Quickstart: Working with Narrow Data

If you like working with multi-indexed pandas.DataFrame variables, you might want to use the “narrow data” format as an alternative to get_timeseries-based tuples.

This example shows how to use narrow data in combination with Seaborn to apply summarization methods on groups of timeseries.

import numpy
import pandas
import pathlib
from matplotlib import pyplot, cm
import seaborn

import bletl

Parse the raw data file

filepath = pathlib.Path(r"..\tests\data\BL1\NT_1200rpm_30C_DO-GFP75-pH-BS10_12min_20171221_121339.csv")
bldata = bletl.parse(filepath, lot_number=1515, temp=30)
bldata

BLData(model=BL1) {
  "DO": FilterTimeSeries(103 cycles, 48 wells),
  "GFP75": FilterTimeSeries(103 cycles, 48 wells),
  "pH": FilterTimeSeries(103 cycles, 48 wells),
  "BS10": FilterTimeSeries(103 cycles, 48 wells),
}

Get narrow data

You can access the narrow data via methods on the BLData or FilterTimeSeries objects:

narrow = bldata.get_unified_narrow_data()
narrow.head()

	well	cycle	time	DO	GFP75	pH	BS10
0	A01	1	0.00523	96.605204	54.76	7.590411	11.786574
1	A01	2	0.21047	98.859258	54.62	7.576478	12.167130
2	A01	3	0.41047	99.428472	54.84	7.562804	12.210841
3	A01	4	0.61056	102.309616	55.01	7.549377	12.474002
4	A01	5	0.81047	104.657712	54.90	7.549377	11.918877

A quick plot using seaborn

The following cell uses pandas grouping to create a DataFrame that is fed to seaborn.

seaborn.lineplot(data=narrow.groupby("well").get_group("A01"), x="time", y="BS10");

Replicate condensation using seaborn

In some cases, you might want to group several biological replicates and apply mean/std summarization statistics instead of plotting each well individually.

The next cell does exactly that using a pandas query and summarization with seaborn.

replicate_group = ["A01", "B01", "C01"]
sub = narrow.query('well in @replicate_group')

fig, ax = pyplot.subplots(3, figsize=(4,6), sharex=True, dpi=120)
seaborn.lineplot(data=sub, x="time", y="BS10", ci="sd", ax=ax[0])
seaborn.lineplot(data=sub, x="time", y="pH", ci="sd", ax=ax[1])
seaborn.lineplot(data=sub, x="time", y="DO", ci="sd", ax=ax[2])
fig.align_ylabels()

%load_ext watermark
%watermark -n -u -v -iv -w

Last updated: Fri Jul 02 2021

Python implementation: CPython
Python version       : 3.7.9
IPython version      : 7.19.0

pandas    : 1.2.1
matplotlib: 3.3.2
seaborn   : 0.11.1
numpy     : 1.19.2
bletl     : 1.0.0

Watermark: 2.1.0