Skip to main content

A plugin based data load library

Project description

A plugin based data load and manimupulate library.

Usage

Assume that there are three kinds of samples and each samples have 5 indipendent experimental results. All filenames are written as the following format:

sample-type<type number>.<experiment number>.txt

And files are saved in data directory like:

+- data
    |
    +- sample-type1.001.txt
    +- sample-type1.002.txt
    +- sample-type1.003.txt
    +- sample-type1.004.txt
    +- sample-type1.005.txt
    +- sample-type2.001.txt
    +- sample-type2.002.txt
    +- sample-type2.003.txt
    +- sample-type2.004.txt
    +- sample-type2.005.txt
    +- sample-type3.001.txt
    +- sample-type3.002.txt
    +- sample-type3.003.txt
    +- sample-type3.004.txt
    +- sample-type3.005.txt

Then, the code for plotting the data will be:

>>> import matplotlib.pyplot as plt
>>> import maidenhair
>>> import maidenhair.statistics
>>> dataset = []
>>> dataset += maidenhair.load('data/sample-type1.*.txt', unite=True)
>>> dataset += maidenhair.load('data/sample-type2.*.txt', unite=True)
>>> dataset += maidenhair.load('data/sample-type3.*.txt', unite=True)
>>> nameset = ['Type1', 'Type2', 'Type3']
>>> for name, (x, y) in zip(nameset, dataset):
...     xa = maidenhair.statistics.average(x)
...     ya = maidenhair.statistics.average(y)
...     ye = maidenhair.statistics.confidential_interval(y)
...     plt.errorbar(xa, ya, yerr=ye, label=name)
...
>>> plt.show()

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

maidenhair-0.1.1.tar.gz (8.8 kB view hashes)

Uploaded Source

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page