absolut path issue

.gitignore

@@ -1,8 +1,12 @@
+demos
+SimCAD
+simularions
+setup.py
+build
+
 .ipynb_checkpoints
 .DS_Store
 .idea
-notebooks/.ipynb_checkpoints
-notebooks/multithreading.ipynb
 SimCAD.egg-info
 __pycache__
 Pipfile

LICENSE.txt

@@ -0,0 +1,119 @@
+TRIAL LICENSE AGREEMENT
+
+BACKGROUND
+
+Company has developed and intends to market and license a certain software product and service called ”SimCAD” which,
+among other things, is a scientific engineering simulation tool (“Software”). Company wishes to provide access, on a
+trial basis, to users of a “beta” version of the Software to test and provide feedback to Company. Licensee wishes to
+participate in Company’s beta trial of the Software and to provide feedback to Company with respect to Licensee’s use
+thereof.
+
+Accordingly, the parties hereby agree as follows:
+
+1. BETA PRODUCT.
+
+This Agreement applies to any pre­release version of the Software and any updates and changes thereto during the Term
+(collectively, “Beta Product”). As an essential condition of this Agreement, Licensee understands and acknowledges that:
+(a) Licensee is participating in a beta test of the Beta Product; (b) the Beta Product has not been field tested or
+trialed; and (c) the Beta Product may not operate properly or be error free and may not perform all functions for
+which it is intended or represented.
+
+2. FEEDBACK.
+
+As a condition of this Agreement, during the Term of this Agreement, Licensee agrees to provide Company with comments,
+feedback, criticisms, and suggestions for changes to the Beta Product (“Feedback”), and to help Company identify errors
+or malfunctions, and performance issues, in the operation of the Beta Product, as Company may reasonably request. All
+rights to any Feedback or other intellectual property derived from Licensee’s use of or relating to the Beta Product,
+as well any data collected from the use of the Beta Product, belong solely to Company and Licensee hereby irrevocably
+assigns all such rights to Company. Company reserves the right to use all Feedback and data collected as a result of the
+use of the Beta Product to advertise and promote the Company and the Software.
+
+3. LICENSE AND RESERVATION OF RIGHTS.
+
+3.1 Subject to the terms and conditions set forth in this Agreement, Company hereby grants Licensee, and Licensee
+accepts, during the Term, a non­exclusive, royalty­free, revocable, non­transferable, limited license to access and use
+the Beta Product for its internal, non­commercial use for evaluation purposes only, and to give permission to employees
+of Licensee and employees of Licensee’s subsidiaries (“Permitted Users”) to use the Beta Product in accordance with the
+foregoing.
+
+3.2 The Beta Product and the Software comprise the intellectual property of Company. All right, title and interest in
+and to the Beta Product (and, more generally, in and to the Software), and to all Feedback and data arising from its
+use, in whole or in part, and all patent, copyright, trade­marks, trade secret and all other intellectual and industrial
+property rights therein and the structure, sequence and organization of same, and the media on which such material is
+contained belong exclusively to Company. Licensee and its Permitted Users will not, directly or indirectly: reverse
+engineer, decompile, disassemble or otherwise attempt to discover the source code, object code or underlying structure,
+ideas, know­how or algorithms relevant to the Beta Product; modify, adapt, alter, edit, correct, translate, publish,
+sell, transfer, assign, convey, rent, lease, loan, pledge, sublicense, distribute, export, enhance or create derivative
+works based on the Beta Product; or remove, alter, cover or otherwise obscure any proprietary notices or labels
+displayed on or within the Beta Product any documentation relating thereto.
+
+4. DISCLAIMER.
+
+4.1 COMPANY MAKES NO WARRANTIES, WHETHER EXPRESS, IMPLIED, STATUTORY OR OTHERWISE, WITH RESPECT TO THE BETA PRODUCT,
+INCLUDING, BUT NOT LIMITED TO, THE AVAILABILITY, QUALITY OR PERFORMANCE OF THE BETA PRODUCT. COMPANY SPECIFICALLY
+DISCLAIMS ALL EXPRESS, STATUTORY AND IMPLIED WARRANTIES AND CONDITIONS, INCLUDING, WITHOUT LIMITATION (A) THE IMPLIED
+WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NON­INFRINGEMENT, (B) ANY WARRANTIES AGAINST HIDDEN
+OR LATENT DEFECTS, (C) AND ANY WARRANTIES AND CONDITIONS ARISING OUT OF COURSE OF DEALING OR USAGE OF TRADE AND (D) ANY
+WARRANTY OR REPRESENTATION THAT THE BETA PRODUCT IS ERROR­FREE, VIRUS­FREE, SECURE, UNINTERRUPTED, OR FREE FROM
+UNAUTHORIZED ACCESS (INCLUDING, BUT NOT LIMITED TO, THIRD PARTY HACKERS OR DENIAL OF SERVICE ATTACKS). THE BETA PRODUCT
+IS SUPPLIED ON AN “AS IS”, “AS AVAILABLE” BASIS WITHOUT WARRANTY.
+
+4.2 NEITHER PARTY SHALL BE LIABLE FOR SPECIAL, INCIDENTAL, PUNITIVE, CONSEQUENTIAL OR INDIRECT DAMAGES OR LOSS
+(INCLUDING DEATH AND PERSONAL INJURY), IRRESPECTIVE OF THEIR CAUSE, NOTWITHSTANDING THAT A PARTY HAS BEEN ADVISED OF THE
+POSSIBILITY OF SUCH LOSS OR DAMAGE, NOR FOR ANY CLAIMS FOR SUCH LOSS OR DAMAGE INSTITUTED AGAINST A PARTY OR ITS
+CUSTOMERS BY ANY THIRD PARTY.
+
+5. CONFIDENTIALITY
+
+5.1 All Confidential Information disclosed by either party shall be kept by the receiving party in strict confidence and
+shall not be disclosed to any third party without the disclosing party’s express written consent. For purposes of this
+Agreement, “Confidential Information” means all information regarding either party’s business which has been marked or
+is otherwise communicated as being “proprietary” or “confidential” or which reasonably should be known by the receiving
+party to be proprietary or confidential information. Without limiting the generality of the foregoing, Confidential
+Information of Company includes non­public information regarding features, functionality and performance of the Beta
+Product, including all Feedback and related data. Notwithstanding the foregoing, each party’s confidentiality
+obligations hereunder shall not apply to information that: (a) is already known to the receiving party without a
+pre­existing restriction as to disclosure; (b) is or becomes publicly available without fault of the receiving party;
+(c) is rightfully obtained by the receiving party from a third party without restriction as to disclosure, or is
+approved for release by written authorization of the disclosing party; (d) is developed independently by the receiving
+party without use of the disclosing party’s Confidential Information; or (e) is required to be disclosed by law or
+regulation, including, but not limited to, supplying such information or making such statements or disclosures relating
+to this Agreement before any competent court, governmental agency or authority in response to a lawful requirement or
+request from a court of governmental agency or authority, provided that the disclosing party shall give the other party
+prompt notice of such request, to the extent practicable, so that the other party may seek (at its sole cost and
+expense) an appropriate protective order or similar relief.
+
+5.2 In the event of a breach of Sections 2, 3 or this Section 5, the non­breaching party shall be entitled to seek
+equitable relief to protect its interests, including, but not limited to, injunctive relief. In the event of expiration
+or earlier termination of this Agreement, each party shall immediately return to the other party such other party’s
+Confidential Information, or at such other party’s option, destroy any remaining Confidential Information and certify
+that such destruction has taken place.
+
+6. FEES; EXPENSES.
+
+Neither party shall be entitled to any compensation in connection with this Agreement or its use or provision of the
+Beta Product. Each party shall bear its own costs and expenses arising from this Agreement and its use or provision of
+the Beta Product, as the case may be.
+
+7. TERM OF AGREEMENT.
+
+This Agreement shall begin on the Effective Date and shall continue until it has been terminated (such period, the
+“Term”). Either party shall have the right to terminate this Agreement at any time on one (1) month written notice to
+the other party, or in the case of a breach of this Agreement by Licensee or its Permitted Users, Company may terminate
+this Agreement immediately on written notice to Licensee. Upon termination of this Agreement, all rights granted to
+Licensee (and any Permitted User) under this Agreement will immediately terminate and Licensee (and all Permitted Users)
+must immediately cease all use of the Beta Product at such time. Notwithstanding any termination of this Agreement,
+Sections 2, 3.2, 4, 5, 6, this Section 7 and Section 8 shall survive and remain binding on the parties.
+
+8. MISCELLANEOUS.
+
+This Agreement shall be governed by and construed in accordance with the laws of the State of New York. All disputes
+relating to this Agreement shall be resolved in the federal and state courts of New York County, New York and the
+parties submit to the jurisdiction of such courts. This Agreement does not create any agency, partnership, or joint
+venture relationship between Licensee and Company. This Agreement is the entire understanding of the parties with
+respect to the subject matter hereof and supersedes any previous or contemporaneous communications, representations,
+warranties, discussions, arrangements or commitments, whether oral or written with respect to such subject matter. This
+Agreement cannot be amended except by a written amendment that expressly refers to this Agreement and is signed by an
+authorized representative of each party. This Agreement may be executed in one or more counterparts, including via
+facsimile or email (or any other electronic means such as “.pdf” or “.tiff” files), each of which shall be deemed an
+original, and all of which shall constitute one and the same Agreement.

README.md

@@ -1,52 +1,57 @@
 # SimCad
 
-**Dependencies:**
+**Warning**:
+**Do not** publish this package / software to **any** software repository **except** one permitted by BlockScience.  
+
+**Description:**
+
+SimCAD is a differential games based simulation software package for research, validation, and Computer \
+Aided Design of economic systems. 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 state rather than direct from 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.
+
+**1. Install Dependencies:**
 ```bash
 pip install -r requirements.txt
-```
-
-**Project:**
-
-Example Runs:
-`/simulations/sim_test.py`
-
-Example Configurations:
-`/simulations/validation/`
-
-**User Interface: Simulation Configuration**
-
-Configurations:
-```bash
-/DiffyQ-SimCAD/ui/config.py
-```
-
-**Build Tool & Package Import:**
-
-Step 1. Build & Install Package locally: 
-```bash
-pip install .
 pip install -e .
 ```
-* [Package Creation Tutorial](https://python-packaging.readthedocs.io/en/latest/minimal.html)
 
-Step 2. Import Package & Run:  
+**2. Configure Simulation:**
+
+Intructions:
+`/Simulation.md`
+
+Examples:
+`/simulations/validation/*`
+
+**3. Import SimCAD & Run Simulation:**
+
+Example:
+`/demos/sim_test.py` or `test.ipynb`
+
 ```python
 import pandas as pd
 from tabulate import tabulate
 
+
 # The following imports NEED to be in the exact same order
+
 from SimCAD.engine import ExecutionMode, ExecutionContext, Executor
 from simulations.validation import config1, config2
 from SimCAD import configs
 
+
 # ToDo: pass ExecutionContext with execution method as ExecutionContext input
 
 exec_mode = ExecutionMode()
 
 
-exec_mode = ExecutionMode()
-
-
 print("Simulation Execution 1")
 print()
 first_config = [configs[0]] # from config1
@@ -54,7 +59,7 @@ single_proc_ctx = ExecutionContext(context=exec_mode.single_proc)
 run1 = Executor(exec_context=single_proc_ctx, configs=first_config)
 run1_raw_result, tensor_field = run1.main()
 result = pd.DataFrame(run1_raw_result)
-# result.to_csv('~/Projects/DiffyQ-SimCAD/results/config4.csv', sep=',')
+
 print()
 print("Tensor Field:")
 print(tabulate(tensor_field, headers='keys', tablefmt='psql'))
@@ -76,14 +81,7 @@ for raw_result, tensor_field in run2.main():
     print()
 ```
 
-Same can be run in Jupyter . 
+The above can be run in Jupyter. 
 ```bash
 jupyter notebook
 ```
-
-Notebooks Directory:  
-`/DiffyQ-SimCAD/notebooks/`
-
-
-**Warning**:
-**Do Not** publish this package / software to **Any** software repository **except** [DiffyQ-SimCAD's staging branch](https://github.com/BlockScience/DiffyQ-SimCAD/tree/staging) or its **Fork** 

SimCAD/configuration/__init__.py

@@ -49,7 +49,6 @@ class Processor:
         self.state_identity = id.state_identity
         self.apply_identity_funcs = id.apply_identity_funcs
 
-    # Make returntype chosen by user.
     def create_matrix_field(self, mechanisms, key):
         if key == 'states':
             identity = self.state_identity
@@ -62,11 +61,8 @@ class Processor:
         else:
             return pd.DataFrame({'empty': []})
 
-    # Maybe Refactor to only use dictionary BUT I used dfs to fill NAs. Perhaps fill
     def generate_config(self, state_dict, mechanisms, exo_proc):
 
-        # ToDo: include False / False case
-        # ToDo: Use Range multiplier instead for loop iterator
         def no_update_handler(bdf, sdf):
             if (bdf.empty == False) and (sdf.empty == True):
                 bdf_values = bdf.values.tolist()

SimCAD/configuration/utils/__init__.py

@@ -8,7 +8,6 @@ class TensorFieldReport:
     def __init__(self, config_proc):
         self.config_proc = config_proc
 
-    # ??? dont for-loop to apply exo_procs, use exo_proc struct
     def create_tensor_field(self, mechanisms, exo_proc, keys=['behaviors', 'states']):
         dfs = [self.config_proc.create_matrix_field(mechanisms, k) for k in keys]
         df = pd.concat(dfs, axis=1)
@@ -34,7 +33,6 @@ def proc_trigger(trigger_step, update_f, step):
         return lambda x: x
 
 
-# accept timedelta instead of timedelta params
 t_delta = timedelta(days=0, minutes=0, seconds=30)
 def time_step(dt_str, dt_format='%Y-%m-%d %H:%M:%S', _timedelta = t_delta):
     dt = datetime.strptime(dt_str, dt_format)
@@ -42,7 +40,6 @@ def time_step(dt_str, dt_format='%Y-%m-%d %H:%M:%S', _timedelta = t_delta):
     return t.strftime(dt_format)
 
 
-# accept timedelta instead of timedelta params
 t_delta = timedelta(days=0, minutes=0, seconds=1)
 def ep_time_step(s, dt_str, fromat_str='%Y-%m-%d %H:%M:%S', _timedelta = t_delta):
     if s['mech_step'] == 0:
@@ -54,7 +51,7 @@ def ep_time_step(s, dt_str, fromat_str='%Y-%m-%d %H:%M:%S', _timedelta = t_delta
 def exo_update_per_ts(ep):
     @curried
     def ep_decorator(f, y, step, sL, s, _input):
-        if s['mech_step'] + 1 == 1:  # inside f body to reduce performance costs
+        if s['mech_step'] + 1 == 1:
             return f(step, sL, s, _input)
         else:
             return (y, s[y])

SimCAD/configuration/utils/behaviorAggregation.py

@@ -27,7 +27,7 @@ def foldr_dict_vals(f, d):
 def sum_dict_values():
     return foldr_dict_vals(add)
 
-# AttributeError: 'int' object has no attribute 'keys'
+
 @curried
 def dict_op(f, d1, d2):
     def set_base_value(target_dict, source_dict, key):
@@ -43,6 +43,3 @@ def dict_op(f, d1, d2):
 
 def dict_elemwise_sum():
     return dict_op(add)
-
-
-# class BehaviorAggregation:

SimCAD/engine/__init__.py

@@ -45,7 +45,6 @@ class Executor:
 
 
     def execute(self):
-
         config_proc = Processor()
         create_tensor_field = TensorFieldReport(config_proc).create_tensor_field
 
@@ -65,6 +64,7 @@ class Executor:
 
             config_idx += 1
 
+
         # Dimensions: N x r x mechs
 
         if self.exec_context == ExecutionMode.single_proc:
@@ -77,4 +77,5 @@ class Executor:
                 results = []
                 for result, mechanism, ep in list(zip(simulations, mechanisms, eps)):
                     results.append((flatten(result), create_tensor_field(mechanism, ep)))
+
                 return results

SimCAD/engine/simulation.py

@@ -1,10 +1,7 @@
 from copy import deepcopy
 from fn.op import foldr, call
-
-from SimCAD.utils import rename
 from SimCAD.engine.utils import engine_exception
 
-
 id_exception = engine_exception(KeyError, KeyError, None)
 
 
@@ -14,7 +11,6 @@ class Executor:
         self.state_update_exception = state_update_exception
         self.behavior_update_exception = behavior_update_exception
 
-    # Data Type reduction
     def get_behavior_input(self, step, sL, s, funcs):
         ops = self.behavior_ops[::-1]
 
@@ -27,18 +23,16 @@ class Executor:
         for state in state_dict.keys():
             if state in list(env_processes.keys()):
                 env_state = env_processes[state]
-                if (env_state.__name__ == '_curried') or (env_state.__name__ == 'proc_trigger'): # might want to change
+                if (env_state.__name__ == '_curried') or (env_state.__name__ == 'proc_trigger'):
                     state_dict[state] = env_state(step)(state_dict[state])
                 else:
                     state_dict[state] = env_state(state_dict[state])
 
-
     def mech_step(self, m_step, sL, state_funcs, behavior_funcs, env_processes, t_step, run):
         last_in_obj = sL[-1]
 
         _input = self.state_update_exception(self.get_behavior_input(m_step, sL, last_in_obj, behavior_funcs))
 
-        # ToDo: add env_proc generator to `last_in_copy` iterator as wrapper function
         last_in_copy = dict([self.behavior_update_exception(f(m_step, sL, last_in_obj, _input)) for f in state_funcs])
 
         for k in last_in_obj:
@@ -47,8 +41,7 @@ class Executor:
 
         del last_in_obj
 
-        # make env proc trigger field agnostic
+        self.apply_env_proc(env_processes, last_in_copy, last_in_copy['timestamp'])
-        self.apply_env_proc(env_processes, last_in_copy, last_in_copy['timestamp']) # mutating last_in_copy
 
         last_in_copy["mech_step"], last_in_copy["time_step"], last_in_copy['run'] = m_step, t_step, run
         sL.append(last_in_copy)
@@ -59,8 +52,6 @@ class Executor:
     def mech_pipeline(self, states_list, configs, env_processes, t_step, run):
         m_step = 0
         states_list_copy = deepcopy(states_list)
-        # print(states_list_copy)
-        # remove copy
         genesis_states = states_list_copy[-1]
         genesis_states['mech_step'], genesis_states['time_step'] = m_step, t_step
         states_list = [genesis_states]
@@ -75,7 +66,6 @@ class Executor:
 
         return states_list
 
-    # rename pipe
     def block_pipeline(self, states_list, configs, env_processes, time_seq, run):
         time_seq = [x + 1 for x in time_seq]
         simulation_list = [states_list]
@@ -87,12 +77,11 @@ class Executor:
 
         return simulation_list
 
-    # Del _ / head
     def simulation(self, states_list, configs, env_processes, time_seq, runs):
         pipe_run = []
         for run in range(runs):
             run += 1
-            states_list_copy = deepcopy(states_list) # WHY ???
+            states_list_copy = deepcopy(states_list)
             head, *tail = self.block_pipeline(states_list_copy, configs, env_processes, time_seq, run)
             genesis = head.pop()
             genesis['mech_step'], genesis['time_step'], genesis['run'] = 0, 0, run

SimCAD/engine/utils.py

@@ -31,11 +31,3 @@ def engine_exception(ErrorType, error_message, exception_function, try_function)
     except ErrorType:
         print(error_message)
         return exception_function
-
-
-# def exception_handler(f, m_step, sL, last_mut_obj, _input):
-#     try:
-#         return f(m_step, sL, last_mut_obj, _input)
-#     except KeyError:
-#         print("Exception")
-#         return f(m_step, sL, sL[-2], _input)

SimCAD/utils/__init__.py

@@ -1,5 +1,3 @@
-# from fn.func import curried
-
 def pipe(x):
     return x
 
@@ -20,13 +18,7 @@ def flatmap(f, items):
 def key_filter(l, keyname):
     return [v[keyname] for k, v in l.items()]
 
-# @curried
+
 def rename(new_name, f):
     f.__name__ = new_name
     return f
-#
-# def rename(newname):
-#     def decorator(f):
-#         f.__name__ = newname
-#         return f
-#     return decorator

demos/sim_test.py

@@ -0,0 +1,37 @@
+import pandas as pd
+from tabulate import tabulate
+
+# The following imports NEED to be in the exact order
+from SimCAD.engine import ExecutionMode, ExecutionContext, Executor
+from simulations.validation import config1, config2
+from SimCAD import configs
+
+exec_mode = ExecutionMode()
+
+
+print("Simulation Execution 1")
+print()
+first_config = [configs[0]] # from config1
+single_proc_ctx = ExecutionContext(context=exec_mode.single_proc)
+run1 = Executor(exec_context=single_proc_ctx, configs=first_config)
+run1_raw_result, tensor_field = run1.main()
+result = pd.DataFrame(run1_raw_result)
+print()
+print("Tensor Field:")
+print(tabulate(tensor_field, headers='keys', tablefmt='psql'))
+print("Output:")
+print(tabulate(result, headers='keys', tablefmt='psql'))
+print()
+
+print("Simulation Execution 2: Pairwise Execution")
+print()
+multi_proc_ctx = ExecutionContext(context=exec_mode.multi_proc)
+run2 = Executor(exec_context=multi_proc_ctx, configs=configs)
+for raw_result, tensor_field in run2.main():
+    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()

demos/test.ipynb

@@ -0,0 +1,137 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# The following imports NEED to be in the exact order\n",
+    "from SimCAD.engine import ExecutionMode, ExecutionContext, Executor\n",
+    "from simulations.validation import config1, config2\n",
+    "from SimCAD import configs\n",
+    "\n",
+    "exec_mode = ExecutionMode()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"Simulation Execution 1\")\n",
+    "print()\n",
+    "first_config = [configs[0]] # from config1\n",
+    "single_proc_ctx = ExecutionContext(context=exec_mode.single_proc)\n",
+    "run1 = Executor(exec_context=single_proc_ctx, configs=first_config)\n",
+    "run1_raw_result, raw_tensor_field = run1.main()\n",
+    "result = pd.DataFrame(run1_raw_result)\n",
+    "tensor_field = pd.DataFrame(raw_tensor_field)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"Tensor Field:\")\n",
+    "tensor_field"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"Output:\")\n",
+    "result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"Simulation Execution 2: Pairwise Execution\")\n",
+    "print()\n",
+    "multi_proc_ctx = ExecutionContext(context=exec_mode.multi_proc)\n",
+    "run2 = Executor(exec_context=multi_proc_ctx, configs=configs)\n",
+    "results = []\n",
+    "tensor_fields = []\n",
+    "for raw_result, raw_tensor_field in run2.main():\n",
+    "    results.append(pd.DataFrame(raw_result))\n",
+    "    tensor_fields.append(pd.DataFrame(raw_tensor_field))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "print(\"Tensor Field A:\")\n",
+    "tensor_fields[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"Output A:\")\n",
+    "results[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"Tensor Field B:\")\n",
+    "tensor_fields[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"Output B:\")\n",
+    "results[1]"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

notebooks/test.ipynb

@@ -1,78 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(\"Simulation Run 2: Pairwise Execution\")\n",
-    "print()\n",
-    "multi_proc_ctx = ExecutionContext(exec_mode.multi_proc)\n",
-    "run2 = Executor(multi_proc_ctx, configs)\n",
-    "run2_raw_results = run2.main()\n",
-    "for raw_result in run2_raw_results:\n",
-    "    result = pd.DataFrame(raw_result)\n",
-    "    print(tabulate(result, headers='keys', tablefmt='psql'))\n",
-    "print()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(\"Simulation Run 1\")\n",
-    "print()\n",
-    "single_config = [configs[0]]\n",
-    "single_proc_ctx = ExecutionContext(exec_mode.single_proc)\n",
-    "run1 = Executor(single_proc_ctx, single_config)\n",
-    "run1_raw_result = run1.main()\n",
-    "result = pd.DataFrame(run1_raw_result)\n",
-    "result"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "from tabulate import tabulate\n",
-    "\n",
-    "from SimCAD.engine import ExecutionMode, ExecutionContext, Executor\n",
-    "from sandboxUX import config1, config2\n",
-    "from SimCAD import configs\n",
-    "\n",
-    "# ToDo: pass ExecutionContext with execution method as ExecutionContext input\n",
-    "\n",
-    "exec_mode = ExecutionMode()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}

requirements.txt

@@ -1,4 +1,3 @@
 pathos
-pipenv
 fn
 tabulate

setup.py

@@ -19,5 +19,5 @@ setup(name='SimCAD',
       author='Joshua E. Jodesty',
       author_email='joshua@block.science',
       license='licenses',
-      packages=['SimCAD'],
+      packages=['SimCAD']
-      zip_safe=False)
+)

simulations/scrapbox/config7c.py

@@ -1,494 +0,0 @@
-from decimal import Decimal
-import numpy as np
-
-from SimCAD import Configuration, configs
-from SimCAD.configuration import exo_update_per_ts, bound_norm_random, \
-    ep_time_step
-
-seed = {
-    'z': np.random.RandomState(1)
-    # 'a': np.random.RandomState(2),
-    # 'b': np.random.RandomState(3),
-    # 'c': np.random.RandomState(3)
-}
-
-#Signals
-# Pr_signal
-#if s['P_Ext_Markets'] != 0:
-#Pr_signal = s['Z']/s['P_Ext_Markets']
-#else Pr_signal = 0
-#    if Pr_signal < s['Z']/s['Buy_Log']:
-beta = Decimal('0.25') #agent response gain
-beta_LT = Decimal('0.1') #LT agent response gain
-alpha = Decimal('0.091') #21 day EMA forgetfullness between 0 and 1, closer to 1 discounts older obs quicker, should be 2/(N+1)
-max_withdraw_factor = Decimal('0.9')
-external_draw = Decimal('0.01') # between 0 and 1 to draw Buy_Log to external
-
-# Stochastic process factors
-correction_factor = Decimal('0.01')
-volatility =  Decimal('5.0')
-
-
-# Buy_Log_signal =
-# Z_signal = 
-# Price_signal =
-# TDR_draw_signal =
-# P_Ext_Markets_signal = 
-
-# Behaviors per Mechanism
-
-# BEHAVIOR 1: EMH Trader
-EMH_portion = Decimal('0.250')
-EMH_Ext_Hold = Decimal('42000.0')
-
-def b1m1(step, sL, s):
- #   y = 'P_Ext_Markets'
-    # Psignal_ext = s['P_Ext_Markets'] / s['Z']
-    # Psignal_int = s['Buy_Log'] /  s['Z']
-    # if Psignal_ext < Psignal_int:
-    #     return beta*(Psignal_int - Psignal_ext) * s['Z']  # Deposited amount in TDR
-    # else:
-    #     return 0 # Decimal(0.000001)
- #   return (y,x)
-    theta = (s['Z']*EMH_portion*s['Price'])/(s['Z']*EMH_portion*s['Price'] + EMH_Ext_Hold * s['P_Ext_Markets'])
-    if s['Price'] < (theta*EMH_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*EMH_portion*(1-theta)):
-        return beta * theta*EMH_Ext_Hold * s['P_Ext_Markets']/(s['Price']*EMH_portion*(1-theta))
-    elif s['Price'] > (theta*EMH_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*EMH_portion*(1-theta)):
-        return 0
-    else:
-        return 0
-
-def b1m2(step, sL, s):
-    theta = (s['Z']*EMH_portion*s['Price'])/(s['Z']*EMH_portion*s['Price'] + EMH_Ext_Hold * s['P_Ext_Markets'])
-    if s['Price'] < (theta*EMH_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*EMH_portion*(1-theta)):
-        return 0
-    elif s['Price'] > (theta*EMH_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*EMH_portion*(1-theta)):
-        return beta * theta*EMH_Ext_Hold * s['P_Ext_Markets']/(s['Price']*EMH_portion*(1-theta))
-    else:
-        return 0
-
-# BEHAVIOR 3: Herding
-
-
-# BEHAVIOR 4: HODLers
-HODL_belief = Decimal('10.0')
-HODL_portion = Decimal('0.250')
-HODL_Ext_Hold = Decimal('4200.0')
-
-def b4m2(step, sL, s):
-    theta = (s['Z']*HODL_portion*s['Price'])/(s['Z']*HODL_portion*s['Price'] + HODL_Ext_Hold * s['P_Ext_Markets'])
-    if s['Price'] <  1/HODL_belief*(theta*HODL_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*HODL_portion*(1-theta)):
-        return beta * theta*HODL_Ext_Hold * s['P_Ext_Markets']/(s['Price']*HODL_portion*(1-theta))
-    elif s['Price'] > (theta*HODL_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*HODL_portion*(1-theta)):
-        return 0
-    else:
-        return 0
-
-
-# BEHAVIOR 2: Withdraw TDR and burn Zeus
-# Selling Agent- Arbitrage on TDR ext v TDR int signals
-# def b2m1(step, sL, s):
-#     Psignal_ext = s['P_Ext_Markets'] / s['Z']
-#     Psignal_int = s['Buy_Log'] /  s['Z']
-#     if Psignal_ext > Psignal_int:
-#         # withdrawn amount in TDR, subject to TDR limit
-#         return - np.minimum(beta*(Psignal_ext - Psignal_int) * s['Z'],s['Buy_Log']*max_withdraw_factor) 
-#     else:
-#         return 0 #- Decimal(0.000001)    
-   # return 0
-
-# BEHAVIOR 1: Deposit TDR and mint Zeus
-# Buying Agent- Arbitrage on Price and Z signals
-# def b1m2(step, sL, s):
-#     # Psignal_ext = s['P_Ext_Markets'] / s['Z']
-#     # Psignal_int = s['Buy_Log'] /  s['Z']
-#     # if Psignal_ext > Psignal_int:
-#     #     # withdrawn amount in TDR, subject to TDR limit
-#     #     return - np.minimum(beta*(Psignal_ext - Psignal_int) * s['Z'],s['Buy_Log']*max_withdraw_factor) 
-#     # else:
-#     #     return 0 #- Decimal(0.000001) 
-#     # 
-#     #   LT more valuable than ST = deposit TDR and mint Z
-#     Psignal_LT =  s['Price'] / s['Z']
-#     if Psignal_LT > 1:
-#         return beta_LT*(Psignal_LT - 1) * s['Z']
-#     else:
-#         return 0
-
-# Behavior will go here- b2m2, putting in mech 3: b1m3 for debugging
-# def b2m2(step, sL, s):
-#     # Psignal_LT =  s['Price'] / s['Z']
-#     # if Psignal_LT > 1:
-#     test = np.arange(1,10)
-#     return test
-
-# Selling Agent- Arbitrage on Price and Z signals
-# def b1m3(step, sL, s):
-#     Psignal_LT =  s['Price'] / s['Z']
-#     if Psignal_LT < 1:
-#         return - np.minimum(beta_LT*(Psignal_LT - 1) * s['Z'], s['Z']*max_withdraw_factor)
-#     else:
-#         return 0
-
-
-# def b2m3(step, sL, s):
-#     return 0
-
-def dummy_behavior(step, sL, s):
-    return 0
-def s1_dummy(step, sL, s, _input):
-    y = 'Z'
-    x = s['Z']
-    return (y, x)
-def s2_dummy(step, sL, s, _input):
-    y = 'Price'
-    x = s['Price']
-    return (y, x)
-def s3_dummy(step, sL, s, _input):
-    y = 'Buy_Log'
-    x = s['Buy_Log']
-    return (y, x)
-def s4_dummy(step, sL, s, _input):
-    y = 'Sell_Log'
-    x = s['Sell_Log']
-    return (y, x)
-def s5_dummy(step, sL, s, _input):
-    y = 'Trans'
-    x = s['Trans']
-    return (y, x)
-def s6_dummy(step, sL, s, _input):
-    y = 'P_Ext_Markets'
-    x = s['P_Ext_Markets']
-    return (y, x)
-# Internal States per Mechanism
-# Deposit TDR/Mint Zeus
-# def s1m1(step, sL, s, _input):
-#     s['Z'] = s['Z'] + _input
-
-
-# STATES
-
-# ZEUS Fixed Supply
-def s1m1(step, sL, s, _input):
-    y = 'Z'
-    x =  s['Z'] #+  _input # / Psignal_int
-    return (y, x)
-
-def s2m1(step, sL, s, _input):
-    y = 'Price'
-    x = (s['P_Ext_Markets'] - _input) /s['Z'] *10000
-    #x= alpha * s['Z'] + (1 - alpha)*s['Price']
-    return (y, x)
-
-def s3m1(step, sL, s, _input):
-    y = 'Buy_Log'
-    x =   _input # / Psignal_int
-    return (y, x)
-
-def s4m2(step, sL, s, _input):
-    y = 'Sell_Log'
-    x =  _input # / Psignal_int
-    print('s4m2 ',type(_input))
-    return (y, x)
-
-def s3m3(step, sL, s, _input):
-    y = 'Buy_Log'
-    x = s['Buy_Log'] +  _input # / Psignal_int
-    return (y, x)
-
-# Price Update
-def s2m3(step, sL, s, _input):
-    print('s2m3 ')
-    print(type(s['Sell_Log']))
-    print(type(s['Z']))
-
-    y = 'Price'
-    x = s['Price'] + s['Buy_Log']/s['Z'] - s['Sell_Log']/s['Z'] 
-     #+ np.divide(s['Buy_Log'],s['Z']) - np.divide() # / Psignal_int
-    return (y, x)
-
-
-
-def s6m1(step, sL, s, _input):
-    y = 'P_Ext_Markets'
-    x = s['P_Ext_Markets'] - _input
-    #x= alpha * s['Z'] + (1 - alpha)*s['Price']
-    return (y, x)
-
-def s2m2(step, sL, s, _input):
-    y = 'Price'
-    x = (s['P_Ext_Markets'] - _input) /s['Z'] *10000
-    #x= alpha * s['Z'] + (1 - alpha)*s['Price']
-    return (y, x)
-
-# def s1m1(step, sL, s, _input):
-#     Psignal_int = s['Buy_Log'] /  s['Z']
-#     y = 'Z'
-#     x =  s['Z'] +  _input / Psignal_int
-#     return (y, x)
-
-# def s2m1(step, sL, s, _input):
-#     y = 'Price'
-#     x= alpha * s['Z'] + (1 - alpha)*s['Price']
-#     return (y, x)
-
-# def s3m1(step, sL, s, _input):
-#     y = 'Buy_Log'
-#     x = s['Buy_Log'] + _input # Input already in TDR * s['Z']
-#     return (y, x)
-
-# #  Withdraw TDR/Burn Zeus
-# def s1m2(step, sL, s, _input):
-#     Psignal_int = s['Buy_Log'] /  s['Z']
-#     y = 'Z'
-#     x =  s['Z'] #+  _input / Psignal_int
-#     return (y, x)
-
-# def s2m2(step, sL, s, _input):
-#     y = 'Price'
-#     x= alpha * s['Z'] + (1 - alpha)*s['Price']
-#     return (y, x)
-
-# def s3m2(step, sL, s, _input):
-#     y = 'Buy_Log'
-#     x = s['Buy_Log'] + _input #* s['Z']
-#     # y = 'Buy_Log'
-#     # x = s['Buy_Log'] + _input
-#     return (y, x)
-
-# def s1m3(step, sL, s, _input):
-#     Psignal_int = s['Buy_Log'] /  s['Z']
-#     y = 'Z'
-#     x =  s['Z'] #+  _input / Psignal_int
-#     return (y, x)
-
-# def s2m3(step, sL, s, _input):
-#     y = 'Price'
-#     x= alpha * s['Z'] + (1 - alpha)*s['Price']
-#     return (y, x)
-
-# def s3m3(step, sL, s, _input):
-#     y = 'Buy_Log'
-#     x = s['Buy_Log'] #+ _input #* s['Z']
-#     # y = 'Buy_Log'
-#     # x = s['Buy_Log'] + _input
-#     return (y, x)
-
-# def s3m4(step, sL, s, _input):
-#     y = 'Buy_Log'
-#     x = s['Buy_Log']*(1-external_draw) + s['Sell_Log']*external_draw # _input #* s['Z']
-#     # y = 'Buy_Log'
-#     # x = s['Buy_Log'] + _input
-#     return (y, x)
-
-# def s1m3(step, sL, s, _input):
-#     s['Z'] = s['Z'] + _input
-# def s2m3(step, sL, s, _input):
-#     s['Price'] = s['Price'] + _input
-
-# Exogenous States
-proc_one_coef_A = -125
-proc_one_coef_B = 125
-# def es3p1(step, sL, s, _input):
-#     s['s3'] = s['s3'] * bound_norm_random(seed['a'], proc_one_coef_A, proc_one_coef_B)
-# def es4p2(step, sL, s, _input):
-#     s['P_Ext_Markets'] = s['P_Ext_Markets'] * bound_norm_random(seed['b'], proc_one_coef_A, proc_one_coef_B) 
-# def es5p2(step, sL, s, _input): # accept timedelta instead of timedelta params
-#     s['timestamp'] = ep_time_step(s, s['timestamp'], seconds=1)
-def es3p1(step, sL, s, _input):
-    y = 's3'
-    x = s['s3'] + 1
-    return (y, x)
-# def es4p2(step, sL, s, _input):
-#     y = 'P_Ext_Markets'
-#     # bound_norm_random defined in utils.py
-
-#     #x  = s['P_Ext_Markets'] * bound_norm_random(seed['b'], proc_one_coef_A, proc_one_coef_B)
-#     expected_change = correction_factor*(s['P_Ext_Markets']-s['Buy_Log'])
-#     vol =  np.random.randint(1,volatility)
-#     change = expected_change * vol
-#     # change_float =  (np.random.normal(expected_change,volatility*expected_change) #Decimal('1.0')
-#     #change = Decimal.from_float(change_float)
-#     x = s['P_Ext_Markets'] + change 
-   
-#     return (y, x)
-
-# A change in belief of actual price, passed onto behaviors to make action
-def es4p2(step, sL, s, _input):
-    y = 'P_Ext_Markets'
-    x = s['P_Ext_Markets'] +  bound_norm_random(seed['z'], proc_one_coef_A, proc_one_coef_B)
-
-    return (y,x)
-
-
-def es5p2(step, sL, s, _input): # accept timedelta instead of timedelta params
-    y = 'timestamp'
-    x = ep_time_step(s, s['timestamp'], seconds=1)
-    return (y, x)
-#Environment States
-# def stochastic(reference, seed, correction = 0.01):
-#     series = np.zeros(len(reference))
-#     series[0] = reference[0]
-#     for i in range(1,len(reference)):
-#         expected_change = correction*(reference[i]-series[i-1])
-#         normalized_expected_change = np.abs(expected_change)*(reference[i])/(reference[i-1])
-#         seed_int = seed.randint(1,10)
-#         change = np.random.normal(expected_change,seed_int*normalized_expected_change)
-                
-#         series[i] = series[i-1]+change
-#         # avoid negative series returns
-#         if series[i] <= 0:
-#             series[i] = .01
-#         #series[i] = series[i-1]+change 
-        
-#     return [series,seed_int]
-# ref3 = np.arange(1,1000)*.1
-# test = stochastic(ref3,seed['b']) 
-
-# def env_a(ref3,seed['b']):
-#     return stochastic(ref3,seed['b'])
-def env_a(x):
-    return 100
-def env_b(x):
-    return 21000000
-# def what_ever(x):
-#     return x + 1
-
-# Genesis States
-state_dict = {
-    'Z': Decimal(21000000.0),
-    'Price': Decimal(100.0),   # Initialize = Z for EMA
-    'Buy_Log': Decimal(0.0),
-    'Sell_Log': Decimal(0.0),
-    'Trans': Decimal(0.0),
-    'P_Ext_Markets': Decimal(25000.0),
-   
-   # 's2': Decimal(0.0),
-   # 's3': Decimal(0.0),
-  #  's4': Decimal(0.0),
-    'timestamp': '2018-10-01 15:16:24'
-}
-
-# exogenous_states = {
-# #    "s3": es3p1,
-#     "P_Ext_Markets": es4p2,
-#     "timestamp": es5p2
-# }
-
-exogenous_states = exo_update_per_ts(
-    {
- #   "s3": es3p1,
-    "P_Ext_Markets": es4p2,
-    "timestamp": es5p2
-    }
-)
-
-env_processes = {
- #   "s3": env_proc('2018-10-01 15:16:25', env_a),
-#    "P_Ext_Markets": env_proc('2018-10-01 15:16:25', env_b)
-}
-
-# test return vs. non-return functions as lambdas
-# test fully defined functions
-mechanisms = {
-    "m1": {
-        "behaviors": {
-            "b1": b1m1, # lambda step, sL, s: s['s1'] + 1,
-#            "b2": b2m1
-        },
-        "states": {
-            "Z": s1m1,
-            "Price": s2_dummy,
-            "Buy_Log": s3m1,
-            "Sell_Log":s4_dummy,
-            "Trans": s5_dummy,
-            "P_Ext_Markets": s6_dummy
-        }
-    },
-        "m2": {
-            "behaviors": {
-                "b1": b1m2,
-                "b4": b4m2
-        },
-            "states": {
-                "Z": s1_dummy,
-                "Price": s2_dummy,
-                "Buy_Log": s3_dummy,
-                "Sell_Log":s4m2,
-                "Trans": s5_dummy,
-                "P_Ext_Markets": s6_dummy
-        }
-    },
-        "m3": {
-            "behaviors": {
-#                "b1": b1m2,
-#                "b4": b4m2
-        },
-            "states": {
-                "Z": s1_dummy,
-                "Price": s2m3,
-                "Buy_Log": s3_dummy,
-                "Sell_Log":s4_dummy,
-                "Trans": s5_dummy,
-                "P_Ext_Markets": s6_dummy
-        }
-    },
-#     "m3": {
-#         "behaviors": {
-#             "b1": b1m3,
-#             "b2": b2m3
-#         },
-#         "states": {
-#             "Z": s1m3,
-#             "Price": s2m3,
-#             "Buy_Log": s3m3,
-#             "Sell_Log": s4_dummy,
-#             "Trans": s5_dummy,
-#             "P_Ext_Markets": s6_dummy
-#         }
-#     },
-#     "m4": {
-#         "behaviors": {
-#             "dummy": dummy_behavior
-#         },
-#         "states": {
-#             "Z": s1_dummy,
-#             "Price": s2_dummy,
-#             "Buy_Log": s3m4,
-#             "Sell_Log": s4_dummy,
-#             "Trans": s5_dummy,
-#             "P_Ext_Markets": s6_dummy
-#         }
-#    },
-    # "m3": {
-    #     "behaviors": {
-    #         "b1": b1m3,
-    #         "b2": b2m3
-    #     },
-    #     "states": {
-    #         "Z": s1m3,
-    #         "Price": s2m3,
-    #     }
-    # }
-     #treat environmental processes as a mechanism
-    "ep": {
-        "behaviors": {
-            "dummy": dummy_behavior
-        },
-        "states": {
-            "Z": s1_dummy,
-            "Price": s2_dummy,
-            "Buy_Log": s3_dummy,
-            "Sell_Log": s4_dummy,
-            "Trans": s5_dummy,
-            "P_Ext_Markets":  es4p2
-        }
-    }
-}
-
-sim_config = {
-    "N": 1,
-    "T": range(1000)
-}
-
-configs.append(Configuration(sim_config, state_dict, seed, exogenous_states, env_processes, mechanisms))

simulations/scrapbox/config8c.py

@@ -1,443 +0,0 @@
-from decimal import Decimal
-import numpy as np
-
-from SimCAD import Configuration, configs
-from SimCAD.configuration import exo_update_per_ts, bound_norm_random, \
-    ep_time_step
-
-# behavior_ops = []
-# behavior_ops = [foldr(dict_elemwise_sum())]
-
-seed = {
-    'z': np.random.RandomState(1)
-    # 'a': np.random.RandomState(2),
-    # 'b': np.random.RandomState(3),
-    # 'c': np.random.RandomState(3)
-}
-
-#Signals
-# Pr_signal
-#if s['P_Ext_Markets'] != 0:
-#Pr_signal = s['Z']/s['P_Ext_Markets']
-#else Pr_signal = 0
-#    if Pr_signal < s['Z']/s['Buy_Log']:
-beta = Decimal('0.25') #agent response gain
-beta_LT = Decimal('0.1') #LT agent response gain
-alpha = Decimal('0.091') #21 day EMA forgetfullness between 0 and 1, closer to 1 discounts older obs quicker, should be 2/(N+1)
-max_withdraw_factor = Decimal('0.9')
-external_draw = Decimal('0.01') # between 0 and 1 to draw Buy_Log to external
-
-# Stochastic process factors
-correction_factor = Decimal('0.01')
-volatility =  Decimal('5.0')
-
-
-# Buy_Log_signal =
-# Z_signal = 
-# Price_signal =
-# TDR_draw_signal =
-# P_Ext_Markets_signal = 
-
-# Behaviors per Mechanism
-
-# BEHAVIOR 1: EMH Trader
-EMH_portion = Decimal('0.250')
-EMH_Ext_Hold = Decimal('42000.0')
-
-def b1m1(step, sL, s):
-    print('b1m1')
- #   y = 'P_Ext_Markets'
-    # Psignal_ext = s['P_Ext_Markets'] / s['Z']
-    # Psignal_int = s['Buy_Log'] /  s['Z']
-    # if Psignal_ext < Psignal_int:
-    #     return beta*(Psignal_int - Psignal_ext) * s['Z']  # Deposited amount in TDR
-    # else:
-    #     return 0 # Decimal(0.000001)
- #   return (y,x)
-    theta = (s['Z']*EMH_portion*s['Price'])/(s['Z']*EMH_portion*s['Price'] + EMH_Ext_Hold * s['P_Ext_Markets'])
-    if s['Price'] < (theta*EMH_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*EMH_portion*(1-theta)):
-        buy = beta * theta*EMH_Ext_Hold * s['P_Ext_Markets']/(s['Price']*EMH_portion*(1-theta))
-        return {'buy_order1': buy}
-    elif s['Price'] > (theta*EMH_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*EMH_portion*(1-theta)):
-        return {'buy_order1': 0}
-    else:
-        return {'buy_order1': 0}
-
-def b1m2(step, sL, s):
-    print('b1m2')
-    theta = (s['Z']*EMH_portion*s['Price'])/(s['Z']*EMH_portion*s['Price'] + EMH_Ext_Hold * s['P_Ext_Markets'])
-    if s['Price'] < (theta*EMH_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*EMH_portion*(1-theta)):
-        return {'sell_order1': 0}
-    elif s['Price'] > (theta*EMH_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*EMH_portion*(1-theta)):
-        sell = beta * theta*EMH_Ext_Hold * s['P_Ext_Markets']/(s['Price']*EMH_portion*(1-theta))
-        return {'sell_order1': sell}
-    else:
-        return {'sell_order1': 0}
-
-# BEHAVIOR 3: Herding
-
-
-# BEHAVIOR 4: HODLers
-HODL_belief = Decimal('10.0')
-HODL_portion = Decimal('0.250')
-HODL_Ext_Hold = Decimal('4200.0')
-
-def b4m2(step, sL, s):
-    print('b4m2')
-    theta = (s['Z']*HODL_portion*s['Price'])/(s['Z']*HODL_portion*s['Price'] + HODL_Ext_Hold * s['P_Ext_Markets'])
-    if s['Price'] <  1/HODL_belief*(theta*HODL_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*HODL_portion*(1-theta)):
-        sell = beta * theta*HODL_Ext_Hold * s['P_Ext_Markets']/(s['Price']*HODL_portion*(1-theta))
-        return {'sell_order2': sell}
-    elif s['Price'] > (theta*HODL_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*HODL_portion*(1-theta)):
-        return {'sell_order2': 0}
-    else:
-        return {'sell_order2': 0}
-
-
-# BEHAVIOR 2: Withdraw TDR and burn Zeus
-# Selling Agent- Arbitrage on TDR ext v TDR int signals
-# def b2m1(step, sL, s):
-#     Psignal_ext = s['P_Ext_Markets'] / s['Z']
-#     Psignal_int = s['Buy_Log'] /  s['Z']
-#     if Psignal_ext > Psignal_int:
-#         # withdrawn amount in TDR, subject to TDR limit
-#         return - np.minimum(beta*(Psignal_ext - Psignal_int) * s['Z'],s['Buy_Log']*max_withdraw_factor) 
-#     else:
-#         return 0 #- Decimal(0.000001)    
-   # return 0
-
-# BEHAVIOR 1: Deposit TDR and mint Zeus
-# Buying Agent- Arbitrage on Price and Z signals
-# def b1m2(step, sL, s):
-#     # Psignal_ext = s['P_Ext_Markets'] / s['Z']
-#     # Psignal_int = s['Buy_Log'] /  s['Z']
-#     # if Psignal_ext > Psignal_int:
-#     #     # withdrawn amount in TDR, subject to TDR limit
-#     #     return - np.minimum(beta*(Psignal_ext - Psignal_int) * s['Z'],s['Buy_Log']*max_withdraw_factor) 
-#     # else:
-#     #     return 0 #- Decimal(0.000001) 
-#     # 
-#     #   LT more valuable than ST = deposit TDR and mint Z
-#     Psignal_LT =  s['Price'] / s['Z']
-#     if Psignal_LT > 1:
-#         return beta_LT*(Psignal_LT - 1) * s['Z']
-#     else:
-#         return 0
-
-# Behavior will go here- b2m2, putting in mech 3: b1m3 for debugging
-# def b2m2(step, sL, s):
-#     # Psignal_LT =  s['Price'] / s['Z']
-#     # if Psignal_LT > 1:
-#     test = np.arange(1,10)
-#     return test
-
-# Selling Agent- Arbitrage on Price and Z signals
-# def b1m3(step, sL, s):
-#     Psignal_LT =  s['Price'] / s['Z']
-#     if Psignal_LT < 1:
-#         return - np.minimum(beta_LT*(Psignal_LT - 1) * s['Z'], s['Z']*max_withdraw_factor)
-#     else:
-#         return 0
-
-
-# def b2m3(step, sL, s):
-#     return 0
-
-# Internal States per Mechanism
-# Deposit TDR/Mint Zeus
-# def s1m1(step, sL, s, _input):
-#     s['Z'] = s['Z'] + _input
-
-
-# STATES
-
-# ZEUS Fixed Supply
-def s1m1(step, sL, s, _input):
-    y = 'Z'
-    x =  s['Z'] #+  _input # / Psignal_int
-    return (y, x)
-
-def s2m1(step, sL, s, _input):
-    y = 'Price'
-    x = (s['P_Ext_Markets'] - _input['buy_order1']) /s['Z'] *10000
-    #x= alpha * s['Z'] + (1 - alpha)*s['Price']
-    return (y, x)
-
-def s3m1(step, sL, s, _input):
-    y = 'Buy_Log'
-    x =   _input['buy_order1'] # / Psignal_int
-    return (y, x)
-
-def s4m2(step, sL, s, _input):
-    y = 'Sell_Log'
-    x = _input['sell_order1'] + _input['sell_order2'] # / Psignal_int
-    return (y, x)
-
-def s3m3(step, sL, s, _input):
-    y = 'Buy_Log'
-    x = s['Buy_Log'] +  _input # / Psignal_int
-    return (y, x)
-
-# Price Update
-def s2m3(step, sL, s, _input):
-
-    y = 'Price'
-    #var1 = Decimal.from_float(s['Buy_Log'])
-    x = s['Price'] + s['Buy_Log'] * 1/s['Z'] - s['Sell_Log']/s['Z'] 
-     #+ np.divide(s['Buy_Log'],s['Z']) - np.divide() # / Psignal_int
-    return (y, x)
-
-
-
-def s6m1(step, sL, s, _input):
-    y = 'P_Ext_Markets'
-    x = s['P_Ext_Markets'] - _input
-    #x= alpha * s['Z'] + (1 - alpha)*s['Price']
-    return (y, x)
-
-def s2m2(step, sL, s, _input):
-    y = 'Price'
-    x = (s['P_Ext_Markets'] - _input) /s['Z'] *10000
-    #x= alpha * s['Z'] + (1 - alpha)*s['Price']
-    return (y, x)
-
-# def s1m1(step, sL, s, _input):
-#     Psignal_int = s['Buy_Log'] /  s['Z']
-#     y = 'Z'
-#     x =  s['Z'] +  _input / Psignal_int
-#     return (y, x)
-
-# def s2m1(step, sL, s, _input):
-#     y = 'Price'
-#     x= alpha * s['Z'] + (1 - alpha)*s['Price']
-#     return (y, x)
-
-# def s3m1(step, sL, s, _input):
-#     y = 'Buy_Log'
-#     x = s['Buy_Log'] + _input # Input already in TDR * s['Z']
-#     return (y, x)
-
-# #  Withdraw TDR/Burn Zeus
-# def s1m2(step, sL, s, _input):
-#     Psignal_int = s['Buy_Log'] /  s['Z']
-#     y = 'Z'
-#     x =  s['Z'] #+  _input / Psignal_int
-#     return (y, x)
-
-# def s2m2(step, sL, s, _input):
-#     y = 'Price'
-#     x= alpha * s['Z'] + (1 - alpha)*s['Price']
-#     return (y, x)
-
-# def s3m2(step, sL, s, _input):
-#     y = 'Buy_Log'
-#     x = s['Buy_Log'] + _input #* s['Z']
-#     # y = 'Buy_Log'
-#     # x = s['Buy_Log'] + _input
-#     return (y, x)
-
-# def s1m3(step, sL, s, _input):
-#     Psignal_int = s['Buy_Log'] /  s['Z']
-#     y = 'Z'
-#     x =  s['Z'] #+  _input / Psignal_int
-#     return (y, x)
-
-# def s2m3(step, sL, s, _input):
-#     y = 'Price'
-#     x= alpha * s['Z'] + (1 - alpha)*s['Price']
-#     return (y, x)
-
-# def s3m3(step, sL, s, _input):
-#     y = 'Buy_Log'
-#     x = s['Buy_Log'] #+ _input #* s['Z']
-#     # y = 'Buy_Log'
-#     # x = s['Buy_Log'] + _input
-#     return (y, x)
-
-# def s3m4(step, sL, s, _input):
-#     y = 'Buy_Log'
-#     x = s['Buy_Log']*(1-external_draw) + s['Sell_Log']*external_draw # _input #* s['Z']
-#     # y = 'Buy_Log'
-#     # x = s['Buy_Log'] + _input
-#     return (y, x)
-
-# def s1m3(step, sL, s, _input):
-#     s['Z'] = s['Z'] + _input
-# def s2m3(step, sL, s, _input):
-#     s['Price'] = s['Price'] + _input
-
-# Exogenous States
-proc_one_coef_A = -125
-proc_one_coef_B = 125
-# def es3p1(step, sL, s, _input):
-#     s['s3'] = s['s3'] * bound_norm_random(seed['a'], proc_one_coef_A, proc_one_coef_B)
-# def es4p2(step, sL, s, _input):
-#     s['P_Ext_Markets'] = s['P_Ext_Markets'] * bound_norm_random(seed['b'], proc_one_coef_A, proc_one_coef_B) 
-# def es5p2(step, sL, s, _input): # accept timedelta instead of timedelta params
-#     s['timestamp'] = ep_time_step(s, s['timestamp'], seconds=1)
-def es3p1(step, sL, s, _input):
-    y = 's3'
-    x = s['s3'] + 1
-    return (y, x)
-# def es4p2(step, sL, s, _input):
-#     y = 'P_Ext_Markets'
-#     # bound_norm_random defined in utils.py
-
-#     #x  = s['P_Ext_Markets'] * bound_norm_random(seed['b'], proc_one_coef_A, proc_one_coef_B)
-#     expected_change = correction_factor*(s['P_Ext_Markets']-s['Buy_Log'])
-#     vol =  np.random.randint(1,volatility)
-#     change = expected_change * vol
-#     # change_float =  (np.random.normal(expected_change,volatility*expected_change) #Decimal('1.0')
-#     #change = Decimal.from_float(change_float)
-#     x = s['P_Ext_Markets'] + change 
-   
-#     return (y, x)
-
-# A change in belief of actual price, passed onto behaviors to make action
-def es4p2(step, sL, s, _input):
-    y = 'P_Ext_Markets'
-    x = s['P_Ext_Markets'] +  bound_norm_random(seed['z'], proc_one_coef_A, proc_one_coef_B)
-
-    return (y,x)
-
-
-def es5p2(step, sL, s, _input): # accept timedelta instead of timedelta params
-    y = 'timestamp'
-    x = ep_time_step(s, s['timestamp'], seconds=1)
-    return (y, x)
-#Environment States
-# def stochastic(reference, seed, correction = 0.01):
-#     series = np.zeros(len(reference))
-#     series[0] = reference[0]
-#     for i in range(1,len(reference)):
-#         expected_change = correction*(reference[i]-series[i-1])
-#         normalized_expected_change = np.abs(expected_change)*(reference[i])/(reference[i-1])
-#         seed_int = seed.randint(1,10)
-#         change = np.random.normal(expected_change,seed_int*normalized_expected_change)
-                
-#         series[i] = series[i-1]+change
-#         # avoid negative series returns
-#         if series[i] <= 0:
-#             series[i] = .01
-#         #series[i] = series[i-1]+change 
-        
-#     return [series,seed_int]
-# ref3 = np.arange(1,1000)*.1
-# test = stochastic(ref3,seed['b']) 
-
-# def env_a(ref3,seed['b']):
-#     return stochastic(ref3,seed['b'])
-def env_a(x):
-    return 100
-def env_b(x):
-    return 21000000
-# def what_ever(x):
-#     return x + 1
-
-# Genesis States
-state_dict = {
-    'Z': Decimal(21000000.0),
-    'Price': Decimal(100.0),   # Initialize = Z for EMA
-    'Buy_Log': Decimal(0.0),
-    'Sell_Log': Decimal(0.0),
-    'Trans': Decimal(0.0),
-    'P_Ext_Markets': Decimal(25000.0),
-   
-   # 's2': Decimal(0.0),
-   # 's3': Decimal(0.0),
-  #  's4': Decimal(0.0),
-    'timestamp': '2018-10-01 15:16:24'
-}
-
-# exogenous_states = {
-# #    "s3": es3p1,
-#     "P_Ext_Markets": es4p2,
-#     "timestamp": es5p2
-# }
-
-exogenous_states = exo_update_per_ts(
-    {
- #   "s3": es3p1,
-    "P_Ext_Markets": es4p2,
-    "timestamp": es5p2
-    }
-)
-
-env_processes = {
- #   "s3": env_proc('2018-10-01 15:16:25', env_a),
-#    "P_Ext_Markets": env_proc('2018-10-01 15:16:25', env_b)
-}
-
-# test return vs. non-return functions as lambdas
-# test fully defined functions
-mechanisms = {
-    "m1": {
-        "behaviors": {
-            "b1": b1m1, # lambda step, sL, s: s['s1'] + 1,
-#            "b2": b2m1
-        },
-        "states": {
-            "Z": s1m1,
-#            "Price": s2_dummy,
-            "Buy_Log": s3m1,
-        }
-    },
-    "m2": {
-         "behaviors": {
-            "b1": b1m2,
-            "b4": b4m2
-         },
-         "states": {
-             "Sell_Log":s4m2,
-        }
-    },
-        "m3": {
-            "behaviors": {
-        },
-            "states": {
-                "Price": s2m3,
-        }
-    },
-#     "m3": {
-#         "behaviors": {
-#             "b1": b1m3,
-#             "b2": b2m3
-#         },
-#         "states": {
-#             "Z": s1m3,
-#             "Price": s2m3,
-#             "Buy_Log": s3m3,
-#         }
-#     },
-#     "m4": {
-#         "behaviors": {
-#         },
-#         "states": {
-#         }
-#    },
-    # "m3": {
-    #     "behaviors": {
-    #         "b1": b1m3,
-    #         "b2": b2m3
-    #     },
-    #     "states": {
-    #         "Z": s1m3,
-    #         "Price": s2m3,
-    #     }
-    # }
-     #treat environmental processes as a mechanism
-    "ep": {
-        "behaviors": {
-        },
-        "states": {
-            "P_Ext_Markets":  es4p2
-        }
-    }
-}
-
-sim_config = {
-    "N": 1,
-    "T": range(1000)
-}
-
-configs.append(Configuration(sim_config, state_dict, seed, exogenous_states, env_processes, mechanisms))

simulations/validation/config1.py

@@ -7,6 +7,7 @@ from SimCAD.configuration import Configuration
 from SimCAD.configuration.utils import exo_update_per_ts, proc_trigger, bound_norm_random, \
     ep_time_step
 
+
 seed = {
     'z': np.random.RandomState(1),
     'a': np.random.RandomState(2),
@@ -14,8 +15,8 @@ seed = {
     'c': np.random.RandomState(3)
 }
 
+
 # Behaviors per Mechanism
-# Different return types per mechanism ?? *** No ***
 def b1m1(step, sL, s):
     return {'param1': 1}
 def b2m1(step, sL, s):
@@ -31,15 +32,15 @@ def b1m3(step, sL, s):
 def b2m3(step, sL, s):
     return {'param1': ['d'], 'param2': np.array([20, 200])}
 
-# deff not more than 2
+
 # Internal States per Mechanism
 def s1m1(step, sL, s, _input):
     y = 's1'
-    x = _input['param1'] #+ [Coef1 x 5]
+    x = _input['param1']
     return (y, x)
 def s2m1(step, sL, s, _input):
     y = 's2'
-    x = _input['param2'] #+ [Coef2 x 5]
+    x = _input['param2']
     return (y, x)
 
 def s1m2(step, sL, s, _input):
@@ -60,6 +61,7 @@ def s2m3(step, sL, s, _input):
     x = _input['param2']
     return (y, x)
 
+
 # Exogenous States
 proc_one_coef_A = 0.7
 proc_one_coef_B = 1.3
@@ -90,6 +92,7 @@ def env_b(x):
 # def what_ever(x):
 #     return x + 1
 
+
 # Genesis States
 genesis_states = {
     's1': Decimal(0.0),
@@ -99,6 +102,7 @@ genesis_states = {
     'timestamp': '2018-10-01 15:16:24'
 }
 
+
 # remove `exo_update_per_ts` to update every ts
 exogenous_states = exo_update_per_ts(
     {
@@ -108,33 +112,20 @@ exogenous_states = exo_update_per_ts(
     }
 )
 
-# ToDo: make env proc trigger field agnostic
+
-# ToDo: input json into function renaming __name__
 env_processes = {
     "s3": env_a,
     "s4": proc_trigger('2018-10-01 15:16:25', env_b)
 }
 
-# lambdas
-# genesis Sites should always be there
-# [1, 2]
-# behavior_ops = [ foldr(_ + _), lambda x: x + 0 ]
-
-# [1, 2] = {'b1': ['a'], 'b2', [1]} =
-# behavior_ops = [ behavior_to_dict, print_fwd, sum_dict_values ]
-# behavior_ops = [foldr(dict_elemwise_sum())]
-# behavior_ops = [foldr(lambda a, b: a + b)]
-
-# need at least 1 behaviour and 1 state function for the 1st mech with behaviors
-# mechanisms = {}
 
 mechanisms = {
     "m1": {
         "behaviors": {
-            "b1": b1m1, # lambda step, sL, s: s['s1'] + 1,
+            "b1": b1m1,
             "b2": b2m1
         },
-        "states": { # exclude only. TypeError: reduce() of empty sequence with no initial value
+        "states": {
             "s1": s1m1,
             "s2": s2m1
         }
@@ -161,11 +152,13 @@ mechanisms = {
     }
 }
 
+
 sim_config = {
     "N": 2,
     "T": range(5)
 }
 
+
 configs.append(
     Configuration(
         sim_config=sim_config,

simulations/validation/config2.py

@@ -15,8 +15,8 @@ seed = {
     'c': np.random.RandomState(3)
 }
 
+
 # Behaviors per Mechanism
-# Different return types per mechanism ?? *** No ***
 def b1m1(step, sL, s):
     return {'param1': 1}
 def b2m1(step, sL, s):
@@ -27,7 +27,6 @@ def b1m2(step, sL, s):
 def b2m2(step, sL, s):
     return {'param1': 'b', 'param2': 4}
 
-
 def b1m3(step, sL, s):
     return {'param1': ['c'], 'param2': np.array([10, 100])}
 def b2m3(step, sL, s):
@@ -62,6 +61,7 @@ def s2m3(step, sL, s, _input):
     x = _input['param2']
     return (y, x)
 
+
 # Exogenous States
 proc_one_coef_A = 0.7
 proc_one_coef_B = 1.3
@@ -92,6 +92,7 @@ def env_b(x):
 # def what_ever(x):
 #     return x + 1
 
+
 # Genesis States
 genesis_states = {
     's1': Decimal(0.0),
@@ -101,8 +102,8 @@ genesis_states = {
     'timestamp': '2018-10-01 15:16:24'
 }
 
+
 # remove `exo_update_per_ts` to update every ts
-# why `exo_update_per_ts` here instead of `env_processes`
 exogenous_states = exo_update_per_ts(
     {
     "s3": es3p1,
@@ -111,32 +112,20 @@ exogenous_states = exo_update_per_ts(
     }
 )
 
-#	make env proc trigger field agnostic
+
 env_processes = {
     "s3": proc_trigger('2018-10-01 15:16:25', env_a),
     "s4": proc_trigger('2018-10-01 15:16:25', env_b)
 }
 
-# lambdas
-# genesis Sites should always be there
-# [1, 2]
-# behavior_ops = [ foldr(_ + _), lambda x: x + 0 ]
 
-
-# [1, 2] = {'b1': ['a'], 'b2', [1]} =
-# behavior_ops = [behavior_to_dict, print_fwd, sum_dict_values]
-# behavior_ops = [foldr(dict_elemwise_sum())]
-# behavior_ops = []
-
-# need at least 1 behaviour and 1 state function for the 1st mech with behaviors
-# mechanisms = {}
 mechanisms = {
     "m1": {
         "behaviors": {
-            "b1": b1m1, # lambda step, sL, s: s['s1'] + 1,
+            "b1": b1m1,
             # "b2": b2m1
         },
-        "states": { # exclude only. TypeError: reduce() of empty sequence with no initial value
+        "states": {
             "s1": s1m1,
             # "s2": s2m1
         }
@@ -163,11 +152,13 @@ mechanisms = {
     }
 }
 
+
 sim_config = {
     "N": 2,
     "T": range(5)
 }
 
+
 configs.append(
     Configuration(
         sim_config=sim_config,