https://github.com/clberube/pydlc
Simplified Python implementation of the Density Line Chart by Moritz & Fisher.
https://github.com/clberube/pydlc
datavisualization density-estimation sequential-data timeseries
Last synced: 9 months ago
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Simplified Python implementation of the Density Line Chart by Moritz & Fisher.
- Host: GitHub
- URL: https://github.com/clberube/pydlc
- Owner: clberube
- License: mit
- Created: 2021-06-05T14:52:34.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2021-07-30T18:29:46.000Z (over 4 years ago)
- Last Synced: 2025-04-28T13:44:23.931Z (10 months ago)
- Topics: datavisualization, density-estimation, sequential-data, timeseries
- Language: Python
- Homepage:
- Size: 849 KB
- Stars: 23
- Watchers: 1
- Forks: 4
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
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# PyDLC - Density Line Charts with Python
Python implementation of the Density Line Chart [(Moritz & Fisher, 2018)](https://arxiv.org/abs/1808.06019) to visualize time series collections.
## Installation
### Python Package Index
```console
$ pip install pydlc
```
### Requirements
- [numpy](numpy.org/)
- [matplotlib](matplotlib.org/)
## Usage
### Example
The following example shows how to import and use the `dense_lines` plotting function.
```python
import numpy as np
import matplotlib.pyplot as plt
from pydlc import dense_lines
# Generate random synthetic time series
x = np.linspace(0, 90, 25)
ys = []
for _ in range(10000):
ys.append(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) # this is slow and cluttered
axs[0].set_title('Line Chart')
im = dense_lines(ys, x=x, ax=axs[1], cmap='magma') # this is fast and clean
axs[1].set_title('Density Lines Chart')
fig.colorbar(im)
fig.tight_layout()
plt.show()
```
### Arguments
- ys (`list` of `1darray`): The lines to plot. Can also be
passed as a `2darray`.
- x (`1darray`, optional): The x values corresponding to
the data passed with `ys`. If not provided, `range(0, len(ys))`
is used.
- ax (`matplotlib axes`, optional): The axes to plot on. If not
provided a new figure will be created.
- ny (`int`, optional): The vertical grid size. Higher values
yield a smoother density estimation. Default: 100.
- y_pad (`float`, optional): The padding fraction to set the
grid limits past the data values. Must be greater than 0.
Default: 0.01.
- normalize (`bool`, optional): Normalize the plot so the density
is between 0 and 1. Default: True.
- **kwargs: Arbitrary keyword arguments to pass to `plt.imshow()`.
## Limitations
- All series to be included in the density estimation and passed in the `ys` argument must have the same length.
- 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](https://idl.cs.washington.edu/papers/dense-lines/)).
