I have data I wish to do some manipulations on, which are easier to do if I convert my data to an image (e.g., Hough transform). For that, I convert the data to an image (see attached figure). However, I do not understand how to properly move between the two coordinate systems (or alternatively, convert the axes instance to image rather than the figure instance, and then transformation would be trivial).
I tried playing around with the transformations framework but with no success.
I attach a figure containing two subplots, on the left of the scatter data, and on the right its image representation.
(also, I don't know if how I turned the scatter data to an image can be done neater, if so, I would be happy to hear suggestions for improvement).
I use python 3.6.9; matplotlib version = 3.3.4
Thanks in advance.
import numpy as np
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from matplotlib.figure import Figure
import matplotlib.pyplot as plt
# --- Study case: generate random data
X = np.random.rand(1000, 2)
# --- Figure configuration
fig = plt.figure(figsize=(25, 25))
ax1 = fig.add_subplot(121)
ax2 = fig.add_subplot(122)
# --- First subplot
ax1.scatter(X[:, 0], X[:, 1], s=2, c='k')
ax1.set_title('ax.scatter')
# --- Scatter plot to image
Fig = Figure()
canvas = FigureCanvas(Fig)
Ax = Fig.gca()
Ax.axis('off')
Ax.scatter(X[:, 0], X[:, 1], s=2, c='k')
Ax.get_yaxis().set_ticks([])
Ax.get_xaxis().set_ticks([])
canvas.draw() # draw the canvas, cache the renderer
width, height = Fig.get_size_inches() * Fig.get_dpi()
height = int(height)
width = int(width)
img = np.frombuffer(canvas.tostring_rgb(), dtype='uint8').reshape(height, width, 3)
# --- Display image
ax2.imshow(img)
plt.title('ax.imshow')
plt.show()
Related
Question background: I am trying to make two geometries in a one plot in python. I have made one geometry which is an object having mesh as shown in figure below. The respective code is also mentioned here.
df_1_new = pd.DataFrame()
df_1_new['X_coordinate']=pd.Series(x_new)
df_1_new['Y_coordinate']=pd.Series(y_new)
df_1_new['node_number'] = df_1_new.index
df_1_new = df_1_new[['node_number','X_coordinate','Y_coordinate']]
plt.scatter(x_new, y_new)
plt.show
The second geometry, which is a circle and I made this geometry running below code.
from matplotlib import pyplot as plt, patches
plt.rcParams["figure.figsize"] = [9.00, 6.50]
plt.rcParams["figure.autolayout"] = True
fig = plt.figure()
ax = fig.add_subplot()
circle1 = plt.Circle((2, 2), radius=5, fill = False)
ax.add_patch(circle1)
ax.axis('equal')
plt.show()
My question: How can I combine both geometries mentioned above in a one plot. I would like to place my circle around my geometry (object). Geometry has a centroid (2, 2) and I want to place my circle's centroid exactly on the centroid of geometry therefore I will be having a circle around my geometry. What code I should write. Kindly help me on this.
For your reference: I want my plot just like in below picture.
you need to do all the plotting between the subplot creation and before you issue the plt.show() command, as any command after it will create a new figure.
from matplotlib import pyplot as plt, patches
plt.rcParams["figure.figsize"] = [9.00, 6.50]
plt.rcParams["figure.autolayout"] = True
fig = plt.figure()
ax = fig.add_subplot()
# other plt.scatter or plt.plot here
plt.scatter([3,4,5,6,4],[5,4,2,3,2]) # example
circle1 = plt.Circle((2, 2), radius=5, fill = False)
ax.add_patch(circle1)
ax.axis('equal')
plt.show()
image example
to get the points inside the circle, you need to play with the circle radius and center till you get it right.
something you can do is to make the circle at the np.median of your x and y values, so you are sure about the center position.
When dealing with overlapping high density scatter or line plots of different colors it can be convenient to implement additive blending schemes, where the RGB colors of each marker add together to produce the final color in the canvas. This is a common operation in 2D and 3D render engines.
However, in Matplotlib I've only found support for alpha/opacity blending. Is there any roundabout way of doing it or am I stuck with rendering to bitmap and then blending them in some paint program?
Edit: Here's some example code and a manual solution.
This will produce two partially overlapping random distributions:
x1 = randn(1000)
y1 = randn(1000)
x2 = randn(1000) * 5
y2 = randn(1000)
scatter(x1,y1,c='b',edgecolors='none')
scatter(x2,y2,c='r',edgecolors='none')
This will produce in matplotlib the following:
As you can see, there are some overlapping blue points that are occluded by red points and we would like to see them. By using alpha/opacity blending in matplotlib, you can do:
scatter(x1,y1,c='b',edgecolors='none',alpha=0.5)
scatter(x2,y2,c='r',edgecolors='none',alpha=0.5)
Which will produce the following:
But what I really want is the following:
I can do it manually by rendering each plot independently to a bitmap:
xlim = plt.xlim()
ylim = plt.ylim()
scatter(x1,y1,c='b',edgecolors='none')
plt.xlim(xlim)
plt.ylim(ylim)
scatter(x2,y2,c='r',edgecolors='none')
plt.xlim(xlim)
plt.ylim(ylim)
plt.savefig(r'scatter_blue.png',transparent=True)
plt.savefig(r'scatter_red.png',transparent=True)
Which gives me the following images:
What you can do then is load them as independent layers in Paint.NET/PhotoShop/gimp and just additive blend them.
Now ideal would be to be able to do this programmatically in Matplotlib, since I'll be processing hundreds of these!
If you only need an image as the result, you can get the canvas buffer as a numpy array, and then do the blending, here is an example:
from matplotlib import pyplot as plt
import numpy as np
fig, ax = plt.subplots()
ax.scatter(x1,y1,c='b',edgecolors='none')
ax.set_xlim(-4, 4)
ax.set_ylim(-4, 4)
ax.patch.set_facecolor("none")
ax.patch.set_edgecolor("none")
fig.canvas.draw()
w, h = fig.canvas.get_width_height()
img = np.frombuffer(fig.canvas.buffer_rgba(), np.uint8).reshape(h, w, -1).copy()
ax.clear()
ax.scatter(x2,y2,c='r',edgecolors='none')
ax.set_xlim(-4, 4)
ax.set_ylim(-4, 4)
ax.patch.set_facecolor("none")
ax.patch.set_edgecolor("none")
fig.canvas.draw()
img2 = np.frombuffer(fig.canvas.buffer_rgba(), np.uint8).reshape(h, w, -1).copy()
img[img[:, :, -1] == 0] = 0
img2[img2[:, :, -1] == 0] = 0
fig.clf()
plt.imshow(np.maximum(img, img2))
plt.subplots_adjust(0, 0, 1, 1)
plt.axis("off")
plt.show()
the result:
This feature is now supported by my matplotlib backend https://github.com/anntzer/mplcairo (master only):
import matplotlib; matplotlib.use("module://mplcairo.qt")
from matplotlib import pyplot as plt
from mplcairo import operator_t
import numpy as np
x1 = np.random.randn(1000)
y1 = np.random.randn(1000)
x2 = np.random.randn(1000) * 5
y2 = np.random.randn(1000)
fig, ax = plt.subplots()
# The figure and axes background must be made transparent.
fig.patch.set(alpha=0)
ax.patch.set(alpha=0)
pc1 = ax.scatter(x1, y1, c='b', edgecolors='none')
pc2 = ax.scatter(x2, y2, c='r', edgecolors='none')
operator_t.ADD.patch_artist(pc2) # Use additive blending.
plt.show()
I am trying to make an animation using ArtistAnimation like this:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
fig, ax = plt.subplots()
ims = []
for i in range(60):
x = np.linspace(0,i,1000)
y = np.sin(x)
im = ax.plot(x,y, color='black')
ims.append(im)
ani = animation.ArtistAnimation(fig, ims, interval=50, blit=True,
repeat_delay=1000)
plt.show()
This animates a sine wave growing across the figure. Currently I'm just adding the Lines2D object returned by ax.plot() to ims. However, I would like to potentially draw multiple overlapping plots on the Axes and adjust the title, legend and x-axis range for each frame. How do I get an object that I can add to ims after plotting and making all the changes I want for each frame?
The list you supply to ArtistAnimation should be a list of lists of artists, one list per frame.
artist_list = [[line1a, line1b, title1], [line2a, line2b, title2], ...]
where the first list is shown in the first frame, the second list in the second frame etc.
The reason your code works is that ax.plot returns a list of lines (in your case only a list of a single line).
In any case, the following might be a more understandable version of your code where an additional text is animated.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
fig, ax = plt.subplots()
artist_list = []
for i in range(60):
x = np.linspace(0,i,1000)
y = np.sin(x)
line, = ax.plot(x,y, color='black')
text = ax.text(i,0,i)
artist_list.append([line, text])
ani = animation.ArtistAnimation(fig, artist_list, interval=50, blit=True,
repeat_delay=1000)
plt.show()
In general, it will be hard to animate changing axes limits with ArtistAnimation, so if that is an ultimate goal consider using a FuncAnimation instead.
I am finding the edges of the images using matplotlib.I have done almost.And i want to rotate the image as 90 degree in plot.But it is not working for me.I have tried many things.Below is my code what i have tried.
from scipy import misc
from skimage import color,measure
import matplotlib.pyplot as plt
from skimage.draw import ellipse
from skimage.measure import find_contours, approximate_polygon, subdivide_polygon
from PIL import Image
import numpy as np
filename = r"images/5601.jpg"
fimg = misc.imread(filename)
gimg = color.colorconv.rgb2grey(fimg)
contours = measure.find_contours(gimg, 0.8)
for n, contour in enumerate(contours):
plt.plot(contour[:, 1], contour[:, 0], linewidth=2)
contour = contours[0]
new_s = contour.copy()
appr_s = approximate_polygon(new_s, tolerance=0.8)
fig, ax2 = plt.subplots(ncols=1, figsize=(7, 5))
ax2.plot(contour[:, 0], contour[:, 1])
#these are all what i have tried
#plt.xticks(rotation='vertical')
# for tick in ax2.get_xticklabels():
# tick.set_rotation(45)
#plt.setp(ax2.xaxis.get_majorticklabels(), rotation=70 )
#ax2.tick_params(axis='both', rotation=45)
#fig.autofmt_xdate(bottom=0.5, rotation=90, ha='right')
#plt.hist(ax2, bins=10, orientation='horizontal')
plt.axis('off')
plt.tick_params(axis='both' , left='off', top='off', right='off', bottom='off', labelleft='off', labeltop='off', labelright='off', labelbottom='off')
plt.savefig("test.svg", format="svg")
The output is:
Expected output is:
Thanks in advance.
There are a lot of options here. It is important to note that rotating the ticks will not help here. Instead, use either of the following.
Flip the axes using invert_yaxis(). This would not rotate the image, but flip the axes the image is shown in vertically.
ax2.plot(contour[:, 1], contour[:, 0])
ax2.invert_yaxis()
Flip the image using numpy.flipud. This would not rotate the image, but flip it vertically before processing it further.
fimg = plt.imread(filename)
fimg = np.flipud(fimg)
# ...
ax2.plot(contour[:, 1], contour[:, 0])
Rotate the image using numpy.rot90. In fact you would need to rotate it by 180 degrees (k=2).
fimg = plt.imread(filename)
fimg = np.rot90(fimg,k=2)
# ...
ax2.plot(contour[:, 1], contour[:, 0])
Rotate the output curve
mat = lambda angle: np.array([[ np.cos(angle), np.sin(angle)],
[-np.sin(angle), np.cos(angle)]])
rotcontour = np.dot(contour, mat(np.deg2rad(180)))
ax2.plot(rotcontour[:, 1], rotcontour[:, 0])
I am trying to plot a scatter plot on a background using basemap. But it's overwriting the background. How do I retain the background?
I am using this code
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
m = Basemap(projection='merc',llcrnrlat=-80,urcrnrlat=80,llcrnrlon=-180,urcrnrlon=180,lat_ts=20,resolution='c')
m.bluemarble()
x, y = m(list(longitude), list(latitude))
plt.scatter(x,y,1,marker='o',color='Red')
plt.show()
But as soon as I run the scatter plot, its overwriting background image. How can I overlay the scatter plot on the image.
This is how to plot a series of points on top of a raster map. Note that the bluemarble image is huge, so a full scale (1.0 or default) plot of it should be avoided. The code is based on yours.
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
# make up some data for scatter plot
lats = np.random.randint(-75, 75, size=20)
lons = np.random.randint(-179, 179, size=20)
fig = plt.gcf()
fig.set_size_inches(8, 6.5)
m = Basemap(projection='merc', \
llcrnrlat=-80, urcrnrlat=80, \
llcrnrlon=-180, urcrnrlon=180, \
lat_ts=20, \
resolution='c')
m.bluemarble(scale=0.2) # full scale will be overkill
m.drawcoastlines(color='white', linewidth=0.2) # add coastlines
x, y = m(lons, lats) # transform coordinates
plt.scatter(x, y, 10, marker='o', color='Red')
plt.show()
The resulting plot:
I realize it's an old question but in case anyone comes here with the same problem as I did.
The trick is to give a higher zorder for the scatter plot than for the .bluemarble().
m.scatter(x, y, 10, marker='o', color='Red', zorder=3)
More info here: https://matplotlib.org/3.1.0/gallery/misc/zorder_demo.html
I'm not entirely sure what you mean by "overwriting the background". When you use plt.scatter(), it will plot the points over the map, so it will display the points over the background.
Just based off the code provided, I think you're issue here is m(list(longitude), list(latitude)).
If you have multiple points in a list, you want to loop over them.
lats = [32, 38, 35]
lons = [-98, -79, -94]
x, y = m(lons, lats)
for i in range(len(lats)):
plt.scatter(x, y, marker = 'o')
If it's only one single point,
lat, lon = 32, -92
x, y = m(lon, lat)
plt.scatter(x, y, marker = 'o')
The styling of the points can be found in the matplotlib documentation.