diff --git a/conviction_cadCAD.ipynb b/conviction_cadCAD.ipynb index 15e3f9b..60e87f1 100644 --- a/conviction_cadCAD.ipynb +++ b/conviction_cadCAD.ipynb @@ -231,7 +231,7 @@ " 'conviction': 0,\n", " 'status': 'candidate',\n", " 'age': 0,\n", - " 'funds_requested': 24045.85625614501,\n", + " 'funds_requested': 44422.72592566299,\n", " 'trigger': inf}" ] }, @@ -254,8 +254,8 @@ "data": { "text/plain": [ "{'type': 'participant',\n", - " 'holdings': 837.7209354737918,\n", - " 'sentiment': 0.9643955178896184}" + " 'holdings': 583.4204491141511,\n", + " 'sentiment': 0.13785461223155082}" ] }, "execution_count": 11, @@ -276,7 +276,7 @@ { "data": { "text/plain": [ - "{'affinity': 0.3515390332022055, 'tokens': 0, 'conviction': 0}" + "{'affinity': 0.18493514019369206, 'tokens': 0, 'conviction': 0}" ] }, "execution_count": 12, @@ -313,7 +313,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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" ] @@ -554,426 +554,6 @@ " 'sentiment': initial_sentiment}" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#functions for partial state update block 1\n", - "\n", - "def gen_new_participant(network, new_participant_holdings):\n", - " \n", - " i = len([node for node in network.nodes])\n", - " \n", - " network.add_node(i)\n", - " network.nodes[i]['type']=\"participant\"\n", - " \n", - " s_rv = np.random.rand() \n", - " network.nodes[i]['sentiment'] = s_rv\n", - " network.nodes[i]['holdings']=new_participant_holdings\n", - " \n", - " for j in get_nodes_by_type(network, 'proposal'):\n", - " network.add_edge(i, j)\n", - " \n", - " rv = np.random.rand()\n", - " a_rv = 1-4*(1-rv)*rv #polarized distribution\n", - " network.edges[(i, j)]['affinity'] = a_rv\n", - " network.edges[(i,j)]['tokens'] = a_rv*network.nodes[i]['holdings']\n", - " network.edges[(i, j)]['conviction'] = 0\n", - " \n", - " return network\n", - " \n", - "\n", - "def gen_new_proposal(network, funds, supply):\n", - " j = len([node for node in network.nodes])\n", - " network.add_node(j)\n", - " network.nodes[j]['type']=\"proposal\"\n", - " \n", - " network.nodes[j]['conviction']=0\n", - " network.nodes[j]['status']='candidate'\n", - " network.nodes[j]['age']=0\n", - " \n", - " rescale = 10000*funds/initial_funds\n", - " r_rv = gamma.rvs(3,loc=0.001, scale=rescale)\n", - " network.node[j]['funds_requested'] = r_rv\n", - " \n", - " network.nodes[j]['trigger']= trigger_threshold(r_rv, funds, supply)\n", - " \n", - " participants = get_nodes_by_type(network, 'participant')\n", - " proposing_participant = np.random.choice(participants)\n", - " \n", - " for i in participants:\n", - " network.add_edge(i, j)\n", - " if i==proposing_participant:\n", - " network.edges[(i, j)]['affinity']=1\n", - " else:\n", - " rv = np.random.rand()\n", - " a_rv = 1-4*(1-rv)*rv #polarized distribution\n", - " network.edges[(i, j)]['affinity'] = a_rv\n", - " \n", - " network.edges[(i, j)]['conviction'] = 0\n", - " network.edges[(i,j)]['tokens'] = 0\n", - " return network\n", - " \n", - " \n", - "\n", - "def driving_process(params, step, sL, s):\n", - " \n", - " #placeholder plumbing for random processes\n", - " arrival_rate = 10/s['sentiment']\n", - " rv1 = np.random.rand()\n", - " new_participant = bool(rv1<1/arrival_rate)\n", - " if new_participant:\n", - " h_rv = expon.rvs(loc=0.0, scale=1000)\n", - " new_participant_holdings = h_rv\n", - " else:\n", - " new_participant_holdings = 0\n", - " \n", - " network = s['network']\n", - " affinities = [network.edges[e]['affinity'] for e in network.edges ]\n", - " median_affinity = np.median(affinities)\n", - " \n", - " proposals = get_nodes_by_type(network, 'proposal')\n", - " fund_requests = [network.nodes[j]['funds_requested'] for j in proposals if network.nodes[j]['status']=='candidate' ]\n", - " \n", - " funds = s['funds']\n", - " total_funds_requested = np.sum(fund_requests)\n", - " \n", - " proposal_rate = 10/median_affinity * total_funds_requested/funds\n", - " rv2 = np.random.rand()\n", - " new_proposal = bool(rv2<1/proposal_rate)\n", - " \n", - " sentiment = s['sentiment']\n", - " funds = s['funds']\n", - " scale_factor = 1+4000*sentiment**2\n", - " \n", - " #this shouldn't happen but expon is throwing domain errors\n", - " if scale_factor > 1: \n", - " funds_arrival = expon.rvs(loc = 0, scale = scale_factor )\n", - " else:\n", - " funds_arrival = 0\n", - " \n", - " return({'new_participant':new_participant,\n", - " 'new_participant_holdings':new_participant_holdings,\n", - " 'new_proposal':new_proposal, \n", - " 'funds_arrival':funds_arrival})\n", - "\n", - "def update_network(params, step, sL, s, _input):\n", - " \n", - " network = s['network']\n", - " funds = s['funds']\n", - " supply = s['supply']\n", - " #placeholder plumbing for new proposals and new participants\n", - " new_participant = _input['new_participant'] #T/F\n", - " new_proposal = _input['new_proposal'] #T/F\n", - " # IF THEN logic to create new nodes // left out for now since always FALSE\n", - " if new_participant:\n", - " new_participant_holdings = _input['new_participant_holdings']\n", - " network = gen_new_participant(network, new_participant_holdings)\n", - " \n", - " if new_proposal:\n", - " network= gen_new_proposal(network,funds,supply )\n", - " \n", - " #update age of the existing proposals\n", - " proposals = get_nodes_by_type(network, 'proposal')\n", - " \n", - " for j in proposals:\n", - " network.nodes[j]['age'] = network.nodes[j]['age']+1\n", - " if network.nodes[j]['status'] == 'candidate':\n", - " requested = network.nodes[j]['funds_requested']\n", - " network.nodes[j]['trigger'] = trigger_threshold(requested, funds, supply)\n", - " else:\n", - " network.nodes[j]['trigger'] = np.nan\n", - " \n", - " key = 'network'\n", - " value = network\n", - " \n", - " return (key, value)\n", - "\n", - "def increment_funds(params, step, sL, s, _input):\n", - " \n", - " funds = s['funds']\n", - " funds_arrival = _input['funds_arrival']\n", - "\n", - " #increment funds\n", - " funds = funds + funds_arrival\n", - " \n", - " key = 'funds'\n", - " value = funds\n", - " \n", - " return (key, value)\n", - "\n", - "def increment_supply(params, step, sL, s, _input):\n", - " \n", - " supply = s['supply']\n", - " supply_arrival = _input['new_participant_holdings']\n", - "\n", - " #increment funds\n", - " supply = supply + supply_arrival\n", - " \n", - " key = 'supply'\n", - " value = supply\n", - " \n", - " return (key, value)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#partial state update block 2\n", - "def check_progress(params, step, sL, s):\n", - " \n", - " network = s['network']\n", - " proposals = get_nodes_by_type(network, 'proposal')\n", - " \n", - " completed = []\n", - " for j in proposals:\n", - " if network.nodes[j]['status'] == 'active':\n", - " grant_size = network.nodes[j]['funds_requested']\n", - " likelihood = 1.0/(min_completion_rate+np.log(grant_size))\n", - " if np.random.rand() < likelihood:\n", - " completed.append(j)\n", - " \n", - " return({'completed':completed})\n", - "\n", - "def complete_proposal(params, step, sL, s, _input):\n", - " \n", - " network = s['network']\n", - " participants = get_nodes_by_type(network, 'participant')\n", - " \n", - " completed = _input['completed']\n", - " for j in completed:\n", - " network.nodes[j]['status']='completed'\n", - " for i in participants:\n", - " force = network.edges[(i,j)]['affinity']\n", - " sentiment = network.node[i]['sentiment']\n", - " network.node[i]['sentiment'] = get_sentimental(sentiment, force, decay=False)\n", - " \n", - " key = 'network'\n", - " value = network\n", - " \n", - " return (key, value)\n", - "\n", - "def update_sentiment_on_completion(params, step, sL, s, _input):\n", - " \n", - " network = s['network']\n", - " proposals = get_nodes_by_type(network, 'proposal')\n", - " completed = _input['completed']\n", - " \n", - " grants_outstanding = np.sum([network.nodes[j]['funds_requested'] for j in proposals if network.nodes[j]['status']=='active'])\n", - " \n", - " grants_completed = np.sum([network.nodes[j]['funds_requested'] for j in completed])\n", - " \n", - " sentiment = s['sentiment']\n", - " \n", - " force = grants_completed/grants_outstanding\n", - " if (force >=0) and (force <=1):\n", - " sentiment = get_sentimental(sentiment, force, True)\n", - " else:\n", - " sentiment = get_sentimental(sentiment, 0, True)\n", - " \n", - " \n", - " key = 'sentiment'\n", - " value = sentiment\n", - " \n", - " return (key, value)\n", - "\n", - "def get_sentimental(sentiment, force, decay=True):\n", - " sentiment = sentiment*(1-int(decay)*mu) + force\n", - " \n", - " if sentiment > 1:\n", - " sentiment = 1\n", - " \n", - " return sentiment" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#partial state update block 3\n", - "def trigger_function(params, step, sL, s):\n", - " \n", - " network = s['network']\n", - " funds = s['funds']\n", - " supply = s['supply']\n", - " proposals = get_nodes_by_type(network, 'proposal')\n", - " \n", - " accepted = []\n", - " triggers = {}\n", - " for j in proposals:\n", - " if network.nodes[j]['status'] == 'candidate':\n", - " requested = network.nodes[j]['funds_requested']\n", - " age = network.nodes[j]['age']\n", - " threshold = trigger_threshold(requested, funds, supply)\n", - " if age > tmin:\n", - " conviction = network.nodes[j]['conviction']\n", - " if conviction >threshold:\n", - " accepted.append(j)\n", - " else:\n", - " threshold = np.nan\n", - " \n", - " triggers[j] = threshold\n", - " \n", - " \n", - " \n", - " return({'accepted':accepted, 'triggers':triggers})\n", - "\n", - "def decrement_funds(params, step, sL, s, _input):\n", - " \n", - " funds = s['funds']\n", - " network = s['network']\n", - " accepted = _input['accepted']\n", - "\n", - " #decrement funds\n", - " for j in accepted:\n", - " funds = funds - network.nodes[j]['funds_requested']\n", - " \n", - " key = 'funds'\n", - " value = funds\n", - " \n", - " return (key, value)\n", - "\n", - "def update_proposals(params, step, sL, s, _input):\n", - " \n", - " network = s['network']\n", - " accepted = _input['accepted']\n", - " triggers = _input['triggers']\n", - " participants = get_nodes_by_type(network, 'participant')\n", - " proposals = get_nodes_by_type(network, 'proposals')\n", - " \n", - " for j in proposals:\n", - " network.nodes[j]['trigger'] = triggers[j]\n", - " \n", - " #bookkeeping conviction and participant sentiment\n", - " for j in accepted:\n", - " network.nodes[j]['status']='active'\n", - " network.nodes[j]['conviction']=np.nan\n", - " #change status to active\n", - " for i in participants:\n", - " \n", - " edge = (i,j)\n", - " #reset tokens assigned to other candidates\n", - " network.edges[(i,j)]['tokens']=0\n", - " network.edges[(i,j)]['conviction'] = np.nan\n", - " \n", - " #update participants sentiments (positive or negative) \n", - " affinities = [network.edges[(i,p)]['affinity'] for p in proposals if not(p in accepted)]\n", - " if len(affinities)>1:\n", - " max_affinity = np.max(affinities)\n", - " force = network.edges[(i,j)]['affinity']-sensitivity*max_affinity\n", - " else:\n", - " force = 0\n", - " \n", - " #based on what their affinities to the accepted proposals\n", - " network.nodes[i]['sentiment'] = get_sentimental(network.nodes[i]['sentiment'], force, False)\n", - " \n", - " \n", - " key = 'network'\n", - " value = network\n", - " \n", - " return (key, value)\n", - "\n", - "def update_sentiment_on_release(params, step, sL, s, _input):\n", - " \n", - " network = s['network']\n", - " proposals = get_nodes_by_type(network, 'proposal')\n", - " accepted = _input['accepted']\n", - " \n", - " proposals_outstanding = np.sum([network.nodes[j]['funds_requested'] for j in proposals if network.nodes[j]['status']=='candidate'])\n", - " \n", - " proposals_accepted = np.sum([network.nodes[j]['funds_requested'] for j in accepted])\n", - " \n", - " sentiment = s['sentiment']\n", - " force = proposals_accepted/proposals_outstanding\n", - " if (force >=0) and (force <=1):\n", - " sentiment = get_sentimental(sentiment, force, False)\n", - " else:\n", - " sentiment = get_sentimental(sentiment, 0, False)\n", - " \n", - " key = 'sentiment'\n", - " value = sentiment\n", - " \n", - " return (key, value)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "def participants_decisions(params, step, sL, s):\n", - " \n", - " network = s['network']\n", - " participants = get_nodes_by_type(network, 'participant')\n", - " proposals = get_nodes_by_type(network, 'proposal')\n", - " candidates = [j for j in proposals if network.nodes[j]['status']=='candidate']\n", - " \n", - " gain = .01\n", - " delta_holdings={}\n", - " proposals_supported ={}\n", - " for i in participants:\n", - " force = network.nodes[i]['sentiment']-sensitivity\n", - " delta_holdings[i] = network.nodes[i]['holdings']*gain*force\n", - " \n", - " support = []\n", - " for j in candidates:\n", - " affinity = network.edges[(i, j)]['affinity']\n", - " cutoff = sensitivity*np.max([network.edges[(i,p)]['affinity'] for p in candidates])\n", - " if cutoff <.5:\n", - " cutoff = .5\n", - " \n", - " if affinity > cutoff:\n", - " support.append(j)\n", - " \n", - " proposals_supported[i] = support\n", - " \n", - " return({'delta_holdings':delta_holdings, 'proposals_supported':proposals_supported})\n", - "\n", - "def update_tokens(params, step, sL, s, _input):\n", - " \n", - " network = s['network']\n", - " delta_holdings = _input['delta_holdings']\n", - " proposals = get_nodes_by_type(network, 'proposal')\n", - " proposals_supported = _input['proposals_supported']\n", - " participants = get_nodes_by_type(network, 'participant')\n", - " \n", - " for i in participants:\n", - " network.nodes[i]['holdings'] = network.nodes[i]['holdings']+delta_holdings[i]\n", - " supported = proposals_supported[i]\n", - " total_affinity = np.sum([ network.edges[(i, j)]['affinity'] for j in supported])\n", - " for j in proposals:\n", - " if j in supported:\n", - " normalized_affinity = network.edges[(i, j)]['affinity']/total_affinity\n", - " network.edges[(i, j)]['tokens'] = normalized_affinity*network.nodes[i]['holdings']\n", - " else:\n", - " network.edges[(i, j)]['tokens'] = 0\n", - " \n", - " prior_conviction = network.edges[(i, j)]['conviction']\n", - " current_tokens = network.edges[(i, j)]['tokens']\n", - " network.edges[(i, j)]['conviction'] =current_tokens+alpha*prior_conviction\n", - " \n", - " for j in proposals:\n", - " network.nodes[j]['conviction'] = np.sum([ network.edges[(i, j)]['conviction'] for i in participants])\n", - " \n", - " key = 'network'\n", - " value = network\n", - " \n", - " return (key, value)\n", - "\n", - "def update_supply(params, step, sL, s, _input):\n", - " \n", - " supply = s['supply']\n", - " delta_holdings = _input['delta_holdings']\n", - " delta_supply = np.sum([v for v in delta_holdings.values()])\n", - " \n", - " supply = supply + delta_supply\n", - " \n", - " key = 'supply'\n", - " value = supply\n", - " \n", - " return (key, value)" - ] - }, { "cell_type": "code", "execution_count": 23, @@ -1054,44 +634,39 @@ "name": "stdout", "output_type": "stream", "text": [ - "single_proc: []\n", - "[{'sensitivity': [0.75], 'tmin': [7], 'sentiment_decay': [0.001], 'alpha': [0.5, 0.9], 'base_completion_rate': [10], 'trigger_func': []}]\n", - "\n" + "multi_proc: []\n" ] }, { "ename": "TypeError", - "evalue": "list indices must be integers or slices, not str", + "evalue": "'NoneType' object is not iterable", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mexec_context\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mExecutionContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexec_mode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msingle_proc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mexecutor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mExecutor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexec_context\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# Pass the configuration object inside an array\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mraw_result\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexecutor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# The `main()` method returns a tuple; its first elements contains the raw results\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/cadCAD/engine/__init__.py\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 96\u001b[0m \u001b[0;31m# ToDO: Deprication Handler - \"sanitize\" in appropriate place\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[0mtensor_field\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcreate_tensor_field\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpartial_state_updates\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meps\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 98\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexec_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msimulation_execs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvar_dict_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstates_lists\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfigs_structs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0menv_processes_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 99\u001b[0m \u001b[0mfinal_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor_field\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 100\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexec_context\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mExecutionMode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmulti_proc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - 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"\u001b[0;31mTypeError\u001b[0m: list indices must be integers or slices, not str" + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0mrun\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mExecutor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexec_context\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mexec_context\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfigs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconfigs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0mexecutor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mExecutor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexec_context\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfigs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# Pass the configuration object inside an array\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m \u001b[0mraw_result\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexecutor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# The `main()` method returns a tuple; its first elements contains the raw results\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m: 'NoneType' object is not iterable" ] } ], "source": [ + "from cadCAD.engine import ExecutionMode, ExecutionContext, Executor\n", + "from cadCAD.configuration import append_configs\n", + "from cadCAD import configs\n", + "\n", + "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #\n", + "# The configurations above are then packaged into a `Configuration` object\n", + "config = append_configs(\n", + " initial_state=initial_conditions, #dict containing variable names and initial values\n", + " partial_state_update_blocks=partial_state_update_blocks, #dict containing state update functions\n", + " sim_configs=simulation_parameters #dict containing simulation parameters\n", + ")\n", + "\n", "exec_mode = ExecutionMode()\n", - "exec_context = ExecutionContext(exec_mode.single_proc)\n", - "executor = Executor(exec_context, [config]) # Pass the configuration object inside an array\n", - "raw_result, tensor = executor.main() # The `main()` method returns a tuple; its first elements contains the raw results" + "exec_context = ExecutionContext(context=exec_mode.multi_proc)\n", + "run = Executor(exec_context=exec_context, configs=configs)\n", + "executor = Executor(exec_context, configs) # Pass the configuration object inside an array\n", + "raw_result, tensor = executor.execute() # The `execute()` method returns a tuple; its first elements contains the raw results" ] }, { diff --git a/conviction_system_logic_sim.py b/conviction_test_sim.py similarity index 100% rename from conviction_system_logic_sim.py rename to conviction_test_sim.py