From b245ebfcb4ca86092a3ddc764ac766464c0e5b55 Mon Sep 17 00:00:00 2001 From: Guillaume Viejo Date: Fri, 17 Nov 2023 10:53:11 -0500 Subject: [PATCH] Update tutorial --- docs/examples/tutorial_calcium_imaging.py | 6 +++--- docs/index.md | 7 ++----- 2 files changed, 5 insertions(+), 8 deletions(-) diff --git a/docs/examples/tutorial_calcium_imaging.py b/docs/examples/tutorial_calcium_imaging.py index 2c9e11b9..2df962cc 100644 --- a/docs/examples/tutorial_calcium_imaging.py +++ b/docs/examples/tutorial_calcium_imaging.py @@ -95,7 +95,7 @@ # --------------------- # Here we compute the tuning curves of all the neurons -tcurves = nap.compute_1d_tuning_curves_continous(transients, angle, nb_bins = 120) +tcurves = nap.compute_1d_tuning_curves_continuous(transients, angle, nb_bins = 120) print(tcurves) @@ -123,8 +123,8 @@ # %% # Now we can compute the tuning curves for each half of the recording and plot the tuning curves for the fifth region of interest. -half1 = nap.compute_1d_tuning_curves_continous(transients, angle, nb_bins = 120, ep = halves.loc[[0]]) -half2 = nap.compute_1d_tuning_curves_continous(transients, angle, nb_bins = 120, ep = halves.loc[[1]]) +half1 = nap.compute_1d_tuning_curves_continuous(transients, angle, nb_bins = 120, ep = halves.loc[[0]]) +half2 = nap.compute_1d_tuning_curves_continuous(transients, angle, nb_bins = 120, ep = halves.loc[[1]]) plt.figure(figsize=(12, 5)) plt.subplot(1,2,1) diff --git a/docs/index.md b/docs/index.md index 4786fc87..d740a960 100644 --- a/docs/index.md +++ b/docs/index.md @@ -22,8 +22,8 @@ pynapple is a light-weight python library for neurophysiological data analysis. ------------------------------------------------------------------------ -:fire::fire::fire::fire::fire::fire::fire::fire: New release :fire::fire::fire::fire::fire::fire::fire::fire::fire::fire: ---------------- +New release :fire: +------------------ Starting with 0.4, pynapple rely on the [numpy array container](https://numpy.org/doc/stable/user/basics.dispatch.html) approach instead of Pandas. Pynapple builtin functions will remain the same except for functions inherited from Pandas. Typically this line of code in `pynapple<=0.3.6` : ```python meantsd = tsdframe.mean(1) @@ -37,9 +37,6 @@ in `pynapple>=0.4.0`. This allows for a better handling of returned objects. Additionaly, it is now possible to define time series objects with more than 2 dimensions with `TsdTensor`. You can also look at this [notebook](https://pynapple-org.github.io/pynapple/generated/gallery/tutorial_pynapple_numpy/) for a demonstration of numpy compatibilities. -:fire::fire::fire::fire::fire::fire::fire::fire::fire::fire::fire::fire::fire::fire::fire::fire::fire::fire::fire::fire::fire::fire::fire: ---------------- - Getting Started ---------------