Visualising your data
Once the behavpy object is created, the print function will just show your data structure. If you want to see your data and the metadata at once, use the built in method
# first load your data and create a behavpy instance of it df.display()
You can also get quick summary statistics of your dataset with
df.summary() # an example output of df.summary() output: behavpy table with: individuals 675 metavariable 9 variables 13 measurements 3370075 # add the argument detailed = True to get information per fly df.summary(detailed = True) output: data_points time_range id 2019-08-02_14-21-23_021d6b|01 5756 86400 -> 431940 2019-08-02_14-21-23_021d6b|02 5481 86400 -> 431940
Be careful with the pandas method
.groupby() as this will return a pandas object back and not a behavpy object. Most other common pandas actions will return a behavpy object.
Visualising your data
Whilst summary statistics are good for a basic overview, visualising the variable of interest over time is usually a lot more informative.
The first port of call when looking at time series data is to create a heatmap to see if there are any obvious irregularities in your experiments.
# To create a heatmap all you need to write is one line of code! # All plot methods will return the figure, the usual etiquette is to save the variable as fig fig = df.heatmap('moving') # enter as a string which ever numerical variable you want plotted inside the brackets # Then all you need to do is the below to generate the figure fig.show()
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. # To achieve the smooth plot a moving average is applied, we found averaging over 30 minutes gave the best results # So if you have your data in rows of 10 seconds you would want the avg_window to be 180 (the default) # Here the data is rows of 60 seconds, so we only need 30 fig = df.plot_overtime( variable = 'moving', wrapped = True, avg_window = 30 ) fig.show() # the plots will show the mean with 95% confidence intervals in a lighter colour around the mean
# 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 fig = 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'] ) fig.show() # 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.