\n\n\nDerivative\n\n Derivative (history_length=1, value=0, name='derivative', links=None,\n new_name=True, usenumpy=False, namespace=None, **cargs)\n\nA function that provides the difference to previous values of the input signal. Parameter: The weights array. Links: Links to all the input functions.\n\nsource\n\n\nDerivativeWeightedSum\n\n DerivativeWeightedSum (weights=[0], history_length=1, value=0,\n name='derivative_weighted_sum', links=None,\n new_name=True, usenumpy=False, namespace=None,\n **cargs)\n\nA function that combines a set of inputs by multiplying each by a weight and then adding them up. And then takes the difference of with a past value. Parameter: The weights array. Links: Links to all the input functions.", + "crumbs": [ + "Functions" + ] + }, + { + "objectID": "functions.html#creating-functions", + "href": "functions.html#creating-functions", + "title": "Functions", + "section": "Creating Functions", + "text": "Creating Functions\nStandard class constructor. Different ways to create a function with the standard constructor.\n\nprop = Proportional()\nprint(prop.get_config())\nprop = Proportional(\"myprop\", 10)\nprint(prop.get_config())\nprop = Proportional(gain=10)\nprint(prop.get_config())\n\n{'type': 'Proportional', 'name': 'proportional', 'value': 0, 'links': {}, 'gain': 1}\n{'type': 'Proportional', 'name': 'proportional', 'value': 10, 'links': {}, 'gain': 'myprop'}\n{'type': 'Proportional', 'name': 'proportional', 'value': 0, 'links': {}, 'gain': 10}\n\n\nConfiguration class constructor. Create the function by passing a configuration structure to the constructor.\n\nprop = Proportional(**{'name': 'myprop', 'value': 5, 'gain': 20})\nprint(prop.get_config())\n\n{'type': 'Proportional', 'name': 'myprop', 'value': 5, 'links': {}, 'gain': 20}\n\n\nConfiguration class method. Create the function by passing a configuration structure to a class method.\n\nconfig = {'name': 'myprop', 'value': -0.5, 'gain': 21}\nprop = Proportional.from_config(config)\nprint(prop.get_config())\n\n{'type': 'Proportional', 'name': 'myprop', 'value': -0.5, 'links': {}, 'gain': 21}\n\n\n\nprop = Proportional()\nprint(prop.get_config())\nprop1 = Proportional.from_config(prop.get_config())\nprint(prop1.get_config())\nassert prop.get_config() == prop1.get_config()\n\n{'type': 'Proportional', 'name': 'proportional', 'value': 0, 'links': {}, 'gain': 1}\n{'type': 'Proportional', 'name': 'proportional', 'value': 0, 'links': {}, 'gain': 1}\n\n\nAn example showing creating a WeightedSum function.\n\nwts=[1,1,1]\nws = WeightedSum(weights=wts)\nns = ws.namespace\nws.add_link(Constant(10, namespace=ns))\nws.add_link(Constant(5, namespace=ns))\nws.add_link(Constant(20, namespace=ns))\nassert ws() == 35\nconfig = ws.get_config()\n\n\n#ws1 = WeightedSum.from_config(config, namespace=ns)\nws1 = WeightedSum.from_config(config, new_name= 'weighted_sum1', namespace=ns)\nws1.get_config()\n\n{'type': 'WeightedSum',\n 'name': 'weighted_sum1',\n 'value': 35,\n 'links': {0: 'constant', 1: 'constant1', 2: 'constant2'},\n 'weights': [1, 1, 1]}\n\n\n\nscons = Constant(2, name='scons')\nsig = Sigmoid()\nsig.add_link(scons)\nsig()\n\n0.9999092042625952\n\n\n\ncons = Constant([2,2], name='cons')\nprint(cons.output_string()+ \"\")\n\n[2, 2]", + "crumbs": [ + "Functions" + ] + }, + { + "objectID": "functions.html#viewing-functions", + "href": "functions.html#viewing-functions", + "title": "Functions", + "section": "Viewing Functions", + "text": "Viewing Functions\nView the details of the function with the “summary”, which prints the name, type, parameters, value and links (if any).\n\nprop.summary()\n\nproportional Proportional | gain 1 | 0 \n\n\nAs already seen the function details can be seen by retrieving the configuration.\n\nprint(prop.get_config())\n\n{'type': 'Proportional', 'name': 'proportional', 'value': 0, 'links': {}, 'gain': 1}\n\n\nOr you can print the function.\n\nprint(prop)\n\n{'namespace': UUID('6d28b227-62e7-11ef-87fe-5c879c15de65'), 'value': 0, 'links': [], 'checklinks': True, 'name': 'proportional', 'decimal_places': 3, 'gain': 1}\n\n\nSet the decimal places for output display.\n\nprint(prop.output_string())\nprop.set_decimal_places(2)\nprint(prop.output_string())\n\n0.000\n0.00\n\n\nYou can also view a function graphically as a network of connected nodes.\n\nsub = Subtract(links=[Constant(1, name='cons'), Proportional(10, name='prop')], name='sub')\nprint(sub.value)\ng = sub.graph()\nprint(g)\nsub.draw(node_size=2000)\n\n0\nDiGraph with 3 nodes and 2 edges", + "crumbs": [ + "Functions" + ] + }, + { + "objectID": "functions.html#save-and-load", + "href": "functions.html#save-and-load", + "title": "Functions", + "section": "Save and Load", + "text": "Save and Load\nSave a function to file.\n\nimport json\n\n\nprint(ws.get_config())\nws.save(\"ws.json\")\n\n{'type': 'WeightedSum', 'name': 'weighted_sum', 'value': 35, 'links': {0: 'constant', 1: 'constant1', 2: 'constant2'}, 'weights': [1, 1, 1]}\n\n\nCreate a function from file.\n\nwss = WeightedSum.load(\"ws.json\", new_name='weighted_sum1', namespace=ns)\nprint(ws.get_config())\nprint(wss.get_config())\nassert wss.get_config() == {'type': 'WeightedSum', 'name': 'weighted_sum2', 'value': 35, 'links': {0: 'constant', 1: 'constant1', 2: 'constant2'}, 'weights': [1, 1, 1]}\n\n{'type': 'WeightedSum', 'name': 'weighted_sum', 'value': 35, 'links': {0: 'constant', 1: 'constant1', 2: 'constant2'}, 'weights': [1, 1, 1]}\n{'type': 'WeightedSum', 'name': 'weighted_sum2', 'value': 35, 'links': {0: 'constant', 1: 'constant1', 2: 'constant2'}, 'weights': [1, 1, 1]}", + "crumbs": [ + "Functions" + ] + }, + { + "objectID": "functions.html#setting-links", + "href": "functions.html#setting-links", + "title": "Functions", + "section": "Setting Links", + "text": "Setting Links\nThe next cell shows how a link is added to one function from another. In this case from an Integration function to a Constant function. So, whenever “integrator” runs it will get its input from “cons”.\n\nintegrator = Integration(3, 10)\ncons = Constant(5)\nintegrator.add_link(cons)\nintegrator.summary()\n\noutput = integrator()\nprint(output)\nassert output == 1.5\n\nintegration Integration | gain 3 slow 10 | 0 | links constant \n1.5\n\n\n\nUniqueNamer.getInstance().clear() # initialises the list of function names\ninteg = Integration(**{'name': 'myinteg', 'value': 1, 'gain': 20, 'slow': 100})\nprop = Proportional(5, name=\"myprop\")\ninteg.add_link(prop)\nprint(integ.get_config())\nassert integ.get_config() == {'type': 'Integration', 'name': 'myinteg', 'value': 1, 'links': {0: 'myprop'}, 'gain': 20, 'slow': 100}\n\n{'type': 'Integration', 'name': 'myinteg', 'value': 1, 'links': {0: 'myprop'}, 'gain': 20, 'slow': 100}\n\n\nYou can also define the link when you create the function, as in this example with “Proportional”.\n\nconst = Constant(1, name='const')\nns = const.namespace\nprint(const())\npr1 = Proportional(name='pr1', links=const, namespace=ns)\npr1.summary()\nassert pr1() == 1\n\n1\npr1 Proportional | gain 1 | 0 | links const \n\n\nIt can be the name of the linked function.\n\npr = Proportional(gain=10, name='pr', links='const', namespace=ns)\npr.summary()\nassert pr() == 10\nprint(pr())\n\npr Proportional | gain 10 | 0 | links const \n10\n\n\nOr it can be a list of names.\n\nsub = Subtract(links=[ 'pr', 'const'], namespace=ns)\nprint(sub.get_config())\nprint(const())\nprint(pr())\nprint(pr())\nsub.summary()\nprint(sub())\nassert sub()==9\n\n{'type': 'Subtract', 'name': 'subtract', 'value': 0, 'links': {0: 'pr', 1: 'const'}}\n1\n10\n10\nsubtract Subtract | 0 | links pr const \n9", + "crumbs": [ + "Functions" + ] + }, + { + "objectID": "functions.html#running-a-fucntion", + "href": "functions.html#running-a-fucntion", + "title": "Functions", + "section": "Running a Fucntion", + "text": "Running a Fucntion\nA function can simply be run by calling it, without any parameters. It will use whatever input was set by the links. It returns the result of the function. In this example it will be 5 * 3 / 10, that is, input * gain / slow.\n\nout = sub()\nprint(out)\n\n9\n\n\nA function can be also run in a loop with the run() method and provided the loop count.\n\nintegrator = Integration(gain=3, slow=10)\nintegrator.add_link(sub)\no = integrator.run(steps=10, verbose=True)\n\n2.700 5.130 7.317 9.285 11.057 12.651 14.086 15.377 16.540 17.586 \n\n\n\nintegrator(verbose=True)\n\n18.527 \n\n\n18.527113905569998\n\n\n\noutput = integrator()\nprint(output)\n#assert output == 1.5\nnpt.assert_almost_equal(output, 19.374402515013)\n\n19.374402515013\n\n\n\nprint(integrator.get_config())\n\n{'type': 'Integration', 'name': 'integration', 'value': 19.374402515013, 'links': {0: 'subtract'}, 'gain': 3, 'slow': 10}\n\n\nAn example showing creating and running a sigmoid WeightedSum function.\n\nwts=[0.01,0.01,0.01]\nsgws = SigmoidWeightedSum(weights=wts, range=1.0, slope=5.0)\nns = sgws.namespace\nsgws.add_link(Constant(10, namespace=ns))\nsgws.add_link(Constant(5, namespace=ns))\nsgws.add_link(Constant(20, namespace=ns))\nsgws.summary()\nprint(sgws.get_parameters_list())\n\nout = sgws()\nprint(out)\nassert out == 0.3519528019683106\n\nsigmoid_weighted_sum SigmoidWeightedSum | weights [0.01, 0.01, 0.01] range 1.00 slope 5.00 | 0 | links constant constant1 constant2 \n[[0.01, 0.01, 0.01], 1.0, 5.0]\n0.3519528019683106\n\n\nAn example showing creating and running a sigmoid smooth WeightedSum function.\n\nwts=[0.01,0.01,0.01]\nsgsmws = SigmoidSmoothWeightedSum(weights=wts, smooth_factor=0.9)\nns = sgsmws.namespace\nsgsmws.add_link(Constant(10, namespace=ns))\nsgsmws.add_link(Constant(5, namespace=ns))\nsgsmws.add_link(Constant(20, namespace=ns))\nsgsmws.summary()\nprint(sgsmws.get_parameters_list())\nprint(sgsmws.get_graph_name())\nlabels = {}\nsgsmws.get_weights_labels_funcdata(labels)\nprint('wts labels',labels)\nfor _ in range(5):\n # print(sgsmsm())\n out = sgsmws()\n print(out)\n\nnpt.assert_almost_equal(out, 0.34373448930708195)\n\nsigmoid_smooth_weighted_sum SigmoidSmoothWeightedSum | weights [0.01, 0.01, 0.01] smooth 0.90 range 2.00 slope 10.00 | 0 | links constant constant1 constant2 \n[[0.01, 0.01, 0.01], 0.9, 2.0, 10.0]\nsigmoid_smooth_weighted_sum\n0.90:2.00|10.00\nwts labels {('sigmoid_smooth_weighted_sum\\n0.90:2.00|10.00', 'constant\\n10.00'): '0.01', ('sigmoid_smooth_weighted_sum\\n0.90:2.00|10.00', 'constant1\\n5.00'): '0.01', ('sigmoid_smooth_weighted_sum\\n0.90:2.00|10.00', 'constant2\\n20.00'): '0.01'}\n0.08727737447415773\n0.16473508145615878\n0.23277835967904958\n0.2921474527779875\n0.34373448930708195\n\n\nAn example showing creating and running a smoothed WeightedSum function.\n\nwts=[1.0,1.0,1.0]\nwts=[0.01,0.01,0.01]\nsmws = SmoothWeightedSum(weights=wts, smooth_factor=0.9)\nns = smws.namespace\nsmws.add_link(Constant(10, namespace=ns))\nsmws.add_link(Constant(5, namespace=ns))\nsmws.add_link(Constant(20, namespace=ns))\nsmws.summary()\nprint(smws.get_parameters_list())\nprint(smws.get_graph_name())\nfor _ in range(5):\n out = smws()\n print(out)\n\nnpt.assert_almost_equal(out, 0.1433285)\n\nsmooth_weighted_sum SmoothWeightedSum | weights [0.01, 0.01, 0.01] smooth 0.90 | 0 | links constant constant1 constant2 \n[[0.01, 0.01, 0.01], 0.9]\nsmooth_weighted_sum\n0.90\n0.034999999999999996\n0.0665\n0.09485\n0.120365\n0.1433285\n\n\nAn example showing creating and running a derivative function.\n\ndv = Derivative(history_length=5)\nns = dv.namespace\ncons = Constant(10, namespace=ns)\ndv.add_link(cons)\ndv.summary()\nprint(dv.get_config())\nprint(dv.get_parameters_list())\nprint(dv.get_graph_name())\nlabels = {}\ndv.get_weights_labels(labels)\nprint(labels)\nfor i in range(20):\n cons.set_value(i*i)\n out = dv()\n print(out, end=\" \")\n\nprint()\nassert out == -136\nprint(dv.history)\nassert dv.history == [225, 256, 289, 324, 361]\n\nderivative Derivative | history_length 5 | 0 | links constant \n{'type': 'Derivative', 'name': 'derivative', 'value': 0, 'links': {0: 'constant'}, 'history_length': 5}\n[5]\nderivative\n5.00\n{}\n0 -1 -4 -9 -16 -24 -32 -40 -48 -56 -64 -72 -80 -88 -96 -104 -112 -120 -128 -136 \n[225, 256, 289, 324, 361]\n\n\nAn example showing creating a derivative WeightedSum function.\n\nwts=[0.01,0.01,0.01]\ndvws = DerivativeWeightedSum(weights=wts, history_length=5)\nns = dvws.namespace\ncons = Constant(10, namespace=ns)\ndvws.add_link(cons)\n\n#dvws.add_link(Constant(10, namespace=ns))\ndvws.add_link(Constant(5, namespace=ns))\ndvws.add_link(Constant(20, namespace=ns))\ndvws.summary()\nprint(dvws.get_config())\nprint(dvws.get_parameters_list())\nprint(dvws.get_graph_name())\nlabels = {}\ndvws.get_weights_labels(labels)\nprint(labels)\nfor i in range(20):\n cons.set_value(i*i)\n out = dvws()\n print(out, end=\" \")\n\nprint()\nnpt.assert_almost_equal(out, -1.3599999999999999)\nprint(dvws.history)\nassert dvws.history == [2.5, 2.81, 3.14, 3.49, 3.86]\n\nderivative_weighted_sum DerivativeWeightedSum | weights [0.01, 0.01, 0.01] history_length 5 | 0 | links constant constant1 constant2 \n{'type': 'DerivativeWeightedSum', 'name': 'derivative_weighted_sum', 'value': 0, 'links': {0: 'constant', 1: 'constant1', 2: 'constant2'}, 'weights': [0.01, 0.01, 0.01], 'history_length': 5}\n[[0.01, 0.01, 0.01], 5]\nderivative_weighted_sum\n5\n{('derivative_weighted_sum', 'constant'): '0.01', ('derivative_weighted_sum', 'constant1'): '0.01', ('derivative_weighted_sum', 'constant2'): '0.01'}\n0.0 -0.010000000000000009 -0.040000000000000036 -0.09000000000000002 -0.16000000000000003 -0.24 -0.31999999999999995 -0.39999999999999997 -0.4800000000000001 -0.56 -0.64 -0.72 -0.7999999999999998 -0.8799999999999999 -0.96 -1.04 -1.12 -1.2000000000000002 -1.2800000000000002 -1.3599999999999999 \n[2.5, 2.81, 3.14, 3.49, 3.86]\n\n\n\nwts=[0.01,0.01,0.01]\ndvws = DerivativeWeightedSum(weights=wts, history_length=0)\nns = dvws.namespace\ncons = Constant(10, namespace=ns)\ndvws.add_link(cons)\ndvws.add_link(Constant(5, namespace=ns))\ndvws.add_link(Constant(20, namespace=ns))\ndvws.summary()\nprint(dvws.get_config())\nprint(dvws.get_parameters_list())\nprint(dvws.get_graph_name())\nlabels = {}\ndvws.get_weights_labels(labels)\nprint(labels)\nfor i in range(20):\n cons.set_value(i*i)\n out = dvws()\n print(out, end=\" \")\n\nprint()\nnpt.assert_almost_equal(out, 3.86)\n\nderivative_weighted_sum DerivativeWeightedSum | weights [0.01, 0.01, 0.01] history_length 0 | 0 | links constant constant1 constant2 \n{'type': 'DerivativeWeightedSum', 'name': 'derivative_weighted_sum', 'value': 0, 'links': {0: 'constant', 1: 'constant1', 2: 'constant2'}, 'weights': [0.01, 0.01, 0.01], 'history_length': 0}\n[[0.01, 0.01, 0.01], 0]\nderivative_weighted_sum\n0\n{('derivative_weighted_sum', 'constant'): '0.01', ('derivative_weighted_sum', 'constant1'): '0.01', ('derivative_weighted_sum', 'constant2'): '0.01'}\n0.25 0.26 0.29000000000000004 0.34 0.41000000000000003 0.5 0.61 0.74 0.8900000000000001 1.06 1.25 1.46 1.69 1.94 2.21 2.5 2.81 3.14 3.49 3.86", + "crumbs": [ + "Functions" + ] + }, + { + "objectID": "hierarchy.html", + "href": "hierarchy.html", + "title": "Hierarchy", + "section": "", + "text": "source", + "crumbs": [ + "Hierarchy" + ] + }, + { + "objectID": "hierarchy.html#defining-the-hierarchy", + "href": "hierarchy.html#defining-the-hierarchy", + "title": "Hierarchy", + "section": "Defining the Hierarchy", + "text": "Defining the Hierarchy\nDefining the PCTHierarchy\n\nsource\n\nPCTHierarchy\n\n PCTHierarchy (levels=0, cols=0, pre=None, post=None, name='pcthierarchy',\n clear_names=True, links='single', history=False,\n build=True, error_collector=None, namespace=None, **pargs)\n\nA hierarchical perceptual control system, of PCTNodes.\n\n# history=True\n# hierarchy, env = PCTHierarchy.load_from_file('testfiles/ARC/ga-000.000-s001-1x1-m007-ARC0010-9ddcf52416e60d65f19007957d07262d-consolidated.properties', min=True, render=True, history=history)\n# hierarchy.summary()\n\n# score, dfig, pfigs = PCTHierarchy.run_and_draw_hierarchy(hierarchy, env, draw_file=True, draw_figsize=(5,5), history = history, plots=\"scEdges,scZero\", steps=50)#, draw_file='/tmp/tmp.png')\n# print('Test score =',score)\n# dfig\n\n\n# for pfig in pfigs:\n# display(pfig)", + "crumbs": [ + "Hierarchy" + ] + }, + { + "objectID": "hierarchy.html#creating-a-hierarchy", + "href": "hierarchy.html#creating-a-hierarchy", + "title": "Hierarchy", + "section": "Creating a Hierarchy", + "text": "Creating a Hierarchy\nCreate a hierarchy by defining the number of rows (levels) and columns.\n\nfrom pct.functions import Constant\n\n\npre=Constant(5, name='precon')\nnamespace=pre.namespace\npost=Constant(10, name='postcon', namespace=namespace)\nhpct = PCTHierarchy(3,3, pre=[pre], post=[post], history=True, clear_names=False, links=\"dense\", namespace=namespace)\nhpct.hierarchy\n\n[[<pct.nodes.PCTNode>,\n <pct.nodes.PCTNode>,\n <pct.nodes.PCTNode>],\n [<pct.nodes.PCTNode>,\n <pct.nodes.PCTNode>,\n <pct.nodes.PCTNode>],\n [<pct.nodes.PCTNode>,\n <pct.nodes.PCTNode>,\n <pct.nodes.PCTNode>]]\n\n\n\nprint(hpct.get_summary())\n\n**************************\nPRE: 5.000 \nL0C0 0.000 0.000 0.000 0.000 \nL0C1 0.000 0.000 0.000 0.000 \nL0C2 0.000 0.000 0.000 0.000 \nL1C0 0.000 0.000 0.000 0.000 \nL1C1 0.000 0.000 0.000 0.000 \nL1C2 0.000 0.000 0.000 0.000 \nL2C0 0.000 0.000 0.000 0.000 \nL2C1 0.000 0.000 0.000 0.000 \nL2C2 0.000 0.000 0.000 0.000 \nPOST: 10.000 \n\n\n\n\nprint(hpct.get_parameters_list())\n\n[[[[5]], [[10]]], [[[[1, 1, 1]], [[0]], [[1]]], [[[1, 1, 1]], [[0]], [[1]]], [[[1, 1, 1]], [[0]], [[1]]]], [[[[1, 1, 1]], [[1, 1, 1]], [[1]]], [[[1, 1, 1]], [[1, 1, 1]], [[1]]], [[[1, 1, 1]], [[1, 1, 1]], [[1]]]], [[[[0]], [[1, 1, 1]], [[1]]], [[[0]], [[1, 1, 1]], [[1]]], [[[0]], [[1, 1, 1]], [[1]]]]]\n\n\n\nprint(hpct.get_grid())\n\n[3, 3, 3]\n\n\n\nhpct.change_namespace()\n\n\nhpct.summary()\n\n**************************\npcthierarchy PCTHierarchy [3, 3, 3] b5096973-bd48-11ef-a948-8cf8c5b8669e\n--------------------------\nPRE: precon Constant | 5 \nLevel 0 Cols 3\nlevel0col0 PCTNode b5096973-bd48-11ef-a948-8cf8c5b8669e\n----------------------------\nREF: weighted_sum WeightedSum | weights [1, 1, 1] | 0 | links proportional3 proportional4 proportional5 \nPER: variable Variable | 0 \nCOM: subtract Subtract | 0 | links weighted_sum variable \nOUT: proportional Proportional | gain 1 | 0 | links subtract \n----------------------------\nlevel0col1 PCTNode b5096973-bd48-11ef-a948-8cf8c5b8669e\n----------------------------\nREF: weighted_sum1 WeightedSum | weights [1, 1, 1] | 0 | links proportional3 proportional4 proportional5 \nPER: variable1 Variable | 0 \nCOM: subtract1 Subtract | 0 | links weighted_sum1 variable1 \nOUT: proportional1 Proportional | gain 1 | 0 | links subtract1 \n----------------------------\nlevel0col2 PCTNode b5096973-bd48-11ef-a948-8cf8c5b8669e\n----------------------------\nREF: weighted_sum2 WeightedSum | weights [1, 1, 1] | 0 | links proportional3 proportional4 proportional5 \nPER: variable2 Variable | 0 \nCOM: subtract2 Subtract | 0 | links weighted_sum2 variable2 \nOUT: proportional2 Proportional | gain 1 | 0 | links subtract2 \n----------------------------\nLevel 1 Cols 3\nlevel1col0 PCTNode b5096973-bd48-11ef-a948-8cf8c5b8669e\n----------------------------\nREF: weighted_sum4 WeightedSum | weights [1, 1, 1] | 0 | links proportional6 proportional7 proportional8 \nPER: weighted_sum3 WeightedSum | weights [1, 1, 1] | 0 | links variable variable1 variable2 \nCOM: subtract3 Subtract | 0 | links weighted_sum4 weighted_sum3 \nOUT: proportional3 Proportional | gain 1 | 0 | links subtract3 \n----------------------------\nlevel1col1 PCTNode b5096973-bd48-11ef-a948-8cf8c5b8669e\n----------------------------\nREF: weighted_sum6 WeightedSum | weights [1, 1, 1] | 0 | links proportional6 proportional7 proportional8 \nPER: weighted_sum5 WeightedSum | weights [1, 1, 1] | 0 | links variable variable1 variable2 \nCOM: subtract4 Subtract | 0 | links weighted_sum6 weighted_sum5 \nOUT: proportional4 Proportional | gain 1 | 0 | links subtract4 \n----------------------------\nlevel1col2 PCTNode b5096973-bd48-11ef-a948-8cf8c5b8669e\n----------------------------\nREF: weighted_sum8 WeightedSum | weights [1, 1, 1] | 0 | links proportional6 proportional7 proportional8 \nPER: weighted_sum7 WeightedSum | weights [1, 1, 1] | 0 | links variable variable1 variable2 \nCOM: subtract5 Subtract | 0 | links weighted_sum8 weighted_sum7 \nOUT: proportional5 Proportional | gain 1 | 0 | links subtract5 \n----------------------------\nLevel 2 Cols 3\nlevel2col0 PCTNode b5096973-bd48-11ef-a948-8cf8c5b8669e\n----------------------------\nREF: constant Constant | 0 \nPER: weighted_sum9 WeightedSum | weights [1, 1, 1] | 0 | links weighted_sum3 weighted_sum5 weighted_sum7 \nCOM: subtract6 Subtract | 0 | links constant weighted_sum9 \nOUT: proportional6 Proportional | gain 1 | 0 | links subtract6 \n----------------------------\nlevel2col1 PCTNode b5096973-bd48-11ef-a948-8cf8c5b8669e\n----------------------------\nREF: constant1 Constant | 0 \nPER: weighted_sum10 WeightedSum | weights [1, 1, 1] | 0 | links weighted_sum3 weighted_sum5 weighted_sum7 \nCOM: subtract7 Subtract | 0 | links constant1 weighted_sum10 \nOUT: proportional7 Proportional | gain 1 | 0 | links subtract7 \n----------------------------\nlevel2col2 PCTNode b5096973-bd48-11ef-a948-8cf8c5b8669e\n----------------------------\nREF: constant2 Constant | 0 \nPER: weighted_sum11 WeightedSum | weights [1, 1, 1] | 0 | links weighted_sum3 weighted_sum5 weighted_sum7 \nCOM: subtract8 Subtract | 0 | links constant2 weighted_sum11 \nOUT: proportional8 Proportional | gain 1 | 0 | links subtract8 \n----------------------------\nPOST: postcon Constant | 10 \n**************************\n\n\n\n#FunctionsList.getInstance().report()\n\nCreate a hierarchy from a configuration.\n\nconfig = hpct.get_config()\nprint(config)\n\n{'type': 'PCTHierarchy', 'name': 'pcthierarchy', 'pre': {'pre0': {'type': 'Constant', 'name': 'precon', 'value': 5, 'links': {}}}, 'levels': {'level0': {'level': 0, 'nodes': {'col0': {'col': 0, 'node': {'type': 'PCTNode', 'name': 'level0col0', 'refcoll': {'0': {'type': 'WeightedSum', 'name': 'weighted_sum', 'value': 0, 'links': {0: 'proportional3', 1: 'proportional4', 2: 'proportional5'}, 'weights': [1, 1, 1]}}, 'percoll': {'0': {'type': 'Variable', 'name': 'variable', 'value': 0, 'links': {}}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract', 'value': 0, 'links': {0: 'weighted_sum', 1: 'variable'}}}, 'outcoll': {'0': {'type': 'Proportional', 'name': 'proportional', 'value': 0, 'links': {0: 'subtract'}, 'gain': 1}}}}, 'col1': {'col': 1, 'node': {'type': 'PCTNode', 'name': 'level0col1', 'refcoll': {'0': {'type': 'WeightedSum', 'name': 'weighted_sum1', 'value': 0, 'links': {0: 'proportional3', 1: 'proportional4', 2: 'proportional5'}, 'weights': [1, 1, 1]}}, 'percoll': {'0': {'type': 'Variable', 'name': 'variable1', 'value': 0, 'links': {}}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract1', 'value': 0, 'links': {0: 'weighted_sum1', 1: 'variable1'}}}, 'outcoll': {'0': {'type': 'Proportional', 'name': 'proportional1', 'value': 0, 'links': {0: 'subtract1'}, 'gain': 1}}}}, 'col2': {'col': 2, 'node': {'type': 'PCTNode', 'name': 'level0col2', 'refcoll': {'0': {'type': 'WeightedSum', 'name': 'weighted_sum2', 'value': 0, 'links': {0: 'proportional3', 1: 'proportional4', 2: 'proportional5'}, 'weights': [1, 1, 1]}}, 'percoll': {'0': {'type': 'Variable', 'name': 'variable2', 'value': 0, 'links': {}}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract2', 'value': 0, 'links': {0: 'weighted_sum2', 1: 'variable2'}}}, 'outcoll': {'0': {'type': 'Proportional', 'name': 'proportional2', 'value': 0, 'links': {0: 'subtract2'}, 'gain': 1}}}}}}, 'level1': {'level': 1, 'nodes': {'col0': {'col': 0, 'node': {'type': 'PCTNode', 'name': 'level1col0', 'refcoll': {'0': {'type': 'WeightedSum', 'name': 'weighted_sum4', 'value': 0, 'links': {0: 'proportional6', 1: 'proportional7', 2: 'proportional8'}, 'weights': [1, 1, 1]}}, 'percoll': {'0': {'type': 'WeightedSum', 'name': 'weighted_sum3', 'value': 0, 'links': {0: 'variable', 1: 'variable1', 2: 'variable2'}, 'weights': [1, 1, 1]}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract3', 'value': 0, 'links': {0: 'weighted_sum4', 1: 'weighted_sum3'}}}, 'outcoll': {'0': {'type': 'Proportional', 'name': 'proportional3', 'value': 0, 'links': {0: 'subtract3'}, 'gain': 1}}}}, 'col1': {'col': 1, 'node': {'type': 'PCTNode', 'name': 'level1col1', 'refcoll': {'0': {'type': 'WeightedSum', 'name': 'weighted_sum6', 'value': 0, 'links': {0: 'proportional6', 1: 'proportional7', 2: 'proportional8'}, 'weights': [1, 1, 1]}}, 'percoll': {'0': {'type': 'WeightedSum', 'name': 'weighted_sum5', 'value': 0, 'links': {0: 'variable', 1: 'variable1', 2: 'variable2'}, 'weights': [1, 1, 1]}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract4', 'value': 0, 'links': {0: 'weighted_sum6', 1: 'weighted_sum5'}}}, 'outcoll': {'0': {'type': 'Proportional', 'name': 'proportional4', 'value': 0, 'links': {0: 'subtract4'}, 'gain': 1}}}}, 'col2': {'col': 2, 'node': {'type': 'PCTNode', 'name': 'level1col2', 'refcoll': {'0': {'type': 'WeightedSum', 'name': 'weighted_sum8', 'value': 0, 'links': {0: 'proportional6', 1: 'proportional7', 2: 'proportional8'}, 'weights': [1, 1, 1]}}, 'percoll': {'0': {'type': 'WeightedSum', 'name': 'weighted_sum7', 'value': 0, 'links': {0: 'variable', 1: 'variable1', 2: 'variable2'}, 'weights': [1, 1, 1]}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract5', 'value': 0, 'links': {0: 'weighted_sum8', 1: 'weighted_sum7'}}}, 'outcoll': {'0': {'type': 'Proportional', 'name': 'proportional5', 'value': 0, 'links': {0: 'subtract5'}, 'gain': 1}}}}}}, 'level2': {'level': 2, 'nodes': {'col0': {'col': 0, 'node': {'type': 'PCTNode', 'name': 'level2col0', 'refcoll': {'0': {'type': 'Constant', 'name': 'constant', 'value': 0, 'links': {}}}, 'percoll': {'0': {'type': 'WeightedSum', 'name': 'weighted_sum9', 'value': 0, 'links': {0: 'weighted_sum3', 1: 'weighted_sum5', 2: 'weighted_sum7'}, 'weights': [1, 1, 1]}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract6', 'value': 0, 'links': {0: 'constant', 1: 'weighted_sum9'}}}, 'outcoll': {'0': {'type': 'Proportional', 'name': 'proportional6', 'value': 0, 'links': {0: 'subtract6'}, 'gain': 1}}}}, 'col1': {'col': 1, 'node': {'type': 'PCTNode', 'name': 'level2col1', 'refcoll': {'0': {'type': 'Constant', 'name': 'constant1', 'value': 0, 'links': {}}}, 'percoll': {'0': {'type': 'WeightedSum', 'name': 'weighted_sum10', 'value': 0, 'links': {0: 'weighted_sum3', 1: 'weighted_sum5', 2: 'weighted_sum7'}, 'weights': [1, 1, 1]}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract7', 'value': 0, 'links': {0: 'constant1', 1: 'weighted_sum10'}}}, 'outcoll': {'0': {'type': 'Proportional', 'name': 'proportional7', 'value': 0, 'links': {0: 'subtract7'}, 'gain': 1}}}}, 'col2': {'col': 2, 'node': {'type': 'PCTNode', 'name': 'level2col2', 'refcoll': {'0': {'type': 'Constant', 'name': 'constant2', 'value': 0, 'links': {}}}, 'percoll': {'0': {'type': 'WeightedSum', 'name': 'weighted_sum11', 'value': 0, 'links': {0: 'weighted_sum3', 1: 'weighted_sum5', 2: 'weighted_sum7'}, 'weights': [1, 1, 1]}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract8', 'value': 0, 'links': {0: 'constant2', 1: 'weighted_sum11'}}}, 'outcoll': {'0': {'type': 'Proportional', 'name': 'proportional8', 'value': 0, 'links': {0: 'subtract8'}, 'gain': 1}}}}}}}, 'post': {'post0': {'type': 'Constant', 'name': 'postcon', 'value': 10, 'links': {}}}}\n\n\n\n# h = PCTHierarchy.from_config(config, namespace=namespace)\nh = PCTHierarchy.from_config(config)\n\n\nassert h.get_config() == hpct.get_config()", + "crumbs": [ + "Hierarchy" + ] + }, + { + "objectID": "hierarchy.html#viewing-a-hierarchy", + "href": "hierarchy.html#viewing-a-hierarchy", + "title": "Hierarchy", + "section": "Viewing a Hierarchy", + "text": "Viewing a Hierarchy\nThe hierarchy details can be viewed as a summary.\n\nhpct.get_node(2,0).get_function('reference').set_value(1)\nhpct.get_node(2,1).get_function('reference').set_value(1)\nhpct.get_node(2,2).get_function('reference').set_value(1)\nfor level in range(3):\n for col in range(3):\n hpct.get_node(level,col).get_function('output').set_property('gain', 10)\nhpct.summary()\n\n**************************\npcthierarchy PCTHierarchy [3, 3, 3] b5096973-bd48-11ef-a948-8cf8c5b8669e\n--------------------------\nPRE: precon Constant | 5 \nLevel 0 Cols 3\nlevel0col0 PCTNode b5096973-bd48-11ef-a948-8cf8c5b8669e\n----------------------------\nREF: weighted_sum WeightedSum | weights [1, 1, 1] | 0 | links proportional3 proportional4 proportional5 \nPER: variable Variable | 0 \nCOM: subtract Subtract | 0 | links weighted_sum variable \nOUT: proportional Proportional | gain 10 | 0 | links subtract \n----------------------------\nlevel0col1 PCTNode b5096973-bd48-11ef-a948-8cf8c5b8669e\n----------------------------\nREF: weighted_sum1 WeightedSum | weights [1, 1, 1] | 0 | links proportional3 proportional4 proportional5 \nPER: variable1 Variable | 0 \nCOM: subtract1 Subtract | 0 | links weighted_sum1 variable1 \nOUT: proportional1 Proportional | gain 10 | 0 | links subtract1 \n----------------------------\nlevel0col2 PCTNode b5096973-bd48-11ef-a948-8cf8c5b8669e\n----------------------------\nREF: weighted_sum2 WeightedSum | weights [1, 1, 1] | 0 | links proportional3 proportional4 proportional5 \nPER: variable2 Variable | 0 \nCOM: subtract2 Subtract | 0 | links weighted_sum2 variable2 \nOUT: proportional2 Proportional | gain 10 | 0 | links subtract2 \n----------------------------\nLevel 1 Cols 3\nlevel1col0 PCTNode b5096973-bd48-11ef-a948-8cf8c5b8669e\n----------------------------\nREF: weighted_sum4 WeightedSum | weights [1, 1, 1] | 0 | links proportional6 proportional7 proportional8 \nPER: weighted_sum3 WeightedSum | weights [1, 1, 1] | 0 | links variable variable1 variable2 \nCOM: subtract3 Subtract | 0 | links weighted_sum4 weighted_sum3 \nOUT: proportional3 Proportional | gain 10 | 0 | links subtract3 \n----------------------------\nlevel1col1 PCTNode b5096973-bd48-11ef-a948-8cf8c5b8669e\n----------------------------\nREF: weighted_sum6 WeightedSum | weights [1, 1, 1] | 0 | links proportional6 proportional7 proportional8 \nPER: weighted_sum5 WeightedSum | weights [1, 1, 1] | 0 | links variable variable1 variable2 \nCOM: subtract4 Subtract | 0 | links weighted_sum6 weighted_sum5 \nOUT: proportional4 Proportional | gain 10 | 0 | links subtract4 \n----------------------------\nlevel1col2 PCTNode b5096973-bd48-11ef-a948-8cf8c5b8669e\n----------------------------\nREF: weighted_sum8 WeightedSum | weights [1, 1, 1] | 0 | links proportional6 proportional7 proportional8 \nPER: weighted_sum7 WeightedSum | weights [1, 1, 1] | 0 | links variable variable1 variable2 \nCOM: subtract5 Subtract | 0 | links weighted_sum8 weighted_sum7 \nOUT: proportional5 Proportional | gain 10 | 0 | links subtract5 \n----------------------------\nLevel 2 Cols 3\nlevel2col0 PCTNode b5096973-bd48-11ef-a948-8cf8c5b8669e\n----------------------------\nREF: constant Constant | 1 \nPER: weighted_sum9 WeightedSum | weights [1, 1, 1] | 0 | links weighted_sum3 weighted_sum5 weighted_sum7 \nCOM: subtract6 Subtract | 0 | links constant weighted_sum9 \nOUT: proportional6 Proportional | gain 10 | 0 | links subtract6 \n----------------------------\nlevel2col1 PCTNode b5096973-bd48-11ef-a948-8cf8c5b8669e\n----------------------------\nREF: constant1 Constant | 1 \nPER: weighted_sum10 WeightedSum | weights [1, 1, 1] | 0 | links weighted_sum3 weighted_sum5 weighted_sum7 \nCOM: subtract7 Subtract | 0 | links constant1 weighted_sum10 \nOUT: proportional7 Proportional | gain 10 | 0 | links subtract7 \n----------------------------\nlevel2col2 PCTNode b5096973-bd48-11ef-a948-8cf8c5b8669e\n----------------------------\nREF: constant2 Constant | 1 \nPER: weighted_sum11 WeightedSum | weights [1, 1, 1] | 0 | links weighted_sum3 weighted_sum5 weighted_sum7 \nCOM: subtract8 Subtract | 0 | links constant2 weighted_sum11 \nOUT: proportional8 Proportional | gain 10 | 0 | links subtract8 \n----------------------------\nPOST: postcon Constant | 10 \n**************************\n\n\nThe hierarchy details can be viewed as a configuration. That configuration can be used to create a hierarchy, as shown above.\n\nhpct.get_config()\n\n{'type': 'PCTHierarchy',\n 'name': 'pcthierarchy',\n 'pre': {'pre0': {'type': 'Constant',\n 'name': 'precon',\n 'value': 5,\n 'links': {}}},\n 'levels': {'level0': {'level': 0,\n 'nodes': {'col0': {'col': 0,\n 'node': {'type': 'PCTNode',\n 'name': 'level0col0',\n 'refcoll': {'0': {'type': 'WeightedSum',\n 'name': 'weighted_sum',\n 'value': 0,\n 'links': {0: 'proportional3', 1: 'proportional4', 2: 'proportional5'},\n 'weights': [1, 1, 1]}},\n 'percoll': {'0': {'type': 'Variable',\n 'name': 'variable',\n 'value': 0,\n 'links': {}}},\n 'comcoll': {'0': {'type': 'Subtract',\n 'name': 'subtract',\n 'value': 0,\n 'links': {0: 'weighted_sum', 1: 'variable'}}},\n 'outcoll': {'0': {'type': 'Proportional',\n 'name': 'proportional',\n 'value': 0,\n 'links': {0: 'subtract'},\n 'gain': 10}}}},\n 'col1': {'col': 1,\n 'node': {'type': 'PCTNode',\n 'name': 'level0col1',\n 'refcoll': {'0': {'type': 'WeightedSum',\n 'name': 'weighted_sum1',\n 'value': 0,\n 'links': {0: 'proportional3', 1: 'proportional4', 2: 'proportional5'},\n 'weights': [1, 1, 1]}},\n 'percoll': {'0': {'type': 'Variable',\n 'name': 'variable1',\n 'value': 0,\n 'links': {}}},\n 'comcoll': {'0': {'type': 'Subtract',\n 'name': 'subtract1',\n 'value': 0,\n 'links': {0: 'weighted_sum1', 1: 'variable1'}}},\n 'outcoll': {'0': {'type': 'Proportional',\n 'name': 'proportional1',\n 'value': 0,\n 'links': {0: 'subtract1'},\n 'gain': 10}}}},\n 'col2': {'col': 2,\n 'node': {'type': 'PCTNode',\n 'name': 'level0col2',\n 'refcoll': {'0': {'type': 'WeightedSum',\n 'name': 'weighted_sum2',\n 'value': 0,\n 'links': {0: 'proportional3', 1: 'proportional4', 2: 'proportional5'},\n 'weights': [1, 1, 1]}},\n 'percoll': {'0': {'type': 'Variable',\n 'name': 'variable2',\n 'value': 0,\n 'links': {}}},\n 'comcoll': {'0': {'type': 'Subtract',\n 'name': 'subtract2',\n 'value': 0,\n 'links': {0: 'weighted_sum2', 1: 'variable2'}}},\n 'outcoll': {'0': {'type': 'Proportional',\n 'name': 'proportional2',\n 'value': 0,\n 'links': {0: 'subtract2'},\n 'gain': 10}}}}}},\n 'level1': {'level': 1,\n 'nodes': {'col0': {'col': 0,\n 'node': {'type': 'PCTNode',\n 'name': 'level1col0',\n 'refcoll': {'0': {'type': 'WeightedSum',\n 'name': 'weighted_sum4',\n 'value': 0,\n 'links': {0: 'proportional6', 1: 'proportional7', 2: 'proportional8'},\n 'weights': [1, 1, 1]}},\n 'percoll': {'0': {'type': 'WeightedSum',\n 'name': 'weighted_sum3',\n 'value': 0,\n 'links': {0: 'variable', 1: 'variable1', 2: 'variable2'},\n 'weights': [1, 1, 1]}},\n 'comcoll': {'0': {'type': 'Subtract',\n 'name': 'subtract3',\n 'value': 0,\n 'links': {0: 'weighted_sum4', 1: 'weighted_sum3'}}},\n 'outcoll': {'0': {'type': 'Proportional',\n 'name': 'proportional3',\n 'value': 0,\n 'links': {0: 'subtract3'},\n 'gain': 10}}}},\n 'col1': {'col': 1,\n 'node': {'type': 'PCTNode',\n 'name': 'level1col1',\n 'refcoll': {'0': {'type': 'WeightedSum',\n 'name': 'weighted_sum6',\n 'value': 0,\n 'links': {0: 'proportional6', 1: 'proportional7', 2: 'proportional8'},\n 'weights': [1, 1, 1]}},\n 'percoll': {'0': {'type': 'WeightedSum',\n 'name': 'weighted_sum5',\n 'value': 0,\n 'links': {0: 'variable', 1: 'variable1', 2: 'variable2'},\n 'weights': [1, 1, 1]}},\n 'comcoll': {'0': {'type': 'Subtract',\n 'name': 'subtract4',\n 'value': 0,\n 'links': {0: 'weighted_sum6', 1: 'weighted_sum5'}}},\n 'outcoll': {'0': {'type': 'Proportional',\n 'name': 'proportional4',\n 'value': 0,\n 'links': {0: 'subtract4'},\n 'gain': 10}}}},\n 'col2': {'col': 2,\n 'node': {'type': 'PCTNode',\n 'name': 'level1col2',\n 'refcoll': {'0': {'type': 'WeightedSum',\n 'name': 'weighted_sum8',\n 'value': 0,\n 'links': {0: 'proportional6', 1: 'proportional7', 2: 'proportional8'},\n 'weights': [1, 1, 1]}},\n 'percoll': {'0': {'type': 'WeightedSum',\n 'name': 'weighted_sum7',\n 'value': 0,\n 'links': {0: 'variable', 1: 'variable1', 2: 'variable2'},\n 'weights': [1, 1, 1]}},\n 'comcoll': {'0': {'type': 'Subtract',\n 'name': 'subtract5',\n 'value': 0,\n 'links': {0: 'weighted_sum8', 1: 'weighted_sum7'}}},\n 'outcoll': {'0': {'type': 'Proportional',\n 'name': 'proportional5',\n 'value': 0,\n 'links': {0: 'subtract5'},\n 'gain': 10}}}}}},\n 'level2': {'level': 2,\n 'nodes': {'col0': {'col': 0,\n 'node': {'type': 'PCTNode',\n 'name': 'level2col0',\n 'refcoll': {'0': {'type': 'Constant',\n 'name': 'constant',\n 'value': 1,\n 'links': {}}},\n 'percoll': {'0': {'type': 'WeightedSum',\n 'name': 'weighted_sum9',\n 'value': 0,\n 'links': {0: 'weighted_sum3', 1: 'weighted_sum5', 2: 'weighted_sum7'},\n 'weights': [1, 1, 1]}},\n 'comcoll': {'0': {'type': 'Subtract',\n 'name': 'subtract6',\n 'value': 0,\n 'links': {0: 'constant', 1: 'weighted_sum9'}}},\n 'outcoll': {'0': {'type': 'Proportional',\n 'name': 'proportional6',\n 'value': 0,\n 'links': {0: 'subtract6'},\n 'gain': 10}}}},\n 'col1': {'col': 1,\n 'node': {'type': 'PCTNode',\n 'name': 'level2col1',\n 'refcoll': {'0': {'type': 'Constant',\n 'name': 'constant1',\n 'value': 1,\n 'links': {}}},\n 'percoll': {'0': {'type': 'WeightedSum',\n 'name': 'weighted_sum10',\n 'value': 0,\n 'links': {0: 'weighted_sum3', 1: 'weighted_sum5', 2: 'weighted_sum7'},\n 'weights': [1, 1, 1]}},\n 'comcoll': {'0': {'type': 'Subtract',\n 'name': 'subtract7',\n 'value': 0,\n 'links': {0: 'constant1', 1: 'weighted_sum10'}}},\n 'outcoll': {'0': {'type': 'Proportional',\n 'name': 'proportional7',\n 'value': 0,\n 'links': {0: 'subtract7'},\n 'gain': 10}}}},\n 'col2': {'col': 2,\n 'node': {'type': 'PCTNode',\n 'name': 'level2col2',\n 'refcoll': {'0': {'type': 'Constant',\n 'name': 'constant2',\n 'value': 1,\n 'links': {}}},\n 'percoll': {'0': {'type': 'WeightedSum',\n 'name': 'weighted_sum11',\n 'value': 0,\n 'links': {0: 'weighted_sum3', 1: 'weighted_sum5', 2: 'weighted_sum7'},\n 'weights': [1, 1, 1]}},\n 'comcoll': {'0': {'type': 'Subtract',\n 'name': 'subtract8',\n 'value': 0,\n 'links': {0: 'constant2', 1: 'weighted_sum11'}}},\n 'outcoll': {'0': {'type': 'Proportional',\n 'name': 'proportional8',\n 'value': 0,\n 'links': {0: 'subtract8'},\n 'gain': 10}}}}}}},\n 'post': {'post0': {'type': 'Constant',\n 'name': 'postcon',\n 'value': 10,\n 'links': {}}}}\n\n\nGet the output function, which will be the output function of the last node, or the last item of the post-processor functions, if present.\n\nlink = hpct.get_output_function()\nprint(link.get_config())\n\n{'type': 'Constant', 'name': 'postcon', 'value': 10, 'links': {}}\n\n\nThe hierarhcy can also be viewed graphically as a network of connected nodes.\n\nimport os\n\n\nahpct = PCTHierarchy(2,2, links=\"dense\")\n\ntest = 3\nif test==1:\n g = ahpct.graph()\n pos=graphviz_layout(g, prog='dot')\n nx.draw(g, pos=pos, with_labels=True, font_size=12, font_weight='bold', node_color='red', node_size=500)\n\nif test ==2:\n g = ahpct.graph()\n pos = nx.multipartite_layout(g, subset_key=\"layer\", align='horizontal')\n pos['constant1'][0]+=0.2\n c = pos['constant1'][0]\n print(c)\n nx.draw(g, pos=pos, with_labels=True, font_weight='bold', node_color='red', node_size=750, arrowsize=25)\n\nif test ==3:\n if os.name=='nt': \n ahpct.draw(file=\"ahpct.png\", node_size=1500, figsize=(10,10))# with_labels=True, font_weight='bold', node_color='red', node_size=500, arrowsize=25, align='vertical'\n\nC:\\Users\\ryoung\\AppData\\Local\\Temp\\ipykernel_988\\790717467.py:277: UserWarning:\n\nThis figure includes Axes that are not compatible with tight_layout, so results might be incorrect.", + "crumbs": [ + "Hierarchy" + ] + }, + { + "objectID": "hierarchy.html#running-a-hierarchy", + "href": "hierarchy.html#running-a-hierarchy", + "title": "Hierarchy", + "section": "Running a hierarchy", + "text": "Running a hierarchy\nThe hierachy can be run once by calling itself. The verbose flag will print the computations to the screen.\n\nhpct(verbose=True)\n\n5.000 \nlevel0col0 0.000 0.000 0.000 0.000 \nlevel0col1 0.000 0.000 0.000 0.000 \nlevel0col2 0.000 0.000 0.000 0.000 \nlevel1col0 0.000 0.000 0.000 0.000 \nlevel1col1 0.000 0.000 0.000 0.000 \nlevel1col2 0.000 0.000 0.000 0.000 \nlevel2col0 1.000 0.000 1.000 10.000 \nlevel2col1 1.000 0.000 1.000 10.000 \nlevel2col2 1.000 0.000 1.000 10.000 \n10.000 \n\n\n10\n\n\nA hierarchy can be executed with the “run()” method, providing the number of iterations to run.\n\nhpct1 = PCTHierarchy(3,3, pre=[pre], post=[post], history=True, links=\"dense\")\nnamespace=hpct1.namespace\nhpct1.get_node(2,0).get_function('reference').set_value(1)\nhpct1.get_node(2,1).get_function('reference').set_value(1)\nhpct1.get_node(2,2).get_function('reference').set_value(1)\nfor level in range(3):\n for col in range(3):\n hpct1.get_node(level,col).get_function('output').set_property('gain', 10)\n\nhpct1.run(10)\n\n10", + "crumbs": [ + "Hierarchy" + ] + }, + { + "objectID": "hierarchy.html#viewing-data", + "href": "hierarchy.html#viewing-data", + "title": "Hierarchy", + "section": "Viewing Data", + "text": "Viewing Data\nIf the hierarchy is created with the “history” flag equal to True, the data can be retrieved for each node. The node is accessed by specifying the row and column within the hierarchy.\n\nprint(hpct1.get_node(1,1).history.data)\nassert hpct1.get_node(1,1).history.data == {'refcoll': {'weighted_sum6': [0.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0]}, 'percoll': {'weighted_sum5': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, 'comcoll': {'subtract4': [0.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0]}, 'outcoll': {'proportional4': [0.0, 300.0, 300.0, 300.0, 300.0, 300.0, 300.0, 300.0, 300.0, 300.0]}}\n\n{'refcoll': {'weighted_sum6': [0, 30, 30, 30, 30, 30, 30, 30, 30, 30]}, 'percoll': {'weighted_sum5': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}, 'comcoll': {'subtract4': [0, 30, 30, 30, 30, 30, 30, 30, 30, 30]}, 'outcoll': {'proportional4': [0, 300, 300, 300, 300, 300, 300, 300, 300, 300]}}", + "crumbs": [ + "Hierarchy" + ] + }, + { + "objectID": "hierarchy.html#save-and-load", + "href": "hierarchy.html#save-and-load", + "title": "Hierarchy", + "section": "Save and Load", + "text": "Save and Load\nSave a hierarchy to file.\n\nimport json\n\n\nhpct1.save(\"hpct.json\")\n\nCreate a hierarchy from file.\n\n#loaded = PCTHierarchy.load(\"hpct.json\", clear=False, namespace=namespace)\nloaded = PCTHierarchy.load(\"hpct.json\", clear=False)\nloaded.summary()\n\n**************************\npcthierarchy PCTHierarchy [3, 3, 3] b5b0dae1-bd48-11ef-831e-8cf8c5b8669e\n--------------------------\nPRE: precon Constant | 5 \nLevel 0 Cols 3\nlevel0col0 PCTNode b5b0dae1-bd48-11ef-831e-8cf8c5b8669e\n----------------------------\nREF: weighted_sum WeightedSum | weights [1, 1, 1] | 900 | links proportional3 proportional4 proportional5 \nPER: variable Variable | 0 \nCOM: subtract Subtract | 900 | links weighted_sum variable \nOUT: proportional Proportional | gain 10 | 9000 | links subtract \n----------------------------\nlevel0col1 PCTNode b5b0dae1-bd48-11ef-831e-8cf8c5b8669e\n----------------------------\nREF: weighted_sum1 WeightedSum | weights [1, 1, 1] | 900 | links proportional3 proportional4 proportional5 \nPER: variable1 Variable | 0 \nCOM: subtract1 Subtract | 900 | links weighted_sum1 variable1 \nOUT: proportional1 Proportional | gain 10 | 9000 | links subtract1 \n----------------------------\nlevel0col2 PCTNode b5b0dae1-bd48-11ef-831e-8cf8c5b8669e\n----------------------------\nREF: weighted_sum2 WeightedSum | weights [1, 1, 1] | 900 | links proportional3 proportional4 proportional5 \nPER: variable2 Variable | 0 \nCOM: subtract2 Subtract | 900 | links weighted_sum2 variable2 \nOUT: proportional2 Proportional | gain 10 | 9000 | links subtract2 \n----------------------------\nLevel 1 Cols 3\nlevel1col0 PCTNode b5b0dae1-bd48-11ef-831e-8cf8c5b8669e\n----------------------------\nREF: weighted_sum4 WeightedSum | weights [1, 1, 1] | 30 | links proportional6 proportional7 proportional8 \nPER: weighted_sum3 WeightedSum | weights [1, 1, 1] | 0 | links variable variable1 variable2 \nCOM: subtract3 Subtract | 30 | links weighted_sum4 weighted_sum3 \nOUT: proportional3 Proportional | gain 10 | 300 | links subtract3 \n----------------------------\nlevel1col1 PCTNode b5b0dae1-bd48-11ef-831e-8cf8c5b8669e\n----------------------------\nREF: weighted_sum6 WeightedSum | weights [1, 1, 1] | 30 | links proportional6 proportional7 proportional8 \nPER: weighted_sum5 WeightedSum | weights [1, 1, 1] | 0 | links variable variable1 variable2 \nCOM: subtract4 Subtract | 30 | links weighted_sum6 weighted_sum5 \nOUT: proportional4 Proportional | gain 10 | 300 | links subtract4 \n----------------------------\nlevel1col2 PCTNode b5b0dae1-bd48-11ef-831e-8cf8c5b8669e\n----------------------------\nREF: weighted_sum8 WeightedSum | weights [1, 1, 1] | 30 | links proportional6 proportional7 proportional8 \nPER: weighted_sum7 WeightedSum | weights [1, 1, 1] | 0 | links variable variable1 variable2 \nCOM: subtract5 Subtract | 30 | links weighted_sum8 weighted_sum7 \nOUT: proportional5 Proportional | gain 10 | 300 | links subtract5 \n----------------------------\nLevel 2 Cols 3\nlevel2col0 PCTNode b5b0dae1-bd48-11ef-831e-8cf8c5b8669e\n----------------------------\nREF: constant Constant | 1 \nPER: weighted_sum9 WeightedSum | weights [1, 1, 1] | 0 | links weighted_sum3 weighted_sum5 weighted_sum7 \nCOM: subtract6 Subtract | 1 | links constant weighted_sum9 \nOUT: proportional6 Proportional | gain 10 | 10 | links subtract6 \n----------------------------\nlevel2col1 PCTNode b5b0dae1-bd48-11ef-831e-8cf8c5b8669e\n----------------------------\nREF: constant1 Constant | 1 \nPER: weighted_sum10 WeightedSum | weights [1, 1, 1] | 0 | links weighted_sum3 weighted_sum5 weighted_sum7 \nCOM: subtract7 Subtract | 1 | links constant1 weighted_sum10 \nOUT: proportional7 Proportional | gain 10 | 10 | links subtract7 \n----------------------------\nlevel2col2 PCTNode b5b0dae1-bd48-11ef-831e-8cf8c5b8669e\n----------------------------\nREF: constant2 Constant | 1 \nPER: weighted_sum11 WeightedSum | weights [1, 1, 1] | 0 | links weighted_sum3 weighted_sum5 weighted_sum7 \nCOM: subtract8 Subtract | 1 | links constant2 weighted_sum11 \nOUT: proportional8 Proportional | gain 10 | 10 | links subtract8 \n----------------------------\nPOST: postcon Constant | 10 \n**************************\n\n\n\nimport networkx as nx\nimport matplotlib.pyplot as plt\n\n\n# https://matplotlib.org/3.1.0/gallery/color/named_colors.html\nloaded.draw(with_edge_labels=True, color_mapping={'w':'aqua','c':'limegreen','s':'goldenrod', 'p':'red', 'v':'silver'})\n\nBackend agg is non-interactive backend. Turning interactive mode off.\n\n\n\n\n\n\n\n\n\n\nExamples\nBuild a hierarchy by adding nodes and functions manually.\nCreate an empty hierarchy.\n\nmyhpct = PCTHierarchy()\nnamespace=myhpct.namespace\nmyhpct.summary(build=False)\n\n**************************\npcthierarchy PCTHierarchy [] b61220c9-bd48-11ef-940c-8cf8c5b8669e\n--------------------------\nPRE: None\nPOST: None\n**************************\n\n\nAdd a node. Then nodes at particular positions.\n\nmyhpct.add_node(PCTNode(namespace=namespace))\nmyhpct.add_node(PCTNode(namespace=namespace), level=1)\nmyhpct.add_node(PCTNode(namespace=namespace), level=0)\nmyhpct.summary(build=False)\n\n**************************\npcthierarchy PCTHierarchy [2, 1] b61220c9-bd48-11ef-940c-8cf8c5b8669e\n--------------------------\nPRE: None\nLevel 0 Cols 2\npctnode2 PCTNode b61220c9-bd48-11ef-940c-8cf8c5b8669e\n----------------------------\nREF: constant2 Constant | 0 \nPER: variable2 Variable | 0 \nCOM: subtract2 Subtract | 0 \nOUT: proportional2 Proportional | gain 1 | 0 \n----------------------------\npctnode PCTNode b61220c9-bd48-11ef-940c-8cf8c5b8669e\n----------------------------\nREF: constant Constant | 0 \nPER: variable Variable | 0 \nCOM: subtract Subtract | 0 \nOUT: proportional Proportional | gain 1 | 0 \n----------------------------\nLevel 1 Cols 1\npctnode1 PCTNode b61220c9-bd48-11ef-940c-8cf8c5b8669e\n----------------------------\nREF: constant1 Constant | 0 \nPER: variable1 Variable | 0 \nCOM: subtract1 Subtract | 0 \nOUT: proportional1 Proportional | gain 1 | 0 \n----------------------------\nPOST: None\n**************************\n\n\nReplace functions at particular positions in the hierarchy.\n\nmyhpct.insert_function(level=0, col=0, collection=\"perception\", function=Proportional(3, name=\"prop2\", namespace=namespace))\nmyhpct.insert_function(level=1, col=0, collection=\"perception\", function=WeightedSum(weights=[1,1], name=\"wsum\", namespace=namespace))\nmyhpct.insert_function(level=0, col=1, collection=\"reference\", function=Proportional(1, name=\"passthru\", namespace=namespace))\n\nAdd pre and post processor functions.\n\nmyhpct.add_preprocessor(Constant(1, name=\"cons1\", namespace=namespace))\nmyhpct.add_preprocessor(Proportional(5, name=\"prop1\", namespace=namespace))\nmyhpct.add_postprocessor(Proportional(5, name=\"postprop1\", namespace=namespace))\nmyhpct.add_postprocessor(Proportional(5, name=\"postprop2\", namespace=namespace))\n\nLink the functions together.\n\nmyhpct.set_links(\"prop1\", \"cons1\")\nmyhpct.set_links(\"prop2\", \"prop1\")\nmyhpct.add_links(\"wsum\", \"prop2\", \"variable\")\nmyhpct.set_links(\"passthru\", \"proportional1\")\nmyhpct.set_links(\"postprop1\", \"proportional\")\nmyhpct.set_links(\"postprop2\", \"postprop1\")\n\n\nmyhpct.get_node(0,0).get_function('reference').set_value(1)\nmyhpct.get_node(1,0).get_function('reference').set_value(1)\nmyhpct.get_node(0,0).get_function('output').set_property('gain', 10)\nmyhpct.get_node(0,1).get_function('output').set_property('gain', 10)\nmyhpct.get_node(1,0).get_function('output').set_property('gain', 10)\nmyhpct.summary(build=True)\n\n**************************\npcthierarchy PCTHierarchy [2, 1] b61220c9-bd48-11ef-940c-8cf8c5b8669e\n--------------------------\nPRE: cons1 Constant | 1 \nprop1 Proportional | gain 5 | 0 | links cons1 \nLevel 0 Cols 2\npctnode2 PCTNode b61220c9-bd48-11ef-940c-8cf8c5b8669e\n----------------------------\nREF: constant2 Constant | 1 \nPER: prop2 Proportional | gain 3 | 0 | links prop1 \nCOM: subtract2 Subtract | 0 | links constant2 prop2 \nOUT: proportional2 Proportional | gain 10 | 0 | links subtract2 \n----------------------------\npctnode PCTNode b61220c9-bd48-11ef-940c-8cf8c5b8669e\n----------------------------\nREF: passthru Proportional | gain 1 | 0 | links proportional1 \nPER: variable Variable | 0 \nCOM: subtract Subtract | 0 | links passthru variable \nOUT: proportional Proportional | gain 10 | 0 | links subtract \n----------------------------\nLevel 1 Cols 1\npctnode1 PCTNode b61220c9-bd48-11ef-940c-8cf8c5b8669e\n----------------------------\nREF: constant1 Constant | 1 \nPER: wsum WeightedSum | weights [1, 1] | 0 | links prop2 variable \nCOM: subtract1 Subtract | 0 | links constant1 wsum \nOUT: proportional1 Proportional | gain 10 | 0 | links subtract1 \n----------------------------\nPOST: postprop1 Proportional | gain 5 | 0 | links proportional \npostprop2 Proportional | gain 5 | 0 | links postprop1 \n**************************\n\n\n\nmyhpctconfig = myhpct.get_config()\n#print(myhpctconfig)\nassert myhpctconfig == {'type': 'PCTHierarchy', 'name': 'pcthierarchy', 'pre': {'pre0': {'type': 'Constant', 'name': 'cons1', 'value': 1, 'links': {}}, 'pre1': {'type': 'Proportional', 'name': 'prop1', 'value': 0, 'links': {0: 'cons1'}, 'gain': 5}}, 'levels': {'level0': {'level': 0, 'nodes': {'col0': {'col': 0, 'node': {'type': 'PCTNode', 'name': 'pctnode2', 'refcoll': {'0': {'type': 'Constant', 'name': 'constant2', 'value': 1, 'links': {}}}, 'percoll': {'0': {'type': 'Proportional', 'name': 'prop2', 'value': 0, 'links': {0: 'prop1'}, 'gain': 3}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract2', 'value': 0, 'links': {0: 'constant2', 1: 'prop2'}}}, 'outcoll': {'0': {'type': 'Proportional', 'name': 'proportional2', 'value': 0, 'links': {0: 'subtract2'}, 'gain': 10}}}}, 'col1': {'col': 1, 'node': {'type': 'PCTNode', 'name': 'pctnode', 'refcoll': {'0': {'type': 'Proportional', 'name': 'passthru', 'value': 0, 'links': {0: 'proportional1'}, 'gain': 1}}, 'percoll': {'0': {'type': 'Variable', 'name': 'variable', 'value': 0, 'links': {}}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract', 'value': 0, 'links': {0: 'passthru', 1: 'variable'}}}, 'outcoll': {'0': {'type': 'Proportional', 'name': 'proportional', 'value': 0, 'links': {0: 'subtract'}, 'gain': 10}}}}}}, 'level1': {'level': 1, 'nodes': {'col0': {'col': 0, 'node': {'type': 'PCTNode', 'name': 'pctnode1', 'refcoll': {'0': {'type': 'Constant', 'name': 'constant1', 'value': 1, 'links': {}}}, 'percoll': {'0': {'type': 'WeightedSum', 'name': 'wsum', 'value': 0, 'links': {0: 'prop2', 1: 'variable'}, 'weights': [1.0, 1.0]}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'subtract1', 'value': 0, 'links': {0: 'constant1', 1: 'wsum'}}}, 'outcoll': {'0': {'type': 'Proportional', 'name': 'proportional1', 'value': 0, 'links': {0: 'subtract1'}, 'gain': 10}}}}}}}, 'post': {'post0': {'type': 'Proportional', 'name': 'postprop1', 'value': 0, 'links': {0: 'proportional'}, 'gain': 5}, 'post1': {'type': 'Proportional', 'name': 'postprop2', 'value': 0, 'links': {0: 'postprop1'}, 'gain': 5}}}\n\nDefine the order in which the node will be processed.\n\nmyhpct.set_order([\"pctnode2\", \"pctnode1\", \"pctnode\"])\n\nRun the hierarchy once.\n\nout = myhpct(verbose=True)\nprint(out)\nassert out == -35000\n\n1.000 5.000 \npctnode2 1.000 15.000 -14.000 -140.000 \npctnode1 1.000 15.000 -14.000 -140.000 \npctnode -140.000 0.000 -140.000 -1400.000 \n-7000.000 -35000.000 \n-35000\n\n\n\n# config = {'type': 'Individual', 'name': 'pcthierarchy', 'pre': {'pre0': {'type': 'CartPoleV1', 'name': 'CartPoleV1', 'value': [0.03498833197860944, 0.20994561633454428, 0.012668159509212712, -0.2705237130920193, 0.047656152654718356], 'links': {0: 'Action1'}, 'env_name': 'CartPole-v1', 'reward': 1.0, 'done': False, 'info': {}}, 'pre1': {'type': 'IndexedParameter', 'name': 'ICV', 'value': 0.20994561633454428, 'links': {0: 'CartPoleV1'}, 'index': 1}, 'pre2': {'type': 'IndexedParameter', 'name': 'ICP', 'value': 0.03498833197860944, 'links': {0: 'CartPoleV1'}, 'index': 0}, 'pre3': {'type': 'IndexedParameter', 'name': 'IPV', 'value': -0.2705237130920193, 'links': {0: 'CartPoleV1'}, 'index': 3}, 'pre4': {'type': 'IndexedParameter', 'name': 'IPA', 'value': 0.012668159509212712, 'links': {0: 'CartPoleV1'}, 'index': 2}}, 'levels': {'level0': {'level': 0, 'nodes': {'col0': {'col': 0, 'node': {'type': 'PCTNode', 'name': 'L0C0', 'refcoll': {'0': {'type': 'EAConstant', 'name': 'RL0C0', 'value': 0, 'links': {}}}, 'percoll': {'0': {'type': 'EAWeightedSum', 'name': 'PL0C0', 'value': -0.2705237130920193, 'links': {0: 'ICV', 1: 'ICP', 2: 'IPV', 3: 'IPA'}, 'weights': [0, 0, 1, 0]}}, 'comcoll': {'0': {'type': 'Subtract', 'name': 'CL0C0', 'value': 0.2705237130920193, 'links': {0: 'RL0C0', 1: 'PL0C0'}}}, 'outcoll': {'0': {'type': 'EAProportional', 'name': 'OL0C0', 'value': -0.05046166000036782, 'links': {0: 'CL0C0'}, 'gain': -0.1865332226280776}}}}}}}, 'post': {'post0': {'type': 'EAWeightedSum', 'name': 'Action1', 'value': -0.005282911840894066, 'links': {0: 'OL0C0'}, 'weights': [0.10469159835121472]}}}\n# ind = PCTHierarchy.from_config(config)\n\n\nfrom pct.errors import RootSumSquaredError, TotalError\n\ner = RootSumSquaredError()\nte = TotalError(error_response=er, limit=250,min=True) \nte.add_error_data([1, 2])\nprint(te)\n\n\nhpct = PCTHierarchy(1,1,error_collector=te)\nhpct.run(steps=5, verbose=True)\n\n\nerr=te.error()\nprint(err)\n\nTotalError limit:250, limit_exceeded:False, : RootSumSquaredError error_response:2.23606797749979\n[0] \nlevel0col0 0.000 0.000 0.000 0.000 \n\nCurrent score=2.23606797749979\n[1] \nlevel0col0 0.000 0.000 0.000 0.000 \n\nCurrent score=2.23606797749979\n[2] \nlevel0col0 0.000 0.000 0.000 0.000 \n\nCurrent score=2.23606797749979\n[3] \nlevel0col0 0.000 0.000 0.000 0.000 \n\nCurrent score=2.23606797749979\n[4] \nlevel0col0 0.000 0.000 0.000 0.000 \n\nCurrent score=2.23606797749979\n2.23606797749979", + "crumbs": [ + "Hierarchy" + ] + }, + { + "objectID": "plotting.html", + "href": "plotting.html", + "title": "Plotting", + "section": "", + "text": "source\n\nSubPlotter\n\n SubPlotter (width, height, title, plotsconfig=[['title', 'xlabel',\n 'ylabel', 1, 0, 111, ['line1']]], linewidth=2,\n legend_fontsize=8)\n\nInitialize self. See help(type(self)) for accurate signature.\n\nsource\n\n\nrun_hpct_animation\n\n run_hpct_animation (hpct, counter=None, plotter=None, interval=1,\n verbose=False)\n\n\nwindow = 20\nplotter = SubPlotter(12, 4, \"MyData\", [[\"Data0\", \"time\", \"y\", 2, window, 121, ['line1','line2']], \n [\"Data1\", \"time\", \"y\", 2, window, 122, ['line1','line2']]])\n\nfor x in range(10):\n y11 = random()-0.5\n y12 = random()-0.5\n plotter.add_data(0, x, [y11, y12])\n y21 = random()-0.5\n y22 = random()-0.5 \n plotter.add_data(1, x, [y21, y22])\n \nplotter.draw()\n\n\n\n\n\n\n\n\n\nwindow = 20\nplotter = SubPlotter(12, 8, \"MyData\", [[\"Data0\", \"time\", \"y\", 2, window, 211, ['line1','line2']], \n [\"Data1\", \"time\", \"y\", 2, window, 212, ['line1','line2']]])\n\nfor x in range(10):\n y11 = random()-0.5\n y12 = random()-0.5\n plotter.add_data(0, x, [y11, y12])\n y21 = random()-0.5\n y22 = random()-0.5 \n plotter.add_data(1, x, [y21, y22])\n \nplotter.draw()\n\n\n\n\n\n\n\n\n\nwindow = 20\nplotter = SubPlotter(12, 8, \"MyData\", [[\"Data0\", \"time\", \"y\", 2, window, 221, ['line1','line2']], \n [\"Data1\", \"time\", \"y\", 2, window, 222, ['line1','line2']],\n [\"Data2\", \"time\", \"y\", 2, window, 212, ['line1','line2']]])\n\nfor x in range(10):\n y11 = random()-0.5\n y12 = random()-0.5\n plotter.add_data(0, x, [y11, y12])\n y21 = random()-0.5\n y22 = random()-0.5 \n plotter.add_data(1, x, [y21, y22])\n y21 = random()-0.5\n y22 = random()-0.5 \n plotter.add_data(2, x, [y21, y22])\n \nplotter.draw()", + "crumbs": [ + "Plotting" + ] + }, + { + "objectID": "environments.html", + "href": "environments.html", + "title": "Environments", + "section": "", + "text": "source\n\nEnvironmentFactory\n\n EnvironmentFactory ()\n\nInitialize self. See help(type(self)) for accurate signature.\n\nsource\n\n\nControlEnvironment\n\n ControlEnvironment (name=None, value=None, links=None, new_name=True,\n namespace=None)\n\nAbstract ControlEnvironment\n\nsource\n\n\nOpenAIGym\n\n OpenAIGym (env_name=None, render=False, render_mode='rgb_array',\n video_wrap=False, value=0, name='gym', seed=None, links=None,\n new_name=True, early_termination=False, namespace=None,\n **cargs)\n\nA function that creates and runs an environment from OpenAI Gym. Parameter: The environment name. Flag to display environment. Links: Link to the action function.\n\nsource\n\n\nCartPoleV1\n\n CartPoleV1 (render=False, render_mode='rgb_array', video_wrap=False,\n value=0, name='CartPoleV1', seed=None, links=None,\n new_name=True, namespace=None, **cargs)\n\nA function that creates an runs the CartPole-v1 environment from OpenAI Gym. Parameter: The environment name. Flag to display environment. Links: Link to the action function.\n\nsource\n\n\nCartPoleDV1\n\n CartPoleDV1 (render=False, render_mode='rgb_array', video_wrap=False,\n value=0, name='CartPoleD-v1', seed=None, links=None,\n new_name=True, namespace=None, **cargs)\n\nA function that creates an runs the CartPole-v1 environment from OpenAI Gym. Parameter: The environment name. Flag to display environment. Links: Link to the action function.\n\nsource\n\n\nPendulum\n\n Pendulum (render=False, render_mode='rgb_array', video_wrap=False,\n value=0, name='Pendulum', seed=None, links=None, new_name=True,\n namespace=None, **cargs)\n\nA function that creates an runs the Pendulum-v1 environment from OpenAI Gym. Parameter: The environment name. Flag to display environment. Links: Link to the action function.\n\nclass Pendulum_1(OpenAIGym):\n \"A function that creates an runs the Pendulum-v1 environment from OpenAI Gym. Parameter: The environment name. Flag to display environment. Links: Link to the action function.\"\n # from obs[0], indices\n # 0 cos(theta) - +1 is up, -1 is down, 0 is left and right\n # 1 sin(theta) - +1 is left, -1 is right, 0 is up and down\n # 2 theta dot - +dot is anti-clockwise, -dot is clockwise\n # 3 theta dot - normalised to +/- 1 \n # 4 theta +1/-1 (added here) 1 is pointing upwards, + is anti-clockwise, - is clockwise\n # reward - -(theta^2 + 0.1*theta_dt^2 + 0.001*action^2)\n\n def __init__(self, render=False, render_mode=\"rgb_array\", video_wrap=False, value=0, name=\"Pendulum_1\", \n seed=None, links=None, new_name=True, namespace=None,**cargs): \n super().__init__('Pendulum-v1', render=render, render_mode=render_mode, video_wrap=video_wrap, value=value, name=name, seed=seed, \n links=links, new_name=new_name, namespace=namespace, **cargs)\n \n def process_hierarchy_values(self):\n self.hierarchy_values = self.links[0].get_value()\n \n def process_actions(self):\n pass\n \n def apply_actions_get_obs(self):\n return self.env.step([self.hierarchy_values])\n \n def parse_obs(self): \n self.value = self.obs[0]\n self.reward = -self.obs[1]\n self.done = self.obs[2]\n self.info = self.obs[3]\n\n def process_values(self):\n vel = self.obs[0][2]/8.0\n self.value = np.append(self.value, vel)\n x = math.copysign(math.acos(self.obs[0][0]), self.obs[0][1])/math.pi\n #theta = 100 - (10 * math.copysign(1-abs(x), x))\n theta = 100 - (10 * x)\n self.value = np.append(self.value, theta)\n \n class Factory:\n def create(self, seed=None): return Pendulum_1(seed=seed)\n class FactoryWithNamespace:\n def create(self, namespace=None, seed=None): return Pendulum_1(namespace=namespace, seed=seed)\n\n\nsource\n\n\nMountainCarV0\n\n MountainCarV0 (render=False, render_mode='rgb_array', video_wrap=False,\n value=0, name='MountainCarV0', seed=None, links=None,\n new_name=True, namespace=None, **cargs)\n\nA function that creates and runs the MountainCar-v0 environment from OpenAI Gym. Parameter: The environment name. Flag to display environment. Links: Link to the action function.\n\nsource\n\n\nMountainCarContinuousV0\n\n MountainCarContinuousV0 (render=False, render_mode='rgb_array',\n video_wrap=False, value=0,\n name='MountainCarContinuousV0', seed=None,\n links=None, new_name=True,\n early_termination=True, namespace=None, **cargs)\n\nA function that creates and runs the MountainCarContinuous-v0 environment from OpenAI Gym. Parameter: The environment name. Flag to display environment. Links: Link to the action function.\n\nsource\n\n\nWindTurbine\n\n WindTurbine (value=0, name='WindTurbine', links=None, new_name=True,\n namespace=None, seed=None, **cargs)\n\nA function that creates and runs the YawEnv environment for a wind turbine. Indexes 0 - action, 1 - yaw error, 2 - wind direction, 3 - wind speed (ignore 0).\n\nsource\n\n\nVelocityModel\n\n VelocityModel (mass=50, value=0, name='VelocityModel', links=None,\n num_links=1, new_name=True, indexes=0, namespace=None,\n **cargs)\n\nA simple model of a moving object of a particular mass. Parameters: The environment name, mass. Links: Link to the action function.\n\nsource\n\n\nDummyModel\n\n DummyModel (name='World', value=0, links=None, new_name=True,\n namespace=None, seed=None, **cargs)\n\nBase class of a PCT function. This class is not used directly by developers, but defines the functionality common to all.\n\nsource\n\n\nWebotsWrestler\n\n WebotsWrestler (render=False, value=0, name='WebotsWrestler', seed=None,\n links=None, new_name=True, early_termination=True,\n namespace=None)\n\nA function that creates and runs a Webots Wrestler robot.\n\nsource\n\n\nWebotsWrestlerSupervisor\n\n WebotsWrestlerSupervisor (render=False, value=0,\n name='WebotsWrestlerSupervisor', seed=None,\n links=None, new_name=True,\n early_termination=True, namespace=None)\n\nA function that creates and runs a Webots Wrestler robot.\n\nsource\n\n\nBridge\n\n Bridge (render=False, value=0, name='Bridge', seed=None, links=None,\n new_name=True, early_termination=True, namespace=None)\n\nAn environment function with sensors set by external system.\n\nsource\n\n\nMicroGrid\n\n MicroGrid (value=0, name='MicroGrid', links=None, new_name=True,\n namespace=None, seed=None, **cargs)\n\nA function that creates and runs the microgrid environment for an energy management system. ‘Deep reinforcement learning for energy management in a microgrid with flexible demand.’ Taha Abdelhalim Nakabi, Pekka Toivanen. https://doi.org/10.1016/j.segan.2020.100413 Inputs - st = [SoCt, BSCt, Cbt, Tt, Gt, Put, Lb,t, t]. 0 - ISC - the average SoC (state-of-charge) of the TCLs, 1 - IL - the current load value of the daily consumption pattern. 2 - IPC - the pricing counter, 3 - IBS - the battery SoC, 4 - IEG - the energy generation, 5 - IT - the temparature, 6 - IEP - the electricty prices, 7 - ITS - the time step, Actions: 0 - TCL action, Atcl, 1 - price action, Ap, 2 - energy deficiency action, Ad, 3 - energy excess action, Ae \n\nfrom pct.microgrid import MicroGridEnv0Plus\n\nenv = MicroGrid(seed=1)\nenv.summary()\n\nMicroGrid MicroGrid | 0 \n\n\n\n\nARC\n\nsource\n\n\nARC\n\n ARC (value:float=0, name:str='ARC', links:Optional[List]=None,\n new_name:bool=True, render:bool=False, seed:int=None,\n namespace:Optional[str]=None, **cargs:dict)\n\nA function that creates and runs an ARC environment from a file given the rask code.\n\nenv = ARC()\nenv.add_link(Constant(1))\n# env.add_link(Constant(0))\nproperties = { 'dir': 'C:\\\\packages\\\\arc-prize-2024', 'file_prefix':'arc-agi_training_', 'code':'007bbfb7', 'dataset': 'train', 'control_set': ['dims'], 'input_set': ['env']}\n# file_name = os.path.join(properties['dir'], properties['file_prefix']) + 'challenges.json' \n# challenges_manager = ChallengesDataManager(file_name)\n# data = challenges_manager.get_data_for_key(properties['code'])\n# properties['data']=data\n\n# env.set_properties(properties)\n# env.set_render(True)\n# env.reset()\n# print(env.env.info)\n# for i in range(6):\n# state = env()\n# env.summary() \n# # print()\n# print(env.get_config())\n# print()\n# print(env.output_string()) \n# print()\n# print(state)\n\n# env.close()\n\n\nenv.close()\n\n\n\nOpenAI Gym\nAn example showing how to use an OpenAI Gym function. And how to have another function which accesses one of the values of the gym environment.\n\nfrom pct.functions import Constant\nfrom pct.functions import IndexedParameter\nfrom pct.putils import FunctionsList\nfrom pct.functions import Proportional\n\n\nrender=False \nprint(render)\nacrobot = OpenAIGym(\"Acrobot-v1\", render=render, seed=1)\nnamespace=acrobot.namespace\nacrobot.add_link(Constant(1, namespace=namespace))\n#acrobot.get_config()\n\nFalse\n\n\n\n#acrobot()\n#print(acrobot.reward)\n#print(getattr(acrobot, \"reward\"))\n\n\nacrobot.output_string()\n\n'0.000'\n\n\n\n#acrobot.value[0]\n\nThe IndexedParameter type retrieves a value from a linked function based upon an index.\n\ncos_angle1 = IndexedParameter(0, name=\"cos_angle1\", namespace=namespace)\ncos_angle1.add_link(acrobot)\nprint(cos_angle1.get_config())\n#cos_angle1()\n\n{'type': 'IndexedParameter', 'name': 'cos_angle1', 'value': 0, 'links': {0: 'gym'}, 'index': 0}\n\n\n\nacrobot.close()\n\n\npen = Pendulum(render=True, namespace=namespace, seed=1)\npen.add_link(Constant([1], namespace=namespace))\nprint(pen.get_config())\n#pen.run(steps=10, verbose=True)\n# why TypeError: size must be two numbers?\n\n{'type': 'Pendulum', 'name': 'Pendulum', 'value': 0, 'links': {0: 'constant1'}, 'env_name': 'Pendulum-v1', 'reward': 0, 'done': False, 'info': {}}\n\n\n\npen.close()\n\n\n\nVelocity Model\n\nvm = VelocityModel(name='VelocityModel', namespace=namespace)\nvm.add_link(Constant(1, name='mycon'))\nprint(vm.summary())\nconfig = vm.get_config()\nprint(config)\nassert config == {'type': 'VelocityModel', 'name': 'VelocityModel', 'value': 0, 'links': {0: 'mycon'}, 'mass': 50}\nvm.run(steps=10, verbose=True)\n\nVelocityModel VelocityModel | 0 | links mycon \nNone\n{'type': 'VelocityModel', 'name': 'VelocityModel', 'value': 0, 'links': {0: 'mycon'}, 'mass': 50}\n0.020 0.040 0.060 0.080 0.100 0.120 0.140 0.160 0.180 0.200 \n\n\n0.19999999999999998", + "crumbs": [ + "Environments" + ] + }, + { + "objectID": "environment_processing.html", + "href": "environment_processing.html", + "title": "Environment processing", + "section": "", + "text": "source\n\n\n\n wind_turbine_results (environment_properties=None, experiment=None,\n root=None, wt='WindTurbine', verbose=None,\n early=None, min=None, comparisons=False,\n comparisons_print_plots=False, property_dir=None,\n property_file=None, plots=None,\n log_testing_to_experiment=False)", + "crumbs": [ + "Environment processing" + ] + }, + { + "objectID": "environment_processing.html#wind-turbine-results", + "href": "environment_processing.html#wind-turbine-results", + "title": "Environment processing", + "section": "", + "text": "source\n\n\n\n wind_turbine_results (environment_properties=None, experiment=None,\n root=None, wt='WindTurbine', verbose=None,\n early=None, min=None, comparisons=False,\n comparisons_print_plots=False, property_dir=None,\n property_file=None, plots=None,\n log_testing_to_experiment=False)", + "crumbs": [ + "Environment processing" + ] + }, + { + "objectID": "environment_processing.html#environmentprocessingfactory", + "href": "environment_processing.html#environmentprocessingfactory", + "title": "Environment processing", + "section": "EnvironmentProcessingFactory", + "text": "EnvironmentProcessingFactory\n\nsource\n\nEnvironmentProcessingFactory\n\n EnvironmentProcessingFactory ()\n\nInitialize self. See help(type(self)) for accurate signature.", + "crumbs": [ + "Environment processing" + ] + }, + { + "objectID": "environment_processing.html#baseenvironmentprocessing", + "href": "environment_processing.html#baseenvironmentprocessing", + "title": "Environment processing", + "section": "BaseEnvironmentProcessing", + "text": "BaseEnvironmentProcessing\n\nsource\n\nBaseEnvironmentProcessing\n\n BaseEnvironmentProcessing ()\n\nBase class of environment processing.", + "crumbs": [ + "Environment processing" + ] + }, + { + "objectID": "environment_processing.html#windturbineenvironmentprocessing", + "href": "environment_processing.html#windturbineenvironmentprocessing", + "title": "Environment processing", + "section": "WindTurbineEnvironmentProcessing", + "text": "WindTurbineEnvironmentProcessing\n\nsource\n\nWindTurbineEnvironmentProcessing\n\n WindTurbineEnvironmentProcessing ()\n\nWindTurbine environment processing.\n\nsource\n\n\nDummyEnvironmentProcessing\n\n DummyEnvironmentProcessing ()\n\nDummy environment processing.", + "crumbs": [ + "Environment processing" + ] + }, + { + "objectID": "environment_processing.html#arcenvironmentprocessing", + "href": "environment_processing.html#arcenvironmentprocessing", + "title": "Environment processing", + "section": "ARCEnvironmentProcessing", + "text": "ARCEnvironmentProcessing\n\nsource\n\nARCEnvironmentProcessing\n\n ARCEnvironmentProcessing ()\n\nARC environment processing.", + "crumbs": [ + "Environment processing" + ] + }, + { + "objectID": "environment_processing.html#arc-test", + "href": "environment_processing.html#arc-test", + "title": "Environment processing", + "section": "ARC Test", + "text": "ARC Test\n\nenv_name = 'ARC'\nargs={'file': 'ARC0701-FitnessError-CurrentError-Mode00', 'seed':1, 'drive':'G:\\\\My Drive\\\\', 'max': False, 'verbosed': {'display_env': True, 'hpct_verbose': False}}\nenv_proc = EnvironmentProcessingFactory.createEnvironmentProcessing(f'{env_name}EnvironmentProcessing')\nenv_proc.set_properties(args=args)\nws=env_proc.get_workspace()\nname = env_proc.get_experiment_name()\nprint(ws, name)\n\narc-challenge 0701-01\n\n\n\n#env_proc.results(filepath='G:\\\\My Drive\\\\data\\\\ga\\\\ARC\\\\FitnessError-MovingSumError-Mode07\\\\ga-000.000-s001-1x1-m007-ARC0009-1e1867dd3cfb6967b085ac003458aa46.properties')\n# env_proc.results(filepath='testfiles\\\\data\\\\ga\\\\ga-000.000-s001-1x1-m007-ARC0010-9ddcf52416e60d65f19007957d07262d-consolidated.properties')", + "crumbs": [ + "Environment processing" + ] + }, + { + "objectID": "yaw_module.html", + "href": "yaw_module.html", + "title": "Yaw Module", + "section": "", + "text": "source\n\nget_yaw_count\n\n get_yaw_count (nacelle_position_diff)\n\n\n\n\n\n\n\n\n\n\nType\nDetails\n\n\n\n\nnacelle_position_diff\nPandas series of nacelle position increment\n\n\n\nReturns\nnumber of yaw actuations\n\n\n\n\n\nsource\n\n\nget_time_yawing\n\n get_time_yawing (nacelle_position_diff)\n\n\n\n\n\n\n\n\n\n\nType\nDetails\n\n\n\n\nnacelle_position_diff\nPandas series of nacelle position increment\n\n\n\nReturns\ntime spent yawing in percent\n\n\n\n\n\nsource\n\n\noriented_angle\n\n oriented_angle (angle)\n\n\n\n\n\nType\nDetails\n\n\n\n\nangle\n\n\n\n\nReturns\noriented angle in range [-180,179]\n\n\n\n\n\nsource\n\n\nget_dataset_from_simu\n\n get_dataset_from_simu (path='dataset.csv', cycle_period=10,\n rolling_average_duration=20)\n\nreturns output dataset obtained of CYCA-S\n\nsource\n\n\nget_properties\n\n get_properties (properties)\n\n\nsource\n\n\nget_indexes\n\n get_indexes (model_params, properties)\n\n\nsource\n\n\ntest_trad_control\n\n test_trad_control (wind_timeseries, wind_timeseries_not_agg, agg, start,\n end, experiment=None, datatype='baseline_simu',\n outdir=None)\n\ntest CYCA-S and CYCA-L\n\nsource\n\n\nYawEnv\n\n YawEnv ()\n\n*The main OpenAI Gym class.\nIt encapsulates an environment with arbitrary behind-the-scenes dynamics. An environment can be partially or fully observed.\nThe main API methods that users of this class need to know are:\n\n:meth:step - Takes a step in the environment using an action returning the next observation, reward, if the environment terminated and observation information.\n:meth:reset - Resets the environment to an initial state, returning the initial observation and observation information.\n:meth:render - Renders the environment observation with modes depending on the output\n:meth:close - Closes the environment, important for rendering where pygame is imported\n\nAnd set the following attributes:\n\n:attr:action_space - The Space object corresponding to valid actions\n:attr:observation_space - The Space object corresponding to valid observations\n:attr:reward_range - A tuple corresponding to the minimum and maximum possible rewards\n:attr:spec - An environment spec that contains the information used to initialise the environment from gym.make\n:attr:metadata - The metadata of the environment, i.e. render modes\n:attr:np_random - The random number generator for the environment\n\nNote: a default reward range set to :math:(-\\infty,+\\infty) already exists. Set it if you want a narrower range.*\n\nsource\n\n\nget_comparaison_metrics\n\n get_comparaison_metrics (wind_direction, power_control, power_simu,\n res_model, res_baseline_simu, yaw_rate,\n yaw_power, width_bin)\n\n\nsource\n\n\ntest_model_wind\n\n test_model_wind (wind_timeseries, start_index, stop_index, ancestors,\n filter_duration, yaw_parameters, experiment=None,\n datatype='test')\n\ntest RLYCA\n\nsource\n\n\ntest_hpct_wind\n\n test_hpct_wind (file=None, plots=None, history=None, verbose=None,\n outdir=None, early=None, environment_properties=None,\n start_index=None, stop_index=None, experiment=None,\n datatype='test', draw_file=None, model_file=None,\n log_testing_to_experiment=False,\n log_experiment_figure=False, min=None)\n\ntest RLYCA", + "crumbs": [ + "Yaw Module" + ] + }, + { + "objectID": "unittests_run_hierarchies.html", + "href": "unittests_run_hierarchies.html", + "title": "unittests_run_hierarchies", + "section": "", + "text": "import unittest\nfrom os import sep\nfrom pct.putils import Timer\nfrom pct.hierarchy import PCTHierarchy\nfrom pct.environment_processing import EnvironmentProcessingFactory\n\n\nclass TestRunWindTurbine(unittest.TestCase):\n\n @classmethod\n def setUpClass(cls):\n pass\n\n\n # def test_runMicrogrid(self):\n\n # file = \"G:\\\\My Drive\\\\data\\\\ga\\\\MicroGrid\\\\RewardError-RootMeanSquareError-Mode04\\\\ga-000.017-s001-3x5-m004-MG0001-9b7851aa082d1178ee05750f4b5815ce.properties\"\n # timer = Timer()\n # timer.start()\n # _, score = PCTHierarchy.run_from_file(file, env_props=None, seed=1, render=False, move=None, min=True, history=False, \n # hpct_verbose= False, runs=None, plots_dir=None, early_termination=False)\n # print(f'Score={score:0.3f}')\n # timer.stop()\n # print(f'Mean time: {timer.mean()}')\n\n\n def test_run_s_0538_03(self):\n\n file = \"testfiles\" + sep + \"data\" + sep + \"ga\" + sep + \"WindTurbine\"+sep+\"ga--1362.401-s003-4x3-m005-WT0538-bddf277b0f729cc630efacf91b9f494f.properties\"\n timer = Timer()\n timer.start()\n _, score = PCTHierarchy.run_from_file(file, env_props=None, seed=1, render=False, move=None, min=True, history=False, \n hpct_verbose= False, runs=None, plots_dir=None, early_termination=False)\n print(f'Score={score:0.3f}')\n timer.stop()\n print(f'Mean time: {timer.mean()}')\n\n self.assertAlmostEqual(score, -1362.401471117955 )\n\n def test_run_v_0416_01(self):\n\n file = \"testfiles\" + sep + \"data\" + sep + \"ga\" + sep + \"WindTurbine\"+sep+ \"ga--2629.009-s001-5x5-m002-WT0416-31ecb19201d49e8c6f9dd1e172bd6944.properties\"\n timer = Timer()\n timer.start()\n _, score = PCTHierarchy.run_from_file(file, env_props=None, seed=1, render=False, move=None, min=True, history=False, \n hpct_verbose= False, runs=None, plots_dir=None, early_termination=False)\n print(f'Score={score:0.3f}')\n timer.stop()\n print(f'Mean time: {timer.mean()}')\n\n self.assertAlmostEqual(score, -2629.008509888531 )\n\n\nres = unittest.main(argv=[''], verbosity=3, exit=False)\n\ntest_run_s_0538_03 (__main__.TestRunWindTurbine) ... ok\ntest_run_v_0416_01 (__main__.TestRunWindTurbine) ... \n\n\nScore=-1362.401\nMean time: 1.1948626999999874\n\n\nok\n\n----------------------------------------------------------------------\nRan 2 tests in 1.758s\n\nOK\n\n\nScore=-2629.009\nMean time: 0.5607209000000068\n\n\n\nassert len(res.result.failures) == 0\nassert len(res.result.errors) == 0", + "crumbs": [ + "unittests_run_hierarchies" + ] + }, + { + "objectID": "arc.html", + "href": "arc.html", + "title": "ARC environment classes", + "section": "", + "text": "source\n\n\n\n ARCFitnessFactory ()\n\nInitialize self. See help(type(self)) for accurate signature.\n\n\n\n\n\nsource\n\n\n\n BaseARCFitness ()\n\nBase class of an ARCFitness. This class is not used directly by developers, but defines the functionality common to all.\n\n\n\n\n\nsource\n\n\n\n SumSquareOfDiff ()\n\nA function that sums the square of the diffrences of two arrays.\n\n\n\n\n\nsource\n\n\n\n AverageMaxOfDiff ()\n\nA function that sums the square of the diffrences of two arrays.\n\n\n\n\n\nsource\n\n\n\n Euclidean ()\n\nA function that sums the square of the diffrences of two arrays.\n\n# import random\n# random.seed(1)\nnp.random.seed(1)\n# Create a 2-dimensional 3 by 3 numpy array of random floats from 0 to 9\nrandom_array = np.random.uniform(4, 5, (3, 3))\nprint(random_array)\narray_of_fours = np.full((3, 3), 4)\nprint(array_of_fours)\neuc = Euclidean()\nmetric = euc(array_of_fours, random_array, ['cells'])\nprint(metric)\nnp.random.seed(2)\nrandom_array = np.random.uniform(4.49, 4.5, (3, 3))\nprint(random_array)\nmetric = euc(array_of_fours, random_array, ['cells'])\nprint(metric)\n\n[[4.417022 4.72032449 4.00011437]\n [4.30233257 4.14675589 4.09233859]\n [4.18626021 4.34556073 4.39676747]]\n[[4 4 4]\n [4 4 4]\n [4 4 4]]\n1.0610244787055576\n[[4.49435995 4.49025926 4.49549662]\n [4.49435322 4.49420368 4.49330335]\n [4.49204649 4.49619271 4.49299655]]\n0.49619270966350637", + "crumbs": [ + "ARC environment classes" + ] + }, + { + "objectID": "arc.html#factory", + "href": "arc.html#factory", + "title": "ARC environment classes", + "section": "", + "text": "source\n\n\n\n ARCFitnessFactory ()\n\nInitialize self. See help(type(self)) for accurate signature.", + "crumbs": [ + "ARC environment classes" + ] + }, + { + "objectID": "arc.html#basearcfitness", + "href": "arc.html#basearcfitness", + "title": "ARC environment classes", + "section": "", + "text": "source\n\n\n\n BaseARCFitness ()\n\nBase class of an ARCFitness. This class is not used directly by developers, but defines the functionality common to all.", + "crumbs": [ + "ARC environment classes" + ] + }, + { + "objectID": "arc.html#sumsquareofdiff", + "href": "arc.html#sumsquareofdiff", + "title": "ARC environment classes", + "section": "", + "text": "source\n\n\n\n SumSquareOfDiff ()\n\nA function that sums the square of the diffrences of two arrays.", + "crumbs": [ + "ARC environment classes" + ] + }, + { + "objectID": "arc.html#averagemaxofdiff", + "href": "arc.html#averagemaxofdiff", + "title": "ARC environment classes", + "section": "", + "text": "source\n\n\n\n AverageMaxOfDiff ()\n\nA function that sums the square of the diffrences of two arrays.", + "crumbs": [ + "ARC environment classes" + ] + }, + { + "objectID": "arc.html#euclidean", + "href": "arc.html#euclidean", + "title": "ARC environment classes", + "section": "", + "text": "source\n\n\n\n Euclidean ()\n\nA function that sums the square of the diffrences of two arrays.\n\n# import random\n# random.seed(1)\nnp.random.seed(1)\n# Create a 2-dimensional 3 by 3 numpy array of random floats from 0 to 9\nrandom_array = np.random.uniform(4, 5, (3, 3))\nprint(random_array)\narray_of_fours = np.full((3, 3), 4)\nprint(array_of_fours)\neuc = Euclidean()\nmetric = euc(array_of_fours, random_array, ['cells'])\nprint(metric)\nnp.random.seed(2)\nrandom_array = np.random.uniform(4.49, 4.5, (3, 3))\nprint(random_array)\nmetric = euc(array_of_fours, random_array, ['cells'])\nprint(metric)\n\n[[4.417022 4.72032449 4.00011437]\n [4.30233257 4.14675589 4.09233859]\n [4.18626021 4.34556073 4.39676747]]\n[[4 4 4]\n [4 4 4]\n [4 4 4]]\n1.0610244787055576\n[[4.49435995 4.49025926 4.49549662]\n [4.49435322 4.49420368 4.49330335]\n [4.49204649 4.49619271 4.49299655]]\n0.49619270966350637", + "crumbs": [ + "ARC environment classes" + ] + }, + { + "objectID": "arc.html#example", + "href": "arc.html#example", + "title": "ARC environment classes", + "section": "Example", + "text": "Example\n\nimport pygame\n\n# Example usage:\nif os.name == 'nt': \n props = {'dir': 'C:\\\\packages\\\\arc-prize-2024', 'file_prefix':'arc-agi_training_', 'code':'007bbfb7', 'control_set': ['dims'], 'input_set': ['env'],'dataset': 'train'}\n file_name = os.path.join(props['dir'], props['file_prefix']) + 'challenges.json' \n challenges_manager = ChallengesDataManager(file_name)\n data = challenges_manager.get_data_for_key(props['code'])\n print(data)\n # arc_dict={}\n # arc_dict['data'] = data\n arc_env = ARCEnv()\n arc_env.initialise(props, data)\n arc_env.render()\n # print(state, fitness, done)\n # print(arc_env.dimensions)\n for i in range(6):\n state, fitness, done, info = arc_env.step([1])\n # print(state, fitness, done)\n print(state, fitness, done, info)\n arc_env.render()\n sleep(1)\n\n{'test': [{'input': [[7, 0, 7], [7, 0, 7], [7, 7, 0]]}], 'train': [{'input': [[0, 7, 7], [7, 7, 7], [0, 7, 7]], 'output': [[0, 0, 0, 0, 7, 7, 0, 7, 7], [0, 0, 0, 7, 7, 7, 7, 7, 7], [0, 0, 0, 0, 7, 7, 0, 7, 7], [0, 7, 7, 0, 7, 7, 0, 7, 7], [7, 7, 7, 7, 7, 7, 7, 7, 7], [0, 7, 7, 0, 7, 7, 0, 7, 7], [0, 0, 0, 0, 7, 7, 0, 7, 7], [0, 0, 0, 7, 7, 7, 7, 7, 7], [0, 0, 0, 0, 7, 7, 0, 7, 7]]}, {'input': [[4, 0, 4], [0, 0, 0], [0, 4, 0]], 'output': [[4, 0, 4, 0, 0, 0, 4, 0, 4], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 0, 0, 0, 0, 0, 4, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 4, 0, 4, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 4, 0, 0, 0, 0]]}, {'input': [[0, 0, 0], [0, 0, 2], [2, 0, 2]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 2], [0, 0, 0, 0, 0, 0, 2, 0, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 2], [2, 0, 2, 0, 0, 0, 2, 0, 2]]}, {'input': [[6, 6, 0], [6, 0, 0], [0, 6, 6]], 'output': [[6, 6, 0, 6, 6, 0, 0, 0, 0], [6, 0, 0, 6, 0, 0, 0, 0, 0], [0, 6, 6, 0, 6, 6, 0, 0, 0], [6, 6, 0, 0, 0, 0, 0, 0, 0], [6, 0, 0, 0, 0, 0, 0, 0, 0], [0, 6, 6, 0, 0, 0, 0, 0, 0], [0, 0, 0, 6, 6, 0, 6, 6, 0], [0, 0, 0, 6, 0, 0, 6, 0, 0], [0, 0, 0, 0, 6, 6, 0, 6, 6]]}, {'input': [[2, 2, 2], [0, 0, 0], [0, 2, 2]], 'output': [[2, 2, 2, 2, 2, 2, 2, 2, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 2, 0, 2, 2, 0, 2, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 2, 2, 2, 2, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 2, 2, 0, 2, 2]]}]}\n{'inputs': {'dims': {'env': (4,)}}} 50 True {'num_actions': 1, 'grid_shape': 'equal', 'dims': 1}\n{'inputs': {'dims': {'env': (5,)}}} 32 True {'num_actions': 1, 'grid_shape': 'equal', 'dims': 1}\n{'inputs': {'dims': {'env': (6,)}}} 18 True {'num_actions': 1, 'grid_shape': 'equal', 'dims': 1}\n{'inputs': {'dims': {'env': (7,)}}} 8 True {'num_actions': 1, 'grid_shape': 'equal', 'dims': 1}\n{'inputs': {'dims': {'env': (8,)}}} 2 True {'num_actions': 1, 'grid_shape': 'equal', 'dims': 1}\n{'inputs': {'dims': {'env': (9,)}}} 0 True {'num_actions': 1, 'grid_shape': 'equal', 'dims': 1}\n\n\n\nsleep(2)\narc_env.close()", + "crumbs": [ + "ARC environment classes" + ] + }, + { + "objectID": "examples.html", + "href": "examples.html", + "title": "Examples", + "section": "", + "text": "The autoreload extension is already loaded. To reload it, use:\n %reload_ext autoreload\nimport gym, warnings\nrender=False\nruns=1\nrender=True\nruns=2000", + "crumbs": [ + "Examples" + ] + }, + { + "objectID": "examples.html#cartpole", + "href": "examples.html#cartpole", + "title": "Examples", + "section": "Cartpole", + "text": "Cartpole\nCartpole is an Open AI gym environment for the inverted pendulum problem. The goal is to keep the pole balanced, by moving the cart left or right.\nThe environment provides observations (perceptions) for the state of the cart and pole.\n0 - Cart Position\n1 - Cart Velocity\n2 - Pole Angle\n3 - Pole Angular Velocity\nIt takes one value, of 0 or 1, for applying a force to the left or right, respectively.\nThe PCT solution is a four-level hierarchy for controlling the perceptions at goal values. Only one goal reference is manually set, the highest level which is the pole angle of 0.\nThis example shows how a perceptual control hierarchy can be implemented with this library.\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom pct.hierarchy import PCTHierarchy\nfrom pct.putils import FunctionsList\nfrom pct.environments import CartPoleV1\nfrom pct.functions import IndexedParameter\nfrom pct.functions import Integration\nfrom pct.functions import GreaterThan\nfrom pct.functions import PassOn\n\nCreate a hierarchy of 4 levels each with one node.\n\ncartpole_hierarchy = PCTHierarchy(levels=4, cols=1, name=\"cartpoleh\", build=False)\nnamespace=cartpole_hierarchy.namespace\ncartpole_hierarchy.get_node(0, 0).name = 'cart_velocity_node'\ncartpole_hierarchy.get_node(1, 0).name = 'cart_position_node'\ncartpole_hierarchy.get_node(2, 0).name = 'pole_velocity_node'\ncartpole_hierarchy.get_node(3, 0).name = 'pole_angle_node'\n#FunctionsList.getInstance().report()\n#cartpole_hierarchy.summary(build=True)\n\nCreate the Cartpole gym environment function. This will apply the “action” output from the hierarchy and provide the new observations.\n\ncartpole = CartPoleV1(name=\"CartPole-v1\", render=render, namespace=namespace, seed=1)\n\nCreate functions for each of the observation parameters of the Cartpole environment. Insert them into the hierarchy at the desired places.\n\ncartpole_hierarchy.insert_function(level=0, col=0, collection=\"perception\", function=IndexedParameter(index=1, name=\"cart_velocity\", links=[cartpole], namespace=namespace))\ncartpole_hierarchy.insert_function(level=1, col=0, collection=\"perception\", function=IndexedParameter(index=0, name=\"cart_position\", links=[cartpole], namespace=namespace))\ncartpole_hierarchy.insert_function(level=2, col=0, collection=\"perception\", function=IndexedParameter(index=3, name=\"pole_velocity\", links=[cartpole], namespace=namespace))\ncartpole_hierarchy.insert_function(level=3, col=0, collection=\"perception\", function=IndexedParameter(index=2, name=\"pole_angle\", links=[cartpole], namespace=namespace))\n\nLink the references to the outputs of the level up.\n\ncartpole_hierarchy.insert_function(level=0, col=0, collection=\"reference\", function=PassOn(name=\"cart_velocity_reference\", links=['proportional1'], namespace=namespace))\ncartpole_hierarchy.insert_function(level=1, col=0, collection=\"reference\", function=PassOn(name=\"cart_position_reference\", links=['proportional2'], namespace=namespace))\ncartpole_hierarchy.insert_function(level=2, col=0, collection=\"reference\", function=PassOn(name=\"pole_velocity_reference\", links=['proportional3'], namespace=namespace))\n\nSet the highest level reference.\n\ntop = cartpole_hierarchy.get_function(level=3, col=0, collection=\"reference\")\ntop.set_name(\"pole_angle_reference\")\ntop.set_value(0)\n\nLink the output of the hierarchy back to the Cartpole environment.\n\ncartpole_hierarchy.summary(build=True)\n\n**************************\ncartpoleh PCTHierarchy [1, 1, 1, 1] 0a8fcbad-d648-11ee-ae91-8cf8c5b8669b\n--------------------------\nPRE: None\nLevel 0 Cols 1\ncart_velocity_node PCTNode 0a8fcbad-d648-11ee-ae91-8cf8c5b8669b\n----------------------------\nREF: cart_velocity_reference PassOn | 0 | links proportional1 \nPER: cart_velocity IndexedParameter | index 1 | 0 | links CartPole-v1 \nCOM: subtract Subtract | 0 | links cart_velocity_reference cart_velocity \nOUT: proportional Proportional | gain 1 | 0 | links subtract \n----------------------------\nLevel 1 Cols 1\ncart_position_node PCTNode 0a8fcbad-d648-11ee-ae91-8cf8c5b8669b\n----------------------------\nREF: cart_position_reference PassOn | 0 | links proportional2 \nPER: cart_position IndexedParameter | index 0 | 0 | links CartPole-v1 \nCOM: subtract1 Subtract | 0 | links cart_position_reference cart_position \nOUT: proportional1 Proportional | gain 1 | 0 | links subtract1 \n----------------------------\nLevel 2 Cols 1\npole_velocity_node PCTNode 0a8fcbad-d648-11ee-ae91-8cf8c5b8669b\n----------------------------\nREF: pole_velocity_reference PassOn | 0 | links proportional3 \nPER: pole_velocity IndexedParameter | index 3 | 0 | links CartPole-v1 \nCOM: subtract2 Subtract | 0 | links pole_velocity_reference pole_velocity \nOUT: proportional2 Proportional | gain 1 | 0 | links subtract2 \n----------------------------\nLevel 3 Cols 1\npole_angle_node PCTNode 0a8fcbad-d648-11ee-ae91-8cf8c5b8669b\n----------------------------\nREF: pole_angle_reference Constant | 0 \nPER: pole_angle IndexedParameter | index 2 | 0 | links CartPole-v1 \nCOM: subtract3 Subtract | 0 | links pole_angle_reference pole_angle \nOUT: proportional3 Proportional | gain 1 | 0 | links subtract3 \n----------------------------\nPOST: None\n**************************\n\n\n\ncartpole_hierarchy.insert_function(level=0, col=0, collection=\"output\", function=Integration(gain=-0.05, slow=4, name=\"force\", links='subtract', namespace=namespace))\n\nSet the names and gains of the output functions. This also shows another way of getting a function, by name.\n\nFunctionsList.getInstance().get_function(namespace=namespace, name=\"proportional3\").set_name(\"pole_angle_output\")\nFunctionsList.getInstance().get_function(namespace=namespace, name=\"pole_angle_output\").set_property('gain', 3.5)\n\nFunctionsList.getInstance().get_function(namespace=namespace, name=\"proportional2\").set_name(\"pole_velocity_output\")\nFunctionsList.getInstance().get_function(namespace=namespace, name=\"pole_velocity_output\").set_property('gain', 0.5)\n\nFunctionsList.getInstance().get_function(namespace=namespace, name=\"proportional1\").set_name(\"cart_position_output\")\nFunctionsList.getInstance().get_function(namespace=namespace, name=\"cart_position_output\").set_property('gain', 2)\n\nAdd a post function to convert the output to 1 or 0 as required by the Cartpole environment.\n\ngreaterthan = GreaterThan(threshold=0, upper=1, lower=0, links='force', namespace=namespace)\ncartpole_hierarchy.add_postprocessor(greaterthan)\n\nAdd the cartpole function as one that is executed before the actual hierarchy.\n\ncartpole_hierarchy.add_preprocessor(cartpole)\n\nSet the output of the hierachy as the action input to the Cartpole environment.\n\n#link = cartpole_hierarchy.get_output_function()\ncartpole.add_link(greaterthan)\n\nSit back and observe the brilliance of your efforts.\n\ncartpole_hierarchy.set_order(\"Down\")\n\n\ncartpole_hierarchy.summary()\n\n**************************\ncartpoleh PCTHierarchy [1, 1, 1, 1] 0a8fcbad-d648-11ee-ae91-8cf8c5b8669b\n--------------------------\nPRE: CartPole-v1 CartPoleV1 | 0 | links greaterthan \nLevel 3 Cols 1\npole_angle_node PCTNode 0a8fcbad-d648-11ee-ae91-8cf8c5b8669b\n----------------------------\nREF: pole_angle_reference Constant | 0 \nPER: pole_angle IndexedParameter | index 2 | 0 | links CartPole-v1 \nCOM: subtract3 Subtract | 0 | links pole_angle_reference pole_angle \nOUT: pole_angle_output Proportional | gain 3.5 | 0 | links subtract3 \n----------------------------\nLevel 2 Cols 1\npole_velocity_node PCTNode 0a8fcbad-d648-11ee-ae91-8cf8c5b8669b\n----------------------------\nREF: pole_velocity_reference PassOn | 0 | links pole_angle_output \nPER: pole_velocity IndexedParameter | index 3 | 0 | links CartPole-v1 \nCOM: subtract2 Subtract | 0 | links pole_velocity_reference pole_velocity \nOUT: pole_velocity_output Proportional | gain 0.5 | 0 | links subtract2 \n----------------------------\nLevel 1 Cols 1\ncart_position_node PCTNode 0a8fcbad-d648-11ee-ae91-8cf8c5b8669b\n----------------------------\nREF: cart_position_reference PassOn | 0 | links pole_velocity_output \nPER: cart_position IndexedParameter | index 0 | 0 | links CartPole-v1 \nCOM: subtract1 Subtract | 0 | links cart_position_reference cart_position \nOUT: cart_position_output Proportional | gain 2 | 0 | links subtract1 \n----------------------------\nLevel 0 Cols 1\ncart_velocity_node PCTNode 0a8fcbad-d648-11ee-ae91-8cf8c5b8669b\n----------------------------\nREF: cart_velocity_reference PassOn | 0 | links cart_position_output \nPER: cart_velocity IndexedParameter | index 1 | 0 | links CartPole-v1 \nCOM: subtract Subtract | 0 | links cart_velocity_reference cart_velocity \nOUT: force Integration | gain -0.05 slow 4 | 0 | links subtract \n----------------------------\nPOST: greaterthan GreaterThan | threshold 0 upper 1 lower 0 | 0 | links force \n**************************\n\n\n\ncartpole_hierarchy.draw(font_size=10, figsize=(8,12), move={'CartPole-v1': [-0.075, 0]}, node_size=1000, node_color='red')\n\n\ncartpole_hierarchy.save(\"cartpole.json\")\n\n\nimport networkx as nx\ngr = cartpole_hierarchy.graph()\nwith warnings.catch_warnings():\n warnings.simplefilter(\"ignore\")\n print(nx.info(gr))\n print(gr.nodes())\n\nDiGraph with 18 nodes and 21 edges\n['greaterthan', 'force', 'CartPole-v1', 'cart_velocity_reference', 'cart_position_output', 'subtract', 'cart_velocity', 'cart_position_reference', 'pole_velocity_output', 'subtract1', 'cart_position', 'pole_velocity_reference', 'pole_angle_output', 'subtract2', 'pole_velocity', 'pole_angle_reference', 'subtract3', 'pole_angle']\n\n\nRun the hierarchy for 500 steps.\n\ncartpole_hierarchy.run(1,verbose=False)\n\n1\n\n\n\ncartpole_hierarchy.run(runs,verbose=False)\n\n1\n\n\n\ncartpole.close()", + "crumbs": [ + "Examples" + ] + } +] \ No newline at end of file diff --git a/site_libs/bootstrap/bootstrap-ebc57992554dae5230fa85c1bd5a55fa.min.css b/site_libs/bootstrap/bootstrap-ebc57992554dae5230fa85c1bd5a55fa.min.css new file mode 100644 index 00000000..1a395db2 --- /dev/null +++ b/site_libs/bootstrap/bootstrap-ebc57992554dae5230fa85c1bd5a55fa.min.css @@ -0,0 +1,12 @@ +/*! + * Bootstrap v5.3.1 (https://getbootstrap.com/) + * Copyright 2011-2023 The Bootstrap Authors + * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE) + */@import"https://fonts.googleapis.com/css2?family=Source+Sans+Pro:wght@300;400;700&display=swap";:root,[data-bs-theme=light]{--bs-blue: #2780e3;--bs-indigo: #6610f2;--bs-purple: #613d7c;--bs-pink: #e83e8c;--bs-red: #ff0039;--bs-orange: #f0ad4e;--bs-yellow: #ff7518;--bs-green: #3fb618;--bs-teal: #20c997;--bs-cyan: #9954bb;--bs-black: #000;--bs-white: #fff;--bs-gray: #6c757d;--bs-gray-dark: #343a40;--bs-gray-100: #f8f9fa;--bs-gray-200: #e9ecef;--bs-gray-300: #dee2e6;--bs-gray-400: #ced4da;--bs-gray-500: #adb5bd;--bs-gray-600: #6c757d;--bs-gray-700: #495057;--bs-gray-800: #343a40;--bs-gray-900: #212529;--bs-default: #343a40;--bs-primary: #2780e3;--bs-secondary: #343a40;--bs-success: #3fb618;--bs-info: #9954bb;--bs-warning: #ff7518;--bs-danger: #ff0039;--bs-light: #f8f9fa;--bs-dark: #343a40;--bs-default-rgb: 52, 58, 64;--bs-primary-rgb: 39, 128, 227;--bs-secondary-rgb: 52, 58, 64;--bs-success-rgb: 63, 182, 24;--bs-info-rgb: 153, 84, 187;--bs-warning-rgb: 255, 117, 24;--bs-danger-rgb: 255, 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.dropdown-toggle-split::before{margin-right:0}.btn-sm+.dropdown-toggle-split,.btn-group-sm>.btn+.dropdown-toggle-split{padding-right:.375rem;padding-left:.375rem}.btn-lg+.dropdown-toggle-split,.btn-group-lg>.btn+.dropdown-toggle-split{padding-right:.75rem;padding-left:.75rem}.btn-group-vertical{flex-direction:column;-webkit-flex-direction:column;align-items:flex-start;-webkit-align-items:flex-start;justify-content:center;-webkit-justify-content:center}.btn-group-vertical>.btn,.btn-group-vertical>.btn-group{width:100%}.btn-group-vertical>.btn:not(:first-child),.btn-group-vertical>.btn-group:not(:first-child){margin-top:calc(1px*-1)}.nav{--bs-nav-link-padding-x: 1rem;--bs-nav-link-padding-y: 0.5rem;--bs-nav-link-font-weight: ;--bs-nav-link-color: #2761e3;--bs-nav-link-hover-color: #1f4eb6;--bs-nav-link-disabled-color: rgba(52, 58, 64, 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.offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex 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0.75rem;position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;min-width:0;height:var(--bs-card-height);color:var(--bs-body-color);word-wrap:break-word;background-color:var(--bs-card-bg);background-clip:border-box;border:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card>hr{margin-right:0;margin-left:0}.card>.list-group{border-top:inherit;border-bottom:inherit}.card>.list-group:first-child{border-top-width:0}.card>.list-group:last-child{border-bottom-width:0}.card>.card-header+.list-group,.card>.list-group+.card-footer{border-top:0}.card-body{flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-card-spacer-y) var(--bs-card-spacer-x);color:var(--bs-card-color)}.card-title{margin-bottom:var(--bs-card-title-spacer-y);color:var(--bs-card-title-color)}.card-subtitle{margin-top:calc(-0.5*var(--bs-card-title-spacer-y));margin-bottom:0;color:var(--bs-card-subtitle-color)}.card-text:last-child{margin-bottom:0}.card-link+.card-link{margin-left:var(--bs-card-spacer-x)}.card-header{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);margin-bottom:0;color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-bottom:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-footer{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-top:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-header-tabs{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-bottom:calc(-1*var(--bs-card-cap-padding-y));margin-left:calc(-0.5*var(--bs-card-cap-padding-x));border-bottom:0}.card-header-tabs .nav-link.active{background-color:var(--bs-card-bg);border-bottom-color:var(--bs-card-bg)}.card-header-pills{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-left:calc(-0.5*var(--bs-card-cap-padding-x))}.card-img-overlay{position:absolute;top:0;right:0;bottom:0;left:0;padding:var(--bs-card-img-overlay-padding)}.card-img,.card-img-top,.card-img-bottom{width:100%}.card-group>.card{margin-bottom:var(--bs-card-group-margin)}@media(min-width: 576px){.card-group{display:flex;display:-webkit-flex;flex-flow:row wrap;-webkit-flex-flow:row wrap}.card-group>.card{flex:1 0 0%;-webkit-flex:1 0 0%;margin-bottom:0}.card-group>.card+.card{margin-left:0;border-left:0}}.accordion{--bs-accordion-color: #343a40;--bs-accordion-bg: #fff;--bs-accordion-transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out, border-radius 0.15s ease;--bs-accordion-border-color: #dee2e6;--bs-accordion-border-width: 1px;--bs-accordion-border-radius: 0.25rem;--bs-accordion-inner-border-radius: calc(0.25rem - 1px);--bs-accordion-btn-padding-x: 1.25rem;--bs-accordion-btn-padding-y: 1rem;--bs-accordion-btn-color: #343a40;--bs-accordion-btn-bg: #fff;--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23343a40'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-icon-width: 1.25rem;--bs-accordion-btn-icon-transform: rotate(-180deg);--bs-accordion-btn-icon-transition: transform 0.2s ease-in-out;--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%2310335b'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: #93c0f1;--bs-accordion-btn-focus-box-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: #10335b;--bs-accordion-active-bg: #d4e6f9}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media(prefers-reduced-motion: reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1*var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:not(:first-of-type){border-top:0}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%237db3ee'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%237db3ee'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0;--bs-breadcrumb-padding-y: 0;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color: rgba(52, 58, 64, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(52, 58, 64, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #2761e3;--bs-pagination-bg: #fff;--bs-pagination-border-width: 1px;--bs-pagination-border-color: #dee2e6;--bs-pagination-border-radius: 0.25rem;--bs-pagination-hover-color: #1f4eb6;--bs-pagination-hover-bg: #f8f9fa;--bs-pagination-hover-border-color: #dee2e6;--bs-pagination-focus-color: #1f4eb6;--bs-pagination-focus-bg: #e9ecef;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #2780e3;--bs-pagination-active-border-color: #2780e3;--bs-pagination-disabled-color: rgba(52, 58, 64, 0.75);--bs-pagination-disabled-bg: #e9ecef;--bs-pagination-disabled-border-color: #dee2e6;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(1px*-1)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.2em}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: 0.25rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 0 solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.25rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:.5rem}}.progress,.progress-stacked{--bs-progress-height: 0.5rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #e9ecef;--bs-progress-border-radius: 0.25rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #2780e3;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #343a40;--bs-list-group-bg: #fff;--bs-list-group-border-color: #dee2e6;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.25rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(52, 58, 64, 0.75);--bs-list-group-action-hover-color: #000;--bs-list-group-action-hover-bg: #f8f9fa;--bs-list-group-action-active-color: #343a40;--bs-list-group-action-active-bg: #e9ecef;--bs-list-group-disabled-color: rgba(52, 58, 64, 0.75);--bs-list-group-disabled-bg: #fff;--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #2780e3;--bs-list-group-active-border-color: #2780e3;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1*var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media(min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: 0.5;--bs-btn-close-hover-opacity: 0.75;--bs-btn-close-focus-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: 0.25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:rgba(0,0,0,0) var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme=dark] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex: 1090;--bs-toast-padding-x: 0.75rem;--bs-toast-padding-y: 0.5rem;--bs-toast-spacing: 1.5rem;--bs-toast-max-width: 350px;--bs-toast-font-size:0.875rem;--bs-toast-color: ;--bs-toast-bg: rgba(255, 255, 255, 0.85);--bs-toast-border-width: 1px;--bs-toast-border-color: rgba(0, 0, 0, 0.175);--bs-toast-border-radius: 0.25rem;--bs-toast-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-toast-header-color: rgba(52, 58, 64, 0.75);--bs-toast-header-bg: rgba(255, 255, 255, 0.85);--bs-toast-header-border-color: rgba(0, 0, 0, 0.175);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 1090;position:absolute;z-index:var(--bs-toast-zindex);width:max-content;width:-webkit-max-content;width:-moz-max-content;width:-ms-max-content;width:-o-max-content;max-width:100%;pointer-events:none}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x);color:var(--bs-toast-header-color);background-color:var(--bs-toast-header-bg);background-clip:padding-box;border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color)}.toast-header .btn-close{margin-right:calc(-0.5*var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1055;--bs-modal-width: 500px;--bs-modal-padding: 1rem;--bs-modal-margin: 0.5rem;--bs-modal-color: ;--bs-modal-bg: #fff;--bs-modal-border-color: rgba(0, 0, 0, 0.175);--bs-modal-border-width: 1px;--bs-modal-border-radius: 0.5rem;--bs-modal-box-shadow: 0 0.125rem 0.25rem rgba(0, 0, 0, 0.075);--bs-modal-inner-border-radius: calc(0.5rem - 1px);--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: #dee2e6;--bs-modal-header-border-width: 1px;--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: 0.5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: #dee2e6;--bs-modal-footer-border-width: 1px;position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform .3s ease-out;transform:translate(0, -50px)}@media(prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static .modal-dialog{transform:scale(1.02)}.modal-dialog-scrollable{height:calc(100% - var(--bs-modal-margin)*2)}.modal-dialog-scrollable .modal-content{max-height:100%;overflow:hidden}.modal-dialog-scrollable .modal-body{overflow-y:auto}.modal-dialog-centered{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;min-height:calc(100% - var(--bs-modal-margin)*2)}.modal-content{position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;width:100%;color:var(--bs-modal-color);pointer-events:auto;background-color:var(--bs-modal-bg);background-clip:padding-box;border:var(--bs-modal-border-width) solid var(--bs-modal-border-color);outline:0}.modal-backdrop{--bs-backdrop-zindex: 1050;--bs-backdrop-bg: #000;--bs-backdrop-opacity: 0.5;position:fixed;top:0;left:0;z-index:var(--bs-backdrop-zindex);width:100vw;height:100vh;background-color:var(--bs-backdrop-bg)}.modal-backdrop.fade{opacity:0}.modal-backdrop.show{opacity:var(--bs-backdrop-opacity)}.modal-header{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-modal-header-padding);border-bottom:var(--bs-modal-header-border-width) solid var(--bs-modal-header-border-color)}.modal-header .btn-close{padding:calc(var(--bs-modal-header-padding-y)*.5) calc(var(--bs-modal-header-padding-x)*.5);margin:calc(-0.5*var(--bs-modal-header-padding-y)) calc(-0.5*var(--bs-modal-header-padding-x)) calc(-0.5*var(--bs-modal-header-padding-y)) auto}.modal-title{margin-bottom:0;line-height:var(--bs-modal-title-line-height)}.modal-body{position:relative;flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-modal-padding)}.modal-footer{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;flex-wrap:wrap;-webkit-flex-wrap:wrap;align-items:center;-webkit-align-items:center;justify-content:flex-end;-webkit-justify-content:flex-end;padding:calc(var(--bs-modal-padding) - var(--bs-modal-footer-gap)*.5);background-color:var(--bs-modal-footer-bg);border-top:var(--bs-modal-footer-border-width) solid var(--bs-modal-footer-border-color)}.modal-footer>*{margin:calc(var(--bs-modal-footer-gap)*.5)}@media(min-width: 576px){.modal{--bs-modal-margin: 1.75rem;--bs-modal-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15)}.modal-dialog{max-width:var(--bs-modal-width);margin-right:auto;margin-left:auto}.modal-sm{--bs-modal-width: 300px}}@media(min-width: 992px){.modal-lg,.modal-xl{--bs-modal-width: 800px}}@media(min-width: 1200px){.modal-xl{--bs-modal-width: 1140px}}.modal-fullscreen{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen .modal-content{height:100%;border:0}.modal-fullscreen .modal-body{overflow-y:auto}@media(max-width: 575.98px){.modal-fullscreen-sm-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-sm-down .modal-content{height:100%;border:0}.modal-fullscreen-sm-down .modal-body{overflow-y:auto}}@media(max-width: 767.98px){.modal-fullscreen-md-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-md-down .modal-content{height:100%;border:0}.modal-fullscreen-md-down .modal-body{overflow-y:auto}}@media(max-width: 991.98px){.modal-fullscreen-lg-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-lg-down .modal-content{height:100%;border:0}.modal-fullscreen-lg-down .modal-body{overflow-y:auto}}@media(max-width: 1199.98px){.modal-fullscreen-xl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xl-down .modal-content{height:100%;border:0}.modal-fullscreen-xl-down .modal-body{overflow-y:auto}}@media(max-width: 1399.98px){.modal-fullscreen-xxl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xxl-down .modal-content{height:100%;border:0}.modal-fullscreen-xxl-down .modal-body{overflow-y:auto}}.tooltip{--bs-tooltip-zindex: 1080;--bs-tooltip-max-width: 200px;--bs-tooltip-padding-x: 0.5rem;--bs-tooltip-padding-y: 0.25rem;--bs-tooltip-margin: ;--bs-tooltip-font-size:0.875rem;--bs-tooltip-color: #fff;--bs-tooltip-bg: #000;--bs-tooltip-border-radius: 0.25rem;--bs-tooltip-opacity: 0.9;--bs-tooltip-arrow-width: 0.8rem;--bs-tooltip-arrow-height: 0.4rem;z-index:var(--bs-tooltip-zindex);display:block;margin:var(--bs-tooltip-margin);font-family:"Source Sans Pro",-apple-system,BlinkMacSystemFont,"Segoe UI",Roboto,"Helvetica Neue",Arial,sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI 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#ff99b0;--quarto-scss-export-light-border-subtle: #e9ecef;--quarto-scss-export-dark-border-subtle: #adb5bd;--quarto-scss-export-body-text-align: ;--quarto-scss-export-body-bg: #fff;--quarto-scss-export-body-secondary-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-body-secondary-bg: #e9ecef;--quarto-scss-export-body-tertiary-color: rgba(52, 58, 64, 0.5);--quarto-scss-export-body-tertiary-bg: #f8f9fa;--quarto-scss-export-body-emphasis-color: #000;--quarto-scss-export-link-hover-color: #1f4eb6;--quarto-scss-export-link-hover-decoration: ;--quarto-scss-export-border-color-translucent: rgba(0, 0, 0, 0.175);--quarto-scss-export-component-active-bg: #2780e3;--quarto-scss-export-component-active-color: #fff;--quarto-scss-export-focus-ring-color: rgba(39, 128, 227, 0.25);--quarto-scss-export-headings-font-family: ;--quarto-scss-export-headings-font-style: ;--quarto-scss-export-display-font-family: ;--quarto-scss-export-display-font-style: ;--quarto-scss-export-blockquote-footer-color: #6c757d;--quarto-scss-export-blockquote-border-color: #e9ecef;--quarto-scss-export-hr-bg-color: ;--quarto-scss-export-hr-height: ;--quarto-scss-export-hr-border-color: ;--quarto-scss-export-legend-font-weight: ;--quarto-scss-export-mark-bg: #ffe3d1;--quarto-scss-export-table-color: #343a40;--quarto-scss-export-table-bg: #fff;--quarto-scss-export-table-accent-bg: transparent;--quarto-scss-export-table-th-font-weight: ;--quarto-scss-export-table-striped-color: #343a40;--quarto-scss-export-table-striped-bg: rgba(0, 0, 0, 0.05);--quarto-scss-export-table-active-color: #343a40;--quarto-scss-export-table-active-bg: rgba(0, 0, 0, 0.1);--quarto-scss-export-table-hover-color: #343a40;--quarto-scss-export-table-hover-bg: rgba(0, 0, 0, 0.075);--quarto-scss-export-table-caption-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-input-btn-font-family: ;--quarto-scss-export-input-btn-focus-color: rgba(39, 128, 227, 0.25);--quarto-scss-export-btn-color: #343a40;--quarto-scss-export-btn-font-family: ;--quarto-scss-export-btn-white-space: ;--quarto-scss-export-btn-link-color: #2761e3;--quarto-scss-export-btn-link-hover-color: #1f4eb6;--quarto-scss-export-btn-link-disabled-color: #6c757d;--quarto-scss-export-form-text-font-style: ;--quarto-scss-export-form-text-font-weight: ;--quarto-scss-export-form-text-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-form-label-font-size: ;--quarto-scss-export-form-label-font-style: ;--quarto-scss-export-form-label-font-weight: ;--quarto-scss-export-form-label-color: ;--quarto-scss-export-input-font-family: ;--quarto-scss-export-input-disabled-color: ;--quarto-scss-export-input-disabled-bg: #e9ecef;--quarto-scss-export-input-disabled-border-color: ;--quarto-scss-export-input-color: #343a40;--quarto-scss-export-input-focus-bg: #fff;--quarto-scss-export-input-focus-border-color: #93c0f1;--quarto-scss-export-input-focus-color: #343a40;--quarto-scss-export-input-placeholder-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-input-plaintext-color: #343a40;--quarto-scss-export-form-check-label-color: ;--quarto-scss-export-form-check-transition: ;--quarto-scss-export-form-check-input-bg: #fff;--quarto-scss-export-form-check-input-focus-border: #93c0f1;--quarto-scss-export-form-check-input-checked-color: #fff;--quarto-scss-export-form-check-input-checked-bg-color: #2780e3;--quarto-scss-export-form-check-input-checked-border-color: #2780e3;--quarto-scss-export-form-check-input-indeterminate-color: #fff;--quarto-scss-export-form-check-input-indeterminate-bg-color: #2780e3;--quarto-scss-export-form-check-input-indeterminate-border-color: #2780e3;--quarto-scss-export-form-switch-color: rgba(0, 0, 0, 0.25);--quarto-scss-export-form-switch-focus-color: #93c0f1;--quarto-scss-export-form-switch-checked-color: #fff;--quarto-scss-export-input-group-addon-color: #343a40;--quarto-scss-export-input-group-addon-bg: #f8f9fa;--quarto-scss-export-input-group-addon-border-color: #dee2e6;--quarto-scss-export-form-select-font-family: ;--quarto-scss-export-form-select-color: #343a40;--quarto-scss-export-form-select-bg: #fff;--quarto-scss-export-form-select-disabled-color: ;--quarto-scss-export-form-select-disabled-bg: #e9ecef;--quarto-scss-export-form-select-disabled-border-color: ;--quarto-scss-export-form-select-indicator-color: #343a40;--quarto-scss-export-form-select-border-color: #dee2e6;--quarto-scss-export-form-select-focus-border-color: #93c0f1;--quarto-scss-export-form-range-track-bg: #f8f9fa;--quarto-scss-export-form-range-thumb-bg: #2780e3;--quarto-scss-export-form-range-thumb-active-bg: #bed9f7;--quarto-scss-export-form-range-thumb-disabled-bg: rgba(52, 58, 64, 0.75);--quarto-scss-export-form-file-button-color: #343a40;--quarto-scss-export-form-file-button-bg: #f8f9fa;--quarto-scss-export-form-file-button-hover-bg: #e9ecef;--quarto-scss-export-form-floating-label-disabled-color: #6c757d;--quarto-scss-export-form-feedback-font-style: ;--quarto-scss-export-form-feedback-valid-color: #3fb618;--quarto-scss-export-form-feedback-invalid-color: #ff0039;--quarto-scss-export-form-feedback-icon-valid-color: #3fb618;--quarto-scss-export-form-feedback-icon-invalid-color: #ff0039;--quarto-scss-export-form-valid-color: #3fb618;--quarto-scss-export-form-valid-border-color: #3fb618;--quarto-scss-export-form-invalid-color: #ff0039;--quarto-scss-export-form-invalid-border-color: #ff0039;--quarto-scss-export-nav-link-font-size: ;--quarto-scss-export-nav-link-font-weight: ;--quarto-scss-export-nav-link-color: #2761e3;--quarto-scss-export-nav-link-hover-color: #1f4eb6;--quarto-scss-export-nav-link-disabled-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-nav-tabs-border-color: #dee2e6;--quarto-scss-export-nav-tabs-link-hover-border-color: #e9ecef #e9ecef #dee2e6;--quarto-scss-export-nav-tabs-link-active-color: #000;--quarto-scss-export-nav-tabs-link-active-bg: #fff;--quarto-scss-export-nav-pills-link-active-bg: #2780e3;--quarto-scss-export-nav-pills-link-active-color: #fff;--quarto-scss-export-nav-underline-link-active-color: #000;--quarto-scss-export-navbar-padding-x: ;--quarto-scss-export-navbar-light-contrast: #fff;--quarto-scss-export-navbar-dark-contrast: #fff;--quarto-scss-export-navbar-light-icon-color: rgba(255, 255, 255, 0.75);--quarto-scss-export-navbar-dark-icon-color: rgba(255, 255, 255, 0.75);--quarto-scss-export-dropdown-color: #343a40;--quarto-scss-export-dropdown-bg: #fff;--quarto-scss-export-dropdown-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-divider-bg: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-link-color: #343a40;--quarto-scss-export-dropdown-link-hover-color: #343a40;--quarto-scss-export-dropdown-link-hover-bg: #f8f9fa;--quarto-scss-export-dropdown-link-active-bg: #2780e3;--quarto-scss-export-dropdown-link-active-color: #fff;--quarto-scss-export-dropdown-link-disabled-color: rgba(52, 58, 64, 0.5);--quarto-scss-export-dropdown-header-color: #6c757d;--quarto-scss-export-dropdown-dark-color: #dee2e6;--quarto-scss-export-dropdown-dark-bg: #343a40;--quarto-scss-export-dropdown-dark-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-dark-divider-bg: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-dark-box-shadow: ;--quarto-scss-export-dropdown-dark-link-color: #dee2e6;--quarto-scss-export-dropdown-dark-link-hover-color: #fff;--quarto-scss-export-dropdown-dark-link-hover-bg: rgba(255, 255, 255, 0.15);--quarto-scss-export-dropdown-dark-link-active-color: #fff;--quarto-scss-export-dropdown-dark-link-active-bg: #2780e3;--quarto-scss-export-dropdown-dark-link-disabled-color: #adb5bd;--quarto-scss-export-dropdown-dark-header-color: #adb5bd;--quarto-scss-export-pagination-color: #2761e3;--quarto-scss-export-pagination-bg: #fff;--quarto-scss-export-pagination-border-color: #dee2e6;--quarto-scss-export-pagination-focus-color: #1f4eb6;--quarto-scss-export-pagination-focus-bg: #e9ecef;--quarto-scss-export-pagination-hover-color: #1f4eb6;--quarto-scss-export-pagination-hover-bg: #f8f9fa;--quarto-scss-export-pagination-hover-border-color: #dee2e6;--quarto-scss-export-pagination-active-color: #fff;--quarto-scss-export-pagination-active-bg: #2780e3;--quarto-scss-export-pagination-active-border-color: #2780e3;--quarto-scss-export-pagination-disabled-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-pagination-disabled-bg: #e9ecef;--quarto-scss-export-pagination-disabled-border-color: #dee2e6;--quarto-scss-export-card-title-color: ;--quarto-scss-export-card-subtitle-color: ;--quarto-scss-export-card-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-card-box-shadow: ;--quarto-scss-export-card-cap-color: ;--quarto-scss-export-card-height: ;--quarto-scss-export-card-color: ;--quarto-scss-export-card-bg: #fff;--quarto-scss-export-accordion-color: #343a40;--quarto-scss-export-accordion-bg: #fff;--quarto-scss-export-accordion-border-color: #dee2e6;--quarto-scss-export-accordion-button-color: #343a40;--quarto-scss-export-accordion-button-bg: #fff;--quarto-scss-export-accordion-button-active-bg: #d4e6f9;--quarto-scss-export-accordion-button-active-color: #10335b;--quarto-scss-export-accordion-button-focus-border-color: #93c0f1;--quarto-scss-export-accordion-icon-color: #343a40;--quarto-scss-export-accordion-icon-active-color: #10335b;--quarto-scss-export-tooltip-color: #fff;--quarto-scss-export-tooltip-bg: #000;--quarto-scss-export-tooltip-margin: ;--quarto-scss-export-tooltip-arrow-color: ;--quarto-scss-export-form-feedback-tooltip-line-height: ;--quarto-scss-export-popover-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-popover-header-bg: #e9ecef;--quarto-scss-export-popover-body-color: #343a40;--quarto-scss-export-popover-arrow-color: #fff;--quarto-scss-export-popover-arrow-outer-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-toast-color: ;--quarto-scss-export-toast-background-color: rgba(255, 255, 255, 0.85);--quarto-scss-export-toast-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-toast-header-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-toast-header-background-color: rgba(255, 255, 255, 0.85);--quarto-scss-export-toast-header-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-badge-color: #fff;--quarto-scss-export-modal-content-color: ;--quarto-scss-export-modal-content-bg: #fff;--quarto-scss-export-modal-content-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-modal-backdrop-bg: #000;--quarto-scss-export-modal-header-border-color: #dee2e6;--quarto-scss-export-modal-footer-bg: ;--quarto-scss-export-modal-footer-border-color: #dee2e6;--quarto-scss-export-progress-bg: #e9ecef;--quarto-scss-export-progress-bar-color: #fff;--quarto-scss-export-progress-bar-bg: #2780e3;--quarto-scss-export-list-group-color: #343a40;--quarto-scss-export-list-group-bg: #fff;--quarto-scss-export-list-group-border-color: #dee2e6;--quarto-scss-export-list-group-hover-bg: #f8f9fa;--quarto-scss-export-list-group-active-bg: #2780e3;--quarto-scss-export-list-group-active-color: #fff;--quarto-scss-export-list-group-active-border-color: #2780e3;--quarto-scss-export-list-group-disabled-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-list-group-disabled-bg: #fff;--quarto-scss-export-list-group-action-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-list-group-action-hover-color: #000;--quarto-scss-export-list-group-action-active-color: #343a40;--quarto-scss-export-list-group-action-active-bg: #e9ecef;--quarto-scss-export-thumbnail-bg: #fff;--quarto-scss-export-thumbnail-border-color: #dee2e6;--quarto-scss-export-figure-caption-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-breadcrumb-font-size: ;--quarto-scss-export-breadcrumb-bg: ;--quarto-scss-export-breadcrumb-divider-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-breadcrumb-active-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-breadcrumb-border-radius: ;--quarto-scss-export-carousel-control-color: #fff;--quarto-scss-export-carousel-indicator-active-bg: #fff;--quarto-scss-export-carousel-caption-color: #fff;--quarto-scss-export-carousel-dark-indicator-active-bg: #000;--quarto-scss-export-carousel-dark-caption-color: #000;--quarto-scss-export-btn-close-color: #000;--quarto-scss-export-offcanvas-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-offcanvas-bg-color: #fff;--quarto-scss-export-offcanvas-color: #343a40;--quarto-scss-export-offcanvas-backdrop-bg: #000;--quarto-scss-export-code-color-dark: white;--quarto-scss-export-kbd-color: #fff;--quarto-scss-export-kbd-bg: #343a40;--quarto-scss-export-nested-kbd-font-weight: ;--quarto-scss-export-pre-bg: #f8f9fa;--quarto-scss-export-pre-color: #000;--quarto-scss-export-bslib-sidebar-bg: rgba(var(--bs-emphasis-color-rgb, 0, 0, 0), 0.05);--quarto-scss-export-bslib-sidebar-toggle-bg: rgba(var(--bs-emphasis-color-rgb, 0, 0, 0), 0.1);--quarto-scss-export-bslib-page-sidebar-title-bg: #2780e3;--quarto-scss-export-bslib-page-sidebar-title-color: #fff;--quarto-scss-export-sidebar-color: #595959;--quarto-scss-export-sidebar-hover-color: rgba(33, 81, 191, 0.8);--quarto-scss-export-sidebar-disabled-color: rgba(89, 89, 89, 0.75);--quarto-scss-export-valuebox-bg-primary: #5397e9;--quarto-scss-export-valuebox-bg-secondary: #343a40;--quarto-scss-export-valuebox-bg-success: #3aa716;--quarto-scss-export-valuebox-bg-info: rgba(153, 84, 187, 0.7019607843);--quarto-scss-export-valuebox-bg-warning: #fa6400;--quarto-scss-export-valuebox-bg-danger: rgba(255, 0, 57, 0.7019607843);--quarto-scss-export-valuebox-bg-light: #f8f9fa;--quarto-scss-export-valuebox-bg-dark: #343a40;--quarto-scss-export-mermaid-bg-color: #fff;--quarto-scss-export-mermaid-edge-color: #343a40;--quarto-scss-export-mermaid-node-fg-color: #343a40;--quarto-scss-export-mermaid-fg-color: #343a40;--quarto-scss-export-mermaid-fg-color--lighter: #4b545c;--quarto-scss-export-mermaid-fg-color--lightest: #626d78;--quarto-scss-export-mermaid-label-bg-color: #fff;--quarto-scss-export-mermaid-label-fg-color: #2780e3;--quarto-scss-export-mermaid-node-bg-color: rgba(39, 128, 227, 0.1);--quarto-scss-export-code-block-border-left-color: #dee2e6;--quarto-scss-export-callout-color-note: #2780e3;--quarto-scss-export-callout-color-tip: #3fb618;--quarto-scss-export-callout-color-important: #ff0039;--quarto-scss-export-callout-color-caution: #f0ad4e;--quarto-scss-export-callout-color-warning: #ff7518} \ No newline at end of file diff --git a/site_libs/bootstrap/bootstrap-icons.css b/site_libs/bootstrap/bootstrap-icons.css new file mode 100644 index 00000000..285e4448 --- /dev/null +++ b/site_libs/bootstrap/bootstrap-icons.css @@ -0,0 +1,2078 @@ +/*! + * Bootstrap Icons v1.11.1 (https://icons.getbootstrap.com/) + * Copyright 2019-2023 The Bootstrap Authors + * Licensed under MIT (https://github.com/twbs/icons/blob/main/LICENSE) + */ + +@font-face { + font-display: block; + font-family: "bootstrap-icons"; + src: +url("./bootstrap-icons.woff?2820a3852bdb9a5832199cc61cec4e65") format("woff"); +} + +.bi::before, +[class^="bi-"]::before, +[class*=" bi-"]::before { + display: inline-block; + font-family: bootstrap-icons !important; + font-style: normal; + font-weight: normal !important; + font-variant: normal; + text-transform: none; + line-height: 1; + vertical-align: -.125em; + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; +} + +.bi-123::before { content: "\f67f"; } +.bi-alarm-fill::before { content: "\f101"; } +.bi-alarm::before { content: "\f102"; } +.bi-align-bottom::before { content: "\f103"; } +.bi-align-center::before { content: "\f104"; } +.bi-align-end::before { content: "\f105"; } +.bi-align-middle::before { content: "\f106"; } +.bi-align-start::before { content: "\f107"; } +.bi-align-top::before { content: "\f108"; } +.bi-alt::before { content: "\f109"; } +.bi-app-indicator::before { content: "\f10a"; } +.bi-app::before { content: 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this._getItems().indexOf(t)}_setActiveIndicatorElement(t){if(!this._indicatorsElement)return;const e=z.findOne(wt,this._indicatorsElement);e.classList.remove(yt),e.removeAttribute("aria-current");const i=z.findOne(`[data-bs-slide-to="${t}"]`,this._indicatorsElement);i&&(i.classList.add(yt),i.setAttribute("aria-current","true"))}_updateInterval(){const t=this._activeElement||this._getActive();if(!t)return;const e=Number.parseInt(t.getAttribute("data-bs-interval"),10);this._config.interval=e||this._config.defaultInterval}_slide(t,e=null){if(this._isSliding)return;const i=this._getActive(),n=t===at,s=e||b(this._getItems(),i,n,this._config.wrap);if(s===i)return;const o=this._getItemIndex(s),r=e=>N.trigger(this._element,e,{relatedTarget:s,direction:this._orderToDirection(t),from:this._getItemIndex(i),to:o});if(r(dt).defaultPrevented)return;if(!i||!s)return;const a=Boolean(this._interval);this.pause(),this._isSliding=!0,this._setActiveIndicatorElement(o),this._activeElement=s;const l=n?"carousel-item-start":"carousel-item-end",c=n?"carousel-item-next":"carousel-item-prev";s.classList.add(c),d(s),i.classList.add(l),s.classList.add(l),this._queueCallback((()=>{s.classList.remove(l,c),s.classList.add(yt),i.classList.remove(yt,c,l),this._isSliding=!1,r(ut)}),i,this._isAnimated()),a&&this.cycle()}_isAnimated(){return this._element.classList.contains("slide")}_getActive(){return z.findOne(Et,this._element)}_getItems(){return z.find(At,this._element)}_clearInterval(){this._interval&&(clearInterval(this._interval),this._interval=null)}_directionToOrder(t){return p()?t===ct?lt:at:t===ct?at:lt}_orderToDirection(t){return p()?t===lt?ct:ht:t===lt?ht:ct}static jQueryInterface(t){return this.each((function(){const e=xt.getOrCreateInstance(this,t);if("number"!=typeof t){if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}else e.to(t)}))}}N.on(document,bt,"[data-bs-slide], 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i=`scroll${e[0].toUpperCase()+e.slice(1)}`;this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(Mt),this._element.classList.add(Pt,Nt),this._element.style[e]="",N.trigger(this._element,St)}),this._element,!0),this._element.style[e]=`${this._element[i]}px`}hide(){if(this._isTransitioning||!this._isShown())return;if(N.trigger(this._element,Dt).defaultPrevented)return;const t=this._getDimension();this._element.style[t]=`${this._element.getBoundingClientRect()[t]}px`,d(this._element),this._element.classList.add(Mt),this._element.classList.remove(Pt,Nt);for(const t of this._triggerArray){const e=z.getElementFromSelector(t);e&&!this._isShown(e)&&this._addAriaAndCollapsedClass([t],!1)}this._isTransitioning=!0,this._element.style[t]="",this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(Mt),this._element.classList.add(Pt),N.trigger(this._element,$t)}),this._element,!0)}_isShown(t=this._element){return 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e=t.state,i=t.name,n=e.rects.reference,s=e.rects.popper,o=e.modifiersData.preventOverflow,r=ii(e,{elementContext:"reference"}),a=ii(e,{altBoundary:!0}),l=oi(r,n),c=oi(a,s,o),h=ri(l),d=ri(c);e.modifiersData[i]={referenceClippingOffsets:l,popperEscapeOffsets:c,isReferenceHidden:h,hasPopperEscaped:d},e.attributes.popper=Object.assign({},e.attributes.popper,{"data-popper-reference-hidden":h,"data-popper-escaped":d})}},li={name:"offset",enabled:!0,phase:"main",requires:["popperOffsets"],fn:function(t){var e=t.state,i=t.options,n=t.name,s=i.offset,o=void 0===s?[0,0]:s,r=ee.reduce((function(t,i){return t[i]=function(t,e,i){var n=be(t),s=[Vt,zt].indexOf(n)>=0?-1:1,o="function"==typeof i?i(Object.assign({},e,{placement:t})):i,r=o[0],a=o[1];return 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J,Z="x"===y?zt:Vt,tt="x"===y?Rt:qt,et=A[w],it="y"===w?"height":"width",nt=et+g[Z],st=et-g[tt],ot=-1!==[zt,Vt].indexOf(_),rt=null!=(J=null==x?void 0:x[w])?J:0,at=ot?nt:et-E[it]-T[it]-rt+O.altAxis,lt=ot?et+E[it]+T[it]-rt-O.altAxis:st,ct=f&&ot?function(t,e,i){var n=Ne(t,e,i);return n>i?i:n}(at,et,lt):Ne(f?at:nt,et,f?lt:st);A[w]=ct,k[w]=ct-et}e.modifiersData[n]=k}},requiresIfExists:["offset"]};function di(t,e,i){void 0===i&&(i=!1);var n,s,o=me(e),r=me(e)&&function(t){var e=t.getBoundingClientRect(),i=we(e.width)/t.offsetWidth||1,n=we(e.height)/t.offsetHeight||1;return 1!==i||1!==n}(e),a=Le(e),l=Te(t,r,i),c={scrollLeft:0,scrollTop:0},h={x:0,y:0};return(o||!o&&!i)&&(("body"!==ue(e)||Ue(a))&&(c=(n=e)!==fe(n)&&me(n)?{scrollLeft:(s=n).scrollLeft,scrollTop:s.scrollTop}:Xe(n)),me(e)?((h=Te(e,!0)).x+=e.clientLeft,h.y+=e.clientTop):a&&(h.x=Ye(a))),{x:l.left+c.scrollLeft-h.x,y:l.top+c.scrollTop-h.y,width:l.width,height:l.height}}function ui(t){var e=new Map,i=new Set,n=[];function s(t){i.add(t.name),[].concat(t.requires||[],t.requiresIfExists||[]).forEach((function(t){if(!i.has(t)){var n=e.get(t);n&&s(n)}})),n.push(t)}return t.forEach((function(t){e.set(t.name,t)})),t.forEach((function(t){i.has(t.name)||s(t)})),n}var fi={placement:"bottom",modifiers:[],strategy:"absolute"};function pi(){for(var t=arguments.length,e=new Array(t),i=0;iNumber.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(F.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,...g(this._config.popperConfig,[t])}}_selectMenuItem({key:t,target:e}){const i=z.find(".dropdown-menu 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e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Ei,Ti].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Ii)?this:z.prev(this,Ii)[0]||z.next(this,Ii)[0]||z.findOne(Ii,t.delegateTarget.parentNode),o=qi.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}N.on(document,Si,Ii,qi.dataApiKeydownHandler),N.on(document,Si,Pi,qi.dataApiKeydownHandler),N.on(document,Li,qi.clearMenus),N.on(document,Di,qi.clearMenus),N.on(document,Li,Ii,(function(t){t.preventDefault(),qi.getOrCreateInstance(this).toggle()})),m(qi);const Vi="backdrop",Ki="show",Qi=`mousedown.bs.${Vi}`,Xi={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},Yi={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class Ui extends H{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Xi}static get DefaultType(){return Yi}static get NAME(){return Vi}show(t){if(!this._config.isVisible)return void g(t);this._append();const e=this._getElement();this._config.isAnimated&&d(e),e.classList.add(Ki),this._emulateAnimation((()=>{g(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ki),this._emulateAnimation((()=>{this.dispose(),g(t)}))):g(t)}dispose(){this._isAppended&&(N.off(this._element,Qi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=r(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),N.on(t,Qi,(()=>{g(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){_(t,this._getElement(),this._config.isAnimated)}}const Gi=".bs.focustrap",Ji=`focusin${Gi}`,Zi=`keydown.tab${Gi}`,tn="backward",en={autofocus:!0,trapElement:null},nn={autofocus:"boolean",trapElement:"element"};class sn extends H{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return en}static get DefaultType(){return nn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),N.off(document,Gi),N.on(document,Ji,(t=>this._handleFocusin(t))),N.on(document,Zi,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,N.off(document,Gi))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=z.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===tn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?tn:"forward")}}const on=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",rn=".sticky-top",an="padding-right",ln="margin-right";class cn{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,an,(e=>e+t)),this._setElementAttributes(on,an,(e=>e+t)),this._setElementAttributes(rn,ln,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,an),this._resetElementAttributes(on,an),this._resetElementAttributes(rn,ln)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&F.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=F.getDataAttribute(t,e);null!==i?(F.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(o(t))e(t);else for(const i of z.find(t,this._element))e(i)}}const hn=".bs.modal",dn=`hide${hn}`,un=`hidePrevented${hn}`,fn=`hidden${hn}`,pn=`show${hn}`,mn=`shown${hn}`,gn=`resize${hn}`,_n=`click.dismiss${hn}`,bn=`mousedown.dismiss${hn}`,vn=`keydown.dismiss${hn}`,yn=`click${hn}.data-api`,wn="modal-open",An="show",En="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},Cn={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class On extends W{constructor(t,e){super(t,e),this._dialog=z.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new cn,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return Cn}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||N.trigger(this._element,pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(wn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(N.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(An),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){N.off(window,hn),N.off(this._dialog,hn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Ui({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=z.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),d(this._element),this._element.classList.add(An),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,N.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){N.on(this._element,vn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),N.on(window,gn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),N.on(this._element,bn,(t=>{N.one(this._element,_n,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(wn),this._resetAdjustments(),this._scrollBar.reset(),N.trigger(this._element,fn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(N.trigger(this._element,un).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(En)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(En),this._queueCallback((()=>{this._element.classList.remove(En),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=p()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=p()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=On.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}N.on(document,yn,'[data-bs-toggle="modal"]',(function(t){const e=z.getElementFromSelector(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),N.one(e,pn,(t=>{t.defaultPrevented||N.one(e,fn,(()=>{a(this)&&this.focus()}))}));const i=z.findOne(".modal.show");i&&On.getInstance(i).hide(),On.getOrCreateInstance(e).toggle(this)})),R(On),m(On);const xn=".bs.offcanvas",kn=".data-api",Ln=`load${xn}${kn}`,Sn="show",Dn="showing",$n="hiding",In=".offcanvas.show",Nn=`show${xn}`,Pn=`shown${xn}`,Mn=`hide${xn}`,jn=`hidePrevented${xn}`,Fn=`hidden${xn}`,Hn=`resize${xn}`,Wn=`click${xn}${kn}`,Bn=`keydown.dismiss${xn}`,zn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class qn extends W{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return zn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||N.trigger(this._element,Nn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new cn).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Dn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Sn),this._element.classList.remove(Dn),N.trigger(this._element,Pn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(N.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add($n),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Sn,$n),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new cn).reset(),N.trigger(this._element,Fn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ui({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():N.trigger(this._element,jn)}:null})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_addEventListeners(){N.on(this._element,Bn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():N.trigger(this._element,jn))}))}static jQueryInterface(t){return this.each((function(){const e=qn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}N.on(document,Wn,'[data-bs-toggle="offcanvas"]',(function(t){const e=z.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this))return;N.one(e,Fn,(()=>{a(this)&&this.focus()}));const i=z.findOne(In);i&&i!==e&&qn.getInstance(i).hide(),qn.getOrCreateInstance(e).toggle(this)})),N.on(window,Ln,(()=>{for(const t of z.find(In))qn.getOrCreateInstance(t).show()})),N.on(window,Hn,(()=>{for(const t of z.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&qn.getOrCreateInstance(t).hide()})),R(qn),m(qn);const Vn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Xn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Yn={allowList:Vn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"
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")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Gn)}_setContent(t,e,i){const n=z.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?o(e)?this._putElementInTemplate(r(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Xn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return g(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const Zn=new Set(["sanitize","allowList","sanitizeFn"]),ts="fade",es="show",is=".modal",ns="hide.bs.modal",ss="hover",os="focus",rs={AUTO:"auto",TOP:"top",RIGHT:p()?"left":"right",BOTTOM:"bottom",LEFT:p()?"right":"left"},as={allowList:Vn,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,6],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'',title:"",trigger:"hover focus"},ls={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class cs extends W{constructor(t,e){if(void 0===vi)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,e),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return as}static get DefaultType(){return ls}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),N.off(this._element.closest(is),ns,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=N.trigger(this._element,this.constructor.eventName("show")),e=(c(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),N.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.on(t,"mouseover",h);this._queueCallback((()=>{N.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!N.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,"mouseover",h);this._activeTrigger.click=!1,this._activeTrigger[os]=!1,this._activeTrigger[ss]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),N.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(ts,es),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(ts),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new Jn({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(ts)}_isShown(){return this.tip&&this.tip.classList.contains(es)}_createPopper(t){const e=g(this._config.placement,[this,t,this._element]),i=rs[e.toUpperCase()];return bi(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return g(t,[this._element])}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"preSetPlacement",enabled:!0,phase:"beforeMain",fn:t=>{this._getTipElement().setAttribute("data-popper-placement",t.state.placement)}}]};return{...e,...g(this._config.popperConfig,[e])}}_setListeners(){const t=this._config.trigger.split(" ");for(const e of t)if("click"===e)N.on(this._element,this.constructor.eventName("click"),this._config.selector,(t=>{this._initializeOnDelegatedTarget(t).toggle()}));else if("manual"!==e){const t=e===ss?this.constructor.eventName("mouseenter"):this.constructor.eventName("focusin"),i=e===ss?this.constructor.eventName("mouseleave"):this.constructor.eventName("focusout");N.on(this._element,t,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusin"===t.type?os:ss]=!0,e._enter()})),N.on(this._element,i,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusout"===t.type?os:ss]=e._element.contains(t.relatedTarget),e._leave()}))}this._hideModalHandler=()=>{this._element&&this.hide()},N.on(this._element.closest(is),ns,this._hideModalHandler)}_fixTitle(){const t=this._element.getAttribute("title");t&&(this._element.getAttribute("aria-label")||this._element.textContent.trim()||this._element.setAttribute("aria-label",t),this._element.setAttribute("data-bs-original-title",t),this._element.removeAttribute("title"))}_enter(){this._isShown()||this._isHovered?this._isHovered=!0:(this._isHovered=!0,this._setTimeout((()=>{this._isHovered&&this.show()}),this._config.delay.show))}_leave(){this._isWithActiveTrigger()||(this._isHovered=!1,this._setTimeout((()=>{this._isHovered||this.hide()}),this._config.delay.hide))}_setTimeout(t,e){clearTimeout(this._timeout),this._timeout=setTimeout(t,e)}_isWithActiveTrigger(){return Object.values(this._activeTrigger).includes(!0)}_getConfig(t){const e=F.getDataAttributes(this._element);for(const t of Object.keys(e))Zn.has(t)&&delete e[t];return t={...e,..."object"==typeof t&&t?t:{}},t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t.container=!1===t.container?document.body:r(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),t}_getDelegateConfig(){const t={};for(const[e,i]of Object.entries(this._config))this.constructor.Default[e]!==i&&(t[e]=i);return t.selector=!1,t.trigger="manual",t}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null),this.tip&&(this.tip.remove(),this.tip=null)}static jQueryInterface(t){return this.each((function(){const e=cs.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}m(cs);const hs={...cs.Default,content:"",offset:[0,8],placement:"right",template:'',trigger:"click"},ds={...cs.DefaultType,content:"(null|string|element|function)"};class us extends cs{static get Default(){return hs}static get DefaultType(){return ds}static get NAME(){return"popover"}_isWithContent(){return this._getTitle()||this._getContent()}_getContentForTemplate(){return{".popover-header":this._getTitle(),".popover-body":this._getContent()}}_getContent(){return this._resolvePossibleFunction(this._config.content)}static jQueryInterface(t){return this.each((function(){const e=us.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}m(us);const fs=".bs.scrollspy",ps=`activate${fs}`,ms=`click${fs}`,gs=`load${fs}.data-api`,_s="active",bs="[href]",vs=".nav-link",ys=`${vs}, .nav-item > ${vs}, .list-group-item`,ws={offset:null,rootMargin:"0px 0px -25%",smoothScroll:!1,target:null,threshold:[.1,.5,1]},As={offset:"(number|null)",rootMargin:"string",smoothScroll:"boolean",target:"element",threshold:"array"};class Es extends W{constructor(t,e){super(t,e),this._targetLinks=new Map,this._observableSections=new 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e=this._observableSections.get(t.target.hash);if(e){t.preventDefault();const i=this._rootElement||window,n=e.offsetTop-this._element.offsetTop;if(i.scrollTo)return void i.scrollTo({top:n,behavior:"smooth"});i.scrollTop=n}})))}_getNewObserver(){const t={root:this._rootElement,threshold:this._config.threshold,rootMargin:this._config.rootMargin};return new IntersectionObserver((t=>this._observerCallback(t)),t)}_observerCallback(t){const e=t=>this._targetLinks.get(`#${t.target.id}`),i=t=>{this._previousScrollData.visibleEntryTop=t.target.offsetTop,this._process(e(t))},n=(this._rootElement||document.documentElement).scrollTop,s=n>=this._previousScrollData.parentScrollTop;this._previousScrollData.parentScrollTop=n;for(const o of t){if(!o.isIntersecting){this._activeTarget=null,this._clearActiveClass(e(o));continue}const t=o.target.offsetTop>=this._previousScrollData.visibleEntryTop;if(s&&t){if(i(o),!n)return}else s||t||i(o)}}_initializeTargetsAndObservables(){this._targetLinks=new Map,this._observableSections=new Map;const t=z.find(bs,this._config.target);for(const e of t){if(!e.hash||l(e))continue;const t=z.findOne(decodeURI(e.hash),this._element);a(t)&&(this._targetLinks.set(decodeURI(e.hash),e),this._observableSections.set(e.hash,t))}}_process(t){this._activeTarget!==t&&(this._clearActiveClass(this._config.target),this._activeTarget=t,t.classList.add(_s),this._activateParents(t),N.trigger(this._element,ps,{relatedTarget:t}))}_activateParents(t){if(t.classList.contains("dropdown-item"))z.findOne(".dropdown-toggle",t.closest(".dropdown")).classList.add(_s);else for(const e of z.parents(t,".nav, .list-group"))for(const t of z.prev(e,ys))t.classList.add(_s)}_clearActiveClass(t){t.classList.remove(_s);const e=z.find(`${bs}.${_s}`,t);for(const t of e)t.classList.remove(_s)}static jQueryInterface(t){return this.each((function(){const e=Es.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No 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file mode 100644 index 00000000..74e04ef1 --- /dev/null +++ b/site_libs/quarto-html/quarto-syntax-highlighting-e26003cea8cd680ca0c55a263523d882.css @@ -0,0 +1,205 @@ +/* quarto syntax highlight colors */ +:root { + --quarto-hl-ot-color: #003B4F; + --quarto-hl-at-color: #657422; + --quarto-hl-ss-color: #20794D; + --quarto-hl-an-color: #5E5E5E; + --quarto-hl-fu-color: #4758AB; + --quarto-hl-st-color: #20794D; + --quarto-hl-cf-color: #003B4F; + --quarto-hl-op-color: #5E5E5E; + --quarto-hl-er-color: #AD0000; + --quarto-hl-bn-color: #AD0000; + --quarto-hl-al-color: #AD0000; + --quarto-hl-va-color: #111111; + --quarto-hl-bu-color: inherit; + --quarto-hl-ex-color: inherit; + --quarto-hl-pp-color: #AD0000; + --quarto-hl-in-color: #5E5E5E; + --quarto-hl-vs-color: #20794D; + --quarto-hl-wa-color: #5E5E5E; + --quarto-hl-do-color: #5E5E5E; + --quarto-hl-im-color: #00769E; + --quarto-hl-ch-color: #20794D; + --quarto-hl-dt-color: #AD0000; + --quarto-hl-fl-color: #AD0000; + --quarto-hl-co-color: #5E5E5E; + --quarto-hl-cv-color: #5E5E5E; + --quarto-hl-cn-color: #8f5902; + --quarto-hl-sc-color: #5E5E5E; + --quarto-hl-dv-color: #AD0000; + --quarto-hl-kw-color: #003B4F; +} + +/* other quarto variables */ +:root { + --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; +} + +pre > code.sourceCode > span { + color: #003B4F; +} + +code span { + color: #003B4F; +} + +code.sourceCode > span { + color: #003B4F; +} + +div.sourceCode, +div.sourceCode pre.sourceCode { + color: #003B4F; +} + +code span.ot { + color: #003B4F; + font-style: inherit; +} + +code span.at { + color: #657422; + font-style: inherit; +} + +code span.ss { + color: #20794D; + font-style: inherit; +} + +code span.an { + color: #5E5E5E; + font-style: inherit; +} + +code span.fu { + color: #4758AB; + font-style: inherit; +} + +code span.st { + color: #20794D; + font-style: inherit; +} + +code span.cf { + color: #003B4F; + font-weight: bold; + font-style: inherit; +} + +code span.op { + color: #5E5E5E; + font-style: inherit; +} + +code span.er { + color: #AD0000; + font-style: inherit; +} + +code span.bn { + color: #AD0000; + font-style: inherit; +} + +code span.al { + color: #AD0000; + font-style: inherit; +} + +code span.va { + color: #111111; + font-style: inherit; +} + +code span.bu { + font-style: inherit; +} + +code span.ex { + font-style: inherit; +} + +code span.pp { + color: #AD0000; + font-style: inherit; +} + +code span.in { + color: #5E5E5E; + font-style: inherit; +} + +code span.vs { + color: #20794D; + font-style: inherit; +} + +code span.wa { + color: #5E5E5E; + font-style: italic; +} + +code span.do { + color: #5E5E5E; + font-style: italic; +} + +code span.im { + color: #00769E; + font-style: inherit; +} + +code span.ch { + color: #20794D; + font-style: inherit; +} + +code span.dt { + color: #AD0000; + font-style: inherit; +} + +code span.fl { + color: #AD0000; + font-style: inherit; +} + +code span.co { + color: #5E5E5E; + font-style: inherit; +} + +code span.cv { + color: #5E5E5E; + font-style: italic; +} + +code span.cn { + color: #8f5902; + font-style: inherit; +} + +code span.sc { + color: #5E5E5E; + font-style: inherit; +} + +code span.dv { + color: #AD0000; + font-style: inherit; +} + +code span.kw { + color: #003B4F; + font-weight: bold; + font-style: inherit; +} + +.prevent-inlining { + content: " { + // Find any conflicting margin elements and add margins to the + // top to prevent overlap + const marginChildren = window.document.querySelectorAll( + ".column-margin.column-container > *, .margin-caption, .aside" + ); + + let lastBottom = 0; + for (const marginChild of marginChildren) { + if (marginChild.offsetParent !== null) { + // clear the top margin so we recompute it + marginChild.style.marginTop = null; + const top = marginChild.getBoundingClientRect().top + window.scrollY; + if (top < lastBottom) { + const marginChildStyle = window.getComputedStyle(marginChild); + const marginBottom = parseFloat(marginChildStyle["marginBottom"]); + const margin = lastBottom - top + marginBottom; + marginChild.style.marginTop = `${margin}px`; + } + const styles = window.getComputedStyle(marginChild); + const marginTop = parseFloat(styles["marginTop"]); + lastBottom = top + marginChild.getBoundingClientRect().height + marginTop; + } + } +}; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Recompute the position of margin elements anytime the body size changes + if (window.ResizeObserver) { + const resizeObserver = new window.ResizeObserver( + throttle(() => { + layoutMarginEls(); + if ( + window.document.body.getBoundingClientRect().width < 990 && + isReaderMode() + ) { + quartoToggleReader(); + } + }, 50) + ); + resizeObserver.observe(window.document.body); + } + + const tocEl = window.document.querySelector('nav.toc-active[role="doc-toc"]'); + const sidebarEl = window.document.getElementById("quarto-sidebar"); + const leftTocEl = window.document.getElementById("quarto-sidebar-toc-left"); + const marginSidebarEl = window.document.getElementById( + "quarto-margin-sidebar" + ); + // function to determine whether the element has a previous sibling that is active + const prevSiblingIsActiveLink = (el) => { + const sibling = el.previousElementSibling; + if (sibling && sibling.tagName === "A") { + return sibling.classList.contains("active"); + } else { + return false; + } + }; + + // fire slideEnter for bootstrap tab activations (for htmlwidget resize behavior) + function fireSlideEnter(e) { + const event = window.document.createEvent("Event"); + event.initEvent("slideenter", true, true); + window.document.dispatchEvent(event); + } + const tabs = window.document.querySelectorAll('a[data-bs-toggle="tab"]'); + tabs.forEach((tab) => { + tab.addEventListener("shown.bs.tab", fireSlideEnter); + }); + + // fire slideEnter for tabby tab activations (for htmlwidget resize behavior) + document.addEventListener("tabby", fireSlideEnter, false); + + // Track scrolling and mark TOC links as active + // get table of contents and sidebar (bail if we don't have at least one) + const tocLinks = tocEl + ? [...tocEl.querySelectorAll("a[data-scroll-target]")] + : []; + const makeActive = (link) => tocLinks[link].classList.add("active"); + const removeActive = (link) => tocLinks[link].classList.remove("active"); + const removeAllActive = () => + [...Array(tocLinks.length).keys()].forEach((link) => removeActive(link)); + + // activate the anchor for a section associated with this TOC entry + tocLinks.forEach((link) => { + link.addEventListener("click", () => { + if (link.href.indexOf("#") !== -1) { + const anchor = link.href.split("#")[1]; + const heading = window.document.querySelector( + `[data-anchor-id="${anchor}"]` + ); + if (heading) { + // Add the class + heading.classList.add("reveal-anchorjs-link"); + + // function to show the anchor + const handleMouseout = () => { + heading.classList.remove("reveal-anchorjs-link"); + heading.removeEventListener("mouseout", handleMouseout); + }; + + // add a function to clear the anchor when the user mouses out of it + heading.addEventListener("mouseout", handleMouseout); + } + } + }); + }); + + const sections = tocLinks.map((link) => { + const target = link.getAttribute("data-scroll-target"); + if (target.startsWith("#")) { + return window.document.getElementById(decodeURI(`${target.slice(1)}`)); + } else { + return window.document.querySelector(decodeURI(`${target}`)); + } + }); + + const sectionMargin = 200; + let currentActive = 0; + // track whether we've initialized state the first time + let init = false; + + const updateActiveLink = () => { + // The index from bottom to top (e.g. reversed list) + let sectionIndex = -1; + if ( + window.innerHeight + window.pageYOffset >= + window.document.body.offsetHeight + ) { + // This is the no-scroll case where last section should be the active one + sectionIndex = 0; + } else { + // This finds the last section visible on screen that should be made active + sectionIndex = [...sections].reverse().findIndex((section) => { + if (section) { + return window.pageYOffset >= section.offsetTop - sectionMargin; + } else { + return false; + } + }); + } + if (sectionIndex > -1) { + const current = sections.length - sectionIndex - 1; + if (current !== currentActive) { + removeAllActive(); + currentActive = current; + makeActive(current); + if (init) { + window.dispatchEvent(sectionChanged); + } + init = true; + } + } + }; + + const inHiddenRegion = (top, bottom, hiddenRegions) => { + for (const region of hiddenRegions) { + if (top <= region.bottom && bottom >= region.top) { + return true; + } + } + return false; + }; + + const categorySelector = "header.quarto-title-block .quarto-category"; + const activateCategories = (href) => { + // Find any categories + // Surround them with a link pointing back to: + // #category=Authoring + try { + const categoryEls = window.document.querySelectorAll(categorySelector); + for (const categoryEl of categoryEls) { + const categoryText = categoryEl.textContent; + if (categoryText) { + const link = `${href}#category=${encodeURIComponent(categoryText)}`; + const linkEl = window.document.createElement("a"); + linkEl.setAttribute("href", link); + for (const child of categoryEl.childNodes) { + linkEl.append(child); + } + categoryEl.appendChild(linkEl); + } + } + } catch { + // Ignore errors + } + }; + function hasTitleCategories() { + return window.document.querySelector(categorySelector) !== null; + } + + function offsetRelativeUrl(url) { + const offset = getMeta("quarto:offset"); + return offset ? offset + url : url; + } + + function offsetAbsoluteUrl(url) { + const offset = getMeta("quarto:offset"); + const baseUrl = new URL(offset, window.location); + + const projRelativeUrl = url.replace(baseUrl, ""); + if (projRelativeUrl.startsWith("/")) { + return projRelativeUrl; + } else { + return "/" + projRelativeUrl; + } + } + + // read a meta tag value + function getMeta(metaName) { + const metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; + } + + async function findAndActivateCategories() { + // Categories search with listing only use path without query + const currentPagePath = offsetAbsoluteUrl( + window.location.origin + window.location.pathname + ); + const response = await fetch(offsetRelativeUrl("listings.json")); + if (response.status == 200) { + return response.json().then(function (listingPaths) { + const listingHrefs = []; + for (const listingPath of listingPaths) { + const pathWithoutLeadingSlash = listingPath.listing.substring(1); + for (const item of listingPath.items) { + if ( + item === currentPagePath || + item === currentPagePath + "index.html" + ) { + // Resolve this path against the offset to be sure + // we already are using the correct path to the listing + // (this adjusts the listing urls to be rooted against + // whatever root the page is actually running against) + const relative = offsetRelativeUrl(pathWithoutLeadingSlash); + const baseUrl = window.location; + const resolvedPath = new URL(relative, baseUrl); + listingHrefs.push(resolvedPath.pathname); + break; + } + } + } + + // Look up the tree for a nearby linting and use that if we find one + const nearestListing = findNearestParentListing( + offsetAbsoluteUrl(window.location.pathname), + listingHrefs + ); + if (nearestListing) { + activateCategories(nearestListing); + } else { + // See if the referrer is a listing page for this item + const referredRelativePath = offsetAbsoluteUrl(document.referrer); + const referrerListing = listingHrefs.find((listingHref) => { + const isListingReferrer = + listingHref === referredRelativePath || + listingHref === referredRelativePath + "index.html"; + return isListingReferrer; + }); + + if (referrerListing) { + // Try to use the referrer if possible + activateCategories(referrerListing); + } else if (listingHrefs.length > 0) { + // Otherwise, just fall back to the first listing + activateCategories(listingHrefs[0]); + } + } + }); + } + } + if (hasTitleCategories()) { + findAndActivateCategories(); + } + + const findNearestParentListing = (href, listingHrefs) => { + if (!href || !listingHrefs) { + return undefined; + } + // Look up the tree for a nearby linting and use that if we find one + const relativeParts = href.substring(1).split("/"); + while (relativeParts.length > 0) { + const path = relativeParts.join("/"); + for (const listingHref of listingHrefs) { + if (listingHref.startsWith(path)) { + return listingHref; + } + } + relativeParts.pop(); + } + + return undefined; + }; + + const manageSidebarVisiblity = (el, placeholderDescriptor) => { + let isVisible = true; + let elRect; + + return (hiddenRegions) => { + if (el === null) { + return; + } + + // Find the last element of the TOC + const lastChildEl = el.lastElementChild; + + if (lastChildEl) { + // Converts the sidebar to a menu + const convertToMenu = () => { + for (const child of el.children) { + child.style.opacity = 0; + child.style.overflow = "hidden"; + child.style.pointerEvents = "none"; + } + + nexttick(() => { + const toggleContainer = window.document.createElement("div"); + toggleContainer.style.width = "100%"; + toggleContainer.classList.add("zindex-over-content"); + toggleContainer.classList.add("quarto-sidebar-toggle"); + toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom + toggleContainer.id = placeholderDescriptor.id; + toggleContainer.style.position = "fixed"; + + const toggleIcon = window.document.createElement("i"); + toggleIcon.classList.add("quarto-sidebar-toggle-icon"); + toggleIcon.classList.add("bi"); + toggleIcon.classList.add("bi-caret-down-fill"); + + const toggleTitle = window.document.createElement("div"); + const titleEl = window.document.body.querySelector( + placeholderDescriptor.titleSelector + ); + if (titleEl) { + toggleTitle.append( + titleEl.textContent || titleEl.innerText, + toggleIcon + ); + } + toggleTitle.classList.add("zindex-over-content"); + toggleTitle.classList.add("quarto-sidebar-toggle-title"); + toggleContainer.append(toggleTitle); + + const toggleContents = window.document.createElement("div"); + toggleContents.classList = el.classList; + toggleContents.classList.add("zindex-over-content"); + toggleContents.classList.add("quarto-sidebar-toggle-contents"); + for (const child of el.children) { + if (child.id === "toc-title") { + continue; + } + + const clone = child.cloneNode(true); + clone.style.opacity = 1; + clone.style.pointerEvents = null; + clone.style.display = null; + toggleContents.append(clone); + } + toggleContents.style.height = "0px"; + const positionToggle = () => { + // position the element (top left of parent, same width as parent) + if (!elRect) { + elRect = el.getBoundingClientRect(); + } + toggleContainer.style.left = `${elRect.left}px`; + toggleContainer.style.top = `${elRect.top}px`; + toggleContainer.style.width = `${elRect.width}px`; + }; + positionToggle(); + + toggleContainer.append(toggleContents); + el.parentElement.prepend(toggleContainer); + + // Process clicks + let tocShowing = false; + // Allow the caller to control whether this is dismissed + // when it is clicked (e.g. sidebar navigation supports + // opening and closing the nav tree, so don't dismiss on click) + const clickEl = placeholderDescriptor.dismissOnClick + ? toggleContainer + : toggleTitle; + + const closeToggle = () => { + if (tocShowing) { + toggleContainer.classList.remove("expanded"); + toggleContents.style.height = "0px"; + tocShowing = false; + } + }; + + // Get rid of any expanded toggle if the user scrolls + window.document.addEventListener( + "scroll", + throttle(() => { + closeToggle(); + }, 50) + ); + + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + positionToggle(); + }, 50) + ); + + window.addEventListener("quarto-hrChanged", () => { + elRect = undefined; + }); + + // Process the click + clickEl.onclick = () => { + if (!tocShowing) { + toggleContainer.classList.add("expanded"); + toggleContents.style.height = null; + tocShowing = true; + } else { + closeToggle(); + } + }; + }); + }; + + // Converts a sidebar from a menu back to a sidebar + const convertToSidebar = () => { + for (const child of el.children) { + child.style.opacity = 1; + child.style.overflow = null; + child.style.pointerEvents = null; + } + + const placeholderEl = window.document.getElementById( + placeholderDescriptor.id + ); + if (placeholderEl) { + placeholderEl.remove(); + } + + el.classList.remove("rollup"); + }; + + if (isReaderMode()) { + convertToMenu(); + isVisible = false; + } else { + // Find the top and bottom o the element that is being managed + const elTop = el.offsetTop; + const elBottom = + elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight; + + if (!isVisible) { + // If the element is current not visible reveal if there are + // no conflicts with overlay regions + if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToSidebar(); + isVisible = true; + } + } else { + // If the element is visible, hide it if it conflicts with overlay regions + // and insert a placeholder toggle (or if we're in reader mode) + if (inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToMenu(); + isVisible = false; + } + } + } + } + }; + }; + + const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]'); + for (const tabEl of tabEls) { + const id = tabEl.getAttribute("data-bs-target"); + if (id) { + const columnEl = document.querySelector( + `${id} .column-margin, .tabset-margin-content` + ); + if (columnEl) + tabEl.addEventListener("shown.bs.tab", function (event) { + const el = event.srcElement; + if (el) { + const visibleCls = `${el.id}-margin-content`; + // walk up until we find a parent tabset + let panelTabsetEl = el.parentElement; + while (panelTabsetEl) { + if (panelTabsetEl.classList.contains("panel-tabset")) { + break; + } + panelTabsetEl = panelTabsetEl.parentElement; + } + + if (panelTabsetEl) { + const prevSib = panelTabsetEl.previousElementSibling; + if ( + prevSib && + prevSib.classList.contains("tabset-margin-container") + ) { + const childNodes = prevSib.querySelectorAll( + ".tabset-margin-content" + ); + for (const childEl of childNodes) { + if (childEl.classList.contains(visibleCls)) { + childEl.classList.remove("collapse"); + } else { + childEl.classList.add("collapse"); + } + } + } + } + } + + layoutMarginEls(); + }); + } + } + + // Manage the visibility of the toc and the sidebar + const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, { + id: "quarto-toc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, { + id: "quarto-sidebarnav-toggle", + titleSelector: ".title", + dismissOnClick: false, + }); + let tocLeftScrollVisibility; + if (leftTocEl) { + tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, { + id: "quarto-lefttoc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + } + + // Find the first element that uses formatting in special columns + const conflictingEls = window.document.body.querySelectorAll( + '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]' + ); + + // Filter all the possibly conflicting elements into ones + // the do conflict on the left or ride side + const arrConflictingEls = Array.from(conflictingEls); + const leftSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return false; + } + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + className.startsWith("column-") && + !className.endsWith("right") && + !className.endsWith("container") && + className !== "column-margin" + ); + }); + }); + const rightSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return true; + } + + const hasMarginCaption = Array.from(el.classList).find((className) => { + return className == "margin-caption"; + }); + if (hasMarginCaption) { + return true; + } + + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + !className.endsWith("container") && + className.startsWith("column-") && + !className.endsWith("left") + ); + }); + }); + + const kOverlapPaddingSize = 10; + function toRegions(els) { + return els.map((el) => { + const boundRect = el.getBoundingClientRect(); + const top = + boundRect.top + + document.documentElement.scrollTop - + kOverlapPaddingSize; + return { + top, + bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize, + }; + }); + } + + let hasObserved = false; + const visibleItemObserver = (els) => { + let visibleElements = [...els]; + const intersectionObserver = new IntersectionObserver( + (entries, _observer) => { + entries.forEach((entry) => { + if (entry.isIntersecting) { + if (visibleElements.indexOf(entry.target) === -1) { + visibleElements.push(entry.target); + } + } else { + visibleElements = visibleElements.filter((visibleEntry) => { + return visibleEntry !== entry; + }); + } + }); + + if (!hasObserved) { + hideOverlappedSidebars(); + } + hasObserved = true; + }, + {} + ); + els.forEach((el) => { + intersectionObserver.observe(el); + }); + + return { + getVisibleEntries: () => { + return visibleElements; + }, + }; + }; + + const rightElementObserver = visibleItemObserver(rightSideConflictEls); + const leftElementObserver = visibleItemObserver(leftSideConflictEls); + + const hideOverlappedSidebars = () => { + marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries())); + sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries())); + if (tocLeftScrollVisibility) { + tocLeftScrollVisibility( + toRegions(leftElementObserver.getVisibleEntries()) + ); + } + }; + + window.quartoToggleReader = () => { + // Applies a slow class (or removes it) + // to update the transition speed + const slowTransition = (slow) => { + const manageTransition = (id, slow) => { + const el = document.getElementById(id); + if (el) { + if (slow) { + el.classList.add("slow"); + } else { + el.classList.remove("slow"); + } + } + }; + + manageTransition("TOC", slow); + manageTransition("quarto-sidebar", slow); + }; + const readerMode = !isReaderMode(); + setReaderModeValue(readerMode); + + // If we're entering reader mode, slow the transition + if (readerMode) { + slowTransition(readerMode); + } + highlightReaderToggle(readerMode); + hideOverlappedSidebars(); + + // If we're exiting reader mode, restore the non-slow transition + if (!readerMode) { + slowTransition(!readerMode); + } + }; + + const highlightReaderToggle = (readerMode) => { + const els = document.querySelectorAll(".quarto-reader-toggle"); + if (els) { + els.forEach((el) => { + if (readerMode) { + el.classList.add("reader"); + } else { + el.classList.remove("reader"); + } + }); + } + }; + + const setReaderModeValue = (val) => { + if (window.location.protocol !== "file:") { + window.localStorage.setItem("quarto-reader-mode", val); + } else { + localReaderMode = val; + } + }; + + const isReaderMode = () => { + if (window.location.protocol !== "file:") { + return window.localStorage.getItem("quarto-reader-mode") === "true"; + } else { + return localReaderMode; + } + }; + let localReaderMode = null; + + const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded"); + const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1; + + // Walk the TOC and collapse/expand nodes + // Nodes are expanded if: + // - they are top level + // - they have children that are 'active' links + // - they are directly below an link that is 'active' + const walk = (el, depth) => { + // Tick depth when we enter a UL + if (el.tagName === "UL") { + depth = depth + 1; + } + + // It this is active link + let isActiveNode = false; + if (el.tagName === "A" && el.classList.contains("active")) { + isActiveNode = true; + } + + // See if there is an active child to this element + let hasActiveChild = false; + for (child of el.children) { + hasActiveChild = walk(child, depth) || hasActiveChild; + } + + // Process the collapse state if this is an UL + if (el.tagName === "UL") { + if (tocOpenDepth === -1 && depth > 1) { + // toc-expand: false + el.classList.add("collapse"); + } else if ( + depth <= tocOpenDepth || + hasActiveChild || + prevSiblingIsActiveLink(el) + ) { + el.classList.remove("collapse"); + } else { + el.classList.add("collapse"); + } + + // untick depth when we leave a UL + depth = depth - 1; + } + return hasActiveChild || isActiveNode; + }; + + // walk the TOC and expand / collapse any items that should be shown + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + + // Throttle the scroll event and walk peridiocally + window.document.addEventListener( + "scroll", + throttle(() => { + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 5) + ); + window.addEventListener( + "resize", + throttle(() => { + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 10) + ); + hideOverlappedSidebars(); + highlightReaderToggle(isReaderMode()); +}); + +// grouped tabsets +window.addEventListener("pageshow", (_event) => { + function getTabSettings() { + const data = localStorage.getItem("quarto-persistent-tabsets-data"); + if (!data) { + localStorage.setItem("quarto-persistent-tabsets-data", "{}"); + return {}; + } + if (data) { + return JSON.parse(data); + } + } + + function setTabSettings(data) { + localStorage.setItem( + "quarto-persistent-tabsets-data", + JSON.stringify(data) + ); + } + + function setTabState(groupName, groupValue) { + const data = getTabSettings(); + data[groupName] = groupValue; + setTabSettings(data); + } + + function toggleTab(tab, active) { + const tabPanelId = tab.getAttribute("aria-controls"); + const tabPanel = document.getElementById(tabPanelId); + if (active) { + tab.classList.add("active"); + tabPanel.classList.add("active"); + } else { + tab.classList.remove("active"); + tabPanel.classList.remove("active"); + } + } + + function toggleAll(selectedGroup, selectorsToSync) { + for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) { + const active = selectedGroup === thisGroup; + for (const tab of tabs) { + toggleTab(tab, active); + } + } + } + + function findSelectorsToSyncByLanguage() { + const result = {}; + const tabs = Array.from( + document.querySelectorAll(`div[data-group] a[id^='tabset-']`) + ); + for (const item of tabs) { + const div = item.parentElement.parentElement.parentElement; + const group = div.getAttribute("data-group"); + if (!result[group]) { + result[group] = {}; + } + const selectorsToSync = result[group]; + const value = item.innerHTML; + if (!selectorsToSync[value]) { + selectorsToSync[value] = []; + } + selectorsToSync[value].push(item); + } + return result; + } + + function setupSelectorSync() { + const selectorsToSync = findSelectorsToSyncByLanguage(); + Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => { + Object.entries(tabSetsByValue).forEach(([value, items]) => { + items.forEach((item) => { + item.addEventListener("click", (_event) => { + setTabState(group, value); + toggleAll(value, selectorsToSync[group]); + }); + }); + }); + }); + return selectorsToSync; + } + + const selectorsToSync 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b/site_libs/quarto-nav/quarto-nav.js @@ -0,0 +1,325 @@ +const headroomChanged = new CustomEvent("quarto-hrChanged", { + detail: {}, + bubbles: true, + cancelable: false, + composed: false, +}); + +const announceDismiss = () => { + const annEl = window.document.getElementById("quarto-announcement"); + if (annEl) { + annEl.remove(); + + const annId = annEl.getAttribute("data-announcement-id"); + window.localStorage.setItem(`quarto-announce-${annId}`, "true"); + } +}; + +const announceRegister = () => { + const annEl = window.document.getElementById("quarto-announcement"); + if (annEl) { + const annId = annEl.getAttribute("data-announcement-id"); + const isDismissed = + window.localStorage.getItem(`quarto-announce-${annId}`) || false; + if (isDismissed) { + announceDismiss(); + return; + } else { + annEl.classList.remove("hidden"); + } + + const actionEl = annEl.querySelector(".quarto-announcement-action"); + if (actionEl) { + actionEl.addEventListener("click", function (e) { + e.preventDefault(); + // Hide the bar immediately + announceDismiss(); + }); + } + } +}; + +window.document.addEventListener("DOMContentLoaded", function () { + let init = false; + + announceRegister(); + + // Manage the back to top button, if one is present. + let lastScrollTop = window.pageYOffset || document.documentElement.scrollTop; + const scrollDownBuffer = 5; + const scrollUpBuffer = 35; + const btn = document.getElementById("quarto-back-to-top"); + const hideBackToTop = () => { + btn.style.display = "none"; + }; + const showBackToTop = () => { + btn.style.display = "inline-block"; + }; + if (btn) { + window.document.addEventListener( + "scroll", + function () { + const currentScrollTop = + window.pageYOffset || document.documentElement.scrollTop; + + // Shows and hides the button 'intelligently' as the user scrolls + if (currentScrollTop - scrollDownBuffer > lastScrollTop) { + hideBackToTop(); + lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop; + } else if (currentScrollTop < lastScrollTop - scrollUpBuffer) { + showBackToTop(); + lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop; + } + + // Show the button at the bottom, hides it at the top + if (currentScrollTop <= 0) { + hideBackToTop(); + } else if ( + window.innerHeight + currentScrollTop >= + document.body.offsetHeight + ) { + showBackToTop(); + } + }, + false + ); + } + + function throttle(func, wait) { + var timeout; + return function () { + const context = this; + const args = arguments; + const later = function () { + clearTimeout(timeout); + timeout = null; + func.apply(context, args); + }; + + if (!timeout) { + timeout = setTimeout(later, wait); + } + }; + } + + function headerOffset() { + // Set an offset if there is are fixed top navbar + const headerEl = window.document.querySelector("header.fixed-top"); + if (headerEl) { + return headerEl.clientHeight; + } else { + return 0; + } + } + + function footerOffset() { + const footerEl = window.document.querySelector("footer.footer"); + if (footerEl) { + return footerEl.clientHeight; + } else { + return 0; + } + } + + function dashboardOffset() { + const dashboardNavEl = window.document.getElementById( + "quarto-dashboard-header" + ); + if (dashboardNavEl !== null) { + return dashboardNavEl.clientHeight; + } else { + return 0; + } + } + + function updateDocumentOffsetWithoutAnimation() { + updateDocumentOffset(false); + } + + function updateDocumentOffset(animated) { + // set body offset + const topOffset = headerOffset(); + const bodyOffset = topOffset + footerOffset() + dashboardOffset(); + const bodyEl = window.document.body; + bodyEl.setAttribute("data-bs-offset", topOffset); + bodyEl.style.paddingTop = topOffset + "px"; + + // deal with sidebar offsets + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + if (!animated) { + sidebar.classList.add("notransition"); + // Remove the no transition class after the animation has time to complete + setTimeout(function () { + sidebar.classList.remove("notransition"); + }, 201); + } + + if (window.Headroom && sidebar.classList.contains("sidebar-unpinned")) { + sidebar.style.top = "0"; + sidebar.style.maxHeight = "100vh"; + } else { + sidebar.style.top = topOffset + "px"; + sidebar.style.maxHeight = "calc(100vh - " + topOffset + "px)"; + } + }); + + // allow space for footer + const mainContainer = window.document.querySelector(".quarto-container"); + if (mainContainer) { + mainContainer.style.minHeight = "calc(100vh - " + bodyOffset + "px)"; + } + + // link offset + let linkStyle = window.document.querySelector("#quarto-target-style"); + if (!linkStyle) { + linkStyle = window.document.createElement("style"); + linkStyle.setAttribute("id", "quarto-target-style"); + window.document.head.appendChild(linkStyle); + } + while (linkStyle.firstChild) { + linkStyle.removeChild(linkStyle.firstChild); + } + if (topOffset > 0) { + linkStyle.appendChild( + window.document.createTextNode(` + section:target::before { + content: ""; + display: block; + height: ${topOffset}px; + margin: -${topOffset}px 0 0; + }`) + ); + } + if (init) { + window.dispatchEvent(headroomChanged); + } + init = true; + } + + // initialize headroom + var header = window.document.querySelector("#quarto-header"); + if (header && window.Headroom) { + const headroom = new window.Headroom(header, { + tolerance: 5, + onPin: function () { + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + sidebar.classList.remove("sidebar-unpinned"); + }); + updateDocumentOffset(); + }, + onUnpin: function () { + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + sidebar.classList.add("sidebar-unpinned"); + }); + updateDocumentOffset(); + }, + }); + headroom.init(); + + let frozen = false; + window.quartoToggleHeadroom = function () { + if (frozen) { + headroom.unfreeze(); + frozen = false; + } else { + headroom.freeze(); + frozen = true; + } + }; + } + + window.addEventListener( + "hashchange", + function (e) { + if ( + getComputedStyle(document.documentElement).scrollBehavior !== "smooth" + ) { + window.scrollTo(0, window.pageYOffset - headerOffset()); + } + }, + false + ); + + // Observe size changed for the header + const headerEl = window.document.querySelector("header.fixed-top"); + if (headerEl && window.ResizeObserver) { + const observer = new window.ResizeObserver(() => { + setTimeout(updateDocumentOffsetWithoutAnimation, 0); + }); + observer.observe(headerEl, { + attributes: true, + childList: true, + characterData: true, + }); + } else { + window.addEventListener( + "resize", + throttle(updateDocumentOffsetWithoutAnimation, 50) + ); + } + setTimeout(updateDocumentOffsetWithoutAnimation, 250); + + // fixup index.html links if we aren't on the filesystem + if (window.location.protocol !== "file:") { + const links = window.document.querySelectorAll("a"); + for (let i = 0; i < links.length; i++) { + if (links[i].href) { + links[i].dataset.originalHref = links[i].href; + links[i].href = links[i].href.replace(/\/index\.html/, "/"); + } + } + + // Fixup any sharing links that require urls + // Append url to any sharing urls + const sharingLinks = window.document.querySelectorAll( + "a.sidebar-tools-main-item, a.quarto-navigation-tool, a.quarto-navbar-tools, a.quarto-navbar-tools-item" + ); + for (let i = 0; i < sharingLinks.length; i++) { + const sharingLink = sharingLinks[i]; + const href = sharingLink.getAttribute("href"); + if (href) { + sharingLink.setAttribute( + "href", + href.replace("|url|", window.location.href) + ); + } + } + + // Scroll the active navigation item into view, if necessary + const navSidebar = window.document.querySelector("nav#quarto-sidebar"); + if (navSidebar) { + // Find the active item + const activeItem = navSidebar.querySelector("li.sidebar-item a.active"); + if (activeItem) { + // Wait for the scroll height and height to resolve by observing size changes on the + // nav element that is scrollable + const resizeObserver = new ResizeObserver((_entries) => { + // The bottom of the element + const elBottom = activeItem.offsetTop; + const viewBottom = navSidebar.scrollTop + navSidebar.clientHeight; + + // The element height and scroll height are the same, then we are still loading + if (viewBottom !== navSidebar.scrollHeight) { + // Determine if the item isn't visible and scroll to it + if (elBottom >= viewBottom) { + navSidebar.scrollTop = elBottom; + } + + // stop observing now since we've completed the scroll + resizeObserver.unobserve(navSidebar); + } + }); + resizeObserver.observe(navSidebar); + } + } + } +}); diff --git a/site_libs/quarto-search/autocomplete.umd.js b/site_libs/quarto-search/autocomplete.umd.js new file mode 100644 index 00000000..ae0063aa --- /dev/null +++ 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0!==arguments[1]?arguments[1]:{},n=t.limit,r=void 0===n?-1:n,i=this.options,o=i.includeMatches,c=i.includeScore,a=i.shouldSort,s=i.sortFn,u=i.ignoreFieldNorm,h=g(e)?g(this._docs[0])?this._searchStringList(e):this._searchObjectList(e):this._searchLogical(e);return fe(h,{ignoreFieldNorm:u}),a&&h.sort(s),y(r)&&r>-1&&(h=h.slice(0,r)),ge(h,this._docs,{includeMatches:o,includeScore:c})}},{key:"_searchStringList",value:function(e){var t=re(e,this.options),n=this._myIndex.records,r=[];return n.forEach((function(e){var n=e.v,i=e.i,o=e.n;if(k(n)){var c=t.searchIn(n),a=c.isMatch,s=c.score,u=c.indices;a&&r.push({item:n,idx:i,matches:[{score:s,value:n,norm:o,indices:u}]})}})),r}},{key:"_searchLogical",value:function(e){var t=this,n=function(e,t){var n=(arguments.length>2&&void 0!==arguments[2]?arguments[2]:{}).auto,r=void 0===n||n,i=function e(n){var i=Object.keys(n),o=ue(n);if(!o&&i.length>1&&!se(n))return e(le(n));if(he(n)){var c=o?n[ce]:i[0],a=o?n[ae]:n[c];if(!g(a))throw new Error(x(c));var s={keyId:j(c),pattern:a};return r&&(s.searcher=re(a,t)),s}var u={children:[],operator:i[0]};return i.forEach((function(t){var r=n[t];v(r)&&r.forEach((function(t){u.children.push(e(t))}))})),u};return se(e)||(e=le(e)),i(e)}(e,this.options),r=function e(n,r,i){if(!n.children){var o=n.keyId,c=n.searcher,a=t._findMatches({key:t._keyStore.get(o),value:t._myIndex.getValueForItemAtKeyId(r,o),searcher:c});return a&&a.length?[{idx:i,item:r,matches:a}]:[]}for(var s=[],u=0,h=n.children.length;u1&&void 0!==arguments[1]?arguments[1]:{},n=t.getFn,r=void 0===n?I.getFn:n,i=t.fieldNormWeight,o=void 0===i?I.fieldNormWeight:i,c=e.keys,a=e.records,s=new $({getFn:r,fieldNormWeight:o});return s.setKeys(c),s.setIndexRecords(a),s},ye.config=I,function(){ne.push.apply(ne,arguments)}(te),ye},"object"==typeof exports&&"undefined"!=typeof module?module.exports=t():"function"==typeof define&&define.amd?define(t):(e="undefined"!=typeof globalThis?globalThis:e||self).Fuse=t(); \ No newline at end of file diff --git a/site_libs/quarto-search/quarto-search.js b/site_libs/quarto-search/quarto-search.js new file mode 100644 index 00000000..d788a958 --- /dev/null +++ b/site_libs/quarto-search/quarto-search.js @@ -0,0 +1,1290 @@ +const kQueryArg = "q"; +const kResultsArg = "show-results"; + +// If items don't provide a URL, then both the navigator and the onSelect +// function aren't called (and therefore, the default implementation is used) +// +// We're using this sentinel URL to signal to those handlers that this +// item is a more item (along with the type) and can be handled appropriately +const kItemTypeMoreHref = "0767FDFD-0422-4E5A-BC8A-3BE11E5BBA05"; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Ensure that search is available on this page. If it isn't, + // should return early and not do anything + var searchEl = window.document.getElementById("quarto-search"); + if (!searchEl) return; + + const { autocomplete } = window["@algolia/autocomplete-js"]; + + let quartoSearchOptions = {}; + let language = {}; + const searchOptionEl = window.document.getElementById( + "quarto-search-options" + ); + if (searchOptionEl) { + const jsonStr = searchOptionEl.textContent; + quartoSearchOptions = JSON.parse(jsonStr); + language = quartoSearchOptions.language; + } + + // note the search mode + if (quartoSearchOptions.type === "overlay") { + searchEl.classList.add("type-overlay"); + } else { + searchEl.classList.add("type-textbox"); + } + + // Used to determine highlighting behavior for this page + // A `q` query param is expected when the user follows a search + // to this page + const currentUrl = new URL(window.location); + const query = currentUrl.searchParams.get(kQueryArg); + const showSearchResults = currentUrl.searchParams.get(kResultsArg); + const mainEl = window.document.querySelector("main"); + + // highlight matches on the page + if (query && mainEl) { + // perform any highlighting + highlight(escapeRegExp(query), mainEl); + + // fix up the URL to remove the q query param + const replacementUrl = new URL(window.location); + replacementUrl.searchParams.delete(kQueryArg); + window.history.replaceState({}, "", replacementUrl); + } + + // function to clear highlighting on the page when the search query changes + // (e.g. if the user edits the query or clears it) + let highlighting = true; + const resetHighlighting = (searchTerm) => { + if (mainEl && highlighting && query && searchTerm !== query) { + clearHighlight(query, mainEl); + highlighting = false; + } + }; + + // Clear search highlighting when the user scrolls sufficiently + const resetFn = () => { + resetHighlighting(""); + window.removeEventListener("quarto-hrChanged", resetFn); + window.removeEventListener("quarto-sectionChanged", resetFn); + }; + + // Register this event after the initial scrolling and settling of events + // on the page + window.addEventListener("quarto-hrChanged", resetFn); + window.addEventListener("quarto-sectionChanged", resetFn); + + // Responsively switch to overlay mode if the search is present on the navbar + // Note that switching the sidebar to overlay mode requires more coordinate (not just + // the media query since we generate different HTML for sidebar overlays than we do + // for sidebar input UI) + const detachedMediaQuery = + quartoSearchOptions.type === "overlay" ? "all" : "(max-width: 991px)"; + + // If configured, include the analytics client to send insights + const plugins = configurePlugins(quartoSearchOptions); + + let lastState = null; + const { setIsOpen, setQuery, setCollections } = autocomplete({ + container: searchEl, + detachedMediaQuery: detachedMediaQuery, + defaultActiveItemId: 0, + panelContainer: "#quarto-search-results", + panelPlacement: quartoSearchOptions["panel-placement"], + debug: false, + openOnFocus: true, + plugins, + classNames: { + form: "d-flex", + }, + placeholder: language["search-text-placeholder"], + translations: { + clearButtonTitle: language["search-clear-button-title"], + detachedCancelButtonText: language["search-detached-cancel-button-title"], + submitButtonTitle: language["search-submit-button-title"], + }, + initialState: { + query, + }, + getItemUrl({ item }) { + return item.href; + }, + onStateChange({ state }) { + // If this is a file URL, note that + + // Perhaps reset highlighting + resetHighlighting(state.query); + + // If the panel just opened, ensure the panel is positioned properly + if (state.isOpen) { + if (lastState && !lastState.isOpen) { + setTimeout(() => { + positionPanel(quartoSearchOptions["panel-placement"]); + }, 150); + } + } + + // Perhaps show the copy link + showCopyLink(state.query, quartoSearchOptions); + + lastState = state; + }, + reshape({ sources, state }) { + return sources.map((source) => { + try { + const items = source.getItems(); + + // Validate the items + validateItems(items); + + // group the items by document + const groupedItems = new Map(); + items.forEach((item) => { + const hrefParts = item.href.split("#"); + const baseHref = hrefParts[0]; + const isDocumentItem = hrefParts.length === 1; + + const items = groupedItems.get(baseHref); + if (!items) { + groupedItems.set(baseHref, [item]); + } else { + // If the href for this item matches the document + // exactly, place this item first as it is the item that represents + // the document itself + if (isDocumentItem) { + items.unshift(item); + } else { + items.push(item); + } + groupedItems.set(baseHref, items); + } + }); + + const reshapedItems = []; + let count = 1; + for (const [_key, value] of groupedItems) { + const firstItem = value[0]; + reshapedItems.push({ + ...firstItem, + type: kItemTypeDoc, + }); + + const collapseMatches = quartoSearchOptions["collapse-after"]; + const collapseCount = + typeof collapseMatches === "number" ? collapseMatches : 1; + + if (value.length > 1) { + const target = `search-more-${count}`; + const isExpanded = + state.context.expanded && + state.context.expanded.includes(target); + + const remainingCount = value.length - collapseCount; + + for (let i = 1; i < value.length; i++) { + if (collapseMatches && i === collapseCount) { + reshapedItems.push({ + target, + title: isExpanded + ? language["search-hide-matches-text"] + : remainingCount === 1 + ? `${remainingCount} ${language["search-more-match-text"]}` + : `${remainingCount} ${language["search-more-matches-text"]}`, + type: kItemTypeMore, + href: kItemTypeMoreHref, + }); + } + + if (isExpanded || !collapseMatches || i < collapseCount) { + reshapedItems.push({ + ...value[i], + type: kItemTypeItem, + target, + }); + } + } + } + count += 1; + } + + return { + ...source, + getItems() { + return reshapedItems; + }, + }; + } catch (error) { + // Some form of error occurred + return { + ...source, + getItems() { + return [ + { + title: error.name || "An Error Occurred While Searching", + text: + error.message || + "An unknown error occurred while attempting to perform the requested search.", + type: kItemTypeError, + }, + ]; + }, + }; + } + }); + }, + navigator: { + navigate({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + window.location.assign(itemUrl); + } + }, + navigateNewTab({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + const windowReference = window.open(itemUrl, "_blank", "noopener"); + if (windowReference) { + windowReference.focus(); + } + } + }, + navigateNewWindow({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + window.open(itemUrl, "_blank", "noopener"); + } + }, + }, + getSources({ state, setContext, setActiveItemId, refresh }) { + return [ + { + sourceId: "documents", + getItemUrl({ item }) { + if (item.href) { + return offsetURL(item.href); + } else { + return undefined; + } + }, + onSelect({ + item, + state, + setContext, + setIsOpen, + setActiveItemId, + refresh, + }) { + if (item.type === kItemTypeMore) { + toggleExpanded(item, state, setContext, setActiveItemId, refresh); + + // Toggle more + setIsOpen(true); + } + }, + getItems({ query }) { + if (query === null || query === "") { + return []; + } + + const limit = quartoSearchOptions.limit; + if (quartoSearchOptions.algolia) { + return algoliaSearch(query, limit, quartoSearchOptions.algolia); + } else { + // Fuse search options + const fuseSearchOptions = { + isCaseSensitive: false, + shouldSort: true, + minMatchCharLength: 2, + limit: limit, + }; + + return readSearchData().then(function (fuse) { + return fuseSearch(query, fuse, fuseSearchOptions); + }); + } + }, + templates: { + noResults({ createElement }) { + const hasQuery = lastState.query; + + return createElement( + "div", + { + class: `quarto-search-no-results${ + hasQuery ? "" : " no-query" + }`, + }, + language["search-no-results-text"] + ); + }, + header({ items, createElement }) { + // count the documents + const count = items.filter((item) => { + return item.type === kItemTypeDoc; + }).length; + + if (count > 0) { + return createElement( + "div", + { class: "search-result-header" }, + `${count} ${language["search-matching-documents-text"]}` + ); + } else { + return createElement( + "div", + { class: "search-result-header-no-results" }, + `` + ); + } + }, + footer({ _items, createElement }) { + if ( + quartoSearchOptions.algolia && + quartoSearchOptions.algolia["show-logo"] + ) { + const libDir = quartoSearchOptions.algolia["libDir"]; + const logo = createElement("img", { + src: offsetURL( + `${libDir}/quarto-search/search-by-algolia.svg` + ), + class: "algolia-search-logo", + }); + return createElement( + "a", + { href: "http://www.algolia.com/" }, + logo + ); + } + }, + + item({ item, createElement }) { + return renderItem( + item, + createElement, + state, + setActiveItemId, + setContext, + refresh, + quartoSearchOptions + ); + }, + }, + }, + ]; + }, + }); + + window.quartoOpenSearch = () => { + setIsOpen(false); + setIsOpen(true); + focusSearchInput(); + }; + + document.addEventListener("keyup", (event) => { + const { key } = event; + const kbds = quartoSearchOptions["keyboard-shortcut"]; + const focusedEl = document.activeElement; + + const isFormElFocused = [ + "input", + "select", + "textarea", + "button", + "option", + ].find((tag) => { + return focusedEl.tagName.toLowerCase() === tag; + }); + + if ( + kbds && + kbds.includes(key) && + !isFormElFocused && + !document.activeElement.isContentEditable + ) { + event.preventDefault(); + window.quartoOpenSearch(); + } + }); + + // Remove the labeleledby attribute since it is pointing + // to a non-existent label + if (quartoSearchOptions.type === "overlay") { + const inputEl = window.document.querySelector( + "#quarto-search .aa-Autocomplete" + ); + if (inputEl) { + inputEl.removeAttribute("aria-labelledby"); + } + } + + function throttle(func, wait) { + let waiting = false; + return function () { + if (!waiting) { + func.apply(this, arguments); + waiting = true; + setTimeout(function () { + waiting = false; + }, wait); + } + }; + } + + // If the main document scrolls dismiss the search results + // (otherwise, since they're floating in the document they can scroll with the document) + window.document.body.onscroll = throttle(() => { + // Only do this if we're not detached + // Bug #7117 + // This will happen when the keyboard is shown on ios (resulting in a scroll) + // which then closed the search UI + if (!window.matchMedia(detachedMediaQuery).matches) { + setIsOpen(false); + } + }, 50); + + if (showSearchResults) { + setIsOpen(true); + focusSearchInput(); + } +}); + +function configurePlugins(quartoSearchOptions) { + const autocompletePlugins = []; + const algoliaOptions = quartoSearchOptions.algolia; + if ( + algoliaOptions && + algoliaOptions["analytics-events"] && + algoliaOptions["search-only-api-key"] && + algoliaOptions["application-id"] + ) { + const apiKey = algoliaOptions["search-only-api-key"]; + const appId = algoliaOptions["application-id"]; + + // Aloglia insights may not be loaded because they require cookie consent + // Use deferred loading so events will start being recorded when/if consent + // is granted. + const algoliaInsightsDeferredPlugin = deferredLoadPlugin(() => { + if ( + window.aa && + window["@algolia/autocomplete-plugin-algolia-insights"] + ) { + window.aa("init", { + appId, + apiKey, + useCookie: true, + }); + + const { createAlgoliaInsightsPlugin } = + window["@algolia/autocomplete-plugin-algolia-insights"]; + // Register the insights client + const algoliaInsightsPlugin = createAlgoliaInsightsPlugin({ + insightsClient: window.aa, + onItemsChange({ insights, insightsEvents }) { + const events = insightsEvents.flatMap((event) => { + // This API limits the number of items per event to 20 + const chunkSize = 20; + const itemChunks = []; + const eventItems = event.items; + for (let i = 0; i < eventItems.length; i += chunkSize) { + itemChunks.push(eventItems.slice(i, i + chunkSize)); + } + // Split the items into multiple events that can be sent + const events = itemChunks.map((items) => { + return { + ...event, + items, + }; + }); + return events; + }); + + for (const event of events) { + insights.viewedObjectIDs(event); + } + }, + }); + return algoliaInsightsPlugin; + } + }); + + // Add the plugin + autocompletePlugins.push(algoliaInsightsDeferredPlugin); + return autocompletePlugins; + } +} + +// For plugins that may not load immediately, create a wrapper +// plugin and forward events and plugin data once the plugin +// is initialized. This is useful for cases like cookie consent +// which may prevent the analytics insights event plugin from initializing +// immediately. +function deferredLoadPlugin(createPlugin) { + let plugin = undefined; + let subscribeObj = undefined; + const wrappedPlugin = () => { + if (!plugin && subscribeObj) { + plugin = createPlugin(); + if (plugin && plugin.subscribe) { + plugin.subscribe(subscribeObj); + } + } + return plugin; + }; + + return { + subscribe: (obj) => { + subscribeObj = obj; + }, + onStateChange: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onStateChange) { + plugin.onStateChange(obj); + } + }, + onSubmit: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onSubmit) { + plugin.onSubmit(obj); + } + }, + onReset: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onReset) { + plugin.onReset(obj); + } + }, + getSources: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.getSources) { + return plugin.getSources(obj); + } else { + return Promise.resolve([]); + } + }, + data: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.data) { + plugin.data(obj); + } + }, + }; +} + +function validateItems(items) { + // Validate the first item + if (items.length > 0) { + const item = items[0]; + const missingFields = []; + if (item.href == undefined) { + missingFields.push("href"); + } + if (!item.title == undefined) { + missingFields.push("title"); + } + if (!item.text == undefined) { + missingFields.push("text"); + } + + if (missingFields.length === 1) { + throw { + name: `Error: Search index is missing the ${missingFields[0]} field.`, + message: `The items being returned for this search do not include all the required fields. Please ensure that your index items include the ${missingFields[0]} field or use index-fields in your _quarto.yml file to specify the field names.`, + }; + } else if (missingFields.length > 1) { + const missingFieldList = missingFields + .map((field) => { + return `${field}`; + }) + .join(", "); + + throw { + name: `Error: Search index is missing the following fields: ${missingFieldList}.`, + message: `The items being returned for this search do not include all the required fields. Please ensure that your index items includes the following fields: ${missingFieldList}, or use index-fields in your _quarto.yml file to specify the field names.`, + }; + } + } +} + +let lastQuery = null; +function showCopyLink(query, options) { + const language = options.language; + lastQuery = query; + // Insert share icon + const inputSuffixEl = window.document.body.querySelector( + ".aa-Form .aa-InputWrapperSuffix" + ); + + if (inputSuffixEl) { + let copyButtonEl = window.document.body.querySelector( + ".aa-Form .aa-InputWrapperSuffix .aa-CopyButton" + ); + + if (copyButtonEl === null) { + copyButtonEl = window.document.createElement("button"); + copyButtonEl.setAttribute("class", "aa-CopyButton"); + copyButtonEl.setAttribute("type", "button"); + copyButtonEl.setAttribute("title", language["search-copy-link-title"]); + copyButtonEl.onmousedown = (e) => { + e.preventDefault(); + e.stopPropagation(); + }; + + const linkIcon = "bi-clipboard"; + const checkIcon = "bi-check2"; + + const shareIconEl = window.document.createElement("i"); + shareIconEl.setAttribute("class", `bi ${linkIcon}`); + copyButtonEl.appendChild(shareIconEl); + inputSuffixEl.prepend(copyButtonEl); + + const clipboard = new window.ClipboardJS(".aa-CopyButton", { + text: function (_trigger) { + const copyUrl = new URL(window.location); + copyUrl.searchParams.set(kQueryArg, lastQuery); + copyUrl.searchParams.set(kResultsArg, "1"); + return copyUrl.toString(); + }, + }); + clipboard.on("success", function (e) { + // Focus the input + + // button target + const button = e.trigger; + const icon = button.querySelector("i.bi"); + + // flash "checked" + icon.classList.add(checkIcon); + icon.classList.remove(linkIcon); + setTimeout(function () { + icon.classList.remove(checkIcon); + icon.classList.add(linkIcon); + }, 1000); + }); + } + + // If there is a query, show the link icon + if (copyButtonEl) { + if (lastQuery && options["copy-button"]) { + copyButtonEl.style.display = "flex"; + } else { + copyButtonEl.style.display = "none"; + } + } + } +} + +/* Search Index Handling */ +// create the index +var fuseIndex = undefined; +var shownWarning = false; + +// fuse index options +const kFuseIndexOptions = { + keys: [ + { name: "title", weight: 20 }, + { name: "section", weight: 20 }, + { name: "text", weight: 10 }, + ], + ignoreLocation: true, + threshold: 0.1, +}; + +async function readSearchData() { + // Initialize the search index on demand + if (fuseIndex === undefined) { + if (window.location.protocol === "file:" && !shownWarning) { + window.alert( + "Search requires JavaScript features disabled when running in file://... URLs. In order to use search, please run this document in a web server." + ); + shownWarning = true; + return; + } + const fuse = new window.Fuse([], kFuseIndexOptions); + + // fetch the main search.json + const response = await fetch(offsetURL("search.json")); + if (response.status == 200) { + return response.json().then(function (searchDocs) { + searchDocs.forEach(function (searchDoc) { + fuse.add(searchDoc); + }); + fuseIndex = fuse; + return fuseIndex; + }); + } else { + return Promise.reject( + new Error( + "Unexpected status from search index request: " + response.status + ) + ); + } + } + + return fuseIndex; +} + +function inputElement() { + return window.document.body.querySelector(".aa-Form .aa-Input"); +} + +function focusSearchInput() { + setTimeout(() => { + const inputEl = inputElement(); + if (inputEl) { + inputEl.focus(); + } + }, 50); +} + +/* Panels */ +const kItemTypeDoc = "document"; +const kItemTypeMore = "document-more"; +const kItemTypeItem = "document-item"; +const kItemTypeError = "error"; + +function renderItem( + item, + createElement, + state, + setActiveItemId, + setContext, + refresh, + quartoSearchOptions +) { + switch (item.type) { + case kItemTypeDoc: + return createDocumentCard( + createElement, + "file-richtext", + item.title, + item.section, + item.text, + item.href, + item.crumbs, + quartoSearchOptions + ); + case kItemTypeMore: + return createMoreCard( + createElement, + item, + state, + setActiveItemId, + setContext, + refresh + ); + case kItemTypeItem: + return createSectionCard( + createElement, + item.section, + item.text, + item.href + ); + case kItemTypeError: + return createErrorCard(createElement, item.title, item.text); + default: + return undefined; + } +} + +function createDocumentCard( + createElement, + icon, + title, + section, + text, + href, + crumbs, + quartoSearchOptions +) { + const iconEl = createElement("i", { + class: `bi bi-${icon} search-result-icon`, + }); + const titleEl = createElement("p", { class: "search-result-title" }, title); + const titleContents = [iconEl, titleEl]; + const showParent = quartoSearchOptions["show-item-context"]; + if (crumbs && showParent) { + let crumbsOut = undefined; + const crumbClz = ["search-result-crumbs"]; + if (showParent === "root") { + crumbsOut = crumbs.length > 1 ? crumbs[0] : undefined; + } else if (showParent === "parent") { + crumbsOut = crumbs.length > 1 ? crumbs[crumbs.length - 2] : undefined; + } else { + crumbsOut = crumbs.length > 1 ? crumbs.join(" > ") : undefined; + crumbClz.push("search-result-crumbs-wrap"); + } + + const crumbEl = createElement( + "p", + { class: crumbClz.join(" ") }, + crumbsOut + ); + titleContents.push(crumbEl); + } + + const titleContainerEl = createElement( + "div", + { class: "search-result-title-container" }, + titleContents + ); + + const textEls = []; + if (section) { + const sectionEl = createElement( + "p", + { class: "search-result-section" }, + section + ); + textEls.push(sectionEl); + } + const descEl = createElement("p", { + class: "search-result-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + textEls.push(descEl); + + const textContainerEl = createElement( + "div", + { class: "search-result-text-container" }, + textEls + ); + + const containerEl = createElement( + "div", + { + class: "search-result-container", + }, + [titleContainerEl, textContainerEl] + ); + + const linkEl = createElement( + "a", + { + href: offsetURL(href), + class: "search-result-link", + }, + containerEl + ); + + const classes = ["search-result-doc", "search-item"]; + if (!section) { + classes.push("document-selectable"); + } + + return createElement( + "div", + { + class: classes.join(" "), + }, + linkEl + ); +} + +function createMoreCard( + createElement, + item, + state, + setActiveItemId, + setContext, + refresh +) { + const moreCardEl = createElement( + "div", + { + class: "search-result-more search-item", + onClick: (e) => { + // Handle expanding the sections by adding the expanded + // section to the list of expanded sections + toggleExpanded(item, state, setContext, setActiveItemId, refresh); + e.stopPropagation(); + }, + }, + item.title + ); + + return moreCardEl; +} + +function toggleExpanded(item, state, setContext, setActiveItemId, refresh) { + const expanded = state.context.expanded || []; + if (expanded.includes(item.target)) { + setContext({ + expanded: expanded.filter((target) => target !== item.target), + }); + } else { + setContext({ expanded: [...expanded, item.target] }); + } + + refresh(); + setActiveItemId(item.__autocomplete_id); +} + +function createSectionCard(createElement, section, text, href) { + const sectionEl = createSection(createElement, section, text, href); + return createElement( + "div", + { + class: "search-result-doc-section search-item", + }, + sectionEl + ); +} + +function createSection(createElement, title, text, href) { + const descEl = createElement("p", { + class: "search-result-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + + const titleEl = createElement("p", { class: "search-result-section" }, title); + const linkEl = createElement( + "a", + { + href: offsetURL(href), + class: "search-result-link", + }, + [titleEl, descEl] + ); + return linkEl; +} + +function createErrorCard(createElement, title, text) { + const descEl = createElement("p", { + class: "search-error-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + + const titleEl = createElement("p", { + class: "search-error-title", + dangerouslySetInnerHTML: { + __html: ` ${title}`, + }, + }); + const errorEl = createElement("div", { class: "search-error" }, [ + titleEl, + descEl, + ]); + return errorEl; +} + +function positionPanel(pos) { + const panelEl = window.document.querySelector( + "#quarto-search-results .aa-Panel" + ); + const inputEl = window.document.querySelector( + "#quarto-search .aa-Autocomplete" + ); + + if (panelEl && inputEl) { + panelEl.style.top = `${Math.round(panelEl.offsetTop)}px`; + if (pos === "start") { + panelEl.style.left = `${Math.round(inputEl.left)}px`; + } else { + panelEl.style.right = `${Math.round(inputEl.offsetRight)}px`; + } + } +} + +/* Highlighting */ +// highlighting functions +function highlightMatch(query, text) { + if (text) { + const start = text.toLowerCase().indexOf(query.toLowerCase()); + if (start !== -1) { + const startMark = ""; + const endMark = ""; + + const end = start + query.length; + text = + text.slice(0, start) + + startMark + + text.slice(start, end) + + endMark + + text.slice(end); + const startInfo = clipStart(text, start); + const endInfo = clipEnd( + text, + startInfo.position + startMark.length + endMark.length + ); + text = + startInfo.prefix + + text.slice(startInfo.position, endInfo.position) + + endInfo.suffix; + + return text; + } else { + return text; + } + } else { + return text; + } +} + +function clipStart(text, pos) { + const clipStart = pos - 50; + if (clipStart < 0) { + // This will just return the start of the string + return { + position: 0, + prefix: "", + }; + } else { + // We're clipping before the start of the string, walk backwards to the first space. + const spacePos = findSpace(text, pos, -1); + return { + position: spacePos.position, + prefix: "", + }; + } +} + +function clipEnd(text, pos) { + const clipEnd = pos + 200; + if (clipEnd > text.length) { + return { + position: text.length, + suffix: "", + }; + } else { + const spacePos = findSpace(text, clipEnd, 1); + return { + position: spacePos.position, + suffix: spacePos.clipped ? "…" : "", + }; + } +} + +function findSpace(text, start, step) { + let stepPos = start; + while (stepPos > -1 && stepPos < text.length) { + const char = text[stepPos]; + if (char === " " || char === "," || char === ":") { + return { + position: step === 1 ? stepPos : stepPos - step, + clipped: stepPos > 1 && stepPos < text.length, + }; + } + stepPos = stepPos + step; + } + + return { + position: stepPos - step, + clipped: false, + }; +} + +// removes highlighting as implemented by the mark tag +function clearHighlight(searchterm, el) { + const childNodes = el.childNodes; + for (let i = childNodes.length - 1; i >= 0; i--) { + const node = childNodes[i]; + if (node.nodeType === Node.ELEMENT_NODE) { + if ( + node.tagName === "MARK" && + node.innerText.toLowerCase() === searchterm.toLowerCase() + ) { + el.replaceChild(document.createTextNode(node.innerText), node); + } else { + clearHighlight(searchterm, node); + } + } + } +} + +function escapeRegExp(string) { + return string.replace(/[.*+?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string +} + +// highlight matches +function highlight(term, el) { + const termRegex = new RegExp(term, "ig"); + const childNodes = el.childNodes; + + // walk back to front avoid mutating elements in front of us + for (let i = childNodes.length - 1; i >= 0; i--) { + const node = childNodes[i]; + + if (node.nodeType === Node.TEXT_NODE) { + // Search text nodes for text to highlight + const text = node.nodeValue; + + let startIndex = 0; + let matchIndex = text.search(termRegex); + if (matchIndex > -1) { + const markFragment = document.createDocumentFragment(); + while (matchIndex > -1) { + const prefix = text.slice(startIndex, matchIndex); + markFragment.appendChild(document.createTextNode(prefix)); + + const mark = document.createElement("mark"); + mark.appendChild( + document.createTextNode( + text.slice(matchIndex, matchIndex + term.length) + ) + ); + markFragment.appendChild(mark); + + startIndex = matchIndex + term.length; + matchIndex = text.slice(startIndex).search(new RegExp(term, "ig")); + if (matchIndex > -1) { + matchIndex = startIndex + matchIndex; + } + } + if (startIndex < text.length) { + markFragment.appendChild( + document.createTextNode(text.slice(startIndex, text.length)) + ); + } + + el.replaceChild(markFragment, node); + } + } else if (node.nodeType === Node.ELEMENT_NODE) { + // recurse through elements + highlight(term, node); + } + } +} + +/* Link Handling */ +// get the offset from this page for a given site root relative url +function offsetURL(url) { + var offset = getMeta("quarto:offset"); + return offset ? offset + url : url; +} + +// read a meta tag value +function getMeta(metaName) { + var metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; +} + +function algoliaSearch(query, limit, algoliaOptions) { + const { getAlgoliaResults } = window["@algolia/autocomplete-preset-algolia"]; + + const applicationId = algoliaOptions["application-id"]; + const searchOnlyApiKey = algoliaOptions["search-only-api-key"]; + const indexName = algoliaOptions["index-name"]; + const indexFields = algoliaOptions["index-fields"]; + const searchClient = window.algoliasearch(applicationId, searchOnlyApiKey); + const searchParams = algoliaOptions["params"]; + const searchAnalytics = !!algoliaOptions["analytics-events"]; + + return getAlgoliaResults({ + searchClient, + queries: [ + { + indexName: indexName, + query, + params: { + hitsPerPage: limit, + clickAnalytics: searchAnalytics, + ...searchParams, + }, + }, + ], + transformResponse: (response) => { + if (!indexFields) { + return response.hits.map((hit) => { + return hit.map((item) => { + return { + ...item, + text: highlightMatch(query, item.text), + }; + }); + }); + } else { + const remappedHits = response.hits.map((hit) => { + return hit.map((item) => { + const newItem = { ...item }; + ["href", "section", "title", "text", "crumbs"].forEach( + (keyName) => { + const mappedName = indexFields[keyName]; + if ( + mappedName && + item[mappedName] !== undefined && + mappedName !== keyName + ) { + newItem[keyName] = item[mappedName]; + delete newItem[mappedName]; + } + } + ); + newItem.text = highlightMatch(query, newItem.text); + return newItem; + }); + }); + return remappedHits; + } + }, + }); +} + +let subSearchTerm = undefined; +let subSearchFuse = undefined; +const kFuseMaxWait = 125; + +async function fuseSearch(query, fuse, fuseOptions) { + let index = fuse; + // Fuse.js using the Bitap algorithm for text matching which runs in + // O(nm) time (no matter the structure of the text). In our case this + // means that long search terms mixed with large index gets very slow + // + // This injects a subIndex that will be used once the terms get long enough + // Usually making this subindex is cheap since there will typically be + // a subset of results matching the existing query + if (subSearchFuse !== undefined && query.startsWith(subSearchTerm)) { + // Use the existing subSearchFuse + index = subSearchFuse; + } else if (subSearchFuse !== undefined) { + // The term changed, discard the existing fuse + subSearchFuse = undefined; + subSearchTerm = undefined; + } + + // Search using the active fuse + const then = performance.now(); + const resultsRaw = await index.search(query, fuseOptions); + const now = performance.now(); + + const results = resultsRaw.map((result) => { + const addParam = (url, name, value) => { + const anchorParts = url.split("#"); + const baseUrl = anchorParts[0]; + const sep = baseUrl.search("\\?") > 0 ? "&" : "?"; + anchorParts[0] = baseUrl + sep + name + "=" + value; + return anchorParts.join("#"); + }; + + return { + title: result.item.title, + section: result.item.section, + href: addParam(result.item.href, kQueryArg, query), + text: highlightMatch(query, result.item.text), + crumbs: result.item.crumbs, + }; + }); + + // If we don't have a subfuse and the query is long enough, go ahead + // and create a subfuse to use for subsequent queries + if ( + now - then > kFuseMaxWait && + subSearchFuse === undefined && + resultsRaw.length < fuseOptions.limit + ) { + subSearchTerm = query; + subSearchFuse = new window.Fuse([], kFuseIndexOptions); + resultsRaw.forEach((rr) => { + subSearchFuse.add(rr.item); + }); + } + return results; +} diff --git a/sitemap.xml b/sitemap.xml new file mode 100644 index 00000000..89571c46 --- /dev/null +++ b/sitemap.xml @@ -0,0 +1,91 @@ + + + + https://perceptualrobots.github.io/pct/webots.html + 2024-12-18T17:06:54.649Z + + + https://perceptualrobots.github.io/pct/helpers.html + 2024-12-18T17:06:54.725Z + + + https://perceptualrobots.github.io/pct/structure.html + 2024-12-18T17:06:54.325Z + + + https://perceptualrobots.github.io/pct/pct_examples.html + 2024-12-18T17:06:54.101Z + + + https://perceptualrobots.github.io/pct/network.html + 2024-12-18T17:06:53.953Z + + + https://perceptualrobots.github.io/pct/microgrid.html + 2024-12-18T17:06:54.609Z + + + https://perceptualrobots.github.io/pct/errors.html + 2024-12-18T17:06:53.681Z + + + https://perceptualrobots.github.io/pct/nodes.html + 2024-12-18T17:06:53.497Z + + + https://perceptualrobots.github.io/pct/index.html + 2024-12-18T17:06:53.297Z + + + https://perceptualrobots.github.io/pct/plot.html + 2024-12-18T17:06:53.001Z + + + https://perceptualrobots.github.io/pct/architectures.html + 2024-12-18T17:06:53.441Z + + + https://perceptualrobots.github.io/pct/putils.html + 2024-12-18T17:06:53.249Z + + + https://perceptualrobots.github.io/pct/applications.html + 2024-12-18T17:06:53.325Z + + + https://perceptualrobots.github.io/pct/functions.html + 2024-12-18T17:06:53.449Z + + + https://perceptualrobots.github.io/pct/hierarchy.html + 2024-12-18T17:06:53.921Z + + + https://perceptualrobots.github.io/pct/plotting.html + 2024-12-18T17:06:53.525Z + + + https://perceptualrobots.github.io/pct/environments.html + 2024-12-18T17:06:54.713Z + + + https://perceptualrobots.github.io/pct/environment_processing.html + 2024-12-18T17:06:53.877Z + + + https://perceptualrobots.github.io/pct/yaw_module.html + 2024-12-18T17:06:54.449Z + + + https://perceptualrobots.github.io/pct/unittests_run_hierarchies.html + 2024-12-18T17:06:54.145Z + + + https://perceptualrobots.github.io/pct/arc.html + 2024-12-18T17:06:54.549Z + + + https://perceptualrobots.github.io/pct/examples.html + 2024-12-18T17:06:54.601Z + + diff --git a/structure.html b/structure.html new file mode 100644 index 00000000..b88b5b11 --- /dev/null +++ b/structure.html @@ -0,0 +1,794 @@ + + + + + + + + + + +Structure – pct + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Structure

+
+ +
+
+ Classes to create hierachy configurations. +
+
+ + +
+ + + + +
+ + + +
+ + + +
+
+
+
The autoreload extension is already loaded. To reload it, use:
+  %reload_ext autoreload
+
+
+
+
#sys.path.append("..")
+
+
+

source

+
+

LevelKey

+
+
 LevelKey (value, names=None, module=None, qualname=None, type=None,
+           start=1)
+
+

An enumeration.

+
+

source

+
+
+

ArchitectureStructure

+
+
 ArchitectureStructure (references=None, config=None, attr_mut_pb=None,
+                        lower_float=None, upper_float=None,
+                        levels_limit=None, columns_limit=None, sigma=None,
+                        mu=None, alpha=None, modes=None, namespace=None,
+                        **cargs)
+
+

ArchitectureStructure

+
+
arch = ArchitectureStructure()
+
+
+
config = arch.get_config()
+print(arch.get_config())
+target =  {'parameters': { 'modes' : {LevelKey.ZERO:3, LevelKey.N:3,LevelKey.TOP:4,LevelKey.ZEROTOP :4} }}
+assert config == target
+
+
{'parameters': {'modes': {<LevelKey.ZERO: 'level0'>: 3, <LevelKey.N: 'leveln'>: 3, <LevelKey.TOP: 'leveltop'>: 4, <LevelKey.ZEROTOP: 'level0top'>: 4}}}
+
+
+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/styles.css b/styles.css new file mode 100644 index 00000000..66ccc49e --- /dev/null +++ b/styles.css @@ -0,0 +1,37 @@ +.cell { + margin-bottom: 1rem; +} + +.cell > .sourceCode { + margin-bottom: 0; +} + +.cell-output > pre { + margin-bottom: 0; +} + +.cell-output > pre, .cell-output > .sourceCode > pre, .cell-output-stdout > pre { + margin-left: 0.8rem; + margin-top: 0; + background: none; + border-left: 2px solid lightsalmon; + border-top-left-radius: 0; + border-top-right-radius: 0; +} + +.cell-output > .sourceCode { + border: none; +} + +.cell-output > .sourceCode { + background: none; + margin-top: 0; +} + +div.description { + padding-left: 2px; + padding-top: 5px; + font-style: italic; + font-size: 135%; + opacity: 70%; +} diff --git a/unittests_run_hierarchies.html b/unittests_run_hierarchies.html new file mode 100644 index 00000000..a4136ce1 --- /dev/null +++ b/unittests_run_hierarchies.html @@ -0,0 +1,814 @@ + + + + + + + + + +unittests_run_hierarchies – pct + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

unittests_run_hierarchies

+
+ + + +
+ + + + +
+ + + +
+ + + +
+
import unittest
+from os import sep
+from pct.putils import Timer
+from pct.hierarchy import PCTHierarchy
+from pct.environment_processing import EnvironmentProcessingFactory
+
+
+
class TestRunWindTurbine(unittest.TestCase):
+
+    @classmethod
+    def setUpClass(cls):
+        pass
+
+
+    # def test_runMicrogrid(self):
+
+    #     file = "G:\\My Drive\\data\\ga\\MicroGrid\\RewardError-RootMeanSquareError-Mode04\\ga-000.017-s001-3x5-m004-MG0001-9b7851aa082d1178ee05750f4b5815ce.properties"
+    #     timer = Timer()
+    #     timer.start()
+    #     _, score = PCTHierarchy.run_from_file(file, env_props=None, seed=1, render=False, move=None, min=True, history=False, 
+    #                                                 hpct_verbose= False, runs=None, plots_dir=None, early_termination=False)
+    #     print(f'Score={score:0.3f}')
+    #     timer.stop()
+    #     print(f'Mean time: {timer.mean()}')
+
+
+    def test_run_s_0538_03(self):
+
+        file = "testfiles" + sep + "data" + sep + "ga" + sep + "WindTurbine"+sep+"ga--1362.401-s003-4x3-m005-WT0538-bddf277b0f729cc630efacf91b9f494f.properties"
+        timer = Timer()
+        timer.start()
+        _, score = PCTHierarchy.run_from_file(file, env_props=None, seed=1, render=False, move=None, min=True, history=False, 
+                                                    hpct_verbose= False, runs=None, plots_dir=None, early_termination=False)
+        print(f'Score={score:0.3f}')
+        timer.stop()
+        print(f'Mean time: {timer.mean()}')
+
+        self.assertAlmostEqual(score, -1362.401471117955 )
+
+    def test_run_v_0416_01(self):
+
+        file = "testfiles" + sep + "data" + sep + "ga" + sep + "WindTurbine"+sep+ "ga--2629.009-s001-5x5-m002-WT0416-31ecb19201d49e8c6f9dd1e172bd6944.properties"
+        timer = Timer()
+        timer.start()
+        _, score = PCTHierarchy.run_from_file(file, env_props=None, seed=1, render=False, move=None, min=True, history=False, 
+                                                    hpct_verbose= False, runs=None, plots_dir=None, early_termination=False)
+        print(f'Score={score:0.3f}')
+        timer.stop()
+        print(f'Mean time: {timer.mean()}')
+
+        self.assertAlmostEqual(score, -2629.008509888531 )
+
+
+
res = unittest.main(argv=[''], verbosity=3, exit=False)
+
+
test_run_s_0538_03 (__main__.TestRunWindTurbine) ... ok
+test_run_v_0416_01 (__main__.TestRunWindTurbine) ...
+
+
+
Score=-1362.401
+Mean time: 1.1948626999999874
+
+
+
ok
+
+----------------------------------------------------------------------
+Ran 2 tests in 1.758s
+
+OK
+
+
+
Score=-2629.009
+Mean time: 0.5607209000000068
+
+
+
+
assert len(res.result.failures) == 0
+assert len(res.result.errors) == 0
+
+ + + +
+ +
+ + + + + \ No newline at end of file diff --git a/webots.html b/webots.html new file mode 100644 index 00000000..99065426 --- /dev/null +++ b/webots.html @@ -0,0 +1,723 @@ + + + + + + + + + + +Webots – pct + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Webots

+
+ +
+
+ A helper class for the Webots environment. +
+
+ + +
+ + + + +
+ + + +
+ + + +
+

source

+
+

WebotsHelper

+
+
 WebotsHelper (name=None, mode=1)
+
+

Initialize self. See help(type(self)) for accurate signature.

+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/yaw_module.html b/yaw_module.html new file mode 100644 index 00000000..8146e870 --- /dev/null +++ b/yaw_module.html @@ -0,0 +1,914 @@ + + + + + + + + + +Yaw Module – pct + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Yaw Module

+
+ + + +
+ + + + +
+ + + +
+ + + +
+

source

+
+

get_yaw_count

+
+
 get_yaw_count (nacelle_position_diff)
+
+ +++++ + + + + + + + + + + + + + + + + + + + +
TypeDetails
nacelle_position_diffPandas series of nacelle position increment
Returnsnumber of yaw actuations
+
+

source

+
+
+

get_time_yawing

+
+
 get_time_yawing (nacelle_position_diff)
+
+ +++++ + + + + + + + + + + + + + + + + + + + +
TypeDetails
nacelle_position_diffPandas series of nacelle position increment
Returnstime spent yawing in percent
+
+

source

+
+
+

oriented_angle

+
+
 oriented_angle (angle)
+
+ + + + + + + + + + + + + + + + + + + + +
TypeDetails
angle
Returnsoriented angle in range [-180,179]
+
+

source

+
+
+

get_dataset_from_simu

+
+
 get_dataset_from_simu (path='dataset.csv', cycle_period=10,
+                        rolling_average_duration=20)
+
+

returns output dataset obtained of CYCA-S

+
+

source

+
+
+

get_properties

+
+
 get_properties (properties)
+
+
+

source

+
+
+

get_indexes

+
+
 get_indexes (model_params, properties)
+
+
+

source

+
+
+

test_trad_control

+
+
 test_trad_control (wind_timeseries, wind_timeseries_not_agg, agg, start,
+                    end, experiment=None, datatype='baseline_simu',
+                    outdir=None)
+
+

test CYCA-S and CYCA-L

+
+

source

+
+
+

YawEnv

+
+
 YawEnv ()
+
+

*The main OpenAI Gym class.

+

It encapsulates an environment with arbitrary behind-the-scenes dynamics. An environment can be partially or fully observed.

+

The main API methods that users of this class need to know are:

+
    +
  • :meth:step - Takes a step in the environment using an action returning the next observation, reward, if the environment terminated and observation information.
    • +
  • :meth:reset - Resets the environment to an initial state, returning the initial observation and observation information.
    • +
  • :meth:render - Renders the environment observation with modes depending on the output
    • +
  • :meth:close - Closes the environment, important for rendering where pygame is imported
    • +
+

And set the following attributes:

+
    +
  • :attr:action_space - The Space object corresponding to valid actions
    • +
  • :attr:observation_space - The Space object corresponding to valid observations
    • +
  • :attr:reward_range - A tuple corresponding to the minimum and maximum possible rewards
    • +
  • :attr:spec - An environment spec that contains the information used to initialise the environment from gym.make
    • +
  • :attr:metadata - The metadata of the environment, i.e. render modes
    • +
  • :attr:np_random - The random number generator for the environment
    • +
+

Note: a default reward range set to :math:(-\infty,+\infty) already exists. Set it if you want a narrower range.*

+
+

source

+
+
+

get_comparaison_metrics

+
+
 get_comparaison_metrics (wind_direction, power_control, power_simu,
+                          res_model, res_baseline_simu, yaw_rate,
+                          yaw_power, width_bin)
+
+
+

source

+
+
+

test_model_wind

+
+
 test_model_wind (wind_timeseries, start_index, stop_index, ancestors,
+                  filter_duration, yaw_parameters, experiment=None,
+                  datatype='test')
+
+

test RLYCA

+
+

source

+
+
+

test_hpct_wind

+
+
 test_hpct_wind (file=None, plots=None, history=None, verbose=None,
+                 outdir=None, early=None, environment_properties=None,
+                 start_index=None, stop_index=None, experiment=None,
+                 datatype='test', draw_file=None, model_file=None,
+                 log_testing_to_experiment=False,
+                 log_experiment_figure=False, min=None)
+
+

test RLYCA

+ + +
+ +
+ +
+ + + + + \ No newline at end of file