| plt.text | textalloc (2.1s) |
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pip install 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(ax,x,y,
text_list,
x_scatter=x, y_scatter=y,
textsize=10)
plt.show()
| plt.text | textalloc |
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Text-boxes input parameters are x, y and text_list, which define the text-strings to be plotted and the positions that the texts should point to. x_scatter, y_scatter, x_lines and y_lines define all points and lines in the plot that should not overlap with the text-boxes. Note that the scattered points do not have to be the same as x and y for the text-boxes, but can include more, or different scattered points.
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
pairs of x-coordinates of all lines in the plot (start and endpoint).
y_lines: (array-like), default None
pairs of y-coordinates of all lines in the plot (start and endpoint).
scatter_sizes: (array-like), default None
sizes of all scattered objects in plot list of 1d arrays/lists.
scatter_plot:
provide a scatterplot object (scatter_plot=ax.scatter(...))
for more exact placement instead of x_scatter, y_scatter, scatter_sizes etc.. default None.
text_scatter_sizes: (array-like), default None
sizes of text scattered objects in plot list of 1d arrays/lists.
textsize: (Union[int, List[int]]), default 10
Size of text.
margin: (float), default 0.0
Parameter for margins between objects. Recommendation: keep this lower than min_distance.
Increase for larger margins to points and lines.
Given in proportion of x-ax dimensions (0-1)
min_distance: (float), default 0.015
Parameter for min distance from textbox to
its plotted position.
Given in proportion of x-ax dimensions (0-1)
max_distance: (float), default 0.2
Parameter for max distance from textbox to
its plotted position.
Given in proportion of x-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: (Union[str, List[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 200
Sets the number of candidates used.
linewidth: (float), default 1
Width of line between textbox and it's origin.
textcolor: (Union[str, List[str]]), default "k"
Color code of the text.
seed: (int), default 0
Seeds order of text allocations.
direction: (str), default None
Sets the preferred direction of the boxes with options:
(south, north, east, west, northeast, northwest, southeast, southwest).
avoid_label_lines_overlap: (bool), default False
If set to True, avoids overlap for drawn lines to text labels.
plot_kwargs: (dict), default None
kwargs for the plt.plot of the lines if draw_lines is True.
**kwargs: ()
kwargs for the plt.text() call.
If transform is used, it only needs to be provided here, i.e. not also in plot_kwargs.
The allocate call returns a tuple containing the resulting positions used to plot the text labels and the connecting label lines
The implementation aims to plot as many text-boxes as possible in the free space in the plot. There are three main steps of the algorithm:
For each textbox to be plotted:
- Generate a large number of candidate boxes near the original point with size that matches the fontsize.
- Find the candidates that do not overlap any points, lines, plot boundaries, or already allocated boxes.
- Allocate the text to the first candidate box with no overlaps.
The plot in the top of this Readme was generated in 2.1s on a 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].
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(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) |
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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(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) |
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textalloc now also supports plotting on images and using transforms. Below is an eample of using the PlateCarree transform to plot on top of a downloaded OSM-map (thank you @nebukadnezar for the example).
import numpy as np
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import cartopy.io.img_tiles as cimgt
import textalloc as ta
np.random.seed(1)
x = np.random.rand(100)
x += 150.5
y = np.random.rand(100)
y -= 34.5
dpi = 72
fig = plt.figure(figsize=(800/dpi, 800/dpi), dpi=dpi)
zoom = 10
sitelon = np.mean(x)
sitelat = np.mean(y)
radius = (np.max(x) - np.min(x) ) / 1.5
ll_lon = sitelon - radius * (1/np.cos(np.radians(sitelat)))
ll_lat = sitelat - radius
ur_lon = sitelon + radius * (1/np.cos(np.radians(sitelat)))
ur_lat = sitelat + radius
extent = [ll_lon, ur_lon, ll_lat, ur_lat]
request = cimgt.OSM(desired_tile_form="L")
ax = plt.axes(projection=request.crs)
ax.set_extent(extent)
ax.add_image(request, zoom, alpha=0.5, cmap='gray')
ax.scatter(x, y, c='b', transform=ccrs.PlateCarree())
text_list = [f'Text{i}' for i in range(len(x))]
ta.allocate(ax,x,y,
text_list,
x_scatter=x, y_scatter=y,
textsize=10,
draw_lines=True,
linewidth=0.5,
draw_all=False,
transform=ccrs.PlateCarree(),
avoid_label_lines_overlap=True)
plt.show()
