diff --git a/docs/_static/batch_size.png b/docs/_static/batch_size.png new file mode 100644 index 000000000..48c0c0a2b Binary files /dev/null and b/docs/_static/batch_size.png differ diff --git a/docs/_static/cpu_gpu.png b/docs/_static/cpu_gpu.png new file mode 100644 index 000000000..8213b2473 Binary files /dev/null and b/docs/_static/cpu_gpu.png differ diff --git a/docs/_static/isi_distribution.png b/docs/_static/isi_distribution.png index 696ae96de..67171a89f 100644 Binary files a/docs/_static/isi_distribution.png and b/docs/_static/isi_distribution.png differ diff --git a/docs/_static/synaptic_scaling.png b/docs/_static/synaptic_scaling.png new file mode 100644 index 000000000..c2fd9ae48 Binary files /dev/null and b/docs/_static/synaptic_scaling.png differ diff --git a/docs/benchmarks/benchmarks.rst b/docs/benchmarks/benchmarks.rst new file mode 100644 index 000000000..4e30c1931 --- /dev/null +++ b/docs/benchmarks/benchmarks.rst @@ -0,0 +1,68 @@ +############################ +Memory usage and performance +############################ + +.. currentmodule:: spikeometric.models + +In this section, we discuss the memory usage and performance of core spikeometric functionality. +The benchmarks are run in `Google Colab `_ and the code is available in the `examples` folder of the +`GitHub repository `_. The GPU used for the experiments in this section is the Nvidia V100 with 16GB of memory +and the CPU is the Intel Xeon with 13GB of RAM. + +Memory usage +------------ +The main memory consumer when running a simulation is the synaptic weights. The GLM models support time filters on the synaptic weights +to capture persisting effects of the pre-synaptic spikes over time, while the synaptic activation models (:class:`ThresholdSAM` and :class:`RectifiedSAM`) +only consider spike-events from the previous time step. In general, the weights are stored as a :math:`[N_{syn}, T]` tensor, where :math:`N_{syn}` is the number of synapses +and :math:`T` is the length of the time filter. In addition, we need an edge index to specify the pre- and post-synaptic neurons of each synapse, which is stored as a :math:`[2, N_{syn}]` tensor. +One exception is the :class:`BernoulliGLM`, which also includes a refractory period modeled as a negative self-connection, adding +an additional :math:`N_{neurons}` synapses. + +We also need to store the number of spikes of each neuron per time step, which by default consumes 32 bytes. +In most cases, however, we don't expect the number of spikes per time step for any neuron to exceed 127, which means we can safely reduce the memory conumption to +8 bytes by passing :code:`torch.int8` as the :code:`store_as_dtype` argument of the :meth:`simulate` method if we need additional memory. + +Concretely, the total memory usage (in bytes) of GLM models can be estimated as + +.. math:: + M_{GLM} = 32 \cdot T \cdot N_{syn} + 32 \cdot 2 \cdot N_{syn} + 8 \cdot N_{neurons} \cdot N_{steps} + +where :math:`N_{syn}` is the number of synapses, :math:`N_{neurons}` is the number of neurons, and :math:`N_{steps}` is the +number. For :class:`BernoulliGLM`, we simply add on another :math:`32 \cdot T \cdot N_{neurons}`. +In addition to the spikes, the synaptic activation models also need to store a 32 byte floating-point state for each neuron per time step. +The memory usage of the synaptic activation models can therefore be estimated as + +.. math:: + M_{SAM} = 32 \cdot T \cdot N_{syn} + 32 \cdot 2 \cdot N_{syn} + 8 \cdot N_{neurons} \cdot N_{steps} + 32 \cdot N_{neurons} \cdot N_{steps} + + +Performance +----------- +The performance of the Spikeometric models is measured in terms of the time per iteration. Up to ~2 500 000 synapses, +the time per iteration is roughly constant at ~0.3 ms per iteration for most models, with the :class:`ThresholdSAM` being slightly slower due to +due to the cost of computing its more involved background input. If we run the models for 1000 iterations +for networks with ~2 500 000 synapses up to ~50 000 000 synapses, we can see that the run time eventually increases +approximately linearly with the number of synapses. We also run experiments where we increase +the number of time steps in the time filters up to 100 in steps of 5 for a network of ~2 500 000 synapses. The results are shown in the figure below. + +.. image:: ../_static/synaptic_scaling.png + + +CPU vs GPU +---------- + +Spikeometric is designed to work well with both CPU and GPU architectures, but utilizing a GPU +brings significant performance benefits when simulating larger networks. +For networks with up to 10 000 synapses, the CPU is faster due to overhead on the GPU, but while time per iteration remains +constant at about 0.3 ms up to about 2 500 000 synapses on the GPU, it increases from 0.15 ms at 1000 synapses to 30 ms per iteration at 2 500 000 synapses on the CPU. + +.. figure:: ../_static/cpu_gpu.png + + +Speed-up from batching +---------------------- +The performance of the Spikeometric models can be further improved by batching the simulation of multiple networks. For example, using +the :class:`BernoulliGLM` model, simulating networks with 2 500 000 synapses can be improved to ~0.15 ms per iteration on average +by batching at least 10 networks at a time. The speed-up from batching is shown in the figure below. + +.. image:: ../_static/batch_size.png \ No newline at end of file diff --git a/docs/index.rst b/docs/index.rst index 36c793148..e623bb258 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -44,6 +44,12 @@ Then you can install `spikeometric` using pip: tutorials/implement_model tutorials/stimuli +.. toctree:: + :maxdepth: 1 + :caption: Benchmarks + + benchmarks/benchmarks + .. toctree:: :maxdepth: 1 :caption: API Reference diff --git a/docs/introduction/introduction.rst b/docs/introduction/introduction.rst index 9c4080820..9d38a67a8 100644 --- a/docs/introduction/introduction.rst +++ b/docs/introduction/introduction.rst @@ -22,6 +22,8 @@ is invertible. Introductory example ==================== +.. currentmodule:: spikeometric.datasets +.. currentmodule:: spikeometric.stimulus .. currentmodule:: spikeometric.models .. note:: @@ -118,11 +120,12 @@ For each of our ten networks, we will stimulate 4 random excitatory neurons. stimulus_masks = [torch.isin(torch.arange(n_neurons), torch.randperm(n_neurons//2)[:4]) for _ in range(10)] stimulus = RegularStimulus( strength=5.0, - interval=100, - duration=100_000, + period=100, + stop=100_000, tau=10, dt=1, stimulus_masks=stimulus_masks, + batch_size=5 ) model.add_stimulus(stimulus) diff --git a/docs/modules/generated/spikeometric.stimulus.BaseStimulus.rst b/docs/modules/generated/spikeometric.stimulus.BaseStimulus.rst new file mode 100644 index 000000000..2cdcd27c9 --- /dev/null +++ b/docs/modules/generated/spikeometric.stimulus.BaseStimulus.rst @@ -0,0 +1,9 @@ +BaseStimulus +============ + +.. currentmodule:: spikeometric.stimulus + +.. autoclass:: BaseStimulus + :show-inheritance: + :members: + :undoc-members: \ No newline at end of file diff --git a/docs/modules/generated/spikeometric.stimulus.LoadedStimulus.rst b/docs/modules/generated/spikeometric.stimulus.LoadedStimulus.rst new file mode 100644 index 000000000..3a2dbac73 --- /dev/null +++ b/docs/modules/generated/spikeometric.stimulus.LoadedStimulus.rst @@ -0,0 +1,9 @@ +LoadedStimulus +============== + +.. currentmodule:: spikeometric.stimulus + +.. autoclass:: LoadedStimulus + :show-inheritance: + :members: + :undoc-members: \ No newline at end of file diff --git a/docs/modules/stimulus.rst b/docs/modules/stimulus.rst index 342e6f4fd..3a72abda5 100644 --- a/docs/modules/stimulus.rst +++ b/docs/modules/stimulus.rst @@ -5,13 +5,15 @@ Stimulus The stimlation classes are used to define the external input to the network. They inherit from the :class:`Module` class from torch, which allows us to tune the parameters of the stimulus with the usual torch methods. A couple of stimulus models are provided in the package, but it is easy to implement -new ones by taking inspiration from the existing ones and extending the :class:`Module` class from torch. +new ones by taking inspiration from the existing ones and extending the :class:`BaseStimulus` class. .. autosummary:: :toctree: generated/ :nosignatures: :template: autosummary/class.rst - + + BaseStimulus PoissonStimulus RegularStimulus - SinStimulus \ No newline at end of file + SinStimulus + LoadedStimulus \ No newline at end of file diff --git a/docs/tutorials/implement_model.rst b/docs/tutorials/implement_model.rst index d9944ab12..894501888 100644 --- a/docs/tutorials/implement_model.rst +++ b/docs/tutorials/implement_model.rst @@ -111,13 +111,13 @@ Stimulus filter ---------------- We want the model to handle a multidimensional stimulus given to some of the neurons, and must implement a :py:meth:`stimulus_filter` method to determine how this stimulus will be integrated into an input. -The function :py:func:`k` that we pass to the model will help us with this. -Note that we are assuming that all targeted neurons receive the same stimulus and that they have the same stimulus filter. +The function :py:func:`k` that we pass to the model will decide how the different channels in the multidimensional stimulus are weighted. +Note that we are assuming that all targeted neurons they have the same stimulus filter. .. code-block:: python def stimulus_filter(self, stimulus: torch.Tensor) -> torch.Tensor: - return torch.sum(stimulus*self.k(torch.arange(stimulus.shape[0]))) + return torch.sum(stimulus*self.k(torch.arange(stimulus.shape[1])), dim=1) Input ------ @@ -133,7 +133,7 @@ Non-linearity -------------- After we have collected the input to the neurons, we need to apply a non-linearity to it. In this case, we use a rectified linear non-linearity. The rate is scaled by the time step to ensure that increasing the resolution -of the simulation doesn't artificially increase the firing rate. +of the simulation doesn't artificially change the firing rate. .. code-block:: python @@ -196,7 +196,7 @@ Summary return W.flip(1) def stimulus_filter(self, stimulus: torch.Tensor) -> torch.Tensor: - return torch.sum(stimulus*self.k(torch.arange(stimulus.shape[0]))) + return torch.sum(stimulus*self.k(torch.arange(stimulus.shape[1])), dim=1) def input(self, edge_index: torch.Tensor, W: torch.Tensor, state: torch.Tensor, t=-1) -> torch.Tensor: return self.synaptic_input(edge_index, W, state) + self.stimulus_input(t) @@ -209,30 +209,39 @@ Summary Testing the model ------------------ -To test the model, we will simulate some networks with 50 neurons. We will use a random connectivity matrix with normal weights and a random periodic stimulus. +To test the model, we will simulate some networks with 50 neurons. We will use a random connectivity matrix with normal weights and a periodic stimulus +with random amplitudes in five frequency bins. Since we are batching the simulations in groups of 2 networks, +we will first generate a stimulus plan and make use of the :py:class:`LoadedStimulus` class to take care of the batching for us. .. code-block:: n_neurons = 50 - test_data = NormalGenerator(n_neurons, mean=0, std=0.1, glorot=True).generate(10) - loader = DataLoader(test_data, batch_size=5, shuffle=False) + test_data = NormalGenerator(n_neurons, mean=0, std=0.5, glorot=True).generate(10) + loader = DataLoader(test_data, batch_size=2, shuffle=False) def r(t): - return -100.*(t < 2) + -100*torch.exp(-2*(t-2))*(t >= 2)*(t<5) + return -1000.*(t < 2) + -1000*torch.exp(-2*(t-2))*(t >= 2)*(t<5) def w(t): return torch.exp(-t/2) def k(f): - return torch.exp(-f/5) # weight lower frequencies more + return torch.exp(-f) - def stimulus(t): - targets = torch.isin(torch.arange(50), torch.arange(50)[::10]).unsqueeze - return 0.1*torch.rand(5) * (t % 100 < 20)*targets + + stimulus_plan = torch.zeros(50, 500, 5, 10) # 50 neurons, 500 time steps, 5 frequency bins, 10 networks + t = torch.arange(500) + for i in range(10): + # For each network, we stimulate the first 10 neurons (out of 50), for 20 time steps every 100 time steps, with random amplitudes in five frequency bins. + # The dimensions returned are [n_neurons, n_time_steps (length of t), n_frequency_bins] + stimulus_plan[:, :, :, i] = ((torch.arange(50) < 10).unsqueeze(1) * (t % 100 < 20)).unsqueeze(2)*torch.rand(50, t.shape[0], 5) + + torch.save(stimulus_plan, "stimulus_plan.pt") + stimulus = LoadedStimulus("stimulus_plan.pt", batch_size=2) model = FilRectLNP(lambda_0=1, theta=-0.01, dt=1, T=20, r=r, w=w, k=k) -Since we don't know what parameters to use, we'll make a guess and then tune them to give a firing rate of 10 Hz. +We don't know what parameters to use, so we'll make a guess and then tune them to give a firing rate of 10 Hz. We'll use a learning rate of 1e-4 and train for 100 epochs with 500 time steps per epoch. Note that the model is not trained to fit a set of spike trains, but rather to give a firing rate of 10 Hz. Note also that we might need to try a few different inital parameters for the model not to immediately blow up. @@ -250,9 +259,10 @@ We can now add the stimulus and simulate the model on the data. .. code-block:: model.add_stimulus(stimulus) - results = torch.zeros((len(test_data), n_neurons)) + n_steps = 10_000 + results = torch.zeros((50*10, n_steps)) for i, data in enumerate(loader): - results[i*data.num_nodes:(i+1)*data.num_nodes] = model.simulate(data, n_steps=500) + results[i*data.num_nodes:(i+1)*data.num_nodes] = model.simulate(data, n_steps=n_steps) And plot the results to get the following PSTH, ISI and raster plot diff --git a/docs/tutorials/stimuli.rst b/docs/tutorials/stimuli.rst index 7f28926ea..e7ab102f6 100644 --- a/docs/tutorials/stimuli.rst +++ b/docs/tutorials/stimuli.rst @@ -132,7 +132,7 @@ Synchronizing the network activity with a stimulus -------------------------------------------------- In this final example we'll try to synchronize the activity of a network with the frequency of a sinusoidal stimulus. -We'll use the :py:class:`SinusoidalStimulus` to model the stimulus, and tune it to achieve an average firing rate of the network +We'll use the :py:class:`SinStimulus` to model the stimulus, and tune it to achieve an average firing rate of the network that is close to the frequency of the stimulus. .. code-block:: python @@ -162,7 +162,8 @@ that is close to the frequency of the stimulus. amplitude=1, period=100, # frequency = 1/period = 10 Hz duration=n_steps, - stimulus_masks=stim_masks + stimulus_masks=stim_masks, + batch_size=10, ) model.add_stimulus(stimulus) diff --git a/environment.yml b/environment.yml index 3d2488558..2b0af879a 100644 --- a/environment.yml +++ b/environment.yml @@ -8,8 +8,8 @@ channels: dependencies: - python - pip - - pytorch=1.13 + - pytorch - cpuonly - pyg - pip: - - -r requirements.txt + - -r requirements_dev.txt diff --git a/examples/implement_new_model.ipynb b/examples/implement_new_model.ipynb index 86f0cce6a..14d5b55da 100644 --- a/examples/implement_new_model.ipynb +++ b/examples/implement_new_model.ipynb @@ -24,7 +24,8 @@ "\n", "sys.path.append(\"../\")\n", "from spikeometric.models import BaseModel\n", - "from spikeometric.datasets import NormalGenerator" + "from spikeometric.datasets import NormalGenerator\n", + "from spikeometric.stimulus import LoadedStimulus" ] }, { @@ -185,7 +186,7 @@ "outputs": [], "source": [ "def stimulus_filter(self, stimulus: torch.Tensor) -> torch.Tensor:\n", - " return torch.sum(stimulus*self.k(torch.arange(stimulus.shape[0])))" + " return torch.sum(stimulus*self.k(torch.arange(stimulus.shape[1])), dim=1)" ] }, { @@ -256,7 +257,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -301,7 +302,7 @@ " def stimulus_filter(self, stimulus: torch.Tensor) -> torch.Tensor:\n", " if stimulus.ndim == 0:\n", " stimulus = stimulus.unsqueeze(0).unsqueeze(1)\n", - " return torch.sum(stimulus*self.k(torch.arange(stimulus.shape[1])))\n", + " return torch.sum(stimulus*self.k(torch.arange(stimulus.shape[1])), dim=1)\n", " \n", " def input(self, edge_index: torch.Tensor, W: torch.Tensor, state: torch.Tensor, t=-1) -> torch.Tensor:\n", " return self.synaptic_input(edge_index, W, state) + self.stimulus_input(t)\n", @@ -318,14 +319,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We'll run our model on a dataset of 10 networks with 50 neurons each, with weights drawn from a glorot normal distribution. There are an equal amount of exciitatory and inhibitory neurons, and the networks are 50% sparse.\n", + "We'll run our model on a dataset of 10 networks with 50 neurons each, with weights drawn from a glorot normal distribution. There are an equal amount of excitatory and inhibitory neurons, and the networks are 50% sparse.\n", "\n", - "For the filters we'll use exponential filters both for the synaptic weights and the stimulus filter. The stimulus will be noisy periodic in 10 frequency bins with a 20 ms stimulus every 100 ms. " + "For the filters we'll use exponential filters both for the synaptic weights and the stimulus filter. The stimulus will be noisy periodic in 5 frequency bins with a 20 ms stimulus every 100 ms, and only the 10 first neurons in each network will receive stimulus. For convenience, we first generate the entire stimulus plan, and then load it into the `LoadedStimulus` class to handle the batching." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 55, "metadata": {}, "outputs": [], "source": [ @@ -342,8 +343,15 @@ "def k(f):\n", " return torch.exp(-f)\n", "\n", - "def stimulus(t):\n", - " return (torch.arange(50*2) % 50 < 5).unsqueeze(1) * (0.05*torch.rand(5) * (t % 100 < 20))\n", + "stimulus_plan = torch.zeros(50, 500, 5, 10) # 50 neurons, 500 time steps, 5 frequency bins, 10 networks\n", + "t = torch.arange(500)\n", + "for i in range(10):\n", + " # For each network, we stimulate the first 10 neurons (out of 50), for 20 time steps every 100 time steps, with random amplitudes in five frequency bins.\n", + " # The dimensions returned are [n_neurons, n_time_steps (length of t), n_frequency_bins]\n", + " stimulus_plan[:, :, :, i] = ((torch.arange(50) < 10).unsqueeze(1) * (t % 100 < 20)).unsqueeze(2)*torch.rand(50, t.shape[0], 5)\n", + "\n", + "torch.save(stimulus_plan, \"stimulus_plan.pt\")\n", + "stimulus = LoadedStimulus(\"stimulus_plan.pt\", batch_size=2)\n", "\n", "model = FilRectLNP(lambda_0=1, theta=-0.01, dt=1, T=20, r=r, w=w, k=k)" ] @@ -358,18 +366,18 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Tuning... fr=2.04912: 100%|\u001b[38;2;62;86;65m██████████\u001b[0m| 100/100 [00:12<00:00, 8.32it/s]\n", - "Tuning... fr=9.66347: 100%|\u001b[38;2;62;86;65m██████████\u001b[0m| 100/100 [00:11<00:00, 8.77it/s]\n", - "Tuning... fr=10.14253: 100%|\u001b[38;2;62;86;65m██████████\u001b[0m| 100/100 [00:11<00:00, 8.84it/s]\n", - "Tuning... fr=10.14071: 100%|\u001b[38;2;62;86;65m██████████\u001b[0m| 100/100 [00:11<00:00, 8.74it/s]\n", - "Tuning... fr=10.06790: 100%|\u001b[38;2;62;86;65m██████████\u001b[0m| 100/100 [00:11<00:00, 9.05it/s]\n" + "Tuning... fr=9.15807: 100%|\u001b[38;2;62;86;65m██████████\u001b[0m| 100/100 [00:13<00:00, 7.34it/s]\n", + "Tuning... fr=9.67873: 100%|\u001b[38;2;62;86;65m██████████\u001b[0m| 100/100 [00:13<00:00, 7.45it/s]\n", + "Tuning... fr=9.86260: 100%|\u001b[38;2;62;86;65m██████████\u001b[0m| 100/100 [00:14<00:00, 7.12it/s]\n", + "Tuning... fr=9.64684: 100%|\u001b[38;2;62;86;65m██████████\u001b[0m| 100/100 [00:13<00:00, 7.29it/s]\n", + "Tuning... fr=9.38356: 100%|\u001b[38;2;62;86;65m██████████\u001b[0m| 100/100 [00:13<00:00, 7.21it/s]\n" ] } ], @@ -388,30 +396,56 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "model.add_stimulus(stimulus)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([100])" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.stimulus_input(1).shape" + ] + }, + { + "cell_type": "code", + "execution_count": 50, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", - "text": [ - " 0%|\u001b[38;2;62;86;65m \u001b[0m| 0/10000 [00:00" ] diff --git a/gpu_environment.yml b/gpu_environment.yml index c47ca3fb4..790876d3b 100644 --- a/gpu_environment.yml +++ b/gpu_environment.yml @@ -12,7 +12,7 @@ dependencies: - pip - python - pytorch-cuda=11.7 - - pytorch=1.13 + - pytorch - pyg pip: - - -r requirements.txt \ No newline at end of file + - -r requirements_dev.txt \ No newline at end of file diff --git a/poetry.lock b/poetry.lock index e3e268f3d..2e9124ada 100644 --- a/poetry.lock +++ b/poetry.lock @@ -14,25 +14,25 @@ files = [ [[package]] name = "anyio" -version = "3.7.0" +version = "4.0.0" description = "High level compatibility layer for multiple asynchronous event loop implementations" category = "dev" optional = false -python-versions = ">=3.7" +python-versions = ">=3.8" files = [ - 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"sha256:46ddef89cc2416a81ecfbeaceab1881948c014b1b6e4450b815311a89fb977b0"}, + {file = "sphinx_rtd_theme-1.3.0.tar.gz", hash = "sha256:590b030c7abb9cf038ec053b95e5380b5c70d61591eb0b552063fbe7c41f0931"}, ] [package.dependencies] docutils = "<0.19" -sphinx = ">=1.6,<7" +sphinx = ">=1.6,<8" sphinxcontrib-jquery = ">=4,<5" [package.extras] @@ -2819,26 +2819,6 @@ files = [ lint = ["docutils-stubs", "flake8", "mypy"] test = ["pytest"] -[[package]] -name = "stack-data" -version = "0.6.2" -description = "Extract data from python stack frames and tracebacks for informative displays" -category = "dev" -optional = false -python-versions = "*" -files = [ - {file = "stack_data-0.6.2-py3-none-any.whl", hash = "sha256:cbb2a53eb64e5785878201a97ed7c7b94883f48b87bfb0bbe8b623c74679e4a8"}, - {file = "stack_data-0.6.2.tar.gz", hash = "sha256:32d2dd0376772d01b6cb9fc996f3c8b57a357089dec328ed4b6553d037eaf815"}, -] - -[package.dependencies] -asttokens = ">=2.1.0" -executing = ">=1.2.0" -pure-eval = "*" - -[package.extras] -tests = ["cython", "littleutils", "pygments", "pytest", "typeguard"] - [[package]] name = "terminado" version = "0.17.1" @@ -2926,21 +2906,21 @@ files = [ [[package]] name = "tqdm" -version = "4.65.0" +version = "4.66.1" description = "Fast, Extensible Progress Meter" category = "main" optional = false python-versions = ">=3.7" files = [ - {file = "tqdm-4.65.0-py3-none-any.whl", hash = "sha256:c4f53a17fe37e132815abceec022631be8ffe1b9381c2e6e30aa70edc99e9671"}, - {file = "tqdm-4.65.0.tar.gz", hash = "sha256:1871fb68a86b8fb3b59ca4cdd3dcccbc7e6d613eeed31f4c332531977b89beb5"}, + {file = "tqdm-4.66.1-py3-none-any.whl", hash = "sha256:d302b3c5b53d47bce91fea46679d9c3c6508cf6332229aa1e7d8653723793386"}, + {file = "tqdm-4.66.1.tar.gz", hash = "sha256:d88e651f9db8d8551a62556d3cff9e3034274ca5d66e93197cf2490e2dcb69c7"}, ] [package.dependencies] colorama = {version = "*", markers = "platform_system == \"Windows\""} [package.extras] -dev = ["py-make (>=0.1.0)", "twine", "wheel"] +dev = ["pytest (>=6)", "pytest-cov", "pytest-timeout", "pytest-xdist"] notebook = ["ipywidgets (>=6)"] slack = ["slack-sdk"] telegram = ["requests"] @@ -2961,30 +2941,6 @@ files = [ docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"] test = ["argcomplete (>=2.0)", "pre-commit", "pytest", "pytest-mock"] -[[package]] -name = "typing-extensions" -version = "4.6.3" -description = "Backported and Experimental Type Hints for Python 3.7+" -category = "dev" -optional = false -python-versions = ">=3.7" -files = [ - {file = "typing_extensions-4.6.3-py3-none-any.whl", hash = "sha256:88a4153d8505aabbb4e13aacb7c486c2b4a33ca3b3f807914a9b4c844c471c26"}, - {file = "typing_extensions-4.6.3.tar.gz", hash = "sha256:d91d5919357fe7f681a9f2b5b4cb2a5f1ef0a1e9f59c4d8ff0d3491e05c0ffd5"}, -] - -[[package]] -name = "tzdata" -version = "2023.3" -description = "Provider of IANA time zone data" -category = "main" -optional = false -python-versions = ">=2" -files = [ - {file = "tzdata-2023.3-py2.py3-none-any.whl", hash = "sha256:7e65763eef3120314099b6939b5546db7adce1e7d6f2e179e3df563c70511eda"}, - {file = "tzdata-2023.3.tar.gz", hash = "sha256:11ef1e08e54acb0d4f95bdb1be05da659673de4acbd21bf9c69e94cc5e907a3a"}, -] - [[package]] name = "uri-template" version = "1.3.0" @@ -3002,14 +2958,14 @@ dev = ["flake8", "flake8-annotations", "flake8-bandit", "flake8-bugbear", "flake [[package]] name = "urllib3" -version = "2.0.3" +version = "2.0.4" description = "HTTP library with thread-safe connection pooling, file post, and more." category = "dev" optional = false python-versions = ">=3.7" files = [ - {file = "urllib3-2.0.3-py3-none-any.whl", hash = "sha256:48e7fafa40319d358848e1bc6809b208340fafe2096f1725d05d67443d0483d1"}, - {file = "urllib3-2.0.3.tar.gz", hash = "sha256:bee28b5e56addb8226c96f7f13ac28cb4c301dd5ea8a6ca179c0b9835e032825"}, + {file = "urllib3-2.0.4-py3-none-any.whl", hash = "sha256:de7df1803967d2c2a98e4b11bb7d6bd9210474c46e8a0401514e3a42a75ebde4"}, + {file = "urllib3-2.0.4.tar.gz", hash = "sha256:8d22f86aae8ef5e410d4f539fde9ce6b2113a001bb4d189e0aed70642d602b11"}, ] [package.extras] @@ -3060,50 +3016,50 @@ files = [ [[package]] name = "websocket-client" -version = "1.6.1" +version = "1.6.2" description = "WebSocket client for Python with low level API options" category = "dev" optional = false -python-versions = ">=3.7" +python-versions = ">=3.8" files = [ - {file = "websocket-client-1.6.1.tar.gz", hash = "sha256:c951af98631d24f8df89ab1019fc365f2227c0892f12fd150e935607c79dd0dd"}, - {file = "websocket_client-1.6.1-py3-none-any.whl", hash = "sha256:f1f9f2ad5291f0225a49efad77abf9e700b6fef553900623060dad6e26503b9d"}, + {file = "websocket-client-1.6.2.tar.gz", hash = "sha256:53e95c826bf800c4c465f50093a8c4ff091c7327023b10bfaff40cf1ef170eaa"}, + {file = "websocket_client-1.6.2-py3-none-any.whl", hash = "sha256:ce54f419dfae71f4bdba69ebe65bf7f0a93fe71bc009ad3a010aacc3eebad537"}, ] [package.extras] -docs = ["Sphinx (>=3.4)", "sphinx-rtd-theme (>=0.5)"] +docs = ["Sphinx (>=6.0)", "sphinx-rtd-theme (>=1.1.0)"] optional = ["python-socks", "wsaccel"] test = ["websockets"] [[package]] name = "widgetsnbextension" -version = "4.0.7" +version = "4.0.8" description = "Jupyter interactive widgets for Jupyter Notebook" category = "dev" optional = false python-versions = ">=3.7" files = [ - {file = "widgetsnbextension-4.0.7-py3-none-any.whl", hash = "sha256:be3228a73bbab189a16be2d4a3cd89ecbd4e31948bfdc64edac17dcdee3cd99c"}, - {file = "widgetsnbextension-4.0.7.tar.gz", hash = "sha256:ea67c17a7cd4ae358f8f46c3b304c40698bc0423732e3f273321ee141232c8be"}, + {file = "widgetsnbextension-4.0.8-py3-none-any.whl", hash = "sha256:2e37f0ce9da11651056280c7efe96f2db052fe8fc269508e3724f5cbd6c93018"}, + {file = "widgetsnbextension-4.0.8.tar.gz", hash = "sha256:9ec291ba87c2dfad42c3d5b6f68713fa18be1acd7476569516b2431682315c17"}, ] [[package]] name = "zipp" -version = "3.15.0" +version = "3.16.2" description = "Backport of pathlib-compatible object wrapper for zip files" category = "main" optional = false -python-versions = ">=3.7" +python-versions = ">=3.8" files = [ - {file = "zipp-3.15.0-py3-none-any.whl", hash = "sha256:48904fc76a60e542af151aded95726c1a5c34ed43ab4134b597665c86d7ad556"}, - {file = "zipp-3.15.0.tar.gz", hash = "sha256:112929ad649da941c23de50f356a2b5570c954b65150642bccdd66bf194d224b"}, + {file = "zipp-3.16.2-py3-none-any.whl", hash = "sha256:679e51dd4403591b2d6838a48de3d283f3d188412a9782faadf845f298736ba0"}, + {file = "zipp-3.16.2.tar.gz", hash = "sha256:ebc15946aa78bd63458992fc81ec3b6f7b1e92d51c35e6de1c3804e73b799147"}, ] [package.extras] -docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] -testing = ["big-O", "flake8 (<5)", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)"] +docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] +testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"] [metadata] lock-version = "2.0" python-versions = ">=3.8, <3.12" -content-hash = "33c735a967b9813f2ce0c53f0fec9099206ad7e6aba9fa023ef9a37e7c846c60" +content-hash = "013f5dc2328b493a1e4256ff0d9012aeaa76b05c71d785677c595f9596ba5201" diff --git a/pyproject.toml b/pyproject.toml index 405561869..24dab6b21 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "spikeometric" -version = "1.0.1" +version = "1.0.2" description = "Spikeometric is a Pytorch Geometric based framework for simulating Spiking Neural Networks using Linear Non-linear Cascade models" authors = ["Jakob Sønstebø "] readme = "README.md" @@ -11,12 +11,16 @@ packages = [{include = "spikeometric"}] [tool.poetry.dependencies] python = ">=3.8, <3.12" -numpy = "^1.21.4" -matplotlib = "^3.5.0" +numpy = "<1.24" +matplotlib = "^3.7.1" tqdm = "^4.62.3" scipy = "^1.7.3" seaborn = "^0.12.2" networkx = "^3.0" +pyparsing = "^3.1.1" +pillow = "9.4.0" +kiwisolver = "1.4.4" +pytz = "2023.3" [tool.poetry.dev-dependencies] pytest = "^6.2.5" @@ -25,7 +29,12 @@ pytest-lazy-fixture = "^0.6.3" sphinx = "^4.3.2" sphinx-rtd-theme = "^1.1.1" jupyter = "^1.0.0" -ipython = "^8.8.0" +ipython = "^7.34.0" +ipykernel = "^5.5.6" +pandas = "^1.5.3" +notebook = "^6.5.5" +tornado = "6.3.2" +decorator = "<5.0" [build-system] requires = ["poetry-core"] diff --git a/requirements.txt b/requirements.txt index 8e5355238..f2f883f11 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,138 +1,20 @@ -alabaster==0.7.13 ; python_version >= "3.8" and python_version < "3.12" -anyio==3.7.0 ; python_version >= "3.8" and python_version < "3.12" -appnope==0.1.3 ; python_version >= "3.8" and python_version < "3.12" and sys_platform == "darwin" or python_version >= "3.8" and python_version < "3.12" and platform_system == "Darwin" -argon2-cffi-bindings==21.2.0 ; python_version >= "3.8" and python_version < "3.12" -argon2-cffi==21.3.0 ; python_version >= "3.8" and python_version < "3.12" -arrow==1.2.3 ; python_version >= "3.8" and python_version < "3.12" -asttokens==2.2.1 ; python_version >= "3.8" and python_version < "3.12" -atomicwrites==1.4.1 ; python_version >= "3.8" and python_version < "3.12" and sys_platform == "win32" -attrs==23.1.0 ; python_version >= "3.8" and python_version < "3.12" -babel==2.12.1 ; python_version >= "3.8" and python_version < "3.12" -backcall==0.2.0 ; python_version >= "3.8" and python_version < "3.12" -beautifulsoup4==4.12.2 ; python_version >= "3.8" and python_version < "3.12" -bleach==6.0.0 ; python_version >= "3.8" and python_version < "3.12" -certifi==2023.5.7 ; python_version >= "3.8" and python_version < "3.12" -cffi==1.15.1 ; python_version >= "3.8" and python_version < "3.12" -charset-normalizer==3.1.0 ; python_version >= "3.8" and python_version < "3.12" -colorama==0.4.6 ; python_version >= "3.8" and python_version < "3.12" and platform_system == "Windows" or python_version >= "3.8" and python_version < "3.12" and sys_platform == "win32" -comm==0.1.3 ; python_version >= "3.8" and python_version < "3.12" +colorama==0.4.6 ; python_version >= "3.8" and python_version < "3.12" and platform_system == "Windows" contourpy==1.1.0 ; python_version >= "3.8" and python_version < "3.12" -coverage[toml]==7.2.7 ; python_version >= "3.8" and python_version < "3.12" cycler==0.11.0 ; python_version >= "3.8" and python_version < "3.12" -debugpy==1.6.7 ; python_version >= "3.8" and python_version < "3.12" -decorator==5.1.1 ; python_version >= "3.8" and python_version < "3.12" -defusedxml==0.7.1 ; python_version >= "3.8" and python_version < "3.12" -docutils==0.17.1 ; python_version >= "3.8" and python_version < "3.12" -exceptiongroup==1.1.1 ; python_version >= "3.8" and python_version < "3.11" -executing==1.2.0 ; python_version >= "3.8" and python_version < "3.12" -fastjsonschema==2.17.1 ; python_version >= "3.8" and python_version < "3.12" -fonttools==4.40.0 ; python_version >= "3.8" and python_version < "3.12" -fqdn==1.5.1 ; python_version >= "3.8" and python_version < "3.12" -idna==3.4 ; python_version >= "3.8" and python_version < "3.12" -imagesize==1.4.1 ; python_version >= "3.8" and python_version < "3.12" -importlib-metadata==6.7.0 ; python_version >= "3.8" and python_version < "3.10" -importlib-resources==5.12.0 ; python_version >= "3.8" and python_version < "3.10" -iniconfig==2.0.0 ; python_version >= "3.8" and python_version < "3.12" -ipykernel==6.23.3 ; python_version >= "3.8" and python_version < "3.12" -ipython-genutils==0.2.0 ; python_version >= "3.8" and python_version < "3.12" -ipython==8.12.2 ; python_version >= "3.8" and python_version < "3.12" -ipywidgets==8.0.6 ; python_version >= "3.8" and python_version < "3.12" -isoduration==20.11.0 ; python_version >= "3.8" and python_version < "3.12" -jedi==0.18.2 ; python_version >= "3.8" and python_version < "3.12" -jinja2==3.1.2 ; python_version >= "3.8" and python_version < "3.12" -jsonpointer==2.4 ; python_version >= "3.8" and python_version < "3.12" -jsonschema==4.17.3 ; python_version >= "3.8" and python_version < "3.12" -jsonschema[format-nongpl]==4.17.3 ; python_version >= "3.8" and python_version < "3.12" -jupyter-client==8.3.0 ; python_version >= "3.8" and python_version < "3.12" -jupyter-console==6.6.3 ; python_version >= "3.8" and python_version < "3.12" -jupyter-core==5.3.1 ; python_version >= "3.8" and python_version < "3.12" -jupyter-events==0.6.3 ; python_version >= "3.8" and python_version < "3.12" -jupyter-server-terminals==0.4.4 ; python_version >= "3.8" and python_version < "3.12" -jupyter-server==2.7.0 ; python_version >= "3.8" and python_version < "3.12" -jupyter==1.0.0 ; python_version >= "3.8" and python_version < "3.12" -jupyterlab-pygments==0.2.2 ; python_version >= "3.8" and python_version < "3.12" -jupyterlab-widgets==3.0.7 ; python_version >= "3.8" and python_version < "3.12" +fonttools==4.42.1 ; python_version >= "3.8" and python_version < "3.12" +importlib-resources==6.0.1 ; python_version >= "3.8" and python_version < "3.10" kiwisolver==1.4.4 ; python_version >= "3.8" and python_version < "3.12" -markupsafe==2.1.3 ; python_version >= "3.8" and python_version < "3.12" -matplotlib-inline==0.1.6 ; python_version >= "3.8" and python_version < "3.12" matplotlib==3.7.1 ; python_version >= "3.8" and python_version < "3.12" -mistune==3.0.1 ; python_version >= "3.8" and python_version < "3.12" -nbclassic==1.0.0 ; python_version >= "3.8" and python_version < "3.12" -nbclient==0.8.0 ; python_version >= "3.8" and python_version < "3.12" -nbconvert==7.6.0 ; python_version >= "3.8" and python_version < "3.12" -nbformat==5.9.0 ; python_version >= "3.8" and python_version < "3.12" -nest-asyncio==1.5.6 ; python_version >= "3.8" and python_version < "3.12" networkx==3.1 ; python_version >= "3.8" and python_version < "3.12" -notebook-shim==0.2.3 ; python_version >= "3.8" and python_version < "3.12" -notebook==6.5.4 ; python_version >= "3.8" and python_version < "3.12" -numpy==1.24.4 ; python_version < "3.12" and python_version >= "3.8" -overrides==7.3.1 ; python_version >= "3.8" and python_version < "3.12" +numpy==1.23.5 ; python_version < "3.12" and python_version >= "3.8" packaging==23.1 ; python_version >= "3.8" and python_version < "3.12" -pandas==2.0.2 ; python_version >= "3.8" and python_version < "3.12" -pandocfilters==1.5.0 ; python_version >= "3.8" and python_version < "3.12" -parso==0.8.3 ; python_version >= "3.8" and python_version < "3.12" -pexpect==4.8.0 ; python_version >= "3.8" and python_version < "3.12" and sys_platform != "win32" -pickleshare==0.7.5 ; python_version >= "3.8" and python_version < "3.12" -pillow==9.5.0 ; python_version >= "3.8" and python_version < "3.12" -pkgutil-resolve-name==1.3.10 ; python_version >= "3.8" and python_version < "3.9" -platformdirs==3.8.0 ; python_version >= "3.8" and python_version < "3.12" -pluggy==1.2.0 ; python_version >= "3.8" and python_version < "3.12" -prometheus-client==0.17.0 ; python_version >= "3.8" and python_version < "3.12" -prompt-toolkit==3.0.38 ; python_version >= "3.8" and python_version < "3.12" -psutil==5.9.5 ; python_version >= "3.8" and python_version < "3.12" -ptyprocess==0.7.0 ; python_version >= "3.8" and python_version < "3.12" and sys_platform != "win32" or python_version >= "3.8" and python_version < "3.12" and os_name != "nt" -pure-eval==0.2.2 ; python_version >= "3.8" and python_version < "3.12" -py==1.11.0 ; python_version >= "3.8" and python_version < "3.12" -pycparser==2.21 ; python_version >= "3.8" and python_version < "3.12" -pygments==2.15.1 ; python_version >= "3.8" and python_version < "3.12" -pyparsing==3.1.0 ; python_version >= "3.8" and python_version < "3.12" -pyrsistent==0.19.3 ; python_version >= "3.8" and python_version < "3.12" -pytest-cov==3.0.0 ; python_version >= "3.8" and python_version < "3.12" -pytest-lazy-fixture==0.6.3 ; python_version >= "3.8" and python_version < "3.12" -pytest==6.2.5 ; python_version >= "3.8" and python_version < "3.12" +pandas==1.5.3 ; python_version >= "3.8" and python_version < "3.12" +pillow==9.4.0 ; python_version >= "3.8" and python_version < "3.12" +pyparsing==3.1.1 ; python_version >= "3.8" and python_version < "3.12" python-dateutil==2.8.2 ; python_version >= "3.8" and python_version < "3.12" -python-json-logger==2.0.7 ; python_version >= "3.8" and python_version < "3.12" pytz==2023.3 ; python_version >= "3.8" and python_version < "3.12" -pywin32==306 ; sys_platform == "win32" and platform_python_implementation != "PyPy" and python_version >= "3.8" and python_version < "3.12" -pywinpty==2.0.10 ; python_version >= "3.8" and python_version < "3.12" and os_name == "nt" -pyyaml==6.0 ; python_version >= "3.8" and python_version < "3.12" -pyzmq==25.1.0 ; python_version >= "3.8" and python_version < "3.12" -qtconsole==5.4.3 ; python_version >= "3.8" and python_version < "3.12" -qtpy==2.3.1 ; python_version >= "3.8" and python_version < "3.12" -requests==2.31.0 ; python_version >= "3.8" and python_version < "3.12" -rfc3339-validator==0.1.4 ; python_version >= "3.8" and python_version < "3.12" -rfc3986-validator==0.1.1 ; python_version >= "3.8" and python_version < "3.12" scipy==1.10.1 ; python_version >= "3.8" and python_version < "3.12" seaborn==0.12.2 ; python_version >= "3.8" and python_version < "3.12" -send2trash==1.8.2 ; python_version >= "3.8" and python_version < "3.12" six==1.16.0 ; python_version >= "3.8" and python_version < "3.12" -sniffio==1.3.0 ; python_version >= "3.8" and python_version < "3.12" -snowballstemmer==2.2.0 ; python_version >= "3.8" and python_version < "3.12" -soupsieve==2.4.1 ; python_version >= "3.8" and python_version < "3.12" -sphinx-rtd-theme==1.2.2 ; python_version >= "3.8" and python_version < "3.12" -sphinx==4.5.0 ; python_version >= "3.8" and python_version < "3.12" -sphinxcontrib-applehelp==1.0.4 ; python_version >= "3.8" and python_version < "3.12" -sphinxcontrib-devhelp==1.0.2 ; python_version >= "3.8" and python_version < "3.12" -sphinxcontrib-htmlhelp==2.0.1 ; python_version >= "3.8" and python_version < "3.12" -sphinxcontrib-jquery==4.1 ; python_version >= "3.8" and python_version < "3.12" -sphinxcontrib-jsmath==1.0.1 ; python_version >= "3.8" and python_version < "3.12" -sphinxcontrib-qthelp==1.0.3 ; python_version >= "3.8" and python_version < "3.12" -sphinxcontrib-serializinghtml==1.1.5 ; python_version >= "3.8" and python_version < "3.12" -stack-data==0.6.2 ; python_version >= "3.8" and python_version < "3.12" -terminado==0.17.1 ; python_version >= "3.8" and python_version < "3.12" -tinycss2==1.2.1 ; python_version >= "3.8" and python_version < "3.12" -toml==0.10.2 ; python_version >= "3.8" and python_version < "3.12" -tomli==2.0.1 ; python_full_version <= "3.11.0a6" and python_version >= "3.8" -tornado==6.3.2 ; python_version >= "3.8" and python_version < "3.12" -tqdm==4.65.0 ; python_version >= "3.8" and python_version < "3.12" -traitlets==5.9.0 ; python_version >= "3.8" and python_version < "3.12" -typing-extensions==4.6.3 ; python_version >= "3.8" and python_version < "3.10" -tzdata==2023.3 ; python_version >= "3.8" and python_version < "3.12" -uri-template==1.3.0 ; python_version >= "3.8" and python_version < "3.12" -urllib3==2.0.3 ; python_version >= "3.8" and python_version < "3.12" -wcwidth==0.2.6 ; python_version >= "3.8" and python_version < "3.12" -webcolors==1.13 ; python_version >= "3.8" and python_version < "3.12" -webencodings==0.5.1 ; python_version >= "3.8" and python_version < "3.12" -websocket-client==1.6.1 ; python_version >= "3.8" and python_version < "3.12" -widgetsnbextension==4.0.7 ; python_version >= "3.8" and python_version < "3.12" -zipp==3.15.0 ; python_version >= "3.8" and python_version < "3.10" +tqdm==4.66.1 ; python_version >= "3.8" and python_version < "3.12" +zipp==3.16.2 ; python_version >= "3.8" and python_version < "3.10" diff --git a/requirements_dev.txt b/requirements_dev.txt new file mode 100644 index 000000000..ed83c2299 --- /dev/null +++ b/requirements_dev.txt @@ -0,0 +1,134 @@ +alabaster==0.7.13 ; python_version >= "3.8" and python_version < "3.12" +anyio==4.0.0 ; python_version >= "3.8" and python_version < "3.12" +appnope==0.1.3 ; python_version >= "3.8" and python_version < "3.12" and sys_platform == "darwin" or python_version >= "3.8" and python_version < "3.12" and platform_system == "Darwin" +argon2-cffi-bindings==21.2.0 ; python_version >= "3.8" and python_version < "3.12" +argon2-cffi==23.1.0 ; python_version >= "3.8" and python_version < "3.12" +arrow==1.2.3 ; python_version >= "3.8" and python_version < "3.12" +atomicwrites==1.4.1 ; python_version >= "3.8" and python_version < "3.12" and sys_platform == "win32" +attrs==23.1.0 ; python_version >= "3.8" and python_version < "3.12" +babel==2.12.1 ; python_version >= "3.8" and python_version < "3.12" +backcall==0.2.0 ; python_version >= "3.8" and python_version < "3.12" +beautifulsoup4==4.12.2 ; python_version >= "3.8" and python_version < "3.12" +bleach==6.0.0 ; python_version >= "3.8" and python_version < "3.12" +certifi==2023.7.22 ; python_version >= "3.8" and python_version < "3.12" +cffi==1.15.1 ; python_version >= "3.8" and python_version < "3.12" +charset-normalizer==3.2.0 ; python_version >= "3.8" and python_version < "3.12" +colorama==0.4.6 ; python_version >= "3.8" and python_version < "3.12" and platform_system == "Windows" or python_version >= "3.8" and python_version < "3.12" and sys_platform == "win32" +comm==0.1.4 ; python_version >= "3.8" and python_version < "3.12" +contourpy==1.1.0 ; python_version >= "3.8" and python_version < "3.12" +coverage[toml]==7.3.0 ; python_version >= "3.8" and python_version < "3.12" +cycler==0.11.0 ; python_version >= "3.8" and python_version < "3.12" +decorator==4.4.2 ; python_version >= "3.8" and python_version < "3.12" +defusedxml==0.7.1 ; python_version >= "3.8" and python_version < "3.12" +docutils==0.17.1 ; python_version >= "3.8" and python_version < "3.12" +entrypoints==0.4 ; python_version >= "3.8" and python_version < "3.12" +exceptiongroup==1.1.3 ; python_version >= "3.8" and python_version < "3.11" +fastjsonschema==2.18.0 ; python_version >= "3.8" and python_version < "3.12" +fonttools==4.42.1 ; python_version >= "3.8" and python_version < "3.12" +fqdn==1.5.1 ; python_version >= "3.8" and python_version < "3.12" +idna==3.4 ; python_version >= "3.8" and python_version < "3.12" +imagesize==1.4.1 ; python_version >= "3.8" and python_version < "3.12" +importlib-metadata==6.8.0 ; python_version >= "3.8" and python_version < "3.10" +importlib-resources==6.0.1 ; python_version >= "3.8" and python_version < "3.10" +iniconfig==2.0.0 ; python_version >= "3.8" and python_version < "3.12" +ipykernel==5.5.6 ; python_version >= "3.8" and python_version < "3.12" +ipython-genutils==0.2.0 ; python_version >= "3.8" and python_version < "3.12" +ipython==7.34.0 ; python_version >= "3.8" and python_version < "3.12" +ipywidgets==8.1.0 ; python_version >= "3.8" and python_version < "3.12" +isoduration==20.11.0 ; python_version >= "3.8" and python_version < "3.12" +jedi==0.19.0 ; python_version >= "3.8" and python_version < "3.12" +jinja2==3.1.2 ; python_version >= "3.8" and python_version < "3.12" +jsonpointer==2.4 ; python_version >= "3.8" and python_version < "3.12" +jsonschema-specifications==2023.7.1 ; python_version >= "3.8" and python_version < "3.12" +jsonschema==4.19.0 ; python_version >= "3.8" and python_version < "3.12" +jsonschema[format-nongpl]==4.19.0 ; python_version >= "3.8" and python_version < "3.12" +jupyter-client==7.4.9 ; python_version >= "3.8" and python_version < "3.12" +jupyter-console==6.4.4 ; python_version >= "3.8" and python_version < "3.12" +jupyter-core==5.3.1 ; python_version >= "3.8" and python_version < "3.12" +jupyter-events==0.7.0 ; python_version >= "3.8" and python_version < "3.12" +jupyter-server-terminals==0.4.4 ; python_version >= "3.8" and python_version < "3.12" +jupyter-server==2.7.3 ; python_version >= "3.8" and python_version < "3.12" +jupyter==1.0.0 ; python_version >= "3.8" and python_version < "3.12" +jupyterlab-pygments==0.2.2 ; python_version >= "3.8" and python_version < "3.12" +jupyterlab-widgets==3.0.8 ; python_version >= "3.8" and python_version < "3.12" +kiwisolver==1.4.4 ; python_version >= "3.8" and python_version < "3.12" +markupsafe==2.1.3 ; python_version >= "3.8" and python_version < "3.12" +matplotlib-inline==0.1.6 ; python_version >= "3.8" and python_version < "3.12" +matplotlib==3.7.1 ; python_version >= "3.8" and python_version < "3.12" +mistune==3.0.1 ; python_version >= "3.8" and python_version < "3.12" +nbclassic==1.0.0 ; python_version >= "3.8" and python_version < "3.12" +nbclient==0.8.0 ; python_version >= "3.8" and python_version < "3.12" +nbconvert==7.8.0 ; python_version >= "3.8" and python_version < "3.12" +nbformat==5.9.2 ; python_version >= "3.8" and python_version < "3.12" +nest-asyncio==1.5.7 ; python_version >= "3.8" and python_version < "3.12" +networkx==3.1 ; python_version >= "3.8" and python_version < "3.12" +notebook-shim==0.2.3 ; python_version >= "3.8" and python_version < "3.12" +notebook==6.5.5 ; python_version >= "3.8" and python_version < "3.12" +numpy==1.23.5 ; python_version < "3.12" and python_version >= "3.8" +overrides==7.4.0 ; python_version >= "3.8" and python_version < "3.12" +packaging==23.1 ; python_version >= "3.8" and python_version < "3.12" +pandas==1.5.3 ; python_version >= "3.8" and python_version < "3.12" +pandocfilters==1.5.0 ; python_version >= "3.8" and python_version < "3.12" +parso==0.8.3 ; python_version >= "3.8" and python_version < "3.12" +pexpect==4.8.0 ; python_version >= "3.8" and python_version < "3.12" and sys_platform != "win32" +pickleshare==0.7.5 ; python_version >= "3.8" and python_version < "3.12" +pillow==9.4.0 ; python_version >= "3.8" and python_version < "3.12" +pkgutil-resolve-name==1.3.10 ; python_version >= "3.8" and python_version < "3.9" +platformdirs==3.10.0 ; python_version >= "3.8" and python_version < "3.12" +pluggy==1.3.0 ; python_version >= "3.8" and python_version < "3.12" +prometheus-client==0.17.1 ; python_version >= "3.8" and python_version < "3.12" +prompt-toolkit==3.0.39 ; python_version >= "3.8" and python_version < "3.12" +ptyprocess==0.7.0 ; python_version >= "3.8" and python_version < "3.12" and sys_platform != "win32" or python_version >= "3.8" and python_version < "3.12" and os_name != "nt" +py==1.11.0 ; python_version >= "3.8" and python_version < "3.12" +pycparser==2.21 ; python_version >= "3.8" and python_version < "3.12" +pygments==2.16.1 ; python_version >= "3.8" and python_version < "3.12" +pyparsing==3.1.1 ; python_version >= "3.8" and python_version < "3.12" +pytest-cov==3.0.0 ; python_version >= "3.8" and python_version < "3.12" +pytest-lazy-fixture==0.6.3 ; python_version >= "3.8" and python_version < "3.12" +pytest==6.2.5 ; python_version >= "3.8" and python_version < "3.12" +python-dateutil==2.8.2 ; python_version >= "3.8" and python_version < "3.12" +python-json-logger==2.0.7 ; python_version >= "3.8" and python_version < "3.12" +pytz==2023.3 ; python_version >= "3.8" and python_version < "3.12" +pywin32==306 ; sys_platform == "win32" and platform_python_implementation != "PyPy" and python_version >= "3.8" and python_version < "3.12" +pywinpty==2.0.11 ; python_version >= "3.8" and python_version < "3.12" and os_name == "nt" +pyyaml==6.0.1 ; python_version >= "3.8" and python_version < "3.12" +pyzmq==24.0.1 ; python_version >= "3.8" and python_version < "3.12" +qtconsole==5.4.4 ; python_version >= "3.8" and python_version < "3.12" +qtpy==2.4.0 ; python_version >= "3.8" and python_version < "3.12" +referencing==0.30.2 ; python_version >= "3.8" and python_version < "3.12" +requests==2.31.0 ; python_version >= "3.8" and python_version < "3.12" +rfc3339-validator==0.1.4 ; python_version >= "3.8" and python_version < "3.12" +rfc3986-validator==0.1.1 ; python_version >= "3.8" and python_version < "3.12" +rpds-py==0.10.2 ; python_version >= "3.8" and python_version < "3.12" +scipy==1.10.1 ; python_version >= "3.8" and python_version < "3.12" +seaborn==0.12.2 ; python_version >= "3.8" and python_version < "3.12" +send2trash==1.8.2 ; python_version >= "3.8" and python_version < "3.12" +setuptools==68.1.2 ; python_version >= "3.8" and python_version < "3.12" +six==1.16.0 ; python_version >= "3.8" and python_version < "3.12" +sniffio==1.3.0 ; python_version >= "3.8" and python_version < "3.12" +snowballstemmer==2.2.0 ; python_version >= "3.8" and python_version < "3.12" +soupsieve==2.5 ; python_version >= "3.8" and python_version < "3.12" +sphinx-rtd-theme==1.3.0 ; python_version >= "3.8" and python_version < "3.12" +sphinx==4.5.0 ; python_version >= "3.8" and python_version < "3.12" +sphinxcontrib-applehelp==1.0.4 ; python_version >= "3.8" and python_version < "3.12" +sphinxcontrib-devhelp==1.0.2 ; python_version >= "3.8" and python_version < "3.12" +sphinxcontrib-htmlhelp==2.0.1 ; python_version >= "3.8" and python_version < "3.12" +sphinxcontrib-jquery==4.1 ; python_version >= "3.8" and python_version < "3.12" +sphinxcontrib-jsmath==1.0.1 ; python_version >= "3.8" and python_version < "3.12" +sphinxcontrib-qthelp==1.0.3 ; python_version >= "3.8" and python_version < "3.12" +sphinxcontrib-serializinghtml==1.1.5 ; python_version >= "3.8" and python_version < "3.12" +terminado==0.17.1 ; python_version >= "3.8" and python_version < "3.12" +tinycss2==1.2.1 ; python_version >= "3.8" and python_version < "3.12" +toml==0.10.2 ; python_version >= "3.8" and python_version < "3.12" +tomli==2.0.1 ; python_full_version <= "3.11.0a6" and python_version >= "3.8" +tornado==6.3.2 ; python_version >= "3.8" and python_version < "3.12" +tqdm==4.66.1 ; python_version >= "3.8" and python_version < "3.12" +traitlets==5.9.0 ; python_version >= "3.8" and python_version < "3.12" +uri-template==1.3.0 ; python_version >= "3.8" and python_version < "3.12" +urllib3==2.0.4 ; python_version >= "3.8" and python_version < "3.12" +wcwidth==0.2.6 ; python_version >= "3.8" and python_version < "3.12" +webcolors==1.13 ; python_version >= "3.8" and python_version < "3.12" +webencodings==0.5.1 ; python_version >= "3.8" and python_version < "3.12" +websocket-client==1.6.2 ; python_version >= "3.8" and python_version < "3.12" +widgetsnbextension==4.0.8 ; python_version >= "3.8" and python_version < "3.12" +zipp==3.16.2 ; python_version >= "3.8" and python_version < "3.10" diff --git a/spikeometric/models/poisson_glm_model.py b/spikeometric/models/poisson_glm_model.py index 626595bf6..b3155ea56 100644 --- a/spikeometric/models/poisson_glm_model.py +++ b/spikeometric/models/poisson_glm_model.py @@ -12,12 +12,12 @@ class PoissonGLM(BaseModel): More specifically, we have the following equations: - #. .. math:: g_i(t+1) = r \: \sum_{\tau = 0}^{T-1} \sum_{j\in \mathcal{N}(i)} (W_0)_{j, i} X_j(t-\tau)c(\tau) + b_i + \mathcal{E}_i(t+1) + #. .. math:: g_i(t+1) = r \: \sum_{t' = 0}^{T-1} \sum_{j\in \mathcal{N}(i)} (W_0)_{j, i} X_j(t-t')c(t') + b_i + \mathcal{E}_i(t+1) #. .. math:: \mu_i(t+1) = \frac{\Delta t}{\alpha}\: e^{\beta g_i(t+1)} #. .. math:: X_i(t+1) \sim \text{Pois}(\mu_i(t+1)) The first equation is implemented in the :meth:`input` method and gives the input to neuron :math:`i` at time :math:`t+1` - as a convolution of the spike history of the neighboring neurons with a coupling filter :math:`c`, weighted by the + as a convolution of the spike history of the neighboring neurons with a coupling filter :math:`c(t) = e^{- \Delta t \frac{t}{\tau}}`, weighted by the connectivity matrix :math:`W_0`, and scaled by the recurrent scaling factor :math:`r`. There is also a uniform background input :math:`b_i` and an external stimulus :math:`\mathcal{E}_i(t+1)`. @@ -37,7 +37,7 @@ class PoissonGLM(BaseModel): T : int The number of time steps to consider back in time. tau : float - The time constant of the exponential filter. + The time constant of the exponential coupling filter. dt : float The time step of the simulation in milliseconds. r : float @@ -156,4 +156,4 @@ def connectivity_filter(self, W0: torch.Tensor, edge_index: torch.Tensor) -> tor The edge index of the network. """ t = torch.arange(1, self.T+1, dtype=torch.float32, device=W0.device).repeat(W0.shape[0], 1) - return torch.einsum("i, ij -> ij", W0, torch.exp((t -self.T)*self.dt/self.tau)), edge_index \ No newline at end of file + return torch.einsum("i, ij -> ij", W0, torch.exp((t -self.T)*self.dt/self.tau)), edge_index diff --git a/spikeometric/stimulus/base_stimulus.py b/spikeometric/stimulus/base_stimulus.py index 695bac392..fcdef5253 100644 --- a/spikeometric/stimulus/base_stimulus.py +++ b/spikeometric/stimulus/base_stimulus.py @@ -8,9 +8,15 @@ class BaseStimulus(nn.Module): """ @property def stimulus_masks(self): + r""" + Returns the batched stimulus masks. + """ return torch.split(self.conc_stimulus_masks, self.split_points.tolist(), dim=0) def batch_stimulus_masks(self, stimulus_masks: list, batch_size: int) -> list: + r""" + Batches the stimulus masks into batches of size :obj:`batch_size`, concatenates them, and returns the concatenated stimulus masks and the split points. + """ if batch_size > len(stimulus_masks): raise ValueError("Batch size must be smaller or equal to the number of networks.") n_neurons = torch.tensor([sm.shape[0] for sm in stimulus_masks]) diff --git a/spikeometric/stimulus/loaded_stimulus.py b/spikeometric/stimulus/loaded_stimulus.py index 200f3912f..94e1863b9 100644 --- a/spikeometric/stimulus/loaded_stimulus.py +++ b/spikeometric/stimulus/loaded_stimulus.py @@ -35,8 +35,12 @@ def __init__(self, path: str, batch_size: int = 1): self.register_buffer("n_neurons", torch.tensor(stimuli.shape[0], dtype=torch.int)) self.register_buffer("n_steps", torch.tensor(stimuli.shape[1], dtype=torch.int)) - n_networks = stimuli.shape[2] if len(stimuli.shape) > 2 else 1 - self.register_buffer("n_networks", torch.tensor(n_networks, dtype=torch.int)) + if len(stimuli.shape) < 4: + n_networks = stimuli.shape[2] if len(stimuli.shape) > 2 else 1 + self.register_buffer("n_networks", torch.tensor(n_networks, dtype=torch.int)) + else: + n_networks = stimuli.shape[3] + self.register_buffer("n_networks", torch.tensor(n_networks, dtype=torch.int)) self.register_buffer("batch_size", torch.tensor(batch_size, dtype=torch.int)) if self.n_networks < batch_size: @@ -44,8 +48,10 @@ def __init__(self, path: str, batch_size: int = 1): self.register_buffer("n_batches", torch.tensor(math.ceil(n_networks / batch_size), dtype=torch.int)) - if len(stimuli.shape) > 2: + if len(stimuli.shape) == 3: stimuli = torch.concat(torch.split(stimuli, 1, dim=2), dim=0).squeeze(2) + elif len(stimuli.shape) == 4: + stimuli = torch.concat(torch.split(stimuli, 1, dim=3), dim=0).squeeze(3) neurons_per_batch = [self.n_neurons*self.batch_size] * (self.n_batches - 1) if n_networks % batch_size != 0: @@ -70,7 +76,7 @@ def __call__(self, t: Union[float, torch.Tensor]) -> torch.Tensor: Parameters ---------- t : torch.Tensor or float - Time :math:`t` at which to compute the stimulus (ms). + Time :math:`t` at which to compute the stimulus. Returns ------- @@ -78,8 +84,6 @@ def __call__(self, t: Union[float, torch.Tensor]) -> torch.Tensor: Stimulus at time :math:`t`. """ if torch.is_tensor(t) and not t.dim() == 0: - result = torch.zeros((self.neurons_per_batch[self._idx], t.shape[0])) - result[:, :self.n_steps] = self.stimulus[:, :self.n_steps] - return result + return self.stimulus[..., :t.shape[0], ...] else: - return self.stimulus[:, t] if 0 <= t < self.n_steps else torch.zeros_like(self.stimulus[:, 0]) \ No newline at end of file + return self.stimulus[..., t, ...] if 0 <= t < self.n_steps else torch.zeros(self.neurons_per_batch[self._idx], dtype=torch.float, device=self.stimulus.device) \ No newline at end of file diff --git a/spikeometric/stimulus/sin_stimulus.py b/spikeometric/stimulus/sin_stimulus.py index e0aa552b8..0071fe2e5 100644 --- a/spikeometric/stimulus/sin_stimulus.py +++ b/spikeometric/stimulus/sin_stimulus.py @@ -19,7 +19,7 @@ class SinStimulus(BaseStimulus): Period :math:`T` of stimulus duration : int Duration :math:`\tau` stimulus in total - stimulus_mask: + stimulus_masks: A mask of shape (n_neurons,) indicating which neurons to stimulate. batch_size : int The number of networks to stimulate in parallel. diff --git a/tests/test_data/stim_plan_multidim.pt b/tests/test_data/stim_plan_multidim.pt new file mode 100644 index 000000000..ada209254 Binary files /dev/null and b/tests/test_data/stim_plan_multidim.pt differ diff --git a/tests/test_stimulation.py b/tests/test_stimulation.py index 0a432f409..9d0d63577 100644 --- a/tests/test_stimulation.py +++ b/tests/test_stimulation.py @@ -204,22 +204,24 @@ def test_sin_stimulus_vectorizes(sin_stimulus): t = torch.arange(0, 1000) assert sin_stimulus(t).shape == (20, 1000) -def test_loaded_stimulus_is_batched(): +@pytest.mark.parametrize("stim_plan", ["tests/test_data/stim_plan_multidim.pt", "tests/test_data/stim_plan_4_networks.pt"]) +def test_loaded_stimulus_is_batched(stim_plan): from spikeometric.stimulus import LoadedStimulus - stimulus = LoadedStimulus("tests/test_data/stim_plan_4_networks.pt", batch_size=2) + stimulus = LoadedStimulus(stim_plan, batch_size=2) n_steps = stimulus.n_steps.item() - assert stimulus(0).shape == (40,) - assert stimulus(n_steps).shape == (40,) + assert stimulus(0).shape[0] == 40 + assert stimulus(n_steps).shape[0] == 40 -def test_loaded_stimulus_can_batch_non_uneven_number_of_batches(): +@pytest.mark.parametrize("stim_plan", ["tests/test_data/stim_plan_multidim.pt", "tests/test_data/stim_plan_4_networks.pt"]) +def test_loaded_stimulus_can_batch_non_uneven_number_of_batches(stim_plan): from spikeometric.stimulus import LoadedStimulus - stimulus = LoadedStimulus("tests/test_data/stim_plan_4_networks.pt", batch_size=3) + stimulus = LoadedStimulus(stim_plan, batch_size=3) n_steps = stimulus.n_steps.item() - assert stimulus(0).shape == (60,) - assert stimulus(n_steps+1).shape == (60,) + assert stimulus(0).shape[0] == 60 + assert stimulus(n_steps+1).shape[0] == 60 stimulus.next_batch() - assert stimulus(0).shape == (20,) - assert stimulus(n_steps+1).shape == (20,) + assert stimulus(0).shape[0] == 20 + assert stimulus(n_steps+1).shape[0] == 20 def test_loaded_stimulus_affects_model_output(bernoulli_glm, example_data, loaded_stimulus): initial_spikes = bernoulli_glm.simulate(example_data, n_steps=100, verbose=False) @@ -239,9 +241,10 @@ def test_loaded_stimulus_matches_original_stimulus(loaded_stimulus): def test_loaded_stimulus_is_zero_before_start_of_stimulus(loaded_stimulus): assert torch.allclose(loaded_stimulus(-1), torch.zeros(20)) -def test_loaded_stimulus_cycles_through_batches(): +@pytest.mark.parametrize("stim_plan", ["tests/test_data/stim_plan_multidim.pt", "tests/test_data/stim_plan_4_networks.pt"]) +def test_loaded_stimulus_cycles_through_batches(stim_plan): from spikeometric.stimulus import LoadedStimulus - stimulus = LoadedStimulus("tests/test_data/stim_plan_4_networks.pt", batch_size=2) + stimulus = LoadedStimulus(stim_plan, batch_size=2) n_steps = stimulus.n_steps first_batch = stimulus(torch.arange(0, n_steps)) stimulus.next_batch() @@ -251,23 +254,25 @@ def test_loaded_stimulus_cycles_through_batches(): stimulus.next_batch() assert torch.allclose(second_batch, stimulus(torch.arange(0, n_steps))) -def test_loaded_stimulus_fails_if_batch_size_greater_than_n_networks(): +@pytest.mark.parametrize("stim_plan", ["tests/test_data/stim_plan_multidim.pt", "tests/test_data/stim_plan_4_networks.pt"]) +def test_loaded_stimulus_fails_if_batch_size_greater_than_n_networks(stim_plan): from spikeometric.stimulus import LoadedStimulus with pytest.raises(ValueError): - stimulus = LoadedStimulus("tests/test_data/stim_plan_4_networks.pt", batch_size=5) + stimulus = LoadedStimulus(stim_plan, batch_size=5) -def test_loaded_stimulus_fails_if_idx_out_of_range(): +@pytest.mark.parametrize("stim_plan", ["tests/test_data/stim_plan_multidim.pt", "tests/test_data/stim_plan_4_networks.pt"]) +def test_loaded_stimulus_fails_if_idx_out_of_range(stim_plan): from spikeometric.stimulus import LoadedStimulus - stimulus = LoadedStimulus("tests/test_data/stim_plan_4_networks.pt", batch_size=2) + stimulus = LoadedStimulus(stim_plan, batch_size=2) with pytest.raises(ValueError): stimulus.set_batch(2) -def test_loaded_stimulus_resets(): +@pytest.mark.parametrize("stim_plan", ["tests/test_data/stim_plan_multidim.pt", "tests/test_data/stim_plan_4_networks.pt"]) +def test_loaded_stimulus_resets(stim_plan): from spikeometric.stimulus import LoadedStimulus - stimulus = LoadedStimulus("tests/test_data/stim_plan_4_networks.pt", batch_size=1) + stimulus = LoadedStimulus(stim_plan, batch_size=1) n_steps = stimulus.n_steps first_batch = stimulus(torch.arange(0, n_steps)) stimulus.set_batch(3) stimulus.reset() assert torch.allclose(first_batch, stimulus(torch.arange(0, n_steps))) -