Merge pull request #54 from BlockScience/staging
Open Sourcing cadCad Pt. 2
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de9a708d43
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@ -11,7 +11,6 @@ Pipfile.lock
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results
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results
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.mypy_cache
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.mypy_cache
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*.csv
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*.csv
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*.txt
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simulations/.ipynb_checkpoints
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simulations/.ipynb_checkpoints
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simulations/validation/config3.py
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simulations/validation/config3.py
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cadCAD.egg-info
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cadCAD.egg-info
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@ -0,0 +1,29 @@
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Authors
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=======
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cadCAD was originally implemented by Joshua E. Jodesty and designed by Michael Zargham, Markus B. Koch, and
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Matthew V. Barlin from 2018 to 2019.
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Project Maintainers:
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- Joshua E. Jodesty <joshua@block.science, joshua.jodesty@gmail.com>
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- Markus B. Koch <markus@block.science>
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Contributors:
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- Joshua E. Jodesty
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- Markus B. Koch
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- Matthew V. Barlin
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- Michael Zargham
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- Zixuan Zhang
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We’d also like to thank:
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- Andrew Clark
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- Nikhil Jamdade
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- Nick Hirannet
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- Harry Goodnight
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- Charlie Hoppes
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- Jonathan Gabler
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- Charles Rice
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@ -0,0 +1,21 @@
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MIT License
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Copyright (c) 2018-2019 BlockScience
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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@ -70,11 +70,22 @@ class Executor:
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config_proc = Processor()
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config_proc = Processor()
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create_tensor_field = TensorFieldReport(config_proc).create_tensor_field
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create_tensor_field = TensorFieldReport(config_proc).create_tensor_field
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print(self.exec_context+": "+str(self.configs))
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print(r'''
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__________ ____
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________ __ _____/ ____/ | / __ \
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/ ___/ __` / __ / / / /| | / / / /
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/ /__/ /_/ / /_/ / /___/ ___ |/ /_/ /
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\___/\__,_/\__,_/\____/_/ |_/_____/
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by BlockScience
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''')
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print(f'Execution Mode: {self.exec_context + ": " + str(self.configs)}')
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print(f'Configurations: {self.configs}')
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var_dict_list, states_lists, Ts, Ns, eps, configs_structs, env_processes_list, partial_state_updates, simulation_execs = \
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var_dict_list, states_lists, Ts, Ns, eps, configs_structs, env_processes_list, partial_state_updates, simulation_execs = \
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[], [], [], [], [], [], [], [], []
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[], [], [], [], [], [], [], [], []
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config_idx = 0
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config_idx = 0
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print(self.configs)
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for x in self.configs:
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for x in self.configs:
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Ts.append(x.sim_config['T'])
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Ts.append(x.sim_config['T'])
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@ -1,4 +1,3 @@
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from pprint import pprint
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from typing import Any, Callable, Dict, List, Tuple
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from typing import Any, Callable, Dict, List, Tuple
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from pathos.pools import ThreadPool as TPool
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from pathos.pools import ThreadPool as TPool
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from copy import deepcopy
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from copy import deepcopy
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@ -170,9 +169,6 @@ class Executor:
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time_step += 1
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time_step += 1
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pprint(states_list)
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print()
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return states_list
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return states_list
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# state_update_pipeline
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# state_update_pipeline
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