From 4ea027655a0b4774c21c276cbbe3e8d481a9ba98 Mon Sep 17 00:00:00 2001 From: BalzaniEdoardo Date: Wed, 8 Nov 2023 11:21:11 -0500 Subject: [PATCH] fixed the shapes --- src/nemos/glm.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/src/nemos/glm.py b/src/nemos/glm.py index 35b66fc8..407dd9e3 100644 --- a/src/nemos/glm.py +++ b/src/nemos/glm.py @@ -140,7 +140,7 @@ def predict(self, X: Union[NDArray, jnp.ndarray]) -> jnp.ndarray: Returns ------- : - The predicted rates with shape (n_neurons, n_time_bins). + The predicted rates with shape (n_time_bins, n_neurons). Raises ------ @@ -395,15 +395,15 @@ def simulate( feedforward_input : External input matrix to the model, representing factors like convolved currents, light intensities, etc. When not provided, the simulation is done with coupling-only. - Expected shape: (n_timesteps, n_neurons, n_basis_input). + Expected shape: (n_time_bins, n_neurons, n_basis_input). Returns ------- simulated_activity : Simulated activity (spike counts for PoissonGLMs) for each neuron over time. - Shape: (n_neurons, n_timesteps). + Shape: (n_time_bins, n_neurons). firing_rates : - Simulated rates for each neuron over time. Shape, (n_neurons, n_timesteps). + Simulated rates for each neuron over time. Shape, (n_neurons, n_time_bins). Raises ------ @@ -495,7 +495,7 @@ def simulate_recurrent( feedforward_input : External input matrix to the model, representing factors like convolved currents, light intensities, etc. When not provided, the simulation is done with coupling-only. - Expected shape: (n_timesteps, n_neurons, n_basis_input). + Expected shape: (n_time_bins, n_neurons, n_basis_input). init_y : Initial observation (spike counts for PoissonGLM) matrix that kickstarts the simulation. Expected shape: (window_size, n_neurons). @@ -507,9 +507,9 @@ def simulate_recurrent( ------- simulated_activity : Simulated activity (spike counts for PoissonGLMs) for each neuron over time. - Shape, (n_neurons, n_timesteps). + Shape, (n_time_bins, n_neurons). firing_rates : - Simulated rates for each neuron over time. Shape, (n_neurons, n_timesteps). + Simulated rates for each neuron over time. Shape, (n_time_bins, n_neurons,). Raises ------