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zBkLJ}X;T~14->ka#v*=9xsP4>l(5k>eZsQN0_!5P*ghr;s6f7r8<-pR{cQi`KoSP; z=M*Aq2Mg|ti1R$wcQ!_6pbTQ?E3K8IcedTZ;AiK_g(6+$`qnxtJd0>DkAAdoXll*v z${*6t8FV<3Sl@)^*&x9K5Me5ZM$%16~^f}?@7cm_2K(89MF|ZYv$PJ>jU6f=F>xh`N`?jG)0m%ksj$I@P1O~f_z6A`6oH*M zbWuJNGlk-AW0%rPPoWw5$T%p%)Z@?J_9{pBsxTt=;rXLB=c^I&T*;7gK0Wei&NzHXW m|J&>T{}(;KDF5$kdMu7EFE~igB$>s4KPYLXheeXc0sjMk%EpNR literal 0 HcmV?d00001 diff --git a/src/AoC_2023/Dazbo's_Advent_of_Code_2023.ipynb b/src/AoC_2023/Dazbo's_Advent_of_Code_2023.ipynb index f6ccdbe..1ca875d 100644 --- a/src/AoC_2023/Dazbo's_Advent_of_Code_2023.ipynb +++ b/src/AoC_2023/Dazbo's_Advent_of_Code_2023.ipynb @@ -7344,18 +7344,30 @@ "- It defines the module `name`.\n", "- It contains a list of `outputs`, which are stored as strings that can be used to retrieve the actual `Module`.\n", "- It contains a `ModuleManager`` which coordinates communication between modules.\n", - "- It has an internal `_send_pulse(high: bool)` method. This calls the `pulse_received()` method against each module in this module's `outputs` list. It also increments the relevant _sent counter_.\n", + "- It has a `send_pulse(high: bool)` method. This calls the `manager.send_pulse()` method against each module in this module's `outputs` list. (This is how onward pulses are enqueued.) It also increments the relevant _sent counter_.\n", + "- The `receive_pulse()` method is marked as _abstract_, meaning it MUST be implemented in any concrete subclasses. The job of the `receive_pulse()` method is to process the pulse received, update any internal state, and then call the `send_pulse()` method to enqueue the onward sending.\n", "\n", "Here, I've defined a subclass for each module type, i.e.\n", "\n", "- `FlipFlopModule` - This maintains internal state, and inverts its state (and sends a matching pulse) if it receives a low pulse.\n", "- `ConjunctionModule` - This remembers the last pulse received by each of its inputs, and stores these values in a dictionary. It sends a _low_ pulse if all of its remembered pulses are _high_. Essentially, it's a NAND gate.\n", - "- `Broadcaster` - This forwards on any pulse received to all _outputs_.\n" + "- `Broadcaster` - This forwards on any pulse received to all _outputs_.\n", + "\n", + "Then I defined a `ModuleManager` which acts as the central coordinator for the interactiosn between the modules of the system. \n", + "\n", + "- It is instantiated by passing in all of the modules of the system. \n", + " - The `ModuleManager` assigns itself as the manager to each module.\n", + " - It sets the `BroadcastModule`, since this unique for the system.\n", + " - It links all modules to any `ConjunctionModule` instances.\n", + "\n", + "- The fun stuff happens in the `push_button()` method. This simulates the initial kickoff of the cycle.\n", + " - It enqueues the initial sending of a pulse to the `BroadcastModule`, by placing the `(source, destination, pulse type)` on a queue implementing as a [deque](https://aoc.just2good.co.uk/python/lifo_fifo). This is what the `send_pulse()` method does.\n", + " - Then, it processes all pulses in the queue using a `while` loop. In the loop, we call `receive_pulse()` of the next input module from the queue. Calling `receive_pulse()` will generally enqueue further pulses. So this loop will continue to iterate until there are no further downstream pulses to process." ] }, { "cell_type": "code", - "execution_count": 63, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -7400,7 +7412,7 @@ " \"\"\" The ConjunctionModule implements NAND (Not AND) logic within our system. \n", " It stores the last received state of all of its inputs (defaulting to False). \"\"\"\n", " inputs: dict[str, bool] = field(default_factory=lambda: defaultdict(bool))\n", - " first_high_pulse: int | None = None # record when the first High pulse was received\n", + " high_pulse_sent: int | None = None # record when the first High pulse was received\n", "\n", " def add_input(self, input_name: str):\n", " self.inputs[input_name] = False\n", @@ -7408,9 +7420,9 @@ " def send_pulse(self, high: bool):\n", " assert self.manager is not None\n", " # If this module is sending a high pulse for the first time\n", - " if high and self.first_high_pulse is None:\n", + " if high and self.high_pulse_sent is None:\n", " # Save the index of this high pulse\n", - " self.first_high_pulse = self.manager.button_pushes\n", + " self.high_pulse_sent = self.manager.button_pushes\n", "\n", " return super().send_pulse(high)\n", "\n", @@ -7426,7 +7438,9 @@ " self.send_pulse(high)\n", "\n", "class ModuleManager:\n", - " \"\"\" Perform actions on behalf of the modules \"\"\"\n", + " \"\"\" Perform actions on behalf of the modules.\n", + " Registers itself as the manager of all modules. \n", + " Provides the capability to push the button and run the entire flow. \"\"\"\n", " \n", " def __init__(self, *modules: BaseModule):\n", " \"\"\" Initialise a ModuleManager by passing in a collection of Modules.\n", @@ -7501,19 +7515,20 @@ " return [module for module in self.modules.values()\n", " if output_name in module.outputs]\n", "\n", - "def parse_modules(lines: list[str]) -> ModuleManager:\n", - " \"\"\" Parse a list of lines into a collection of modules. \"\"\"\n", + "def parse_modules(lines: list[str]) -> list[BaseModule]:\n", + " \"\"\" Process the configuration to determine all the input and output modules.\n", + " Then use these to create the ModuleManager. \"\"\"\n", " modules: list[BaseModule] = []\n", "\n", " for line in lines:\n", - " module_part, outputs_part = line.split(\" -> \")\n", - " outputs = outputs_part.split(\", \")\n", + " module_part, outputs_part = [part.strip() for part in line.split(\"->\")]\n", + " outputs = [part.strip() for part in outputs_part.split(\",\")]\n", "\n", " if module_part == \"broadcaster\":\n", " module = BroadcastModule(module_part, outputs)\n", " else:\n", " module_type, module_part = module_part[0], module_part[1:]\n", - " match module_type:\n", + " match module_type: # implement a switch-case\n", " case \"%\":\n", " module = FlipFlopModule(module_part, outputs)\n", " case \"&\":\n", @@ -7523,17 +7538,19 @@ "\n", " modules.append(module)\n", "\n", - " return ModuleManager(*modules)\n" + " return modules" ] }, { "cell_type": "code", - "execution_count": 64, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def solve_part1(data: list[str]):\n", - " manager = parse_modules(data)\n", + " \"\"\" Count pulses after 1000 button pushes \"\"\"\n", + " modules = parse_modules(data)\n", + " manager = ModuleManager(*modules)\n", " \n", " for _ in range(1000):\n", " manager.push_button()\n", @@ -7544,26 +7561,9 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m21:51:15.657:aoc2023 - INF: Tests passed!\u001b[39m\n", - "\u001b[32m21:51:15.692:aoc2023 - INF: Part 1 soln=898731036\u001b[39m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: total: 0 ns\n", - "Wall time: 47.3 ms\n" - ] - } - ], + "outputs": [], "source": [ "%%time\n", "sample_inputs = []\n", @@ -7610,12 +7610,30 @@ "\n", "Plan B...\n", "\n", - "I need to understand the inputs to _rx_ and try to understand what's going on." + "I need to understand the inputs to _rx_ and try to understand what's going on. I started by drawing a graph of the configuration. I used [NetworkX](https://aoc.just2good.co.uk/python/networkx) to do this. With `NetworkX`, it's trivial to build a graph that contains vertices (the string names of the modules) and the edges between the verticies.\n", + "\n", + "![Graph of module configuration](https://aoc.just2good.co.uk/assets/images/module_graph.png)\n", + "\n", + "After drawing the graph, we can see that:\n", + "\n", + "- _rx_ has a single input, called _ns_. The module _ns_ is a `ConjunctionModule`.\n", + "- The module _ns_ itself has our inputs, which are themselves Conjunction modules.\n", + "\n", + "So now we can code a solution. We know that each of these inputs will periodically sent a High pulse to _ns_. We need to determine the button press that causes all four inputs to send High at the same time. We know we can't brute-force this answer. But we can determine the number of button presses required for _each_ input to send a High. So if we take the lowest common multiple (LCM) of these four numbers, we will get the number where all four inputs coincide.\n", + "\n", + "So here's how we do it:\n", + "\n", + "- Retrieve the input of _rx_. Check that there is only one, and that it is a `ConjunctionModule`.\n", + "- Now get the inputs to _that_ input. Check that these are also of type `ConjunctionModule`.\n", + "- Push the button until each of these inputs has its `high_pulse_sent` set. If it is, it will be set to the number of button presses that was required to set this particular input to _High_. When this is true for all inputs, then we have the number of button presses that was required to for each to send a high pulse.\n", + "- It is now trivial to get the LCM. We just call the `math.lcm()` function, and pass in the four button press values.\n", + "\n", + "And that's it!" ] }, { "cell_type": "code", - "execution_count": 66, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -7638,9 +7656,12 @@ " \n", " return graph\n", "\n", - "def draw_nx_graph(rx_input_id, graph):\n", + "def draw_nx_graph(rx_input_id, graph: nx.DiGraph):\n", " pos = nx.spring_layout(graph)\n", " \n", + " # Find upstream nodes of rx_input_id\n", + " immediate_predecessors = set(graph.predecessors(rx_input_id))\n", + " \n", " # Create a list of colors for each node\n", " node_colors = []\n", " for node in graph.nodes():\n", @@ -7648,6 +7669,8 @@ " node_colors.append(\"red\")\n", " elif node == rx_input_id:\n", " node_colors.append(\"yellow\")\n", + " elif node in immediate_predecessors:\n", + " node_colors.append(\"green\")\n", " else:\n", " node_colors.append(\"lightblue\")\n", " \n", @@ -7659,73 +7682,46 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def solve_part2(data: list[str]) -> int:\n", - " \"\"\" When is a low pulse sent to rx?\n", - " - rx has a single input, which is a Conjunction\n", - " - This conjunction has four inputs which are also Conjunctions\n", - " - These inputs send High periodically.\n", - " - Determine when these periods coincide with LCM.\n", - " \"\"\"\n", - " manager = parse_modules(data)\n", + " \"\"\" When is a low pulse sent to rx? \"\"\"\n", + " modules = parse_modules(data)\n", + " manager = ModuleManager(*modules)\n", "\n", " # Get the input to \"rx\"\n", " (input_module,) = manager.get_input_modules(\"rx\")\n", + " assert isinstance(input_module, ConjunctionModule)\n", " input_module = cast(ConjunctionModule, input_module)\n", - " \n", - " # And now the inputs to that Conjunction module...\n", - " pre_input_modules = cast(list[ConjunctionModule],\n", - " manager.get_input_modules(input_module.name))\n", "\n", " # Visualise with a plot\n", " graph = build_nx_module_graph(manager.modules)\n", " draw_nx_graph(input_module.name, graph)\n", + " \n", + " # And now the inputs to that ConjunctionModule...\n", + " inputs_to_rx_input = manager.get_input_modules(input_module.name)\n", + " for mod in inputs_to_rx_input: # check these are also all ConjunctionModules\n", + " assert isinstance(mod, ConjunctionModule)\n", + "\n", + " inputs_to_rx_input = [cast(ConjunctionModule, mod) for mod in inputs_to_rx_input]\n", "\n", " # Push the button until every relevant module outputs a high pulse\n", - " while any(module.first_high_pulse is None for module in pre_input_modules):\n", + " while any(module.high_pulse_sent is None for module in inputs_to_rx_input):\n", " manager.push_button()\n", " \n", - " # Get the LCM of the period lengths\n", - " first_high_pulses = cast(list[int],\n", - " [module.first_high_pulse for module in pre_input_modules])\n", - " \n", - " return math.lcm(*first_high_pulses)" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m21:51:22.571:aoc2023 - INF: Part 2 soln=229414480926893\u001b[39m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: total: 297 ms\n", - "Wall time: 491 ms\n" - ] - } - ], + " high_pulses_sent = [int(module.high_pulse_sent) for module in inputs_to_rx_input\n", + " if module.high_pulse_sent is not None]\n", + " \n", + " return math.lcm(*high_pulses_sent)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "%%time\n", "soln = solve_part2(input_data)\n",