I was able to plot the dots from an image. I am now trying to connect the dots using a line. Essentially mimicking those connect the dot puzzles.
This is my code:
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
original_image = Image.open("jg.jpg")
bw_image = original_image.convert('1')
bw_image_array = np.array(bw_image, dtype=np.int)
black_indices = np.argwhere(bw_image_array == 0)
chosen_black_indices = black_indices[np.random.choice(black_indices.shape[0], replace=False, size=90000)]
plt.figure(figsize=(5, 5), dpi=100)
plt.scatter([x[1] for x in chosen_black_indices], [x[0] for x in chosen_black_indices], color='black', s=1)
plt.gca().invert_yaxis()
plt.xticks([])
plt.yticks([])
plt.show()
I am aiming for this:
import numpy as np
import matplotlib.pyplot as plt
x = np.random.random_sample(size=100)
y = np.random.random_sample(size=100)
fig, ax = plt.subplots()
ax.scatter(x,y)
plt.plot(x, y, '-o')
plt.show()
I am struggling with what would go inside of plt.plot() in order to connect the dots of the image.
It looks like you have the answer there in your second code block. Matplotlib's plot function can handle plotting connected points with the marker='o' argument, so you don't need to call scatter at all. So just change this line:
plt.scatter([x[1] for x in chosen_black_indices],
[x[0] for x in chosen_black_indices],
color='black', s=1)
to this:
plt.plot([x[1] for x in chosen_black_indices],
[x[0] for x in chosen_black_indices],
marker='o',
color='black')
Related
I am trying to draw a curve without a line (skeleton). I want the axis and grid lines only.
Here is the code.
++++++++++
import matplotlib.pyplot as plt
import numpy as np
plt.rcParams["figure.figsize"] = [10.00, 7.00]
plt.rcParams["figure.autolayout"] = True
x = [1.6,2,2.5,3.2,4,5,6.3,8,10,13,16,20,25,32,40,50,63,80,100,130,160,200,250,320,400,500,630,800,1000]
y = range(1,10000,350)#[1,10,100,1000,10000]
# Display grid
plt.grid(True, which="both")
default_x_ticks = range(len(x))
plt.plot(default_x_ticks, y)
plt.yscale('log')
plt.xticks(default_x_ticks, x, rotation=90)
plt.show()
+++++++
Kindly help draw without the curve.
By adding
print(plt.xlim())
print(plt.ylim())
to your code you get the exact axis limits.
These can be used in a second run to create the plot without actually plotting anything:
import matplotlib.pyplot as plt
import numpy as np
plt.rcParams["figure.figsize"] = [10.00, 7.00]
plt.rcParams["figure.autolayout"] = True
x = [1.6,2,2.5,3.2,4,5,6.3,8,10,13,16,20,25,32,40,50,63,80,100,130,160,200,250,320,400,500,630,800,1000]
y = range(1,10000,350)#[1,10,100,1000,10000]
# Display grid
plt.grid(True, which="both")
default_x_ticks = range(len(x))
# plt.plot(default_x_ticks, y)
plt.yscale('log')
plt.xticks(default_x_ticks, x, rotation=90)
plt.xlim(-1.4, 29.4)
plt.ylim(0.6315917965717447, 15517.934294269562)
plt.show()
This is a simplified example of a problem I am having.
import matplotlib.pyplot as plt
for i in range(0,10):
plt.plot(i, i + 1)
plt.show()
shows this. and
x = y = []
for i in range(0,10):
x.append(i)
y.append(i + 1)
plt.plot(x, y,)
plt.show()
shows this.
How can I plot points in a loop so that I don't need to create two arrays?
Try this-
import matplotlib.pyplot as plt
for i in range(0,10):
plt.plot(i, i + 1, color='green', linestyle='solid', linewidth = 3,
marker='o')
plt.show()
Pass array as the first argumet to plt.plot(), this would plot y using x as index array 0..N-1:
import matplotlib.pyplot as plt
# plot y using x as index array 0..N-1
plt.plot(range(10))
plt.show()
You'll find more interesting information at plt.plot().
You can do it with:
import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection
fig, ax = plt.subplots()
max =10
for i in range(0,max):
#scatter:
#s=0 to make dissapeared the scatters
ax.scatter(i, i + 1,s=1,facecolor='blue')
#lines
if i > 0:
lc = LineCollection([[(i-1, i),(i, i+1)]])
ax.add_collection(lc)
plt.show()
result:
I am trying to plot a 1D line along with a 2D surface in matplotlib with Axes3D:
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import matplotlib.pyplot as plt
x = np.arange(-1., 1.1, 0.1)
y = x.copy()
X, Y = np.meshgrid(x, y)
Z = np.abs(X) + np.abs(Y)
plt.close('all')
fig = plt.figure()
ax = fig.gca(projection='3d')
ax.plot(np.zeros_like(y), y, 1, color='k')
ax.plot(x, np.zeros_like(x), 1, color='k')
surf = ax.plot_surface(X, Y, Z, color='w')
plt.show(block=False)
but the 2D plot somehow hides the lines:
If I comment the surf = plot_surface(...) code line, the 1D lines show correctly:
How can I have the lines showing correctly along with the surface?
Axes3D.plot_surface() apparently accepts a transparency (alpha) argument, which actually gets forwarded to a base class, Poly3DCollection.
And of course the line plot() calls accept a linewidth argument.
So if you render the line plots with thicker lines and you render the surface with some transparency, you should be able to find a combination of settings which let you see both the lines and the surface in a balanced way.
https://matplotlib.org/tutorials/toolkits/mplot3d.html#mpl_toolkits.mplot3d.Axes3D.plot_surface
https://matplotlib.org/api/_as_gen/mpl_toolkits.mplot3d.art3d.Poly3DCollection.html#mpl_toolkits.mplot3d.art3d.Poly3DCollection
You can also achieve this by using the zorder in the plot_surface and plot commands to make the lines sit on top of the surface. E.g.
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
x = np.arange(-1., 1.1, 0.1)
y = x.copy()
X, Y = np.meshgrid(x, y)
Z = np.abs(X) + np.abs(Y)
plt.close('all')
fig = plt.figure()
ax = fig.gca(projection='3d')
surf = ax.plot_surface(X, Y, Z, color='w', zorder=1)
ax.plot(np.zeros_like(y), y, 1, color='k', zorder=10)
ax.plot(x, np.zeros_like(x), 1, color='k', zorder=11)
plt.show(block=False)
I'm fairly new to scatter plots and python in general. I am trying to plot a third variable against an x and a y, however, I'm not quite sure how to about specifying that argument? So I would have X values which are ints, y values which are also ints and then on the graph itself I want the model scores to show. Is there any way to do this sort of thing?
Thank you.
You can use color to plot a third value. Here is a very minimal example :
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import numpy as np
x = np.random.rand(100)
y = np.random.rand(100)
z = np.random.rand(100)
plt.scatter(x,y, c=z, s=5, cmap=cm.hsv)
cbar= plt.colorbar()
plt.show()
Edit
You could also use the size of markers, their transparency, hue or rgb values to depict even more information. Here is an example with marker size, alpha level and color on a perceptually uniform colormap.
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.colors as colors
import matplotlib.cm as cmx
x = np.random.rand(100)
y = np.random.rand(100)
z = np.random.rand(100)
t = np.random.rand(100)
w = np.random.rand(100)
fig, ax = plt.subplots(1, 1)
cmap = plt.get_cmap('plasma')
cNorm = colors.Normalize(vmin=0, vmax=max(z))
scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=cmap)
for i in range(100):
ax.scatter(x[i],y[i], c=scalarMap.to_rgba(z[i]), s=t[i]*100, cmap=cmx.plasma, alpha=w[i], edgecolor='none')
scalarMap.set_array([])
fig.colorbar(scalarMap,ax=ax)
for a in [0.1, 0.5, 0.9]:
ax.scatter([], [], c='k', alpha=0.5, s=a*100, label=str(a), edgecolors='none')
l1 = ax.legend(scatterpoints=1, frameon=True, loc='lower left' ,markerscale=1)
for b in [0.25, 0.5, 0.75]:
ax.scatter([], [], c='k', alpha=b, s=50, label=str(b), edgecolors='none')
ax.legend(scatterpoints=1, frameon=True, loc='lower right' ,markerscale=1)
fig.show()
At face value, that question doesn't really make sense because a conventional scatterplot has only two axes, and of course you can't plot points with three dimensions (x, y and accuracy).
However, there are alternative ways to do so.
Use colours
import numpy as np
from matplotlib import pyplot as plt
x = np.random.rand(200)
y = np.random.rand(200)
fig, ax = plt.subplots(figsize=(5, 5))
ax.scatter(x, y, c=(x + y), cmap='RdPu')
scatter takes a c argument, which can be a numeric value, as well as a cmap argument, which can be a string referencing a colormap.
The colormap object translates the numbers provided in c into points along a colour mapping, which you can think of as a gradient bar.
Use 3D axes
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure(figsize=(5, 5))
ax = Axes3D(fig)
ax.scatter(x, y, (x + y))
This turns your 3rd dimension, accuracy, into an ordinary spatial dimension.
Use size of the markers
Very similar to the color option in the first part, you can change the size of the scatter markers (given you have some idea about the scale of the values). So based on the first example, you can also do;
import numpy as np
from matplotlib import pyplot as plt
x = np.random.rand(200)
y = np.random.rand(200)
fig, ax = plt.subplots(figsize=(5, 5))
ax.scatter(x, y, c='k', s=5*(x + y), cmap='RdPu')
scatter takes also the s argument, that changes the size of the markers.
Suppose I have gridded data with dimensions (x,y) and values are in z.
so simply we can make scatter plot for third dimension by:
import numpy as np
import matplotlib.pyplot as plt
x = np.random.random(10)
y = np.random.random(10)
z = np.random.random(10)
plt.scatter(x, y, c = z, s=150, cmap = 'jet')
plt.show()
what i am thinking now is to remove the line color of each circular scatter plot. And also instead of circle can we make it square??
I did not find any way to do that. your help will be highly appreciated.
Pass the argument edgecolors='none' to plt.scatter. The patch boundary will not be drawn.
Pass the argument marker='s' to plt.scatter. The marker style will be square.
Then, we have,
The source code,
import numpy as np
import matplotlib.pyplot as plt
x = np.random.random(10)
y = np.random.random(10)
z = np.random.random(10)
plt.scatter(x, y, c = z, s=150, cmap = 'jet', edgecolors='none', marker='s')
plt.show()
Refer to matplotlib.pyplot.scatter for more information.