Python implementation of the Density Line Chart by Moritz & Fisher.

Overview

PyDLC - Density Line Charts with Python

Python implementation of the Density Line Chart (Moritz & Fisher, 2018) to visualize large collections of time series.

Installation

Python Package Index

$ pip install pydlc

Requirements

Usage

The following example shows how to import and use the dense_lines plotting function.

import numpy as np
import matplotlib.pyplot as plt
from pydlc import dense_lines

# Generate random synthetic time series
x = np.linspace(0, 100, 50)
ys = []
for _ in range(1000):
    ys.append(3 + 1.5*np.random.randn(1)*np.exp(-x/100))

# Plot here
fig, axs = plt.subplots(1, 2, figsize=(8, 3), sharey=True, sharex=True)
axs[0].plot(x, np.array(ys).T, lw=1)
axs[0].set_title('Line Chart')
im = dense_lines(ys, x=x, ax=axs[1], cmap='magma')  # accepts plt.imshow() kwargs
axs[1].set_title('Density Lines Chart')
fig.colorbar(im)
fig.tight_layout()
plt.show()

Limitations

The vertical grid size can be adjusted with the ny parameter. Higher values of ny yield a smoother density visualization. However, the horizontal grid size is currently limited to the same size as the input sequences and there is no parameter to adjust it (yet).

Algorithm

This graphical abstract explains the algorithm (source).

You might also like...
Visualize and compare datasets, target values and associations, with one line of code.
Visualize and compare datasets, target values and associations, with one line of code.

In-depth EDA (target analysis, comparison, feature analysis, correlation) in two lines of code! Sweetviz is an open-source Python library that generat

Automatically Visualize any dataset, any size with a single line of code.  Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.
Automatically Visualize any dataset, any size with a single line of code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.

AutoViz Automatically Visualize any dataset, any size with a single line of code. AutoViz performs automatic visualization of any dataset with one lin

This is simply repo for line drawing rendering using freestyle in Blender.

blender_freestyle_line_drawing This is simply repo for line drawing rendering using freestyle in Blender. how to use blender2935 --background --python

A command line tool for visualizing CSV/spreadsheet-like data
A command line tool for visualizing CSV/spreadsheet-like data

PerfPlotter Read data from CSV files using pandas and generate interactive plots using bokeh, which can then be embedded into HTML pages and served by

Visualize your pandas data with one-line code
Visualize your pandas data with one-line code

PandasEcharts 简介 基于pandas和pyecharts的可视化工具 安装 pip 安装 $ pip install pandasecharts 源码安装 $ git clone https://github.com/gamersover/pandasecharts $ cd pand

Plotly Dash Command Line Tools - Easily create and deploy Plotly Dash projects from templates
Plotly Dash Command Line Tools - Easily create and deploy Plotly Dash projects from templates

🛠️ dash-tools - Create and Deploy Plotly Dash Apps from Command Line | | | | | Create a templated multi-page Plotly Dash app with CLI in less than 7

Create matplotlib visualizations from the command-line
Create matplotlib visualizations from the command-line

MatplotCLI Create matplotlib visualizations from the command-line MatplotCLI is a simple utility to quickly create plots from the command-line, levera

Parallel t-SNE implementation with Python and Torch wrappers.
Parallel t-SNE implementation with Python and Torch wrappers.

Multicore t-SNE This is a multicore modification of Barnes-Hut t-SNE by L. Van der Maaten with python and Torch CFFI-based wrappers. This code also wo

Parallel t-SNE implementation with Python and Torch wrappers.
Parallel t-SNE implementation with Python and Torch wrappers.

Multicore t-SNE This is a multicore modification of Barnes-Hut t-SNE by L. Van der Maaten with python and Torch CFFI-based wrappers. This code also wo

Releases(v0.3)
Owner
Charles L. Bérubé
Assistant professor of applied geophysics
Charles L. Bérubé
Here are my graphs for hw_02

Let's Have A Look At Some Graphs! Graph 1: State Mentions in Congressperson's Tweets on 10/01/2017 The graph below uses this data set to demonstrate h

7 Sep 02, 2022
GitHub Stats Visualizations : Transparent

GitHub Stats Visualizations : Transparent Generate visualizations of GitHub user and repository statistics using GitHub Actions. ⚠️ Disclaimer The pro

YuanYap 7 Apr 05, 2022
🧇 Make Waffle Charts in Python.

PyWaffle PyWaffle is an open source, MIT-licensed Python package for plotting waffle charts. It provides a Figure constructor class Waffle, which coul

Guangyang Li 528 Jan 02, 2023
JupyterHub extension for ContainDS Dashboards

ContainDS Dashboards for JupyterHub A Dashboard publishing solution for Data Science teams to share results with decision makers. Run a private on-pre

Ideonate 179 Nov 29, 2022
This is my favourite function - the Rastrigin function.

This is my favourite function - the Rastrigin function. What sparked my curiosity and interest in the function was its complexity in terms of many local optimum points, which makes it particularly in

1 Dec 27, 2021
An application that allows you to design and test your own stock trading algorithms in an attempt to beat the market.

StockBot is a Python application for designing and testing your own daily stock trading algorithms. Installation Use the

Ryan Cullen 280 Dec 19, 2022
A high performance implementation of HDBSCAN clustering. http://hdbscan.readthedocs.io/en/latest/

HDBSCAN Now a part of scikit-learn-contrib HDBSCAN - Hierarchical Density-Based Spatial Clustering of Applications with Noise. Performs DBSCAN over va

Leland McInnes 91 Dec 29, 2022
A TileDB backend for xarray.

TileDB-xarray This library provides a backend engine to xarray using the TileDB Storage Engine. Example usage: import xarray as xr dataset = xr.open_d

TileDB, Inc. 14 Jun 02, 2021
3D rendered visualization of the austrian monuments registry

Visualization of the Austrian Monuments Visualization of the monument landscape of the austrian monuments registry (Bundesdenkmalamt Denkmalverzeichni

Nikolai Janakiev 3 Oct 24, 2019
This is a super simple visualization toolbox (script) for transformer attention visualization ✌

Trans_attention_vis This is a super simple visualization toolbox (script) for transformer attention visualization ✌ 1. How to prepare your attention m

Mingyu Wang 3 Jul 09, 2022
Generate graphs with NetworkX, natively visualize with D3.js and pywebview

webview_d3 This is some PoC code to render graphs created with NetworkX natively using D3.js and pywebview. The main benifit of this approac

byt3bl33d3r 68 Aug 18, 2022
Automatically Visualize any dataset, any size with a single line of code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.

AutoViz Automatically Visualize any dataset, any size with a single line of code. AutoViz performs automatic visualization of any dataset with one lin

AutoViz and Auto_ViML 1k Jan 02, 2023
Simple and fast histogramming in Python accelerated with OpenMP.

pygram11 Simple and fast histogramming in Python accelerated with OpenMP with help from pybind11. pygram11 provides functions for very fast histogram

Doug Davis 28 Dec 14, 2022
Farhad Davaripour, Ph.D. 1 Jan 05, 2022
Simple plotting for Python. Python wrapper for D3xter - render charts in the browser with simple Python syntax.

PyDexter Simple plotting for Python. Python wrapper for D3xter - render charts in the browser with simple Python syntax. Setup $ pip install PyDexter

D3xter 31 Mar 06, 2021
:art: Diagram as Code for prototyping cloud system architectures

Diagrams Diagram as Code. Diagrams lets you draw the cloud system architecture in Python code. It was born for prototyping a new system architecture d

MinJae Kwon 27.5k Dec 30, 2022
Customizing Visual Styles in Plotly

Customizing Visual Styles in Plotly Code for a workshop originally developed for an Unconference session during the Outlier Conference hosted by Data

Data Design Dimension 9 Aug 03, 2022
Machine learning beginner to Kaggle competitor in 30 days. Non-coders welcome. The program starts Monday, August 2, and lasts four weeks. It's designed for people who want to learn machine learning.

30-Days-of-ML-Kaggle 🔥 About the Hands On Program 💻 Machine learning beginner → Kaggle competitor in 30 days. Non-coders welcome The program starts

Roja Achary 145 Jan 01, 2023
🐞 📊 Ladybug extension to generate 2D charts

ladybug-charts Ladybug extension to generate 2D charts. Installation pip install ladybug-charts QuickStart import ladybug_charts API Documentation Loc

Ladybug Tools 3 Dec 30, 2022
Sci palettes for matplotlib/seaborn

sci palettes for matplotlib/seaborn Installation python3 -m pip install sci-palettes Usage import seaborn as sns import matplotlib.pyplot as plt impor

Qingdong Su 2 Jun 07, 2022