From 892c4120589eec1ba232920ad20a8bd926feedce Mon Sep 17 00:00:00 2001 From: BalzaniEdoardo Date: Wed, 4 Dec 2024 11:49:24 -0500 Subject: [PATCH] fix all tutorials --- docs/tutorials/plot_03_grid_cells.md | 2 +- docs/tutorials/plot_05_place_cells.md | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/tutorials/plot_03_grid_cells.md b/docs/tutorials/plot_03_grid_cells.md index 65bfbc43..b884bc68 100644 --- a/docs/tutorials/plot_03_grid_cells.md +++ b/docs/tutorials/plot_03_grid_cells.md @@ -175,7 +175,7 @@ Now we can "evaluate" the basis for each position of the animal ```{code-cell} ipython3 -position_basis = basis_2d(position["x"], position["y"]) +position_basis = basis_2d.compute_features(position["x"], position["y"]) ``` Now try to make sense of what it is diff --git a/docs/tutorials/plot_05_place_cells.md b/docs/tutorials/plot_05_place_cells.md index ae8e38ac..ce094f28 100644 --- a/docs/tutorials/plot_05_place_cells.md +++ b/docs/tutorials/plot_05_place_cells.md @@ -357,7 +357,7 @@ The object basis only tell us how each basis covers the feature space. For each ```{code-cell} ipython3 -X = basis(position, theta, speed) +X = basis.compute_features(position, theta, speed) ``` `X` is our design matrix. For each timestamps, it contains the information about the current position, @@ -455,7 +455,7 @@ predicted_rates = {} for m in models: print("1. Evaluating basis : ", m) - X = models[m](*features[m]) + X = models[m].compute_features(*features[m]) print("2. Fitting model : ", m) glm.fit(