matplotlib text alignment when using latex - matplotlib

Before, this code:
import matplotlib.pyplot as plt
plt.rcParams.update({
"text.usetex": True})
plt.plot([0, 1], [0, 1])
plt.xlabel('hello\nnew world')
generated a plot with centered alignment in the x label, something like here:
But now I get this left aligned text:
I am not sure if matplotlib or latex version changed, but wondering how should I adapt my code to get back to the usual center alignment.
Thank you!
Related question:
Center title in latex-rendered text for matplotlib figure

Adding plt.rcParams['text.latex.preamble'] = r'\centering' solved the problem. Here is the full code:
import matplotlib.pyplot as plt
plt.rcParams.update({
"text.usetex": True})
plt.rcParams['text.latex.preamble'] = r'\centering'
plt.figure(figsize=(2,2))
plt.plot([0, 1], [0, 1])
plt.xlabel('hello \n new world')
\n can be replaced by \\\ or \\newline (as an additional \ is needed as escape character) or \linebreak (so that it doesn't start a new paragraph).
Another option is to put the centering locally:
import matplotlib.pyplot as plt
plt.rcParams.update({
"text.usetex": True})
plt.figure(figsize=(2,2))
plt.plot([0, 1], [0, 1])
plt.xlabel('\\begin{center} hello \linebreak new world \end{center}')

Related

How to enable MultiCursor for each tab, when using plotWindow to create several tabs in Matplotlib?

This question is about MultiCursor and plotWindow created by #superjax, as seen in this topic, here.
I've found MultiCursor works perfectly for creating a "crosshairs" cursor that extends across multiple subplots.
Example (one single window with no tabs; works correctly by displaying the vertical cursor line across both subplots, when mouse is hovering over either suplot):
import matplotlib.pyplot as plt
from matplotlib.widgets import MultiCursor
fig,axs = plt.subplots(nrows=2, ncols=1, figsize=(5,5), gridspec_kw={'width_ratios': [3], 'height_ratios': [2, 2], 'wspace': 0.4, 'hspace': 0.2} )
multi = MultiCursor(fig.canvas, (axs[0], axs[1]), color='r', lw=1, horizOn=False, vertOn=True)
plt.show()
The problem is that MultiCursor does not seem to work for plotWindow -- with multiple subplots in several tabs.
Example (one single window with three tabs; does not work; vertical cursor does not display at all on any tab, or displays incorrectly/sporadically on final tab only):
import matplotlib.pyplot as plt
from matplotlib.widgets import MultiCursor
from plotWindow.plotWindow import plotWindow
pw = plotWindow()
tabs = ['1','2','3']
for tabName in tabs:
fig,axs = plt.subplots(nrows=2, ncols=1, figsize=(5,5), gridspec_kw={'width_ratios': [3], 'height_ratios': [2, 2], 'wspace': 0.4, 'hspace': 0.2} )
multi = MultiCursor(fig.canvas, (axs[0], axs[1]), color='r', lw=1, horizOn=False, vertOn=True)
pw.addPlot(tabName, fig)
pw.show()
I haven't found any potential solutions for this problem so far, and I don't know what would enable the MultiCursor to display correctly across tabs, since the plotWindow functionality isn't part of the official Matplotlib documentation.
Thanks in advance for your help.

MatplotLib: Scatter of Unicode characters with URL links

I want to export a matplotlib scatter plot as a SVG with a different hyperlink from each scatter points which are not simple markers but Unicode characters.
I've try the following:
import matplotlib.cm as cm
import matplotlib as mpl
import matplotlib.pyplot as pyplot
mpl.rcParams['text.usetex'] = True
mpl.rcParams['text.latex.preamble'] = [r'\usepackage{amsmath}'] #for \text command
f = pyplot.figure()
stemp='6F22'
markerstring=r'$\\unichar{"%s}$' % stemp
print(markerstring)
ss = pyplot.scatter([1, 2, 3], [4, 5, 6],color='black',s=[100, 0, 0],alpha=0.5, marker=markerstring)
ss.set_urls(['http://www.bbc.co.uk/news', 'http://www.google.com', None])
f.savefig('scatter.svg')
It either crashs because "\u" or "\unichar" is unknown
Or displays 6F22 instead of 漢 in the final SVG

Animate a point moving along path between two points

I want to animate a point moving along a path from one location to another on the map.
For example, I drawn a path from New York to New Delhi, using Geodetic transform. Eg. taken from docs Adding data to the map
plt.plot([ny_lon, delhi_lon], [ny_lat, delhi_lat],
color='blue', linewidth=2, marker='o',
transform=ccrs.Geodetic(),
)
Now i want to move a point along this path.
My idea was to somehow get some (say 50) points, along the path and plot a marker on each point for each frame. But I am not able to find a way to get the points on the path.
I found a function transform_points under classCRS, but I am unable to use this, as this gives me the same number of points i have, not the points in between.
Thanks in advance!
There are a couple of approaches to this.
The matplotlib approach
I'll start with perhaps the most basic if you are familiar with matplotlib, but this approach suffers from indirectly using cartopy's functionality, and is therefore harder to configure/extend.
There is a private _get_transformed_path method on a Line2D object (the thing that is returned from plt.plot). The resulting TransformedPath object has a get_transformed_path_and_affine method, which basically will give us the projected line (in the coordinate system of the Axes being drawn).
In [1]: import cartopy.crs as ccrs
In [3]: import matplotlib.pyplot as plt
In [4]: ax = plt.axes(projection=ccrs.Robinson())
In [6]: ny_lon, ny_lat = -75, 43
In [7]: delhi_lon, delhi_lat = 77.23, 28.61
In [8]: [line] = plt.plot([ny_lon, delhi_lon], [ny_lat, delhi_lat],
...: color='blue', linewidth=2, marker='o',
...: transform=ccrs.Geodetic(),
...: )
In [9]: t_path = line._get_transformed_path()
In [10]: path_in_data_coords, _ = t_path.get_transformed_path_and_affine()
In [11]: path_in_data_coords.vertices
Out[11]:
array([[-6425061.82215208, 4594257.92617961],
[-5808923.84969279, 5250795.00604155],
[-5206753.88613758, 5777772.51828996],
[-4554622.94040482, 6244967.03723341],
[-3887558.58343227, 6627927.97123701],
[-3200922.19194864, 6932398.19937816],
[-2480001.76507805, 7165675.95095855],
[-1702269.5101901 , 7332885.72276795],
[ -859899.12295981, 7431215.78426759],
[ 23837.23431173, 7453455.61302756],
[ 889905.10635756, 7397128.77301289],
[ 1695586.66856764, 7268519.87627204],
[ 2434052.81300274, 7073912.54130764],
[ 3122221.22299409, 6812894.40443648],
[ 3782033.80448001, 6478364.28561403],
[ 4425266.18173684, 6062312.15662039],
[ 5049148.25986903, 5563097.6328901 ],
[ 5616318.74912886, 5008293.21452795],
[ 6213232.98764984, 4307186.23400115],
[ 6720608.93929235, 3584542.06839575],
[ 7034261.06659143, 3059873.62740856]])
We can pull this together with matplotlib's animation functionality to do as requested:
import cartopy.crs as ccrs
import matplotlib.animation as animation
import matplotlib.pyplot as plt
ax = plt.axes(projection=ccrs.Robinson())
ax.stock_img()
ny_lon, ny_lat = -75, 43
delhi_lon, delhi_lat = 77.23, 28.61
[line] = plt.plot([ny_lon, delhi_lon], [ny_lat, delhi_lat],
color='blue', linewidth=2, marker='o',
transform=ccrs.Geodetic(),
)
t_path = line._get_transformed_path()
path_in_data_coords, _ = t_path.get_transformed_path_and_affine()
# Draw the point that we want to animate.
[point] = plt.plot(ny_lon, ny_lat, marker='o', transform=ax.projection)
def animate_point(i):
verts = path_in_data_coords.vertices
i = i % verts.shape[0]
# Set the coordinates of the line to the coordinate of the path.
point.set_data(verts[i, 0], verts[i, 1])
ani = animation.FuncAnimation(
ax.figure, animate_point,
frames= path_in_data_coords.vertices.shape[0],
interval=125, repeat=True)
ani.save('point_ani.gif', writer='imagemagick')
plt.show()
The cartopy approach
Under the hood, cartopy's matplotlib implementation (as used above), is calling the project_geometry method. We may as well make use of this directly as it is often more convenient to be using Shapely geometries than it is matplotlib Paths.
With this approach, we simply define a shapely geometry, and then construct the source and target coordinate reference systems that we want to convert the geometry from/to:
target_cs.project_geometry(geometry, source_cs)
The only thing we have to watch out for is that the result can be a MultiLineString (or more generally, any Multi- geometry type). However, in our simple case, we don't need to deal with that (incidentally, the same was true of the simple Path returned in the first example).
The code to produce a similar plot to above:
import cartopy.crs as ccrs
import matplotlib.animation as animation
import matplotlib.pyplot as plt
import numpy as np
import shapely.geometry as sgeom
ax = plt.axes(projection=ccrs.Robinson())
ax.stock_img()
ny_lon, ny_lat = -75, 43
delhi_lon, delhi_lat = 77.23, 28.61
line = sgeom.LineString([[ny_lon, ny_lat], [delhi_lon, delhi_lat]])
projected_line = ccrs.PlateCarree().project_geometry(line, ccrs.Geodetic())
# We only animate along one of the projected lines.
if isinstance(projected_line, sgeom.MultiLineString):
projected_line = projected_line.geoms[0]
ax.add_geometries(
[projected_line], ccrs.PlateCarree(),
edgecolor='blue', facecolor='none')
[point] = plt.plot(ny_lon, ny_lat, marker='o', transform=ccrs.PlateCarree())
def animate_point(i):
verts = np.array(projected_line.coords)
i = i % verts.shape[0]
# Set the coordinates of the line to the coordinate of the path.
point.set_data(verts[i, 0], verts[i, 1])
ani = animation.FuncAnimation(
ax.figure, animate_point,
frames=len(projected_line.coords),
interval=125, repeat=True)
ani.save('projected_line_ani.gif', writer='imagemagick')
plt.show()
Final remaaaaarrrrrrks....
The approach naturally generalises to animating any type of matplotlib Arrrrtist.... in this case, I took a bit more control over the great circle resolution, and I animated an image along the great circle:
import cartopy.crs as ccrs
import matplotlib.animation as animation
import matplotlib.pyplot as plt
import numpy as np
import shapely.geometry as sgeom
ax = plt.axes(projection=ccrs.Mercator())
ax.stock_img()
line = sgeom.LineString([[-5.9845, 37.3891], [-82.3666, 23.1136]])
# Higher resolution version of Mercator. Same workaround as found in
# https://github.com/SciTools/cartopy/issues/8#issuecomment-326987465.
class HighRes(ax.projection.__class__):
#property
def threshold(self):
return super(HighRes, self).threshold / 100
projected_line = HighRes().project_geometry(line, ccrs.Geodetic())
# We only animate along one of the projected lines.
if isinstance(projected_line, sgeom.MultiLineString):
projected_line = projected_line.geoms[0]
# Add the projected line to the map.
ax.add_geometries(
[projected_line], ax.projection,
edgecolor='blue', facecolor='none')
def ll_to_extent(x, y, ax_size=(4000000, 4000000)):
"""
Return an image extent in centered on the given
point with the given width and height.
"""
return [x - ax_size[0] / 2, x + ax_size[0] / 2,
y - ax_size[1] / 2, y + ax_size[1] / 2]
# Image from https://pixabay.com/en/sailing-ship-boat-sail-pirate-28930/.
pirate = plt.imread('pirates.png')
img = ax.imshow(pirate, extent=ll_to_extent(0, 0), transform=ax.projection, origin='upper')
ax.set_global()
def animate_ship(i):
verts = np.array(projected_line.coords)
i = i % verts.shape[0]
# Set the extent of the image to the coordinate of the path.
img.set_extent(ll_to_extent(verts[i, 0], verts[i, 1]))
ani = animation.FuncAnimation(
ax.figure, animate_ship,
frames=len(projected_line.coords),
interval=125, repeat=False)
ani.save('arrrr.gif', writer='imagemagick')
plt.show()
All code and images for this answer can be found at https://gist.github.com/pelson/618a5f4ca003e56f06d43815b21848f6.

matplotlib collection linewidth mapping?

I'm creating some GIS-style plots in matplotlib of road networks and the like, so I'm using LineCollection to store and represent all of the roads and color accordingly. This is working fine, I color the roads based on a criteria and the following map:
from matplotlib.colors import ListedColormap,BoundaryNorm
from matplotlib.collections import LineCollection
cmap = ListedColormap(['grey','blue','green','yellow','orange','red','black'])
norm = BoundaryNorm([0,0.5,0.75,0.9,0.95,1.0,1.5,100],cmap.N)
roads = LineCollection(road_segments, array=ratios, cmap=cmap, norm=norm)
axes.add_collection(roads)
This works fine, however I would really like to have linewidths defined in a similar manner to the color map - ranging from 0.5 to 5 for each color
Does anyone know of a clever way of doing this?
The linewidths keyword.
import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection
axes = plt.axes()
roads = LineCollection([
[[0, 0], [1, 1]],
[[0, 1], [1, 0]]
],
colors=['black', 'red'],
linewidths=[3, 8],
)
axes.add_collection(roads)
plt.show()
HTH

matplotlib: Stretch image to cover the whole figure

I am quite used to working with matlab and now trying to make the shift matplotlib and numpy. Is there a way in matplotlib that an image you are plotting occupies the whole figure window.
import numpy as np
import matplotlib.pyplot as plt
# get image im as nparray
# ........
plt.figure()
plt.imshow(im)
plt.set_cmap('hot')
plt.savefig("frame.png")
I want the image to maintain its aspect ratio and scale to the size of the figure ... so when I do savefig it exactly the same size as the input figure, and it is completely covered by the image.
Thanks.
I did this using the following snippet.
#!/usr/bin/env python
import numpy as np
import matplotlib.cm as cm
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
from pylab import *
delta = 0.025
x = y = np.arange(-3.0, 3.0, delta)
X, Y = np.meshgrid(x, y)
Z1 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
Z = Z2-Z1 # difference of Gaussians
ax = Axes(plt.gcf(),[0,0,1,1],yticks=[],xticks=[],frame_on=False)
plt.gcf().delaxes(plt.gca())
plt.gcf().add_axes(ax)
im = plt.imshow(Z, cmap=cm.gray)
plt.show()
Note the grey border on the sides is related to the aspect rario of the Axes which is altered by setting aspect='equal', or aspect='auto' or your ratio.
Also as mentioned by Zhenya in the comments Similar StackOverflow Question
mentions the parameters to savefig of bbox_inches='tight' and pad_inches=-1 or pad_inches=0
You can use a function like the one below.
It calculates the needed size for the figure (in inches) according to the resolution in dpi you want.
import numpy as np
import matplotlib.pyplot as plt
def plot_im(image, dpi=80):
px,py = im.shape # depending of your matplotlib.rc you may
have to use py,px instead
#px,py = im[:,:,0].shape # if image has a (x,y,z) shape
size = (py/np.float(dpi), px/np.float(dpi)) # note the np.float()
fig = plt.figure(figsize=size, dpi=dpi)
ax = fig.add_axes([0, 0, 1, 1])
# Customize the axis
# remove top and right spines
ax.spines['right'].set_color('none')
ax.spines['left'].set_color('none')
ax.spines['top'].set_color('none')
ax.spines['bottom'].set_color('none')
# turn off ticks
ax.xaxis.set_ticks_position('none')
ax.yaxis.set_ticks_position('none')
ax.xaxis.set_ticklabels([])
ax.yaxis.set_ticklabels([])
ax.imshow(im)
plt.show()
Here's a minimal object-oriented solution:
fig = plt.figure(figsize=(8, 8))
ax = fig.add_axes([0, 0, 1, 1], frameon=False, xticks=[], yticks=[])
Testing it out with
ax.imshow([[0]])
fig.savefig('test.png')
saves out a uniform purple block.
edit: As #duhaime points out below, this requires the figure to have the same aspect as the axes.
If you'd like the axes to resize to the figure, add aspect='auto' to imshow.
If you'd like the figure to resize to be resized to the axes, add
from matplotlib import tight_bbox
bbox = fig.get_tightbbox(fig.canvas.get_renderer())
tight_bbox.adjust_bbox(fig, bbox, fig.canvas.fixed_dpi)
after the imshow call. This is the important bit of matplotlib's tight_layout functionality which is implicitly called by things like Jupyter's renderer.