links to main repo

01 Tutorials/README.md

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-**Robot and Marbles Tutorial Series**
-
-In this series, we introduce basic concepts of cadCAD and system modelling in general using a simple toy model.  
-[Part 1](robot-marbles-part-1/robot-marbles-part-1.ipynb) - States and State Update Functions  
-[Part 2](robot-marbles-part-2/robot-marbles-part-2.ipynb) - Actions and State Dependent Policies  
-[Part 3](robot-marbles-part-3/robot-marbles-part-3.ipynb) - From Synchronous to Asynchronous Time  
-[Part 4](robot-marbles-part-4/robot-marbles-part-4.ipynb) - Uncertainty and Stochastic Processes  
-[Part 5](robot-marbles-part-5/robot-marbles-part-5.ipynb) - Using class objects as state variables  
-
-Check out the [videos](videos) folder for detailed walkthroughs of each one of the tutorials.
+cadCAD is now open source! The tutorials have been moved to the [main repo](https://github.com/BlockScience/cadCAD)

01 Tutorials/videos/README.md

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+cadCAD is now open source! The tutorials have been moved to the [main repo](https://github.com/BlockScience/cadCAD)

README.md

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-cadCAD
-===
-```
-                    __________   ____ 
-  ________ __ _____/ ____/   |  / __ \
- / ___/ __` / __  / /   / /| | / / / /
-/ /__/ /_/ / /_/ / /___/ ___ |/ /_/ / 
-\___/\__,_/\__,_/\____/_/  |_/_____/  
-by BlockScience
-```
-
-**Introduction:**
-
-cadCAD is a Python library that assists in the processes of designing, testing and validating complex systems through 
-simulation. At its core, cadCAD is a differential games engine that supports parameter sweeping and Monte Carlo analyses 
-and can be easily integrated with other scientific computing Python modules and data science workflows.
-
-**Description:**
-
-cadCAD (complex adaptive systems computer-aided design) is a python based, unified modeling framework for stochastic 
-dynamical systems and differential games for research, validation, and Computer Aided Design of economic systems created 
-by BlockScience. It is capable of modeling systems at all levels of abstraction from Agent Based Modeling (ABM) to 
-System Dynamics (SD), and enabling smooth integration of computational social science simulations with empirical data 
-science workflows.
-
-
-An economic system is treated as a state-based model and defined through a set of endogenous and exogenous state 
-variables which are updated through mechanisms and environmental processes, respectively. Behavioral models, which may 
-be deterministic or stochastic, provide the evolution of the system within the action space of the mechanisms. 
-Mathematical formulations of these economic games treat agent utility as derived from the state rather than direct from 
-an action, creating a rich, dynamic modeling framework. Simulations may be run with a range of initial conditions and 
-parameters for states, behaviors, mechanisms, and environmental processes to understand and visualize network behavior 
-under various conditions. Support for A/B testing policies, Monte Carlo analysis, and other common numerical methods is 
-provided.
-
-
-In essence, cadCAD tool allows us to represent a company’s or community’s current business model along with a desired 
-future state and helps make informed, rigorously tested decisions on how to get from today’s stage to the future state. 
-It allows us to use code to solidify our conceptualized ideas and see if the outcome meets our expectations. We can 
-iteratively refine our work until we have constructed a model that closely reflects reality at the start of the model, 
-and see how it evolves. We can then use these results to inform business decisions.
-
-
-#### Documentation:
-* ##### [Tutorials](https://github.com/BlockScience/cadCAD-Tutorials/tree/master/01%20Tutorials)
-* ##### [System Model Configuration](https://github.com/BlockScience/cadCAD-Tutorials/blob/master/documentation/Simulation_Configuration.md)
-* ##### [System Simulation Execution](https://github.com/BlockScience/cadCAD-Tutorials/blob/master/documentation/Simulation_Execution.md)
-
-
-#### 0. Installation:
-
-cadCAD is in private beta. Access to a JupyterHUB instance (or tokens) are issued to participants. If you'd like to 
-participate in the beta program, contact cadcad [at] block [dot] science.
-
-Replace `<TOKEN>` in the installation URL below.
-```bash
-pip3 install pandas pathos fn funcy tabulate 
-pip3 install cadCAD --extra-index-url https://<TOKEN>@repo.fury.io/blockscience/
-```
-
-#### 1. [Configure System Model](https://github.com/BlockScience/cadCAD-Tutorials/blob/master/documentation/Simulation_Configuration.md)
-
-#### 2. [Execute Simulations:](https://github.com/BlockScience/cadCAD-Tutorials/blob/master/documentation/Simulation_Execution.md)
-
-##### Single Process Execution:
-Example System Model Configurations: 
-* [System Model A](https://github.com/BlockScience/cadCAD-Tutorials/blob/master/documentation/examples/sys_model_A.py): 
-`/documentation/examples/sys_model_A.py`
-* [System Model B](https://github.com/BlockScience/cadCAD-Tutorials/blob/master/documentation/examples/sys_model_B.py): 
-`/documentation/examples/sys_model_B.py`
-
-Example Simulation Executions:
-* [System Model A](https://github.com/BlockScience/cadCAD-Tutorials/blob/master/documentation/examples/sys_model_A_exec.py): 
-`/documentation/examples/sys_model_A_exec.py`
-* [System Model B](https://github.com/BlockScience/cadCAD-Tutorials/blob/master/documentation/examples/sys_model_B_exec.py): 
-`/documentation/examples/sys_model_B_exec.py`
-
-```python
-import pandas as pd
-from tabulate import tabulate
-from cadCAD.engine import ExecutionMode, ExecutionContext, Executor
-from documentation.examples import sys_model_A
-from cadCAD import configs
-
-exec_mode = ExecutionMode()
-
-# Single Process Execution using a Single System Model Configuration:
-# sys_model_A
-sys_model_A = [configs[0]] # sys_model_A
-single_proc_ctx = ExecutionContext(context=exec_mode.single_proc)
-sys_model_A_simulation = Executor(exec_context=single_proc_ctx, configs=sys_model_A)
-
-sys_model_A_raw_result, sys_model_A_tensor_field = sys_model_A_simulation.execute()
-sys_model_A_result = pd.DataFrame(sys_model_A_raw_result)
-print()
-print("Tensor Field: sys_model_A")
-print(tabulate(sys_model_A_tensor_field, headers='keys', tablefmt='psql'))
-print("Result: System Events DataFrame")
-print(tabulate(sys_model_A_result, headers='keys', tablefmt='psql'))
-print()
-```
-
-##### Multiple Simulations (Concurrent):
-###### Multiple Simulation Execution (Multi Process Execution)
-System Model Configurations: 
-* [System Model A](https://github.com/BlockScience/cadCAD-Tutorials/blob/master/documentation/examples/sys_model_A.py): 
-`/documentation/examples/sys_model_A.py`
-* [System Model B](https://github.com/BlockScience/cadCAD-Tutorials/blob/master/documentation/examples/sys_model_B.py): 
-`/documentation/examples/sys_model_B.py`
-
-[Example Simulation Executions:](https://github.com/BlockScience/cadCAD-Tutorials/blob/master/documentation/examples/sys_model_AB_exec.py)
-`/documentation/examples/sys_model_AB_exec.py`
-
-```python
-import pandas as pd
-from tabulate import tabulate
-from cadCAD.engine import ExecutionMode, ExecutionContext, Executor
-from documentation.examples import sys_model_A, sys_model_B
-from cadCAD import configs
-
-exec_mode = ExecutionMode()
-
-# # Multiple Processes Execution using Multiple System Model Configurations:
-# # sys_model_A & sys_model_B
-multi_proc_ctx = ExecutionContext(context=exec_mode.multi_proc)
-sys_model_AB_simulation = Executor(exec_context=multi_proc_ctx, configs=configs)
-
-i = 0
-config_names = ['sys_model_A', 'sys_model_B']
-for sys_model_AB_raw_result, sys_model_AB_tensor_field in sys_model_AB_simulation.execute():
-    sys_model_AB_result = pd.DataFrame(sys_model_AB_raw_result)
-    print()
-    print(f"Tensor Field: {config_names[i]}")
-    print(tabulate(sys_model_AB_tensor_field, headers='keys', tablefmt='psql'))
-    print("Result: System Events DataFrame:")
-    print(tabulate(sys_model_AB_result, headers='keys', tablefmt='psql'))
-    print()
-    i += 1
-```
-
-##### Parameter Sweep Simulation (Concurrent):
-[Example:](https://github.com/BlockScience/cadCAD-Tutorials/blob/master/documentation/examples/param_sweep.py) 
-`/documentation/examples/param_sweep.py`
-
-```python
-import pandas as pd
-from tabulate import tabulate
-# The following imports NEED to be in the exact order
-from cadCAD.engine import ExecutionMode, ExecutionContext, Executor
-from documentation.examples import param_sweep
-from cadCAD import configs
-
-exec_mode = ExecutionMode()
-multi_proc_ctx = ExecutionContext(context=exec_mode.multi_proc)
-run = Executor(exec_context=multi_proc_ctx, configs=configs)
-
-for raw_result, tensor_field in run.execute():
-    result = pd.DataFrame(raw_result)
-    print()
-    print("Tensor Field:")
-    print(tabulate(tensor_field, headers='keys', tablefmt='psql'))
-    print("Output:")
-    print(tabulate(result, headers='keys', tablefmt='psql'))
-    print()
-```
+cadCAD is now [open source](https://github.com/BlockScience/cadCAD)