This is a demo from the document of matplotlib
Scatter plot on polar axis
import numpy as np
import matplotlib.pyplot as plt
# Fixing random state for reproducibility
np.random.seed(19680801)
# Compute areas and colors
N = 150
r = 2 * np.random.rand(N)
theta = 2 * np.pi * np.random.rand(N)
area = 200 * r**2
colors = theta
fig = plt.figure()
ax = fig.add_subplot(projection='polar')
c = ax.scatter(theta, r, c=colors, s=area, cmap='hsv', alpha=0.75)
Now I want to replace these dots with some texts, like
What modification should I do to these code?
Further more, I also want to put picture instead of texts, is that possible?
Thanks!!!
This is the original code:
import numpy as np
import matplotlib.pyplot as plt
# Fixing random state for reproducibility
np.random.seed(19680801)
# Compute areas and colors
N = 150
r = 2 * np.random.rand(N)
theta = 2 * np.pi * np.random.rand(N)
area = 200 * r**2
colors = theta
fig = plt.figure()
ax = fig.add_subplot(projection='polar')
c = ax.scatter(theta, r, c=colors, s=area, cmap='hsv', alpha=0.75)
If you add this two lines:
plt.text(0.67, 0.9, 'I am cartesian coordinate', transform=plt.gcf().transFigure)
plt.text(np.pi, r[len(r)-1], 'I am polar coordinate')
You will get
and if you add this code:
im = Image.open('smurf.png')
newax = fig.add_axes([0.5, 0.5, 0.2, 0.2], zorder=1)
newax.imshow(im)
newax.axis('off')
newax = fig.add_axes([0.3, 0.3, 0.2, 0.2], zorder=1)
newax.imshow(im)
newax.axis('off')
You will get
But it requires conversion calculation to get to polar coordinate
You remove the ax.scatter part and instead use ax.text. But be aware that you need to pass the coordinates for the text also in polar coordinates. E.g.:
ax.text(np.pi / 2, 60, 'people', fontsize=20, color='red').
Here you go:
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(projection='polar')
for text,xytext, color in zip(*[['cat', 'car', 'people'],[(0.5, 0.3),(0.5, 0.7),(0.1, 0.5)],['b', 'g','r']]):
ax.annotate(text,
xy=(0,0), # theta, radius
xytext=xytext, # fraction, fraction
textcoords='figure fraction',
horizontalalignment='left',
verticalalignment='bottom',
color=color,
size=20
)
plt.show()
For inserting images there is the following demo.
I am having a problem right now. I have run an extremely heavy simulation and, thus, generated a plot with matplotlib containing the results and saved it (as .jpg). However, there are some elemnts of the plot I would like to change, such as labels size and one vertical line. Is there a straighforward way to do this using matplotlib? I know I could have stored the data and now just replot changing the parameters (and, actually, I have done this), but I was wondering whether there is an easier way. Maybe something like:
fig, ax = plt.figure(path_to_figure)
ax.set_ylabel("Y_label")
...
You can refer to below example, which gives you more idea on how you can do this while plotting everything.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
%matplotlib inline
plt.rc('text', usetex=True)
def f(t):
return t ** 2
t1 = np.arange(0.0, 2.0, 0.1)
noise = np.random.randn(len(t1)) * 0.04
# x coordinates for the lines
xcoords = [0.1, 0.3, 0.5]
# colors for the lines
colors = ['r','k','b']
fig = plt.figure(figsize=(4, 3), dpi=200)
ax = fig.add_subplot(1, 1, 1)
plt.scatter(t1, f(t1 + noise), color = 'hotpink', label='Values obtained by experiment', edgecolors='k')
plt.plot(t1, f(t1), ls='solid', label='Theoretical expectation', color='b')
plt.title(r'This is latex title example $\mathbf{E = m \times c^2}$', fontsize='small')
for xc,c in zip(xcoords,colors):
plt.axvline(x=xc, label='line at x = {}'.format(xc), c=c)
plt.grid()
plt.legend(loc=0)
If you want to make all the fonts bold, you can also use below code to make everything bold:
font = {'weight' : 'bold',
'size' : 14 }
plt.rc('font', **font)
def f(t):
return t ** 2
t1 = np.arange(0.0, 2.0, 0.1)
noise = np.random.randn(len(t1)) * 0.04
# x coordinates for the lines
xcoords = [0.1, 0.3, 0.5]
# colors for the lines
colors = ['r','k','b']
fig = plt.figure(figsize=(4, 3), dpi=200)
ax = fig.add_subplot(1, 1, 1)
plt.scatter(t1, f(t1 + noise), color = 'hotpink', label='Values obtained by experiment', edgecolors='k')
plt.plot(t1, f(t1), ls='solid', label='Theoretical expectation', color='b')
plt.title(r'This is latex title example $\mathbf{E = m \times c^2}$', fontsize='small')
plt.xlabel("This is X-label.", fontsize=12)
plt.ylabel("This is Y-label.", fontsize=16)
for xc,c in zip(xcoords,colors):
plt.axvline(x=xc, label='line at x = {}'.format(xc), c=c)
plt.grid()
plt.legend(loc=(1.15,0.2))
I'd like to draw a lognormal distribution of a given bar plot.
Here's the code
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
import numpy as np; np.random.seed(1)
import scipy.stats as stats
import math
inter = 33
x = np.logspace(-2, 1, num=3*inter+1)
yaxis = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.01,0.03,0.3,0.75,1.24,1.72,2.2,3.1,3.9,
4.3,4.9,5.3,5.6,5.87,5.96,6.01,5.83,5.42,4.97,4.60,4.15,3.66,3.07,2.58,2.19,1.90,1.54,1.24,1.08,0.85,0.73,
0.84,0.59,0.55,0.53,0.48,0.35,0.29,0.15,0.15,0.14,0.12,0.14,0.15,0.05,0.05,0.05,0.04,0.03,0.03,0.03, 0.02,
0.02,0.03,0.01,0.01,0.01,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0,0]
fig, ax = plt.subplots()
ax.bar(x[:-1], yaxis, width=np.diff(x), align="center", ec='k', color='w')
ax.set_xscale('log')
plt.xlabel('Diameter (mm)', fontsize='12')
plt.ylabel('Percentage of Total Particles (%)', fontsize='12')
plt.ylim(0,8)
plt.xlim(0.01, 10)
fig.set_size_inches(12, 12)
plt.savefig("Test.png", dpi=300, bbox_inches='tight')
Resulting plot:
What I'm trying to do is to draw the Probability Density Function exactly like the one shown in red in the graph below:
An idea is to convert everything to logspace, with u = log10(x). Then draw the density histogram in there. And also calculate a kde in the same space. Everything gets drawn as y versus u. When we have u at a top twin axes, x can stay at the bottom. Both axes get aligned by setting the same xlims, but converted to logspace on the top axis. The top axis can be hidden to get the desired result.
import matplotlib.pyplot as plt
import numpy as np
from scipy import stats
inter = 33
u = np.linspace(-2, 1, num=3*inter+1)
x = 10**u
us = np.linspace(u[0], u[-1], 500)
yaxis = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.01,0.03,0.3,0.75,1.24,1.72,2.2,3.1,3.9,
4.3,4.9,5.3,5.6,5.87,5.96,6.01,5.83,5.42,4.97,4.60,4.15,3.66,3.07,2.58,2.19,1.90,1.54,1.24,1.08,0.85,0.73,
0.84,0.59,0.55,0.53,0.48,0.35,0.29,0.15,0.15,0.14,0.12,0.14,0.15,0.05,0.05,0.05,0.04,0.03,0.03,0.03, 0.02,
0.02,0.03,0.01,0.01,0.01,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.01,0,0]
yaxis = np.array(yaxis)
# reconstruct data from the given frequencies
u_data = np.repeat((u[:-1] + u[1:]) / 2, (yaxis * 100).astype(np.int))
kde = stats.gaussian_kde((u[:-1]+u[1:])/2, weights=yaxis, bw_method=0.2)
total_area = (np.diff(u)*yaxis).sum() # total area of all bars; divide by this area to normalize
fig, ax = plt.subplots()
ax2 = ax.twiny()
ax2.bar(u[:-1], yaxis, width=np.diff(u), align="edge", ec='k', color='w', label='frequencies')
ax2.plot(us, total_area*kde(us), color='crimson', label='kde')
ax2.plot(us, total_area * stats.norm.pdf(us, u_data.mean(), u_data.std()), color='dodgerblue', label='lognormal')
ax2.legend()
ax.set_xscale('log')
ax.set_xlabel('Diameter (mm)', fontsize='12')
ax.set_ylabel('Percentage of Total Particles (%)', fontsize='12')
ax.set_ylim(0,8)
xlim = np.array([0.01,10])
ax.set_xlim(xlim)
ax2.set_xlim(np.log10(xlim))
ax2.set_xticks([]) # hide the ticks at the top
plt.tight_layout()
plt.show()
PS: Apparently this also can be achieved directly without explicitly using u (at the cost of being slightly more cryptic):
x = np.logspace(-2, 1, num=3*inter+1)
xs = np.logspace(-2, 1, 500)
total_area = (np.diff(np.log10(x))*yaxis).sum() # total area of all bars; divide by this area to normalize
kde = gaussian_kde((np.log10(x[:-1])+np.log10(x[1:]))/2, weights=yaxis, bw_method=0.2)
ax.bar(x[:-1], yaxis, width=np.diff(x), align="edge", ec='k', color='w')
ax.plot(xs, total_area*kde(np.log10(xs)), color='crimson')
ax.set_xscale('log')
Note that the bandwidth set for gaussian_kde is a somewhat arbitrarily value. Larger values give a more equalized curve, smaller values keep closer to the data. Some experimentation can help.
How do I exactly specify the colorbar labels in matplotlib? Frequently, I need to create very specific color scales, but the colorbar labels display so poorly you can't tell what the scale is. I would like to manually define the text next to the colorbar tick marks, or at least have them display in scientific notation.
Here is an example plot where you can't tell what the bottom four color bins represent:
And here is a working example of how that plot was created:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import colors
# mock up some data
x = np.random.random(50)
y = np.random.random(50)
c = np.arange(0, 1, 1.0/50.0) # color of points
c[0] = 0.00001
c[1] = 0.0001
c[2] = 0.001
c[3] = 0.01
s = 500 * np.random.random(50) + 25 # size of points
# set up some custom color scaling
lcmap = colors.ListedColormap(['#FFFFFF', '#FF99FF', '#8000FF',
'#0000FF', '#0080FF', '#58FAF4',
'#00FF00', '#FFFF00', '#FF8000',
'#FF0000'])
bounds = [0.0, 0.000001, 0.00001, 0.0001,
0.001, 0.01, 0.1, 0.25, 0.5, 0.75, 1.0]
norm = colors.BoundaryNorm(bounds, lcmap.N)
# create some plot
fig, ax = plt.subplots()
im = ax.scatter(x, y, c=c, s=s, cmap=lcmap, norm=norm)
# add the colorbar
fig.colorbar(im, ax=ax)
fig.savefig('temp.jpg')
cbar = fig.colorbar(cax, ticks=[-1, 0, 1])
cbar.ax.set_xticklabels(['Low', 'Medium', 'High'])
and use whatever iterable you want instead of ['Low', 'Medium', 'High']
see: http://matplotlib.org/examples/pylab_examples/colorbar_tick_labelling_demo.html
I have the following code:
from mpl_toolkits.axes_grid.axislines import SubplotZero
from matplotlib.transforms import BlendedGenericTransform
import matplotlib.pyplot as plt
import numpy
if 1:
fig = plt.figure(1)
ax = SubplotZero(fig, 111)
fig.add_subplot(ax)
ax.axhline(linewidth=1.7, color="black")
ax.axvline(linewidth=1.7, color="black")
plt.xticks([1])
plt.yticks([])
ax.text(0, 1.05, 'y', transform=BlendedGenericTransform(ax.transData, ax.transAxes), ha='center')
ax.text(1.05, 0, 'x', transform=BlendedGenericTransform(ax.transAxes, ax.transData), va='center')
for direction in ["xzero", "yzero"]:
ax.axis[direction].set_axisline_style("-|>")
ax.axis[direction].set_visible(True)
for direction in ["left", "right", "bottom", "top"]:
ax.axis[direction].set_visible(False)
x = numpy.linspace(-1, 1, 10000)
ax.plot(x, numpy.tan(2*(x - numpy.pi/2)), linewidth=1.2, color="black")
plt.ylim(-5, 5)
plt.savefig('graph.png')
which produces this graph:
As you can see, not only is the tan graph sketched, but a portion of line is added to join the asymptotic regions of the tan graph, where an asymptote would normally be.
Is there some built in way to skip that section? Or will I graph separate disjoint domains of tan that are bounded by asymptotes (if you get what I mean)?
Something you could try: set a finite threshold and modify your function to provide non-finite values after those points. Practical code modification:
yy = numpy.tan(2*(x - numpy.pi/2))
threshold = 10000
yy[yy>threshold] = numpy.inf
yy[yy<-threshold] = numpy.inf
ax.plot(x, yy, linewidth=1.2, color="black")
Results in:
This code creates a figure and one subplot for tangent function. NaN are inserted when cos(x) is tending to 0 (NaN means "Not a Number" and NaNs are not plotted or connected).
matplot-fmt-pi created by k-donn(https://pypi.org/project/matplot-fmt-pi/) used to change the formatter to make x labels and ticks correspond to multiples of π/8 in fractional format.
plot formatting (grid, legend, limits, axis) is performed as commented.
import matplotlib.pyplot as plt
import numpy as np
from matplot_fmt_pi import MultiplePi
fig, ax = plt.subplots() # creates a figure and one subplot
x = np.linspace(-2 * np.pi, 2 * np.pi, 1000)
y = np.tan(x)
y[np.abs(np.cos(x)) <= np.abs(np.sin(x[1]-x[0]))] = np.nan
# This operation inserts a NaN where cos(x) is reaching 0
# NaN means "Not a Number" and NaNs are not plotted or connected
ax.plot(x, y, lw=2, color="blue", label='Tangent')
# Set up grid, legend, and limits
ax.grid(True)
ax.axhline(0, color='black', lw=.75)
ax.axvline(0, color='black', lw=.75)
ax.set_title("Trigonometric Functions")
ax.legend(frameon=False) # remove frame legend frame
# axis formatting
ax.set_xlim(-2 * np.pi, 2 * np.pi)
pi_manager = MultiplePi(8) # number= ticks between 0 - pi
ax.xaxis.set_major_locator(pi_manager.locator())
ax.xaxis.set_major_formatter(pi_manager.formatter())
plt.ylim(top=10) # y axis limit values
plt.ylim(bottom=-10)
y_ticks = np.arange(-10, 10, 1)
plt.yticks(y_ticks)
fig
[![enter image description here][1]][1]plt.show()