Plots

Plots over time

For an aggregate view of your variable of interest over time, use the .plot_overtime() method to visualise the mean variable over your given time frame or split it into sub groups using the information in your metadata.

# If wrapped is True each specimens data will be aggregated to one 24 day before being aggregated as a whole. If you want to view each day seperately, keep wrapped False.
# A moving average is applied to the data with a window of 30 (30 mins if your time delta is 60 seconds). This can be changed with the parameter avg_window 

df.plot_overtime(
variable = 'moving',
wrapped = True,
avg_window = 30
)

# the plots will show the mean with 95% confidence intervals in a lighter colour around the mean

A plot of movement aggregated to 24 hours using the .plot_overtime() method

# You can seperate out your plots by your specimen labels in the metadata. Specify which column you want fromn the metadata with facet_col and then specify which groups you want with facet_args
# What you enter for facet_args must be in a list and be exactly what is in that column in the metadata
# Don't like the label names in the column, rename the graphing labels with the facet_labels parameter. This can only be done if you have a same length list for facet_arg. Also make sure they are the same order

df.plot_overtime(
variable = 'moving',
facet_col = 'species', 
facet_arg = ['D.vir', 'D.ere', 'D.wil', 'D.sec', 'D.yak'],
facet_labels = ['D.virilis', 'D.erecta', 'D.willistoni', 'D.sechellia', 'D.yakuba']
)

# if you're doing circadian experiments you can specify when night begins with the parameter circadian_night to change the phase bars at the bottom. E.g. circadian_night = 18 for lights off at ZT 18.

Quantifying the above

The plots above look nice, but we often want to quantify the differences to prove what our eyes are telling us. The method .plot_quantify() plots the mean and 95% CI of the specimens to give a strong visual indication of group differences.

# Quantify parameters are near identical to .plot_overtime()
# For all plotting methods in behavpy we can add grid lines to better view the data and you can also add a title, see below for how

df.plot_quantify(
variable = 'moving',
facet_col = 'species', 
facet_arg = ['D.vir', 'D.ere', 'D.wil', 'D.sec', 'D.yak']
title = 'This is a plot for quantifying',
grids = True
)

# Tip! You can have a facet_col argument and nothing for facet_args and the method will automatically plot all groups within that column. Careful though as the order/colour might not be preserved in similar but different plots

Quantification of the Drosophila species movement

If working in a python file workspace the plots should open automatically in your browser as a fully interactable plot. If in a jupyter notebook it'll appear below the cell.

You can save the plots as a pdf for the best resolution or as html file to fully interact with it in the browser.

See a few pages down for examples on how to do this