diff --git a/tutorials/robots_and_marbles/robot-marbles-part-1/robot-marbles-part-1.ipynb b/tutorials/robots_and_marbles/robot-marbles-part-1/robot-marbles-part-1.ipynb index ca44fc4..3bca402 100644 --- a/tutorials/robots_and_marbles/robot-marbles-part-1/robot-marbles-part-1.ipynb +++ b/tutorials/robots_and_marbles/robot-marbles-part-1/robot-marbles-part-1.ipynb @@ -38,7 +38,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": { "pycharm": { "name": "#%%\n" @@ -82,7 +82,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": { "pycharm": { "name": "#%%\n" @@ -113,11 +113,6 @@ }, { "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, "source": [ "# Partial State Update Blocks\n", "Within a timestep, state update functions can be run in any combination of serial or parallel executions. Take the following diagram for example:\n", @@ -167,16 +162,17 @@ "```\n", "\n", "In the case of our robot and marbles example system, we can model the system so that all state update functions are executed in parallel. In other words, we consider the marbles move from one box to the other simultaneously (ie, `box_A + box_B` is constant)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, + ], "metadata": { + "collapsed": false, "pycharm": { - "name": "#%%\n" + "name": "#%% md\n" } - }, + } + }, + { + "cell_type": "code", + "execution_count": 3, "outputs": [], "source": [ "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n", @@ -192,28 +188,30 @@ " }\n", "]\n", "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # " - ] + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } }, { "cell_type": "markdown", + "source": [ + "# Simulation Configuration Parameters\n", + "Lastly, we define the number of timesteps and the number of Monte Carlo runs of the simulation. These parameters must be passed in a dictionary, in `dict_keys` `T` and `N`, respectively. In our example, we'll run the simulation for 10 timesteps. And because we are dealing with a deterministic system, it makes no sense to have multiple Monte Carlo runs, so we set `N=1`. We'll ignore the `M` key for now and set it to an empty `dict`" + ], "metadata": { + "collapsed": false, "pycharm": { "name": "#%% md\n" } - }, - "source": [ - "# Simulation Configuration Parameters\n", - "Lastly, we define the number of timesteps and the number of Monte Carlo runs of the simulation. These parameters must be passed in a dictionary, in `dict_keys` `T` and `N`, respectively. In our example, we'll run the simulation for 10 timesteps. And because we are dealing with a deterministic system, it makes no sense to have multiple Monte Carlo runs, so we set `N=1`. We'll ignore the `M` key for now and set it to an empty `dict`" - ] + } }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, + "execution_count": 4, "outputs": [], "source": [ "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n", @@ -228,28 +226,30 @@ " #'M': {}\n", "}\n", "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # " - ] + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } }, { "cell_type": "markdown", + "source": [ + "# Putting it all together\n", + "We have defined the state variables of our system and their initial conditions, as well as the state update functions, which have been grouped in a single state update block. We have also specified the parameters of the simulation (number of timesteps and runs). We are now ready to put all those pieces together in a `Configuration` object." + ], "metadata": { + "collapsed": false, "pycharm": { "name": "#%% md\n" } - }, - "source": [ - "# Putting it all together\n", - "We have defined the state variables of our system and their initial conditions, as well as the state update functions, which have been grouped in a single state update block. We have also specified the parameters of the simulation (number of timesteps and runs). We are now ready to put all those pieces together in a `Configuration` object." - ] + } }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, + "execution_count": 5, "outputs": [], "source": [ "#imported some addition utilities to help with configuration set-up\n", @@ -266,28 +266,30 @@ " partial_state_update_blocks=partial_state_update_blocks, #dict containing state update functions\n", " sim_configs=c #preprocessed dictionaries containing simulation parameters\n", " )" - ] + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } }, { "cell_type": "markdown", + "source": [ + "# Running the engine\n", + "We are now ready to run the engine with the configuration defined above. Instantiate an ExecutionMode, an ExecutionContext and an Executor objects, passing the Configuration object to the latter. Then run the `execute()` method of the Executor object, which returns the results of the experiment in the first element of a tuple." + ], "metadata": { + "collapsed": false, "pycharm": { "name": "#%% md\n" } - }, - "source": [ - "# Running the engine\n", - "We are now ready to run the engine with the configuration defined above. Instantiate an ExecutionMode, an ExecutionContext and an Executor objects, passing the Configuration object to the latter. Then run the `execute()` method of the Executor object, which returns the results of the experiment in the first element of a tuple." - ] + } }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, + "execution_count": 6, "outputs": [], "source": [ "%%capture\n", @@ -299,35 +301,37 @@ "\n", "simulation = Executor(exec_context=local_mode_ctx, configs=exp.configs) # Pass the configuration object inside an array\n", "raw_system_events, tensor_field, sessions = simulation.execute() # The `execute()` method returns a tuple; its first elements contains the raw results" - ] + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } }, { "cell_type": "markdown", + "source": [ + "# Analyzing the results\n", + "We can now convert the raw results into a DataFrame for analysis" + ], "metadata": { + "collapsed": false, "pycharm": { "name": "#%% md\n" } - }, - "source": [ - "# Analyzing the results\n", - "We can now convert the raw results into a DataFrame for analysis" - ] + } }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, + "execution_count": 7, "outputs": [ { "data": { "text/plain": " box_A box_B simulation\nsubset run timestep substep \n0 1 0 0 10 0 0\n 1 1 9 1 0\n 2 1 8 2 0\n 3 1 7 3 0\n 4 1 6 4 0\n 5 1 5 5 0\n 6 1 5 5 0\n 7 1 5 5 0\n 8 1 5 5 0\n 9 1 5 5 0\n 10 1 5 5 0", "text/html": "
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" }, - "execution_count": 9, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -337,16 +341,17 @@ "import pandas as pd\n", "simulation_result = pd.DataFrame(raw_system_events)\n", "simulation_result.set_index(['subset', 'run', 'timestep', 'substep'])" - ] - }, - { - "cell_type": "code", - "execution_count": 10, + ], "metadata": { + "collapsed": false, "pycharm": { "name": "#%%\n" } - }, + } + }, + { + "cell_type": "code", + "execution_count": 8, "outputs": [ { "data": { @@ -364,27 +369,29 @@ " colormap = 'RdYlGn',\n", " xticks=list(simulation_result['timestep'].drop_duplicates()), \n", " yticks=list(range(1+(simulation_result['box_A']+simulation_result['box_B']).max())));" - ] + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } }, { "cell_type": "markdown", + "source": [ + "Because the number of marbles in the system is even, it converges to an equilibrium with 5 marbles in each box. Simulating a scenario with an odd number of marbles is as easy as modifying the `initial_condition` of the system, recreating the configuration object and rerunning the simulation:" + ], "metadata": { + "collapsed": false, "pycharm": { "name": "#%% md\n" } - }, - "source": [ - "Because the number of marbles in the system is even, it converges to an equilibrium with 5 marbles in each box. Simulating a scenario with an odd number of marbles is as easy as modifying the `initial_condition` of the system, recreating the configuration object and rerunning the simulation:" - ] + } }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, + "execution_count": 9, "outputs": [], "source": [ "%%capture\n", @@ -393,30 +400,31 @@ " 'box_B': 0\n", "}\n", "\n", - "del configs[:]\n", + "del exp.configs[:]\n", "exp.append_configs(initial_state=genesis_states, #dict containing variable names and initial values\n", " partial_state_update_blocks=partial_state_update_blocks, #dict containing state update functions\n", " sim_configs=c #preprocessed dictionaries containing simulation parameters\n", " )\n", "\n", "raw_result, tensor, sessions = simulation.execute()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, + ], "metadata": { + "collapsed": false, "pycharm": { "name": "#%%\n" } - }, + } + }, + { + "cell_type": "code", + "execution_count": 10, "outputs": [ { "data": { - "text/plain": " box_A box_B subset\nsimulation run timestep substep \n0 1 0 0 10 0 0\n 1 1 9 1 0\n 2 1 8 2 0\n 3 1 7 3 0\n 4 1 6 4 0\n 5 1 5 5 0\n 6 1 5 5 0\n 7 1 5 5 0\n 8 1 5 5 0\n 9 1 5 5 0\n 10 1 5 5 0\n1 1 0 0 11 0 0\n 1 1 10 1 0\n 2 1 9 2 0\n 3 1 8 3 0\n 4 1 7 4 0\n 5 1 6 5 0\n 6 1 5 6 0\n 7 1 6 5 0\n 8 1 5 6 0\n 9 1 6 5 0\n 10 1 5 6 0", - "text/html": "
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" }, - "execution_count": 12, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -424,21 +432,22 @@ "source": [ "simulation_result = pd.DataFrame(raw_result)\n", "simulation_result.set_index(['simulation', 'run', 'timestep', 'substep'])" - ] - }, - { - "cell_type": "code", - "execution_count": 13, + ], "metadata": { + "collapsed": false, "pycharm": { "name": "#%%\n" } - }, + } + }, + { + "cell_type": "code", + "execution_count": 11, "outputs": [ { "data": { "text/plain": "
", - "image/png": 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\n" 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\n" }, "metadata": { "needs_background": "light" @@ -451,7 +460,13 @@ " colormap = 'RdYlGn',\n", " xticks=list(simulation_result['timestep'].drop_duplicates()), \n", " yticks=list(range(1+(simulation_result['box_A']+simulation_result['box_B']).max())));" - ] + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } }, { "cell_type": "markdown",