Function Demo: plot_traces

3. Function Demo: plot_traces#

lbm_suite2p_python.plot_traces()

plot_traces is a convenience function for plotting suite2p traces.

Data input is in the same format as F.npy: [n_neurons x n_timepoints].

For the most basic usage, simply pass in a loaded ops file or path to an ops file:

import lbm_suite2p_python as lsp
ops_file = lsp.load_ops("C://Users//RBO//lbm//Plane_07//plane0//ops.npy")
lsp.plot_traces(ops_file)
../_images/plot_traces_raw.png

By default, plot_traces will plot 2 minutes of the DF/F (%) for the first 20 neurons.

The DF/F is calculated by calling lbm_suite2p_python.dff_percentile.

The main features of this function:

  • automatic time and signal scalebars that adjust to the input data

  • automatic offset between traces calculated from the magnitude of the baseline signal

  • lower traces mask into upper traces to highlight the high-magnitude transients

from pathlib import Path
import mbo_utilities as mbo # for convenience
import lbm_suite2p_python as lsp

3.1. Preparation#

mbo_utilities.get_files() lbm_suite2p_python.load_ops() lbm_suite2p_python.load_traces()

This is a lazy way to get any ops.npy files in this directory.

ops_files = mbo.get_files(r"D:\W2_DATA", 'ops.npy', max_depth=10)
ops_files[:3]

We then use the ops.npy file to retrieve the traces:

ops = lsp.load_ops(ops_files[6])
f, fneu, spks = lsp.load_traces(ops) # only need f here

This f variable contains the loaded F.npy pre-filtered to only contain valid cells.

It can then be used to calculate \(\\Delta F / F_0\) :

dff = lsp.dff_percentile(f) * 100

3.2. Single neuron#

lsp.plot_traces(dff, './plot_traces_single.png', signal_units='dff', num_neurons=1)
Single neuron DF/F

Single neuron trace (percentile-based DF/F).#

3.3. Two neurons with manual offset#

lsp.plot_traces(dff, './plot_traces_single_gap50.png', signal_units='dff', num_neurons=2, offset=250)
Two neurons with offset

Two traces with manually specified offset.#

3.4. Default dff#

lsp.plot_traces(dff, './plot_traces_dff.png', signal_units='dff')
Default DF/F traces

Default appearance with vertical stack and scalebars.#

3.5. More neurons#

lsp.plot_traces(dff[:50], './plot_traces_dff_50n.png', signal_units='dff', num_neurons=50)
50 neuron traces

First 50 neurons, default colormap.#

3.5.1. 100 neurons, custom colormap#

lsp.plot_traces(dff[::2], './plot_traces_dff_100n_nipy.png', signal_units='dff', num_neurons=100, cmap="nipy_spectral")
100 neurons, nipy spectral colormap

100 traces using the nipy_spectral colormap.#

3.6. Temporal binning#

3.6.1. 2× binning#

bin_factor = 2
dff_binned = lsp.bin1d(dff, bin_factor)
fps_binned = metadata["frame_rate"] / bin_factor
lsp.plot_traces(dff_binned, './plot_traces_dff_binned_2x.png', signal_units='dff', fps=fps_binned)
Binned 2x

Traces downsampled by factor 2. Scale bar adjusts accordingly.#

3.6.2. 4× binning#

bin_factor = 4
dff_binned_4x = lsp.bin1d(dff, bin_factor)
fps_binned_4x = metadata["frame_rate"] / bin_factor
lsp.plot_traces(dff_binned_4x, './plot_traces_dff_binned_4x.png', signal_units='dff', fps=fps_binned_4x)
Binned 4x

Traces downsampled by factor 4. Horizontal scale bar still valid.#

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