I have a function and I want to draw points on that function.
For example:
def f(x):
return x ** 2 + 2 * x + 4
x_val= np.linspace(-6,6)
graph = f(x_val)
plt.plot(x_val, graph)
This will give the function evaluated at x_val.
I want to plot points on the graph at f(-2), f(2) like this.
You can use plot with the third parameter being a marker. The different markers can be found here. The third parameter of plot is a string where the first letter indicates the color ('r' for red, 'g' for green, ...) and the second letter the marker. More information can be found in the official docs.
import matplotlib.pyplot as plt
import numpy as np
def f(x):
return x ** 2 + 2 * x + 4
x_val= np.linspace(-6,6)
graph = f(x_val)
plt.plot(x_val, graph)
plt.plot(-2, f(-2), 'ro')
plt.plot(2, f(2), 'ro')
plt.show()
In case you have several points, you have two options:
Plot them individually using a for loop
Plot them all at once using a scatter plot via vectorised operation on a NumPy array of points (shown below)
points = np.array([-2, 2])
plt.plot(x_val, graph)
plt.scatter(points, f(points), c='r')
plt.show()
Related
I am programming in Python 3 and I have data structured like this:
coordinates = [(0.15,0.25),(0.35,0.25),(0.55,0.45),(0.65,0.10),(0.15,0.25)]
These are coordinates. Within each pair, the first number is the x coordinate and the second one the y coordinate. Some of the coordinates repeat themselves. I want to plot these data like this:
The coordinates that are most frequently found should appear either as higher intensity (i.e., brighter) points or as points with a different color (for example, red for very frequent coordinates and blue for very infrequent coordinates). Don't worry about the circle and semicircle. That's irrelevant. Is there a matplotlib plot that can do this? Scatter plots do not work because they do not report on the frequency with which each coordinate is found. They just create a cloud.
The answer is:
import matplotlib.pyplot as plt
from scipy.stats import kde
import numpy as np
xvalues = np.random.normal(loc=0.5,scale=0.01,size=50000)
yvalues = np.random.normal(loc=0.25,scale=0.1,size=50000)
nbins=300
k = kde.gaussian_kde([xvalues,yvalues])
xi, yi = np.mgrid[0:1:nbins*1j,0:1:nbins*1j]
zi = k(np.vstack([xi.flatten(),yi.flatten()]))
fig, ax = plt.subplots()
ax.pcolormesh(xi, yi, zi.reshape(xi.shape), shading='auto', cmap=plt.cm.hot)
x = np.arange(0.0,1.01,0.01,dtype=np.float64)
y = np.sqrt((0.5*0.5)-((x-0.5)*(x-0.5)))
ax.axis([0,1,0,0.55])
ax.set_ylabel('S', fontsize=16)
ax.set_xlabel('G', fontsize=16)
ax.tick_params(labelsize=12, width=3)
ax.plot(x,y,'w--')
plt.show()
I'm using the below code to draw the ECC curve y^2+x^3+x^2 =0
import numpy as np
import matplotlib.pyplot as plt
import math
def main():
fig = plt.figure()
ax = fig.add_subplot(111)
y, x = np.ogrid[-2:2:1000j, -2:2:1000j]
ax.contour(x.ravel(), y.ravel(), pow(y, 2) + pow(x, 3) + pow(x, 2) , [0],colors='red')
ax.grid()
plt.show()
if __name__ == '__main__':
main()
The output is
The expected image, however, is this
As we can see, the isolated point at (0,0) is not drawn. Any suggestions to solve this issue?
As already mentioned in the comment, it seems that a single point is not displayed as a contour. The best solution would be if the application indicates such points in some way by itself. Perhaps the library allows this, but I have not found a way and therefore show two workarounds here:
Option 1:
The isolated point at (0,0) could be marked explicitly:
ax.plot(0, 0, color="red", marker = "o", markersize = 2.5, zorder = 10)
In the case of multiple points, a masked array is a good choice, here.
Option 2:
The plot can be slightly varied around z = 0, e.g. z = 0.0002:
z = pow(y,2) + pow(x, 2) + pow(x, 3)
ax.contour(x.ravel(), y.ravel(), z, [0.0002], colors='red', zorder=10)
This will move the whole plot. Alternatively, the area around the isolated point alone could be shifted (by adding a second contour call with a small x,y grid around the isolated point at (0,0)). This does not change the rest.
I have a plot that looks as follows:
I want to put labels for both the lineplot and the markers in red. However the legend is not appearning because its the plot is taking out its space.
Update
it turns out I cannot put several strings in plt.legend()
I made the figure bigger by using the following:
fig = plt.gcf()
fig.set_size_inches(18.5, 10.5)
However now I have only one label in the legend, with the marker appearing on the lineplot while I rather want two: one for the marker alone and another for the line alone:
Updated code:
plt.plot(range(len(y)), y, '-bD', c='blue', markerfacecolor='red', markeredgecolor='k', markevery=rare_cases, label='%s' % target_var_name)
fig = plt.gcf()
fig.set_size_inches(18.5, 10.5)
# changed this over here
plt.legend()
plt.savefig(output_folder + fig_name)
plt.close()
What you want to do (have two labels for a single object) is not completely impossible but it's MUCH easier to plot separately the line and the rare values, e.g.
# boilerplate
import numpy as np
import matplotlib.pyplot as plt
# synthesize some data
N = 501
t = np.linspace(0, 10, N)
s = np.sin(np.pi*t)
rare = np.zeros(N, dtype=bool); rare[:20]=True; np.random.shuffle(rare)
plt.plot(t, s, label='Curve')
plt.scatter(t[rare], s[rare], label='rare')
plt.legend()
plt.show()
Update
[...] it turns out I cannot put several strings in plt.legend()
Well, you can, as long as ① the several strings are in an iterable (a tuple or a list) and ② the number of strings (i.e., labels) equals the number of artists (i.e., thingies) in the plot.
plt.legend(('a', 'b', 'c'))
I have two data-frames in python.
data_A
Name X Y
A 1 0
B 1 1
C 0 0
data_B
Name X Y
A 0 1
B 1 1
C 0 1
I would like to overlap these heatmaps, where if it is a 1 in data_frame A, then the tile is colored purple (or any color), but if it's a 1 in data_frame B, then a circle is drawn (preferably the first one).
So for example, the heatmap would show A[,X][1] colored purple, but those with 1 in both data frames would be purple with a dot. C[,Y][3] would have just a dot, while C[,X][3] would have nothing.
I can seem to mask, with seaborn, and plot two heatmaps with different colors, but the color differential isn't clear enough that a user can simply see that a tile has only one versus both. I think having a circle to denote a positive in one matrix would be better.
Does anyone have an idea of how to plot circles onto a heatmap using seaborn?
To show a heatmap you may use an imshow plot. To show some dots, you may use a scatter plot. Then just plot both in the same axes.
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
dfA = pd.DataFrame([[1,0],[1,1],[0,0]], columns=list("XY"), index=list("ABC"))
dfB = pd.DataFrame([[0,1],[1,1],[0,1]], columns=list("XY"), index=list("ABC"))
assert dfA.shape == dfB.shape
x = np.arange(0,len(dfA.columns))
y = np.arange(0,len(dfB.index))
X,Y=np.meshgrid(x,y)
fig, ax = plt.subplots(figsize=(2.6,3))
ax.invert_yaxis()
ax.imshow(dfA.values, aspect="auto", cmap="Purples")
cond = dfB.values == 1
ax.scatter(X[cond], Y[cond], c="crimson", s=100)
ax.set_xticks(x)
ax.set_yticks(y)
ax.set_xticklabels(dfA.columns)
ax.set_yticklabels(dfA.index)
plt.show()
Alternatives to using a dot to show several datasets on the same heatmap could also
Plotting two distance matrices together on same plot?
something like plt.matshow but with triangles
Now, you can directly plot complex heatmap using python package PyComplexHeatmap: https://github.com/DingWB/PyComplexHeatmap
https://github.com/DingWB/PyComplexHeatmap/blob/main/examples.ipynb
TL;DR -> How can one create a legend for a line graph in Matplotlib's PyPlot without creating any extra variables?
Please consider the graphing script below:
if __name__ == '__main__':
PyPlot.plot(total_lengths, sort_times_bubble, 'b-',
total_lengths, sort_times_ins, 'r-',
total_lengths, sort_times_merge_r, 'g+',
total_lengths, sort_times_merge_i, 'p-', )
PyPlot.title("Combined Statistics")
PyPlot.xlabel("Length of list (number)")
PyPlot.ylabel("Time taken (seconds)")
PyPlot.show()
As you can see, this is a very basic use of matplotlib's PyPlot. This ideally generates a graph like the one below:
Nothing special, I know. However, it is unclear what data is being plotted where (I'm trying to plot the data of some sorting algorithms, length against time taken, and I'd like to make sure people know which line is which). Thus, I need a legend, however, taking a look at the following example below(from the official site):
ax = subplot(1,1,1)
p1, = ax.plot([1,2,3], label="line 1")
p2, = ax.plot([3,2,1], label="line 2")
p3, = ax.plot([2,3,1], label="line 3")
handles, labels = ax.get_legend_handles_labels()
# reverse the order
ax.legend(handles[::-1], labels[::-1])
# or sort them by labels
import operator
hl = sorted(zip(handles, labels),
key=operator.itemgetter(1))
handles2, labels2 = zip(*hl)
ax.legend(handles2, labels2)
You will see that I need to create an extra variable ax. How can I add a legend to my graph without having to create this extra variable and retaining the simplicity of my current script?
Add a label= to each of your plot() calls, and then call legend(loc='upper left').
Consider this sample (tested with Python 3.8.0):
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0, 20, 1000)
y1 = np.sin(x)
y2 = np.cos(x)
plt.plot(x, y1, "-b", label="sine")
plt.plot(x, y2, "-r", label="cosine")
plt.legend(loc="upper left")
plt.ylim(-1.5, 2.0)
plt.show()
Slightly modified from this tutorial: http://jakevdp.github.io/mpl_tutorial/tutorial_pages/tut1.html
You can access the Axes instance (ax) with plt.gca(). In this case, you can use
plt.gca().legend()
You can do this either by using the label= keyword in each of your plt.plot() calls or by assigning your labels as a tuple or list within legend, as in this working example:
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(-0.75,1,100)
y0 = np.exp(2 + 3*x - 7*x**3)
y1 = 7-4*np.sin(4*x)
plt.plot(x,y0,x,y1)
plt.gca().legend(('y0','y1'))
plt.show()
However, if you need to access the Axes instance more that once, I do recommend saving it to the variable ax with
ax = plt.gca()
and then calling ax instead of plt.gca().
Here's an example to help you out ...
fig = plt.figure(figsize=(10,5))
ax = fig.add_subplot(111)
ax.set_title('ADR vs Rating (CS:GO)')
ax.scatter(x=data[:,0],y=data[:,1],label='Data')
plt.plot(data[:,0], m*data[:,0] + b,color='red',label='Our Fitting
Line')
ax.set_xlabel('ADR')
ax.set_ylabel('Rating')
ax.legend(loc='best')
plt.show()
You can add a custom legend documentation
first = [1, 2, 4, 5, 4]
second = [3, 4, 2, 2, 3]
plt.plot(first, 'g--', second, 'r--')
plt.legend(['First List', 'Second List'], loc='upper left')
plt.show()
A simple plot for sine and cosine curves with a legend.
Used matplotlib.pyplot
import math
import matplotlib.pyplot as plt
x=[]
for i in range(-314,314):
x.append(i/100)
ysin=[math.sin(i) for i in x]
ycos=[math.cos(i) for i in x]
plt.plot(x,ysin,label='sin(x)') #specify label for the corresponding curve
plt.plot(x,ycos,label='cos(x)')
plt.xticks([-3.14,-1.57,0,1.57,3.14],['-$\pi$','-$\pi$/2',0,'$\pi$/2','$\pi$'])
plt.legend()
plt.show()
Add labels to each argument in your plot call corresponding to the series it is graphing, i.e. label = "series 1"
Then simply add Pyplot.legend() to the bottom of your script and the legend will display these labels.