textalloc 0.0.5

Efficient Text Allocation in matplotlib using NumPy Broadcasting

textalloc - Efficient matplotlib Text Allocation

plt.text	textalloc (2.1s)

Scatterplot design from scattertext (https://github.com/JasonKessler/scattertext)

Quick-start

Installation

pip install textalloc

Using textalloc

The code below generates the plot to the right:

import textalloc as ta
import numpy as np
import matplotlib.pyplot as plt

np.random.seed(0)
x, y = np.random.random((2,30))
fig, ax = plt.subplots()
ax.scatter(x, y, c='b')
text_list = [f'Text{i}' for i in range(len(x))]
ta.allocate_text(fig,ax,x,y,
                text_list,
                x_scatter=x, y_scatter=y,
                textsize=10)
plt.show()

plt.text	textalloc

Parameters

fig:
    matplotlib figure used for rendering textbox-sizes.
ax:
    matplotlib axes used for plotting.
x: (array-like):
    x-coordinates of texts.
y: (array-like):
    y-coordinates of texts.
text_list: (array-like):
    list of texts.
x_scatter: (array-like), default None
    x-coordinates of all scattered points.
y_scatter: (array-like), default None
    y-coordinates of all scattered points.
x_lines: (array-like), default None
    x-coordinates of all lines in plot.
y_lines: (array-like), default None
    y-coordinates of all lines in plot.
textsize: (int), default 10
    Size of text.
margin: (float), default 0.01
    Parameter for margins between objects.
    Increase for larger margins to points and lines.
    Given in proportion of ax dimensions (0-1)
min_distance: (float), default 0.015
    Parameter for min distance from textbox to
    its plotted position.
    Given in proportion of ax dimensions (0-1)
max_distance: (float), default 0.07
    Parameter for max distance from textbox to
    its plotted position.
    Given in proportion of ax dimensions (0-1)
verbose: (bool), default False
    prints progress using tqdm.
draw_lines: (bool), default True
    draws lines from original points to textboxes.
linecolor: (str), default "r"
    Color code of the lines between points and text-boxes.
draw_all: (bool), default True
    Draws all texts after allocating as many as possible despite overlap.
nbr_candidates: (int), default 100
    Sets the number of candidates used.
linewidth: (float), default 1
    Width of line between textbox and it's origin.
textcolor: (str), default "k"
    Color code of the text.

Implementation and speed

The focus in this implementation is on speed and allocating as many text-boxes as possible into the free space in the plot. There are three main steps of the algorithm:

For each textbox to be plotted:

1. Generate a large number of candidate boxes near the original point with size that matches the fontsize.
2. Find the candidates that do not overlap any points, lines, plot boundaries, or already allocated boxes.
3. Allocate the text to the first candidate box with no overlaps.

Speed

The plot in the top of this Readme was generated in 2.1s on a local laptop, and there are rarely more textboxes that fit into one plot. If the result is still too slow to render, try decreasing nbr_candidates.

The speed is greatly improved by usage of numpy broadcasting in all functions for computing overlap (see textalloc/overlap_functions and textalloc/find_non_overlapping). A simple example from the function non_overlapping_with_boxes which checks if the candidate boxes (expanded with xfrac, yfrac to provide a margin) overlap with already allocated boxes:

candidates[:, 0][:, None] - xfrac > box_arr[:, 2]

The code compares xmin coordinates of all candidates with xmax coordinates of all allocated boxes resulting in a boolean matrix of shape (N_candidates, N_allocated) by use of indexing [:, None].

Types of overlap supported

textalloc supports avoiding overlap with points, lines, and the plot boundary in addition to other text-boxes. See examples below and demo.py for all examples:

import textalloc as ta
import numpy as np
import matplotlib.pyplot as plt

x_line = np.array([0.0, 0.03192317, 0.04101177, 0.26085659, 0.40261173, 0.42142198, 0.87160195, 1.00349979])
y_line = np.array([0. , 0.2, 0.2, 0.4, 0.8, 0.6, 1. , 1. ])
text_list = ['0', '25', '50', '75', '100', '125', '150', '250']
np.random.seed(0)
x, y = np.random.random((2,100))

fig,ax = plt.subplots(dpi=100)
ax.plot(x_line,y_line,color="black")
ax.scatter(x,y,c="b")
ta.allocate_text(fig,ax,x_line,y_line,
                text_list,
                x_scatter=x, y_scatter=y,
                x_lines=[x_line], y_lines=[y_line])
plt.show()

plt.text	textalloc (0.2s)

import textalloc as ta
import numpy as np
import matplotlib.pyplot as plt

np.random.seed(2017)
x_data = np.random.random_sample(100)
y_data = np.random.random_integers(10,50,(100))

f, ax = plt.subplots(dpi=200)
bars = ax.bar(x_data, y_data, width=0.002, facecolor='k')
ta.allocate_text(f,ax,x_data,y_data,
                [str(yy) for yy in list(y_data)],
                x_lines=[np.array([xx,xx]) for xx in list(x_data)],
                y_lines=[np.array([0,yy]) for yy in list(y_data)], 
                textsize=8,
                margin=0.004,
                min_distance=0.005,
                linewidth=0.7,
                nbr_candidates=100,
                textcolor="b")
plt.show()

plt.text	textalloc (0.7s)