```
# Start BPTK and automatically read the scenarios found in the scenarios folder
# this also loads all the Python classes referenced in the scenarios, so we are immediately ready
# to run scenarios and plot results.
from BPTK_Py.bptk import bptk
= bptk() bptk
```

# Choose Data Collector

# Custom Data Collectors

For agent-based models BPTK-Py has a standard data collector which collects the statistics (average, minumum, etc.) for all properties of all **agent types**. Collecting statistics for each agent individually will lead to a huge amount of data. However, if you want to explore this case or other cases which the standard data collector does not cover, you can also implement your own data collector. In this notebook, we explain how to proceed. Therefore we implement a new data collector class which collects the properties of each agent individually.

We first create a new class and choose a name for it: `AgentDataCollector`

. Then we implement the necessary methods and constructors. However, we don’t want to create a whole new class but use the already existing methods of the standard data collector and extend it for our purposes. This saves a lot of work. The new data collector requires the following methods:

- A method which collects the data of each agent
- A method which represents the collected data in a dataframe (this is necessary for the plot method)
- A method which plots the data because the standard visualization class of BPTK-Py cannot handle the new data collector

*The data collector AgentDataCollector is already in the BPTK-Py framework but yet we will explain how to implement your own data collector.*

## 1. Create data collector class

Create a class and name it `AgentDataCollector`

.

## 2. Import libraries

As explained before, we want to derive the new data collector class from the standard class. Therefore we have to import the standard data collector.

`from BPTK_Py.abm import DataCollector`

For plotting the data, we also require pandas and a BPTK-Py config library. The config library is necessary to obtain the same plot design as the standard plot from BPTK-Py.

```
import pandas as pd
import BPTK_Py.config as config
```

Of course, you can add as many libraries as you want. In depends on what you need for your collector.

## 3. Derive standard class

This how you derive the standard data collector:

`class AgentDataCollector(DataCollector)`

## 4. Implement methods

As I explained before, we can either extend the class or replace methods. Since we want to overwrite the collecting method we have to replace `def collect_agent_statistic(self, time, agents)`

. Now you can implement the logic of your own data collector. To save the statistics, you can use the attribute `self.agent_statistics`

which is an empty dictionary.

After collecting all the data we have to bring them into an usable structure to be able to plot the statistics later. I chose to transform the dictionary into multiple dataframes. For each agent there exists one dataframe with its statistics. When we have the statistics in a dataframe we can exploit the functionalities of the library pandas.

The data is prepared and we can now plot them by using the visualization methods of pandas. To obtain the same design as the plot of BPTK-Py you need change the following parameters of the plot method. For the parameter `title`

you can set any title name. For more information about the configuration you can go to the BPTK-Py documentation: Developing Advanced User Interfaces

```
df.plot(kind=config.configuration["kind"],
alpha=config.configuration["alpha"],
stacked=config.configuration["stacked"],
figsize=config.configuration["figsize"],
title=title,
color=config.configuration["colors"],
lw=config.configuration["linewidth"])
```

## 5. Set our new data collector in simulation model

The previous steps explained how to implement a data collector. Now, we have to set the new data collector in our model class. To show you each step, we use our model SPMAgentDataCollector.py which is in `./simulation_models/spm`

.

- You go to the simulation model and open the Python file.
- Import the new data collector:
`from BPTK-Py.abm.datacollectors import AgentDataCollector`

. - Add
`self.data_collector=AgentDataCollector()`

in`instantiate_model(self)`

.

## 6. Run simulation model with new data collector

In this step, we run the simulation model with a specific scenario. All statistics of each agent are collected by the data collector.

```
= bptk.scenario_manager_factory.get_scenario("ABMsmSimpleProjectManagementAgentDataCollector","scenario80").model
model model.run()
```

Now you can plot the stats of one or more specific agents. In our case we want to compare two tasks and see how much effort remains for them.

The following bullet points describe the parameters of the plot method: - agent_ids: choose the stats of the agents you want plot - properties: choose properties you want to plot - title: choose title of the plot - agent_type: choose the agent type

`= model.data_collector.plot_agent_stats(agent_ids=[2,3],properties=['remaining_effort'],title="Scenario80",agent_type="task") ax `

Output the stats in a dataframe

`'task'][2] model.data_collector.get_agent_stats()[`

id | time | agent_state | agent_type | effort | remaining_effort | |
---|---|---|---|---|---|---|

0 | 2 | 0.00 | in_progress | task | 1 | 0.831200 |

1 | 2 | 0.25 | in_progress | task | 1 | 0.662273 |

2 | 2 | 0.50 | in_progress | task | 1 | 0.493219 |

3 | 2 | 0.75 | in_progress | task | 1 | 0.324038 |

4 | 2 | 1.00 | in_progress | task | 1 | 0.154729 |

... | ... | ... | ... | ... | ... | ... |

479 | 2 | 119.75 | closed | task | 1 | 0.000000 |

480 | 2 | 120.00 | closed | task | 1 | 0.000000 |

481 | 2 | 120.25 | closed | task | 1 | 0.000000 |

482 | 2 | 120.50 | closed | task | 1 | 0.000000 |

483 | 2 | 120.75 | closed | task | 1 | 0.000000 |

484 rows × 6 columns

`'task'][3] model.data_collector.get_agent_stats()[`

id | time | agent_state | agent_type | effort | remaining_effort | |
---|---|---|---|---|---|---|

0 | 3 | 0.00 | open | task | 1 | 0.0 |

1 | 3 | 0.25 | open | task | 1 | 0.0 |

2 | 3 | 0.50 | open | task | 1 | 0.0 |

3 | 3 | 0.75 | open | task | 1 | 0.0 |

4 | 3 | 1.00 | open | task | 1 | 0.0 |

... | ... | ... | ... | ... | ... | ... |

479 | 3 | 119.75 | closed | task | 1 | 0.0 |

480 | 3 | 120.00 | closed | task | 1 | 0.0 |

481 | 3 | 120.25 | closed | task | 1 | 0.0 |

482 | 3 | 120.50 | closed | task | 1 | 0.0 |

483 | 3 | 120.75 | closed | task | 1 | 0.0 |

484 rows × 6 columns

If you want to get all stats of each agent you call `model.data_collector.get_agent_stats()`

` model.data_collector.get_agent_stats()`

```
{'staf_member': {0: id time agent_state agent_type
0 0 0.00 busy staf_member
1 0 0.25 busy staf_member
2 0 0.50 busy staf_member
3 0 0.75 busy staf_member
4 0 1.00 busy staf_member
.. .. ... ... ...
479 0 119.75 available staf_member
480 0 120.00 available staf_member
481 0 120.25 available staf_member
482 0 120.50 available staf_member
483 0 120.75 available staf_member
[484 rows x 4 columns]},
'controlling': {1: id time agent_state agent_type productivity schedule_pressure
0 1 0.00 active controlling 0.675200 0.800000
1 1 0.25 active controlling 0.675708 0.800313
2 1 0.50 active controlling 0.676216 0.800626
3 1 0.75 active controlling 0.676725 0.800939
4 1 1.00 active controlling 0.677235 0.801253
.. .. ... ... ... ... ...
479 1 119.75 active controlling 1.000000 1.000000
480 1 120.00 active controlling 1.000000 1.000000
481 1 120.25 active controlling 1.000000 1.000000
482 1 120.50 active controlling 1.000000 1.000000
483 1 120.75 active controlling 1.000000 1.000000
[484 rows x 6 columns]},
'task': {2: id time agent_state agent_type effort remaining_effort
0 2 0.00 in_progress task 1 0.831200
1 2 0.25 in_progress task 1 0.662273
2 2 0.50 in_progress task 1 0.493219
3 2 0.75 in_progress task 1 0.324038
4 2 1.00 in_progress task 1 0.154729
.. .. ... ... ... ... ...
479 2 119.75 closed task 1 0.000000
480 2 120.00 closed task 1 0.000000
481 2 120.25 closed task 1 0.000000
482 2 120.50 closed task 1 0.000000
483 2 120.75 closed task 1 0.000000
[484 rows x 6 columns],
3: id time agent_state agent_type effort remaining_effort
0 3 0.00 open task 1 0.0
1 3 0.25 open task 1 0.0
2 3 0.50 open task 1 0.0
3 3 0.75 open task 1 0.0
4 3 1.00 open task 1 0.0
.. .. ... ... ... ... ...
479 3 119.75 closed task 1 0.0
480 3 120.00 closed task 1 0.0
481 3 120.25 closed task 1 0.0
482 3 120.50 closed task 1 0.0
483 3 120.75 closed task 1 0.0
[484 rows x 6 columns],
4: id time agent_state agent_type effort remaining_effort
0 4 0.00 open task 1 0.0
1 4 0.25 open task 1 0.0
2 4 0.50 open task 1 0.0
3 4 0.75 open task 1 0.0
4 4 1.00 open task 1 0.0
.. .. ... ... ... ... ...
479 4 119.75 closed task 1 0.0
480 4 120.00 closed task 1 0.0
481 4 120.25 closed task 1 0.0
482 4 120.50 closed task 1 0.0
483 4 120.75 closed task 1 0.0
[484 rows x 6 columns],
5: id time agent_state agent_type effort remaining_effort
0 5 0.00 open task 1 0.0
1 5 0.25 open task 1 0.0
2 5 0.50 open task 1 0.0
3 5 0.75 open task 1 0.0
4 5 1.00 open task 1 0.0
.. .. ... ... ... ... ...
479 5 119.75 closed task 1 0.0
480 5 120.00 closed task 1 0.0
481 5 120.25 closed task 1 0.0
482 5 120.50 closed task 1 0.0
483 5 120.75 closed task 1 0.0
[484 rows x 6 columns],
6: id time agent_state agent_type effort remaining_effort
0 6 0.00 open task 1 0.0
1 6 0.25 open task 1 0.0
2 6 0.50 open task 1 0.0
3 6 0.75 open task 1 0.0
4 6 1.00 open task 1 0.0
.. .. ... ... ... ... ...
479 6 119.75 closed task 1 0.0
480 6 120.00 closed task 1 0.0
481 6 120.25 closed task 1 0.0
482 6 120.50 closed task 1 0.0
483 6 120.75 closed task 1 0.0
[484 rows x 6 columns],
7: id time agent_state agent_type effort remaining_effort
0 7 0.00 open task 1 0.0
1 7 0.25 open task 1 0.0
2 7 0.50 open task 1 0.0
3 7 0.75 open task 1 0.0
4 7 1.00 open task 1 0.0
.. .. ... ... ... ... ...
479 7 119.75 closed task 1 0.0
480 7 120.00 closed task 1 0.0
481 7 120.25 closed task 1 0.0
482 7 120.50 closed task 1 0.0
483 7 120.75 closed task 1 0.0
[484 rows x 6 columns],
8: id time agent_state agent_type effort remaining_effort
0 8 0.00 open task 1 0.0
1 8 0.25 open task 1 0.0
2 8 0.50 open task 1 0.0
3 8 0.75 open task 1 0.0
4 8 1.00 open task 1 0.0
.. .. ... ... ... ... ...
479 8 119.75 closed task 1 0.0
480 8 120.00 closed task 1 0.0
481 8 120.25 closed task 1 0.0
482 8 120.50 closed task 1 0.0
483 8 120.75 closed task 1 0.0
[484 rows x 6 columns],
9: id time agent_state agent_type effort remaining_effort
0 9 0.00 open task 1 0.0
1 9 0.25 open task 1 0.0
2 9 0.50 open task 1 0.0
3 9 0.75 open task 1 0.0
4 9 1.00 open task 1 0.0
.. .. ... ... ... ... ...
479 9 119.75 closed task 1 0.0
480 9 120.00 closed task 1 0.0
481 9 120.25 closed task 1 0.0
482 9 120.50 closed task 1 0.0
483 9 120.75 closed task 1 0.0
[484 rows x 6 columns],
10: id time agent_state agent_type effort remaining_effort
0 10 0.00 open task 1 0.0
1 10 0.25 open task 1 0.0
2 10 0.50 open task 1 0.0
3 10 0.75 open task 1 0.0
4 10 1.00 open task 1 0.0
.. .. ... ... ... ... ...
479 10 119.75 closed task 1 0.0
480 10 120.00 closed task 1 0.0
481 10 120.25 closed task 1 0.0
482 10 120.50 closed task 1 0.0
483 10 120.75 closed task 1 0.0
[484 rows x 6 columns],
11: id time agent_state agent_type effort remaining_effort
0 11 0.00 open task 1 0.0
1 11 0.25 open task 1 0.0
2 11 0.50 open task 1 0.0
3 11 0.75 open task 1 0.0
4 11 1.00 open task 1 0.0
.. .. ... ... ... ... ...
479 11 119.75 closed task 1 0.0
480 11 120.00 closed task 1 0.0
481 11 120.25 closed task 1 0.0
482 11 120.50 closed task 1 0.0
483 11 120.75 closed task 1 0.0
[484 rows x 6 columns],
12: id time agent_state agent_type effort remaining_effort
0 12 0.00 open task 1 0.0
1 12 0.25 open task 1 0.0
2 12 0.50 open task 1 0.0
3 12 0.75 open task 1 0.0
4 12 1.00 open task 1 0.0
.. .. ... ... ... ... ...
479 12 119.75 closed task 1 0.0
480 12 120.00 closed task 1 0.0
481 12 120.25 closed task 1 0.0
482 12 120.50 closed task 1 0.0
483 12 120.75 closed task 1 0.0
[484 rows x 6 columns],
13: id time agent_state agent_type effort remaining_effort
0 13 0.00 open task 1 0.0
1 13 0.25 open task 1 0.0
2 13 0.50 open task 1 0.0
3 13 0.75 open task 1 0.0
4 13 1.00 open task 1 0.0
.. .. ... ... ... ... ...
479 13 119.75 closed task 1 0.0
480 13 120.00 closed task 1 0.0
481 13 120.25 closed task 1 0.0
482 13 120.50 closed task 1 0.0
483 13 120.75 closed task 1 0.0
[484 rows x 6 columns],
14: id time agent_state agent_type effort remaining_effort
0 14 0.00 open task 1 0.0
1 14 0.25 open task 1 0.0
2 14 0.50 open task 1 0.0
3 14 0.75 open task 1 0.0
4 14 1.00 open task 1 0.0
.. .. ... ... ... ... ...
479 14 119.75 closed task 1 0.0
480 14 120.00 closed task 1 0.0
481 14 120.25 closed task 1 0.0
482 14 120.50 closed task 1 0.0
483 14 120.75 closed task 1 0.0
[484 rows x 6 columns],
15: id time agent_state agent_type effort remaining_effort
0 15 0.00 open task 1 0.0
1 15 0.25 open task 1 0.0
2 15 0.50 open task 1 0.0
3 15 0.75 open task 1 0.0
4 15 1.00 open task 1 0.0
.. .. ... ... ... ... ...
479 15 119.75 closed task 1 0.0
480 15 120.00 closed task 1 0.0
481 15 120.25 closed task 1 0.0
482 15 120.50 closed task 1 0.0
483 15 120.75 closed task 1 0.0
[484 rows x 6 columns],
16: id time agent_state agent_type effort remaining_effort
0 16 0.00 open task 1 0.0
1 16 0.25 open task 1 0.0
2 16 0.50 open task 1 0.0
3 16 0.75 open task 1 0.0
4 16 1.00 open task 1 0.0
.. .. ... ... ... ... ...
479 16 119.75 closed task 1 0.0
480 16 120.00 closed task 1 0.0
481 16 120.25 closed task 1 0.0
482 16 120.50 closed task 1 0.0
483 16 120.75 closed task 1 0.0
[484 rows x 6 columns],
17: id time agent_state agent_type effort remaining_effort
0 17 0.00 open task 1 0.0
1 17 0.25 open task 1 0.0
2 17 0.50 open task 1 0.0
3 17 0.75 open task 1 0.0
4 17 1.00 open task 1 0.0
.. .. ... ... ... ... ...
479 17 119.75 closed task 1 0.0
480 17 120.00 closed task 1 0.0
481 17 120.25 closed task 1 0.0
482 17 120.50 closed task 1 0.0
483 17 120.75 closed task 1 0.0
[484 rows x 6 columns],
18: id time agent_state agent_type effort remaining_effort
0 18 0.00 open task 1 0.0
1 18 0.25 open task 1 0.0
2 18 0.50 open task 1 0.0
3 18 0.75 open task 1 0.0
4 18 1.00 open task 1 0.0
.. .. ... ... ... ... ...
479 18 119.75 closed task 1 0.0
480 18 120.00 closed task 1 0.0
481 18 120.25 closed task 1 0.0
482 18 120.50 closed task 1 0.0
483 18 120.75 closed task 1 0.0
[484 rows x 6 columns],
19: id time agent_state agent_type effort remaining_effort
0 19 0.00 open task 1 0.0
1 19 0.25 open task 1 0.0
2 19 0.50 open task 1 0.0
3 19 0.75 open task 1 0.0
4 19 1.00 open task 1 0.0
.. .. ... ... ... ... ...
479 19 119.75 closed task 1 0.0
480 19 120.00 closed task 1 0.0
481 19 120.25 closed task 1 0.0
482 19 120.50 closed task 1 0.0
483 19 120.75 closed task 1 0.0
[484 rows x 6 columns],
20: id time agent_state agent_type effort remaining_effort
0 20 0.00 open task 1 0.0
1 20 0.25 open task 1 0.0
2 20 0.50 open task 1 0.0
3 20 0.75 open task 1 0.0
4 20 1.00 open task 1 0.0
.. .. ... ... ... ... ...
479 20 119.75 closed task 1 0.0
480 20 120.00 closed task 1 0.0
481 20 120.25 closed task 1 0.0
482 20 120.50 closed task 1 0.0
483 20 120.75 closed task 1 0.0
[484 rows x 6 columns],
21: id time agent_state agent_type effort remaining_effort
0 21 0.00 open task 1 0.0
1 21 0.25 open task 1 0.0
2 21 0.50 open task 1 0.0
3 21 0.75 open task 1 0.0
4 21 1.00 open task 1 0.0
.. .. ... ... ... ... ...
479 21 119.75 closed task 1 0.0
480 21 120.00 closed task 1 0.0
481 21 120.25 closed task 1 0.0
482 21 120.50 closed task 1 0.0
483 21 120.75 closed task 1 0.0
[484 rows x 6 columns],
22: id time agent_state agent_type effort remaining_effort
0 22 0.00 open task 1 0.0
1 22 0.25 open task 1 0.0
2 22 0.50 open task 1 0.0
3 22 0.75 open task 1 0.0
4 22 1.00 open task 1 0.0
.. .. ... ... ... ... ...
479 22 119.75 closed task 1 0.0
480 22 120.00 closed task 1 0.0
481 22 120.25 closed task 1 0.0
482 22 120.50 closed task 1 0.0
483 22 120.75 closed task 1 0.0
[484 rows x 6 columns],
23: id time agent_state agent_type effort remaining_effort
0 23 0.00 open task 1 0.0
1 23 0.25 open task 1 0.0
2 23 0.50 open task 1 0.0
3 23 0.75 open task 1 0.0
4 23 1.00 open task 1 0.0
.. .. ... ... ... ... ...
479 23 119.75 closed task 1 0.0
480 23 120.00 closed task 1 0.0
481 23 120.25 closed task 1 0.0
482 23 120.50 closed task 1 0.0
483 23 120.75 closed task 1 0.0
[484 rows x 6 columns],
24: id time agent_state agent_type effort remaining_effort
0 24 0.00 open task 1 0.0
1 24 0.25 open task 1 0.0
2 24 0.50 open task 1 0.0
3 24 0.75 open task 1 0.0
4 24 1.00 open task 1 0.0
.. .. ... ... ... ... ...
479 24 119.75 closed task 1 0.0
480 24 120.00 closed task 1 0.0
481 24 120.25 closed task 1 0.0
482 24 120.50 closed task 1 0.0
483 24 120.75 closed task 1 0.0
[484 rows x 6 columns],
25: id time agent_state agent_type effort remaining_effort
0 25 0.00 open task 1 0.0
1 25 0.25 open task 1 0.0
2 25 0.50 open task 1 0.0
3 25 0.75 open task 1 0.0
4 25 1.00 open task 1 0.0
.. .. ... ... ... ... ...
479 25 119.75 closed task 1 0.0
480 25 120.00 closed task 1 0.0
481 25 120.25 closed task 1 0.0
482 25 120.50 closed task 1 0.0
483 25 120.75 closed task 1 0.0
[484 rows x 6 columns],
26: id time agent_state agent_type effort remaining_effort
0 26 0.00 open task 1 0.0
1 26 0.25 open task 1 0.0
2 26 0.50 open task 1 0.0
3 26 0.75 open task 1 0.0
4 26 1.00 open task 1 0.0
.. .. ... ... ... ... ...
479 26 119.75 closed task 1 0.0
480 26 120.00 closed task 1 0.0
481 26 120.25 closed task 1 0.0
482 26 120.50 closed task 1 0.0
483 26 120.75 closed task 1 0.0
[484 rows x 6 columns],
27: id time agent_state agent_type effort remaining_effort
0 27 0.00 open task 1 0.0
1 27 0.25 open task 1 0.0
2 27 0.50 open task 1 0.0
3 27 0.75 open task 1 0.0
4 27 1.00 open task 1 0.0
.. .. ... ... ... ... ...
479 27 119.75 closed task 1 0.0
480 27 120.00 closed task 1 0.0
481 27 120.25 closed task 1 0.0
482 27 120.50 closed task 1 0.0
483 27 120.75 closed task 1 0.0
[484 rows x 6 columns],
28: id time agent_state agent_type effort remaining_effort
0 28 0.00 open task 1 0.0
1 28 0.25 open task 1 0.0
2 28 0.50 open task 1 0.0
3 28 0.75 open task 1 0.0
4 28 1.00 open task 1 0.0
.. .. ... ... ... ... ...
479 28 119.75 closed task 1 0.0
480 28 120.00 closed task 1 0.0
481 28 120.25 closed task 1 0.0
482 28 120.50 closed task 1 0.0
483 28 120.75 closed task 1 0.0
[484 rows x 6 columns],
29: id time agent_state agent_type effort remaining_effort
0 29 0.00 open task 1 0.0
1 29 0.25 open task 1 0.0
2 29 0.50 open task 1 0.0
3 29 0.75 open task 1 0.0
4 29 1.00 open task 1 0.0
.. .. ... ... ... ... ...
479 29 119.75 closed task 1 0.0
480 29 120.00 closed task 1 0.0
481 29 120.25 closed task 1 0.0
482 29 120.50 closed task 1 0.0
483 29 120.75 closed task 1 0.0
[484 rows x 6 columns],
30: id time agent_state agent_type effort remaining_effort
0 30 0.00 open task 1 0.0
1 30 0.25 open task 1 0.0
2 30 0.50 open task 1 0.0
3 30 0.75 open task 1 0.0
4 30 1.00 open task 1 0.0
.. .. ... ... ... ... ...
479 30 119.75 closed task 1 0.0
480 30 120.00 closed task 1 0.0
481 30 120.25 closed task 1 0.0
482 30 120.50 closed task 1 0.0
483 30 120.75 closed task 1 0.0
[484 rows x 6 columns],
31: id time agent_state agent_type effort remaining_effort
0 31 0.00 open task 1 0.0
1 31 0.25 open task 1 0.0
2 31 0.50 open task 1 0.0
3 31 0.75 open task 1 0.0
4 31 1.00 open task 1 0.0
.. .. ... ... ... ... ...
479 31 119.75 closed task 1 0.0
480 31 120.00 closed task 1 0.0
481 31 120.25 closed task 1 0.0
482 31 120.50 closed task 1 0.0
483 31 120.75 closed task 1 0.0
[484 rows x 6 columns],
32: id time agent_state agent_type effort remaining_effort
0 32 0.00 open task 1 0.0
1 32 0.25 open task 1 0.0
2 32 0.50 open task 1 0.0
3 32 0.75 open task 1 0.0
4 32 1.00 open task 1 0.0
.. .. ... ... ... ... ...
479 32 119.75 closed task 1 0.0
480 32 120.00 closed task 1 0.0
481 32 120.25 closed task 1 0.0
482 32 120.50 closed task 1 0.0
483 32 120.75 closed task 1 0.0
[484 rows x 6 columns],
33: id time agent_state agent_type effort remaining_effort
0 33 0.00 open task 1 0.0
1 33 0.25 open task 1 0.0
2 33 0.50 open task 1 0.0
3 33 0.75 open task 1 0.0
4 33 1.00 open task 1 0.0
.. .. ... ... ... ... ...
479 33 119.75 closed task 1 0.0
480 33 120.00 closed task 1 0.0
481 33 120.25 closed task 1 0.0
482 33 120.50 closed task 1 0.0
483 33 120.75 closed task 1 0.0
[484 rows x 6 columns],
34: id time agent_state agent_type effort remaining_effort
0 34 0.00 open task 1 0.0
1 34 0.25 open task 1 0.0
2 34 0.50 open task 1 0.0
3 34 0.75 open task 1 0.0
4 34 1.00 open task 1 0.0
.. .. ... ... ... ... ...
479 34 119.75 closed task 1 0.0
480 34 120.00 closed task 1 0.0
481 34 120.25 closed task 1 0.0
482 34 120.50 closed task 1 0.0
483 34 120.75 closed task 1 0.0
[484 rows x 6 columns],
35: id time agent_state agent_type effort remaining_effort
0 35 0.00 open task 1 0.0
1 35 0.25 open task 1 0.0
2 35 0.50 open task 1 0.0
3 35 0.75 open task 1 0.0
4 35 1.00 open task 1 0.0
.. .. ... ... ... ... ...
479 35 119.75 closed task 1 0.0
480 35 120.00 closed task 1 0.0
481 35 120.25 closed task 1 0.0
482 35 120.50 closed task 1 0.0
483 35 120.75 closed task 1 0.0
[484 rows x 6 columns],
36: id time agent_state agent_type effort remaining_effort
0 36 0.00 open task 1 0.0
1 36 0.25 open task 1 0.0
2 36 0.50 open task 1 0.0
3 36 0.75 open task 1 0.0
4 36 1.00 open task 1 0.0
.. .. ... ... ... ... ...
479 36 119.75 closed task 1 0.0
480 36 120.00 closed task 1 0.0
481 36 120.25 closed task 1 0.0
482 36 120.50 closed task 1 0.0
483 36 120.75 closed task 1 0.0
[484 rows x 6 columns],
37: id time agent_state agent_type effort remaining_effort
0 37 0.00 open task 1 0.0
1 37 0.25 open task 1 0.0
2 37 0.50 open task 1 0.0
3 37 0.75 open task 1 0.0
4 37 1.00 open task 1 0.0
.. .. ... ... ... ... ...
479 37 119.75 closed task 1 0.0
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[484 rows x 6 columns],
38: id time agent_state agent_type effort remaining_effort
0 38 0.00 open task 1 0.0
1 38 0.25 open task 1 0.0
2 38 0.50 open task 1 0.0
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.. .. ... ... ... ... ...
479 38 119.75 closed task 1 0.0
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481 38 120.25 closed task 1 0.0
482 38 120.50 closed task 1 0.0
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[484 rows x 6 columns],
39: id time agent_state agent_type effort remaining_effort
0 39 0.00 open task 1 0.0
1 39 0.25 open task 1 0.0
2 39 0.50 open task 1 0.0
3 39 0.75 open task 1 0.0
4 39 1.00 open task 1 0.0
.. .. ... ... ... ... ...
479 39 119.75 closed task 1 0.0
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481 39 120.25 closed task 1 0.0
482 39 120.50 closed task 1 0.0
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[484 rows x 6 columns],
40: id time agent_state agent_type effort remaining_effort
0 40 0.00 open task 1 0.0
1 40 0.25 open task 1 0.0
2 40 0.50 open task 1 0.0
3 40 0.75 open task 1 0.0
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.. .. ... ... ... ... ...
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481 40 120.25 closed task 1 0.0
482 40 120.50 closed task 1 0.0
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[484 rows x 6 columns],
41: id time agent_state agent_type effort remaining_effort
0 41 0.00 open task 1 0.0
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2 41 0.50 open task 1 0.0
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.. .. ... ... ... ... ...
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[484 rows x 6 columns],
42: id time agent_state agent_type effort remaining_effort
0 42 0.00 open task 1 0.0
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2 42 0.50 open task 1 0.0
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.. .. ... ... ... ... ...
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[484 rows x 6 columns],
43: id time agent_state agent_type effort remaining_effort
0 43 0.00 open task 1 0.0
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.. .. ... ... ... ... ...
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482 43 120.50 closed task 1 0.0
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[484 rows x 6 columns],
44: id time agent_state agent_type effort remaining_effort
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.. .. ... ... ... ... ...
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[484 rows x 6 columns],
45: id time agent_state agent_type effort remaining_effort
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1 45 0.25 open task 1 0.0
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.. .. ... ... ... ... ...
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[484 rows x 6 columns],
46: id time agent_state agent_type effort remaining_effort
0 46 0.00 open task 1 0.0
1 46 0.25 open task 1 0.0
2 46 0.50 open task 1 0.0
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.. .. ... ... ... ... ...
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[484 rows x 6 columns],
47: id time agent_state agent_type effort remaining_effort
0 47 0.00 open task 1 0.0
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.. .. ... ... ... ... ...
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[484 rows x 6 columns],
48: id time agent_state agent_type effort remaining_effort
0 48 0.00 open task 1 0.0
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2 48 0.50 open task 1 0.0
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.. .. ... ... ... ... ...
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482 48 120.50 closed task 1 0.0
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[484 rows x 6 columns],
49: id time agent_state agent_type effort remaining_effort
0 49 0.00 open task 1 0.0
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2 49 0.50 open task 1 0.0
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.. .. ... ... ... ... ...
479 49 119.75 closed task 1 0.0
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482 49 120.50 closed task 1 0.0
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[484 rows x 6 columns],
50: id time agent_state agent_type effort remaining_effort
0 50 0.00 open task 1 0.0
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[484 rows x 6 columns],
51: id time agent_state agent_type effort remaining_effort
0 51 0.00 open task 1 0.0
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.. .. ... ... ... ... ...
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[484 rows x 6 columns],
52: id time agent_state agent_type effort remaining_effort
0 52 0.00 open task 1 0.0
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.. .. ... ... ... ... ...
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[484 rows x 6 columns],
53: id time agent_state agent_type effort remaining_effort
0 53 0.00 open task 1 0.0
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.. .. ... ... ... ... ...
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[484 rows x 6 columns],
54: id time agent_state agent_type effort remaining_effort
0 54 0.00 open task 1 0.0
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.. .. ... ... ... ... ...
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[484 rows x 6 columns],
55: id time agent_state agent_type effort remaining_effort
0 55 0.00 open task 1 0.0
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.. .. ... ... ... ... ...
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[484 rows x 6 columns],
56: id time agent_state agent_type effort remaining_effort
0 56 0.00 open task 1 0.0
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.. .. ... ... ... ... ...
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[484 rows x 6 columns],
57: id time agent_state agent_type effort remaining_effort
0 57 0.00 open task 1 0.0
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[484 rows x 6 columns],
58: id time agent_state agent_type effort remaining_effort
0 58 0.00 open task 1 0.0
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[484 rows x 6 columns],
59: id time agent_state agent_type effort remaining_effort
0 59 0.00 open task 1 0.0
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[484 rows x 6 columns],
60: id time agent_state agent_type effort remaining_effort
0 60 0.00 open task 1 0.0
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[484 rows x 6 columns],
61: id time agent_state agent_type effort remaining_effort
0 61 0.00 open task 1 0.0
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[484 rows x 6 columns],
62: id time agent_state agent_type effort remaining_effort
0 62 0.00 open task 1 0.0
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[484 rows x 6 columns],
63: id time agent_state agent_type effort remaining_effort
0 63 0.00 open task 1 0.0
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.. .. ... ... ... ... ...
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[484 rows x 6 columns],
64: id time agent_state agent_type effort remaining_effort
0 64 0.00 open task 1 0.0
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[484 rows x 6 columns],
65: id time agent_state agent_type effort remaining_effort
0 65 0.00 open task 1 0.0
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[484 rows x 6 columns],
66: id time agent_state agent_type effort remaining_effort
0 66 0.00 open task 1 0.0
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[484 rows x 6 columns],
67: id time agent_state agent_type effort remaining_effort
0 67 0.00 open task 1 0.0
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[484 rows x 6 columns],
68: id time agent_state agent_type effort remaining_effort
0 68 0.00 open task 1 0.0
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[484 rows x 6 columns],
69: id time agent_state agent_type effort remaining_effort
0 69 0.00 open task 1 0.0
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[484 rows x 6 columns],
70: id time agent_state agent_type effort remaining_effort
0 70 0.00 open task 1 0.0
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[484 rows x 6 columns],
71: id time agent_state agent_type effort remaining_effort
0 71 0.00 open task 1 0.0
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[484 rows x 6 columns],
72: id time agent_state agent_type effort remaining_effort
0 72 0.00 open task 1 0.0
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[484 rows x 6 columns],
73: id time agent_state agent_type effort remaining_effort
0 73 0.00 open task 1 0.0
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.. .. ... ... ... ... ...
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[484 rows x 6 columns],
74: id time agent_state agent_type effort remaining_effort
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.. .. ... ... ... ... ...
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483 74 120.75 closed task 1 0.0
[484 rows x 6 columns],
75: id time agent_state agent_type effort remaining_effort
0 75 0.00 open task 1 0.0
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483 75 120.75 closed task 1 0.0
[484 rows x 6 columns],
76: id time agent_state agent_type effort remaining_effort
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[484 rows x 6 columns],
77: id time agent_state agent_type effort remaining_effort
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.. .. ... ... ... ... ...
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[484 rows x 6 columns],
78: id time agent_state agent_type effort remaining_effort
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[484 rows x 6 columns],
79: id time agent_state agent_type effort remaining_effort
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479 79 119.75 closed task 1 0.0
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[484 rows x 6 columns],
80: id time agent_state agent_type effort remaining_effort
0 80 0.00 open task 1 0.0
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[484 rows x 6 columns],
81: id time agent_state agent_type effort remaining_effort
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[484 rows x 6 columns]}}
```