matplotlib show many images in single pdf page - python

Given an image of unknown size as input, the following python script shows it 8 times in a single pdf page:
pdf = PdfPages( './test.pdf' )
gs = gridspec.GridSpec(2, 4)
ax1 = plt.subplot(gs[0])
ax1.imshow( _img )
ax2 = plt.subplot(gs[1])
ax2.imshow( _img )
ax3 = plt.subplot(gs[2])
ax3.imshow( _img )
# so on so forth...
ax8 = plt.subplot(gs[7])
ax8.imshow( _img )
pdf.savefig()
pdf.close()
The input image can have different size (unknown a priori). I tried using the function gs.update(wspace=xxx, hspace=xxx) to change the spacing between the images, hoping that matplotlib would automagically resize and redistribute the images to have least white space possible. However, as you can see below, it didn't work as I expected.
Is there a better way to go to achieve the following?
Have images saved with max resolution possible
Have less white space possible
Ideally I would like the 8 images to completely will the page size of the pdf (with minimum amount of margin needed).

You were on the right path: hspace and wspace control the spaces between the images. You can also control the margins on the figure with top, bottom, left and right:
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import matplotlib.image as mimage
from matplotlib.backends.backend_pdf import PdfPages
_img = mimage.imread('test.jpg')
pdf = PdfPages( 'test.pdf' )
gs = gridspec.GridSpec(2, 4, top=1., bottom=0., right=1., left=0., hspace=0.,
wspace=0.)
for g in gs:
ax = plt.subplot(g)
ax.imshow(_img)
ax.set_xticks([])
ax.set_yticks([])
# ax.set_aspect('auto')
pdf.savefig()
pdf.close()
Result:
If you want your images to really cover all the available space, then you can set the aspect ratio to auto:
ax.set_aspect('auto')
Result:

Related

How to create a Python Pie chart in the form of a coin? [duplicate]

I am using python 3.5.2
I would like to make a pie chart with an png image imbedded. I have pictures of certain bulk products that I would like to insert into the slices. For example strawberries in one slice and raspberries in another. Much like the picture http://www.python-course.eu/images/pie_chart_with_raspberries.png shows.
I can produce images and even plot images instead of points as demonstrated here Matplotlib: How to plot images instead of points?
However, I could not find any approach towards what I am proposing. I suppose it could be manually done in paint, but I was trying to avoid that.
That is sure possible. We can start with a normal pie chart. Then we would need to get the images into the plot. This is done using plt.imread and by using a matplotlib.offsetbox.OffsetImage. We would need to find good coordinates and zoom levels to place the image, such that it overlapps completely with respective pie wedge. Then the Path of the pie's wedge is used as a clip path of the image, such that only the part inside the wedge is left over. Setting the zorder of the unfilled wedge to a high number ensures the borders to be placed on top of the image. This way it looks like the wedges are filled with the image.
import matplotlib.pyplot as plt
from matplotlib.patches import PathPatch
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
total = [5,7,4]
labels = ["Raspberries", "Blueberries", "Blackberries"]
plt.title('Berries')
plt.gca().axis("equal")
wedges, texts = plt.pie(total, startangle=90, labels=labels,
wedgeprops = { 'linewidth': 2, "edgecolor" :"k","fill":False, })
def img_to_pie( fn, wedge, xy, zoom=1, ax = None):
if ax==None: ax=plt.gca()
im = plt.imread(fn, format='png')
path = wedge.get_path()
patch = PathPatch(path, facecolor='none')
ax.add_patch(patch)
imagebox = OffsetImage(im, zoom=zoom, clip_path=patch, zorder=-10)
ab = AnnotationBbox(imagebox, xy, xycoords='data', pad=0, frameon=False)
ax.add_artist(ab)
positions = [(-1,0.3),(0,-0.5),(0.5,0.5)]
zooms = [0.4,0.4,0.4]
for i in range(3):
fn = "data/{}.png".format(labels[i].lower())
img_to_pie(fn, wedges[i], xy=positions[i], zoom=zooms[i] )
wedges[i].set_zorder(10)
plt.show()

How to rasterize a color bar axis spines in Matplotlib?

I am generating a large PDF with numerous pages. I trying to rasterize everything I can within it to save some disk space. However, the color bars are particularly tough to deal with.
Can someone manage to rasterize the color bar axis spines in the example below?
import matplotlib.pyplot as plt
import numpy as np
ax = plt.subplot(111)
im = ax.imshow(np.arange(100).reshape((10, 10)))
cb = plt.colorbar(im)
ax.set_rasterized(True)
plt.suptitle('Title')
plt.savefig('test.pdf')
As it is now, when zooming in, the axes of the image look pixelated but not the axes of the color bar. I want both to be pixelated.
I thought im.colorbar.solids.set_rasterized(True) would make the trick but it did not. Saving the image as a PNG file is not an option since I want to keep the figure title not rasterized (this is the only thing I do not want rasterized)
It turns out it is necessary to access the axis of the color bar directly. The for loop below makes the trick
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure() # <--- New line
ax = plt.subplot(111)
im = ax.imshow(np.arange(100).reshape((10, 10)))
cb = plt.colorbar(im)
for ax in fig.get_axes(): # <--- New line
ax.set_rasterized(True) # <--- New line
plt.suptitle('Title')
plt.savefig('test.pdf')

Add my own picture as marker in animation in Python [duplicate]

I would like to utilize customer markers in both scatter and line charts. How can I make custom marker out of a PNG file?
I don't believe matplotlib can customize markers like that. See here for the level of customization, which falls way short of what you need.
As an alternative, I've coded up this kludge which uses matplotlib.image to place images at the line point locations.
import matplotlib.pyplot as plt
from matplotlib import image
# constant
dpi = 72
path = 'smile.png'
# read in our png file
im = image.imread(path)
image_size = im.shape[1], im.shape[0]
fig = plt.figure(dpi=dpi)
ax = fig.add_subplot(111)
# plot our line with transparent markers, and markersize the size of our image
line, = ax.plot((1,2,3,4),(1,2,3,4),"bo",mfc="None",mec="None",markersize=image_size[0] * (dpi/ 96))
# we need to make the frame transparent so the image can be seen
# only in trunk can you put the image on top of the plot, see this link:
# http://www.mail-archive.com/matplotlib-users#lists.sourceforge.net/msg14534.html
ax.patch.set_alpha(0)
ax.set_xlim((0,5))
ax.set_ylim((0,5))
# translate point positions to pixel positions
# figimage needs pixels not points
line._transform_path()
path, affine = line._transformed_path.get_transformed_points_and_affine()
path = affine.transform_path(path)
for pixelPoint in path.vertices:
# place image at point, centering it
fig.figimage(im,pixelPoint[0]-image_size[0]/2,pixelPoint[1]-image_size[1]/2,origin="upper")
plt.show()
Produces:
Following on from Mark's answer. I just thought I would add to this a bit because I tried to run this and it does what I want with the exception of actually displaying the icons on the graph. Maybe something has changed with matplotlib. It has been 4 years.
The line of code that reads:
ax.get_frame().set_alpha(0)
does not seem to work, however
ax.patch.set_alpha(0)
does work.
The other answer may lead to problems when resizing the figure. Here is a different approach, positionning the images inside annotation boxes, which are anchored in data coordinates.
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
path = "https://upload.wikimedia.org/wikipedia/commons/b/b5/Tango-example_icons.png"
image = plt.imread(path)[116:116+30, 236:236+30]
x = np.arange(10)
y = np.random.rand(10)
fig, ax = plt.subplots()
ax.plot(x,y)
def plot_images(x, y, image, ax=None):
ax = ax or plt.gca()
for xi, yi in zip(x,y):
im = OffsetImage(image, zoom=72/ax.figure.dpi)
im.image.axes = ax
ab = AnnotationBbox(im, (xi,yi), frameon=False, pad=0.0,)
ax.add_artist(ab)
plot_images(x, y, image, ax=ax)
plt.show()

imshow() subplots generate unwanted white spaces

When plotting two (or more) subplots, there is a large areas of white spaces within the plots (on all four sides) as seen here:
Following is the code which I used to plot it.
from pylab import *
from matplotlib import rc, rcParams
import matplotlib.pyplot as plt
for kk in range(57,58):
fn_i=str(kk)
image_file_1='RedshiftOutput00'+fn_i+'_Slice_z_RadioPowerDSA.png'
image_file_2='RedshiftOutput00'+fn_i+'_Slice_z_RadioPowerTRA.png'
image_file_3='RedshiftOutput00'+fn_i+'_Slice_z_RadioPowerDSA+TRA.png'
image_1 = plt.imread(image_file_1)
image_2 = plt.imread(image_file_2)
image_3 = plt.imread(image_file_3)
ax1 = subplot(131)
plt.imshow(image_1)
plt.axis('off') # clear x- and y-axes
ax2 = subplot(132)
plt.imshow(image_2)
plt.axis('off') # clear x- and y-axes
ax3 = subplot(133)
plt.imshow(image_3)
plt.axis('off') # clear x- and y-axes
plt.savefig('RedshiftOutput00'+fn_i+'_all.png')
I am also uploading the 3 images used in this code to making the code a Minimal Working Example
1) https://drive.google.com/file/d/0B6l5iRWTUbHWSTF2R3E1THBGeVk/view?usp=sharing
2) https://drive.google.com/file/d/0B6l5iRWTUbHWaFI4dHAzcWpiOEU/view?usp=sharing
3) https://drive.google.com/file/d/0B6l5iRWTUbHWaG8xclFlcGJNaUk/view?usp=sharing
How we can remove this white space ? I tried by fixing the whole plot size, still white space is comming.
Mel's comment above (use plt.tight_layout()) works in many situations, but sometimes you need a little more control. To manipulate axes more finely (useful, e.g., when you have lots of colorbars or twin-ned axes), you can use plt.subplots_adjust() or a GridSpec object.
GridSpec objects allow you to specify the horizontal and vertical extents of individual axes, as well as their proportional width and height & spacing. subplots_adjust() moves your axes around after you've already plotted stuff on them. I prefer using the first option, but both are documented well.
It also may help to fool around with the size of your figure. If you have lots of whitespace width-wise, make the width of the figure smaller.
Here's some example code that I used to set up a recent plot:
gs = gridspec.GridSpec(
nrows=1, ncols=3, left=0.1, bottom=0.25, right=0.95, top=0.95,
wspace=0.05, hspace=0., width_ratios=[1, 1, 1])
NII_ax = plt.subplot(gs[0])
SII_ax = plt.subplot(gs[1])
OI_ax = plt.subplot(gs[2])
And the result:
Then, if you need a colorbar, adjust the right argument in GridSpec to something like 0.85, and use fig.add_axes() with a list [left_lim, bottom, width, height] and use that as the axis argument for a fig.colorbar()

Matplotlib - unable to save image in same resolution as original image

I am unable to save the image without the white borders and at the initial resolution (1037x627)
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import pyplot, lines
import matplotlib.image as mpimg
from matplotlib.patches import Ellipse
x=[0,0,0,0,0]
y=[0,0,0,0,0]
a=10**1.3*15
inc=25
b=np.cos(np.radians(inc))*a
x[0],y[0]=516.667,313.021
x[1],y[1]=x[0]-a,y[0]
x[2],y[2]=x[0]+a,y[0]
x[3],y[3]=x[0],y[0]+b
x[4],y[4]=x[0],y[0]-b
for pa in range(0,10,5):
fig, ax = plt.subplots()
img=mpimg.imread('IC342.png')
imgplot = plt.imshow(img)
x[1],y[1]=x[0]-a/2*np.cos(np.radians(pa)),y[0]-a/2*np.sin(np.radians(pa))
x[2],y[2]=x[0]+a/2*np.cos(np.radians(pa)),y[0]+a/2*np.sin(np.radians(pa))
x[3],y[3]=x[0]+b/2*np.cos(np.radians(pa+90)),y[0]+b/2*np.sin(np.radians(pa+90))
x[4],y[4]=x[0]-b/2*np.cos(np.radians(pa+90)),y[0]-b/2*np.sin(np.radians(pa+90))
ell = Ellipse(xy=[516.667,313.021], width=a, height=b, angle=pa, edgecolor='b',lw=4, alpha=0.5, facecolor='none')
name='plt'+str(pa)+'.png'
leg='PA='+str(pa)
#ax.text(10, 10, leg, fontsize=15,color='white')
ax.add_artist(ell)
xn=[x[1],x[2],x[0]]
yn=[y[1],y[2],y[0]]
xnw=[x[3],x[4],x[0]]
ynw=[y[3],y[4],y[0]]
line = lines.Line2D(xn, yn, linestyle='-.',lw=5., color='r', alpha=0.4)
line1 = lines.Line2D(xnw, ynw, linestyle='-.',lw=5., color='g', alpha=0.4)
ax.add_line(line)
ax.add_line(line1)
plt.axis('off')
fig.savefig(name, transparent=True, bbox_inches='tight', pad_inches=0,dpi=150 )
initial image
Result
Also I need the white text PA=something to be on the image without changing the resolution. From what I understand adding another figure like text might automatically change the resolution.
Thank you for your time!
There are two factors at play here:
An Axes doesn't take up the entire Figure by default
In matplotlib, the Figure's size is fixed, and the contents are stretched/squeezed/interpolated to fit the figure. You want the Figure's size to be defined by its contents.
To do what you want to do, there are three steps:
Create a figure based on the size of the image and a set DPI
Add a subplot/axes that takes up the entire figure
Save the figure with the DPI you used to calculate figure's size
Let's use a random Hubble image from Nasa http://www.nasa.gov/sites/default/files/thumbnails/image/hubble_friday_12102015.jpg. It's a 1280x1216 pixel image.
Here's a heavily commented example to walk you through it:
import matplotlib.pyplot as plt
# On-screen, things will be displayed at 80dpi regardless of what we set here
# This is effectively the dpi for the saved figure. We need to specify it,
# otherwise `savefig` will pick a default dpi based on your local configuration
dpi = 80
im_data = plt.imread('hubble_friday_12102015.jpg')
height, width, nbands = im_data.shape
# What size does the figure need to be in inches to fit the image?
figsize = width / float(dpi), height / float(dpi)
# Create a figure of the right size with one axes that takes up the full figure
fig = plt.figure(figsize=figsize)
ax = fig.add_axes([0, 0, 1, 1])
# Hide spines, ticks, etc.
ax.axis('off')
# Display the image.
ax.imshow(im_data, interpolation='nearest')
# Add something...
ax.annotate('Look at This!', xy=(590, 650), xytext=(500, 500),
color='cyan', size=24, ha='right',
arrowprops=dict(arrowstyle='fancy', fc='cyan', ec='none'))
# Ensure we're displaying with square pixels and the right extent.
# This is optional if you haven't called `plot` or anything else that might
# change the limits/aspect. We don't need this step in this case.
ax.set(xlim=[-0.5, width - 0.5], ylim=[height - 0.5, -0.5], aspect=1)
fig.savefig('test.jpg', dpi=dpi, transparent=True)
plt.show()
The saved test.jpg will be exactly 1280x1216 pixels. Of course, because we're using a lossy compressed format for both input and output, you won't get a perfect pixel match due to compression artifacts. If you used lossless input and output formats you should, though.

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