I try to make a simple bar chart, where I can monitor a chemical reaction ( A -> B) using a slider for reaction steps.
So far, the following code yields a bar chart for A with a slider for reactionsteps. The print function prints the expected values for A after certain reaction steps. However, the plot won't be updated. I tried plt.draw(), plt.show() and fig.canvas.draw() but none of them worked.
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.widgets import Slider, Button
fig, ax = plt.subplots()
plt.subplots_adjust(bottom=0.25)
fig.canvas.set_window_title('Einfluss der Geschwindigkeitskonstanten')
a0 = 1
b0 = 0
plt.axis([0, 5, 0, 2])
plt.xticks([1, 4], ['A', 'B'])
plt.bar(1, a0, color = 'red')
#slider:
axcolor = 'lightblue'
axrs = plt.axes([0.25, 0.1, 0.65, 0.03], facecolor=axcolor)
srs = Slider(axrs, 'RS', 0, 20, valinit=0)
def slider_val_fkt(t):
ya = []
t = srs.val
ya = [np.exp(-0.6 * t)]
print(ya)
plt.bar(1, ya, color = 'red')
#plt.draw()
#plt.show()
fig.canvas.draw()
srs.on_changed(slider_val_fkt)
plt.show()
The new bar is drawn inside the slider axes instead of the original axes:
To overcome this you should work on the axes objects instead of using pyplot. However, since you anyways want to update the bar instead of drawing a new one, it is sufficient here to work with the bars themselves and update them using set_height.
bars = plt.bar(1, a0, color = 'red')
axrs = plt.axes([0.25, 0.1, 0.65, 0.03], facecolor='lightblue')
srs = Slider(axrs, 'RS', 0, 20, valinit=0)
def slider_val_fkt(t):
t = srs.val
ya = [np.exp(-0.6 * t)]
bars[0].set_height(ya[0])
fig.canvas.draw_idle()
srs.on_changed(slider_val_fkt)
Related
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 don't know why but I am really struggling to get widgets working well in python. I try to look at examples about how to use them but I don't know how to extrapolate that to get it to work with my code. I am trying to get a figure to display widgets such that the type, frequency, phase, and other variables adjust the graph itself.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.widgets as widgets
from scipy import signal
from matplotlib.widgets import RadioButtons
A = 1
ang_f = 5
t = np.linspace(0, 4*np.pi, 1000)
phase = 0
s0 = A*np.sin(ang_f*t + phase)
s2 = A*signal.sawtooth(ang_f*t + phase)
s1 = A*signal.square(ang_f*t + phase)
fig, ax = plt.subplots()
l, = ax.plot(t, s1, lw=2, color='red')
plt.subplots_adjust(left=0.4)
def sinf(x, omega):
return np.sin(omega*x)
def sliderCallback(val):
# """ 'val' is the current value selected by the slider
# Recalculate sine values with val as the frequency """
axesHandle.set_ydata(sinf(x, val))
plt.draw() # Redraw the axes
def clickcallback(val):
# 'val' is the current value selected by the slider
# Recalculate sine values with val as the frequency
axesHandle.set_ydata(sinf(x, val))
plt.draw() # Redraw the axes
def closeCallback(event):
plt.close('all') # Close all open figure windows
fig = plt.figure(figsize=(7, 5))
ax = plt.axes([0.1, 0.2, 0.6, 0.7])
axesHandle, = plt.plot(x, sinf(x, 1), lw=2, color='red')
# Add axis to contain the slider
fax = plt.axes([0.1, 0.04, 0.35, 0.03]) # Frequency
tax = plt.axes([0.1, 0.12, 0.35, 0.03]) # Time
sax_3 = plt.axes([0.60, 0.1, 0.35, 0.03]) # Number of points
pax = plt.axes([0.60, 0.05, 0.35, 0.03]) # Phase
rax = plt.axes([0.85, 0.65, 0.12, 0.15]) # Type
bax = plt.axes([0.85, 0.85, 0.1, 0.1]) # Close
pointshandle = widgets.Slider(sax_3, 'Number of points', 1, 200,
valfmt='%0.0f')
pointshandle.on_changed(sliderCallback)
graphchoice = widgets.RadioButtons(rax, ('Sine', 'Squarewave', 'Sawtooth'))
graphchoice.on_clicked(clickcallback)
freqhandle = widgets.Slider(fax, 'Frequancy (Hz)', 0, 5, valinit=1)
freqhandle.on_changed(sliderCallback)
phasehandle = widgets.Slider(pax, 'Phase', 0, 0*np.pi, valinit=0)
phasehandle.on_changed(sliderCallback)
timehandle = widgets.Slider(tax, 'Time (s)', 1, 10, valinit=1)
timehandle.on_changed(sliderCallback)
buttonHandle = widgets.Button(bax, 'Close')
buttonHandle.on_clicked(closeCallback)
def hzfunc(label):
hzdict = {'Sine': s0, 'Squarewave': s1, 'Sawtooth': s2}
ydata = hzdict[label]
l.set_ydata(ydata)
plt.draw()
graphchoice.on_clicked(hzfunc)
I'm really lost so any tips to put me on the right path would be much appreciated, im just so confused atm.
I need to make some plots with multiple parameters, and I choosed to make it more interactive with matplotlib sliders. For some practise before my actual task I tried to make it relativelly simple, but my sliders does not work. Here is the code, which is inspired from here.
Code:
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.widgets as mw
from scipy import stats
mu = 1
sigma = 3
a = 2
b = 3
axis_color = 'lightgoldenrodyellow'
x = [i for i in range(-100,100,1)]
normal_pdf = stats.norm.pdf(x, mu, sigma)
a_normal_pdf = [i*a for i in normal_pdf]
ab_normal_pdf = [i*b*a for i in normal_pdf]
fig = plt.figure()
ax1 = fig.add_subplot(221)
ax2 = fig.add_subplot(222)
ax3 = fig.add_subplot(223)
ax4 = fig.add_subplot(224)
ax4.axis('off')
#sliders
a_slider_ax = fig.add_axes([0.6, 0.25, 0.25, 0.03], axisbg=axis_color)
a_slider = mw.Slider(a_slider_ax, 'a', 1, 100, valinit = a)
b_slider_ax = fig.add_axes([0.6, 0.4, 0.25, .03], axisbg = axis_color)
b_slider = mw.Slider(b_slider_ax, 'b', 1, 100, valinit = b)
#function for sliders
def sliders_on_change(val):
a_normal_pdf.set_ydata([x*a_slider for x in normal_pdf])
ab_normal_pdf.set_ydata([x*a_slider*b_slider for x in normal_pdf])
fig.canvas.draw_idle()
a_slider.on_changed(sliders_on_change)
b_slider.on_changed(sliders_on_change)
ax1.plot(x, normal_pdf, 'r-')
ax2.plot(x, a_normal_pdf, 'bo')
ax3.plot(x, ab_normal_pdf, 'g*')
plt.show()
I do not fully understand HOW sliders should work, so its maybe the problem instead of idle issue as here, because I tried it in spyder and in jupyter as well, no difference. I can move with sliders, but I cant change the a_normal_pdf nor ab_normal_pdf.
You have two issues in your code:
using the slider object a_slider in place of the slider's current value a_slider.val
the method set_ydata changes the y-data of a Line2D plot object (I saved it in a variable p1 to be able to modify it)
Modified code (hope this helps)
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.widgets as mw
from scipy import stats
mu = 1
sigma = 3
a = 2
b = 3
axis_color = 'lightgoldenrodyellow'
x = [i for i in range(-100,100,1)]
normal_pdf = stats.norm.pdf(x, mu, sigma)
a_normal_pdf = [i*a for i in normal_pdf]
ab_normal_pdf = [i*b*a for i in normal_pdf]
fig = plt.figure()
ax1 = fig.add_subplot(221)
ax2 = fig.add_subplot(222)
ax3 = fig.add_subplot(223)
ax4 = fig.add_subplot(224)
ax4.axis('off')
#sliders
a_slider_ax = fig.add_axes([0.6, 0.25, 0.25, 0.03], axisbg=axis_color)
a_slider = mw.Slider(a_slider_ax, 'a', 1, 100, valinit = a)
b_slider_ax = fig.add_axes([0.6, 0.4, 0.25, .03], axisbg = axis_color)
b_slider = mw.Slider(b_slider_ax, 'b', 1, 100, valinit = b)
#function for sliders
def sliders_on_change(val):
p1.set_ydata([x*a_slider.val for x in normal_pdf])
p2.set_ydata([x*a_slider.val*b_slider.val for x in normal_pdf])
fig.canvas.draw_idle()
a_slider.on_changed(sliders_on_change)
b_slider.on_changed(sliders_on_change)
p1,=ax1.plot(x, normal_pdf, 'r-')
p2,=ax2.plot(x, a_normal_pdf, 'bo')
p3,=ax3.plot(x, ab_normal_pdf, 'g*')
plt.show()
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 am trying to plot the phasors of the voltage across the resistor, capacitor, and inductor in an series R-L-C circuit. I have done all of the calculations and I can get a decent plot with just the normal ax.plot(theta,r,....).
I would like to make the phasor vectors look like arrows. I have been trying to use ax.arrow(0,0,theta,magnitude) but it looks like a line still. The gist to the code that I have written is here : GIST
My image that I create is
I tried to follow the example that I found on this list because it is very similar to what I want to accomplish, it produces the following image:
When I run their code on my computer I get
I am on Xubuntu 14.04 and running matplotlib 1.3.1. I do see that the example I am using was using matplotlib 0.99 in 2009.
Any help would be much appreciated.
Arrow sizes were too big, this:
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
print "matplotlib.__version__ = ", matplotlib.__version__
print "matplotlib.get_backend() = ", matplotlib.get_backend()
# radar green, solid grid lines
plt.rc('grid', color='#316931', linewidth=1, linestyle='-')
plt.rc('xtick', labelsize=15)
plt.rc('ytick', labelsize=15)
# force square figure and square axes looks better for polar, IMO
width, height = matplotlib.rcParams['figure.figsize']
size = min(width, height)
# make a square figure
fig = plt.figure(figsize=(size, size))
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8], polar=True, axisbg='#d5de9c')
r = np.arange(0, 3.0, 0.01)
theta = 2*np.pi*r
ax.plot(theta, r, color='#ee8d18', lw=3)
ax.set_rmax(2.0)
plt.grid(True)
ax.set_title("And there was much rejoicing!", fontsize=20)
#This is the line I added:
arr1 = plt.arrow(0, 0.5, 0, 1, alpha = 0.5, width = 0.015,
edgecolor = 'black', facecolor = 'green', lw = 2, zorder = 5)
# arrow at 45 degree
arr2 = plt.arrow(45/180.*np.pi, 0.5, 0, 1, alpha = 0.5, width = 0.015,
edgecolor = 'black', facecolor = 'green', lw = 2, zorder = 5)
plt.show()
Produces:
Better? :)