I'm just studying Python for a month and have no experience.
I'm trying to hide/show two graphs with one Check button in matplotlib. But with my code, when clicking the button, there is only one graph hidden. Please see my code and show me my mistake.
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import math
from matplotlib.widgets import Button, RadioButtons, CheckButtons
fig = plt.figure()
ax = fig.add_subplot(projection='3d')
p = ax.scatter(5,6,7) and ax.scatter(1,2,3, color='red', marker='+', s=1e2)
lines = [p]
labels = ["Hide/Show"]
def func1(label):
index = labels.index(label)
lines[index].set_visible(not lines[index].get_visible())
fig.canvas.draw()
a = [True]
# xposition, yposition, width, height
ax_check = plt.axes([0, 0.01, 0.25, 0.25])
plot_button = CheckButtons(ax_check, labels, a)
plot_button.on_clicked(func1)
plt.show()
# D.L Your suggestion is perfect.
Just add another line to the figure and in the function fun1 add a calling of the line 2:
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import math
from matplotlib.widgets import Button, RadioButtons, CheckButtons
fig = plt.figure()
ax = fig.add_subplot(projection='3d')
p1 = ax.scatter(5,6,7)
p2 = ax.scatter(1,2,3, color='red', marker='+', s=1e2)
lines = [p1, p2]
labels = ["Hide/Show"]
def func1(label):
index = labels.index(label)
lines[index].set_visible(not lines[index].get_visible())
lines[index+1].set_visible(not lines[index+1].get_visible())
fig.canvas.draw()
a = [True]
# xposition, yposition, width, height
ax_check = plt.axes([0, 0.01, 0.25, 0.25])
plot_button = CheckButtons(ax_check, labels, a)
plot_button.on_clicked(func1)
plt.show()
Related
I'm trying to draw the animation of several 3DScatter with matplotlib. I succeeded to draw all the points but I'm struggling with the colors. Even if I'm calling the function set_color(..) nothing is changed.
Here is what I'm currently doing, to_plot is an array of size total with (5120, 3) float elements and colors is an array of size total with (5120,) elements (equal to 'r' or 'b'):
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.animation as animation
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.patches as mpatches
total = 10
num_whatever = 100 # old = 5120
to_plot = [np.random.rand(num_whatever, 3) for i in range(total)]
colors = [['r' if i%2==0 else 'b' for i in range(num_whatever)] for i in range(total)]
red_patch = mpatches.Patch(color='red', label='Men')
blue_patch = mpatches.Patch(color='blue', label='Women')
fig = plt.figure()
ax3d = Axes3D(fig)
scat3D = ax3d.scatter([],[],[], s=10)
ttl = ax3d.text2D(0.05, 0.95, "", transform=ax3d.transAxes)
def update_plot(i):
print i, to_plot[i].shape
ttl.set_text('PCA on 3 components at step = {}'.format(i*20))
scat3D._offsets3d = np.transpose(to_plot[i])
scat3D.set_color(colors[i])
return scat3D,
def init():
scat3D.set_offsets([[],[],[]])
ax3d.set_xlim(-1.,2.)
ax3d.set_ylim(-0.5,0.7)
ax3d.set_zlim(-1.,0.75)
plt.style.use('ggplot')
plt.legend(handles=[red_patch, blue_patch])
ani = animation.FuncAnimation(fig, update_plot, init_func=init, blit=False, interval=100, frames=xrange(total))
# ani.save(os.path.join(config.workdir, 'gif', 'bins','anim.gif'), writer="imagemagick")
plt.plot()
The scatter plot is a Path3DCollection. It can have a colormap associated to it such that its points are colored according to the color array.
So you can provide a list of numeric values to the scatter via scat3D.set_array(colors[i]), where colors[i] = [0,1,0,...,1,0,1]. Those values are then mapped according to the colormap in use. For blue/red color this is simple, because there exists already a colormap "bwr" from blue to red.
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.animation as animation
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.patches as mpatches
total = 10
num_whatever = 100
to_plot = [np.random.rand(num_whatever, 3) for i in range(total)]
colors = [np.tile([0,1],num_whatever//2) for i in range(total)]
red_patch = mpatches.Patch(color='red', label='Men')
blue_patch = mpatches.Patch(color='blue', label='Women')
fig = plt.figure()
ax3d = Axes3D(fig)
scat3D = ax3d.scatter([],[],[], s=10, cmap="bwr", vmin=0, vmax=1)
scat3D.set_cmap("bwr") # cmap argument above is ignored, so set it manually
ttl = ax3d.text2D(0.05, 0.95, "", transform=ax3d.transAxes)
def update_plot(i):
print i, to_plot[i].shape
ttl.set_text('PCA on 3 components at step = {}'.format(i*20))
scat3D._offsets3d = np.transpose(to_plot[i])
scat3D.set_array(colors[i])
return scat3D,
def init():
scat3D.set_offsets([[],[],[]])
plt.style.use('ggplot')
plt.legend(handles=[red_patch, blue_patch])
ani = animation.FuncAnimation(fig, update_plot, init_func=init,
blit=False, interval=100, frames=xrange(total))
ani.save("ani.gif", writer="imagemagick")
plt.show()
The reason why calling set_color failed is described here: https://github.com/matplotlib/matplotlib/issues/13035
... the bug is due to the set_facecolor not setting _facecolor3d because it is inherited from the base class (Collection) setting _facecolors. The same for edge colors.
And yes, this is a bug in matplotlib.
Therefore, if you want to change the face colors, direct assignment of _facecolor3d just work out fine. Note that you must assign it with an rgba_array, like
scat3D._facecolor3d[0] = [1., 0., 0., 1.]
or instead, if you want to use the presets (like 'r', '.5', etc.), you can do it this way
scat3D.set_color(...)
scat3D._facecolor3d = scat3D.get_facecolor()
scat3D._edgecolor3d = scat3D.get_edgecolor()
I have tested on both python 2.7 and 3.6, and no problem arose.
In the following code, the color of bars changes as the threshold is changed. Instead of using the threshold and plotting the horizontal line in the code, I want to use the y parameter in the OnMouseMove function so that the user can change the location of "threshold". Then, I want the colors to be updated as the y is changed.
I think what I need is called "observer pattern" or perhaps a trick using the animation tools but not sure how to implement it. I appreciate any insight on how to do this. Thanks
%matplotlib notebook
import pandas as pd
import numpy as np
from scipy import stats
import matplotlib.colors as mcol
import matplotlib.cm as cm
import matplotlib.pyplot as plt
np.random.seed(12345)
df = pd.DataFrame([np.random.normal(335,1500,300),
np.random.normal(410,900,300),
np.random.normal(410,1200,300),
np.random.normal(480,550,300)],
index=[1,2,3,4])
fig, ax = plt.subplots()
plt.show()
bars = plt.bar(range(df.shape[0]), df.mean(axis = 1), color = 'lightslategrey')
fig = plt.gcf()
threshold=420
plt.axhline(y = threshold, color = 'grey', alpha = 0.5)
cm1 = mcol.LinearSegmentedColormap.from_list("Test",["b", "white", "purple"])
cpick = cm.ScalarMappable(cmap=cm1)
cpick.set_array([])
percentages = []
for bar in bars:
percentage = (bar.get_height()-threshold)/bar.get_height()
if percentage>1: percentage = 1
if percentage<0: percentage=0
percentages.append(percentage)
cpick.to_rgba(percentages)
bars = plt.bar(range(df.shape[0]), df.mean(axis = 1), color = cpick.to_rgba(percentages))
plt.colorbar(cpick, orientation='horizontal')
def onMouseMove(event):
ax.lines = [ax.lines[0]]
plt.axhline(y=event.ydata, color="k")
fig.canvas.mpl_connect('motion_notify_event', onMouseMove)
plt.xticks(range(df.shape[0]), df.index, alpha = 0.8)
First you should use exactly one bar plot and exactly one axhline (using more will make everything chaotic). You can set the colors of the bars via
for bar in bars:
bar.set_color(..)
and you can update the axhline's position via line.set_ydata(position).
Now, for every mouse move event you need to update the axhline's position, calculate the percentages and apply a new colors to the bars. So those things should be done in a function, which is called every time the mouse move event is triggered. After those settings have been applied the canvas needs to be drawn for them to become visible.
Here is a complete code.
import pandas as pd
import numpy as np
import matplotlib.colors as mcol
import matplotlib.cm as cm
import matplotlib.pyplot as plt
np.random.seed(12345)
df = pd.DataFrame([np.random.normal(335,1500,300),
np.random.normal(410,900,300),
np.random.normal(410,1200,300),
np.random.normal(480,550,300)],
index=[1,2,3,4])
fig, ax = plt.subplots()
threshold=420.
bars = plt.bar(range(df.shape[0]), df.mean(axis = 1), color = 'lightslategrey')
axline = plt.axhline(y = threshold, color = 'grey', alpha = 0.5)
cm1 = mcol.LinearSegmentedColormap.from_list("Test",["b", "white", "purple"])
cpick = cm.ScalarMappable(cmap=cm1)
cpick.set_array([])
plt.colorbar(cpick, orientation='horizontal')
def percentages(threshold):
percentages = []
for bar in bars:
percentage = (bar.get_height()-threshold)/bar.get_height()
if percentage>1: percentage = 1
if percentage<0: percentage=0
percentages.append(percentage)
return percentages
def update(threshold):
axline.set_ydata(threshold)
perc = percentages(threshold)
for bar, p in zip(bars, perc):
bar.set_color(cpick.to_rgba(p))
# update once before showing
update(threshold)
def onMouseMove(event):
if event.inaxes == ax:
update(event.ydata)
fig.canvas.draw_idle()
fig.canvas.mpl_connect('motion_notify_event', onMouseMove)
plt.xticks(range(df.shape[0]), df.index, alpha = 0.8)
plt.show()
I am generating 2D heat map plots of a set of 3D data. I would like to be able to have a mechanism to interactively page through each pane. Below is a simple sample code, I would like to be able to interactively view both panes (ie, z = [0,1]) via a slider bar (or some other means). Is this possible with matplotlib or is this something I'll need to do post processing after generating the image files?
import numpy as np
from matplotlib import pyplot as plt
data = np.random.randint(10, size=(5, 5, 2))
data_slice = np.zeros((5,5))
for i in range(0, 5):
for j in range(0, 5):
data_slice[i][j] = data[i][j][0]
plt.imshow(data_slice, cmap='hot', interpolation='nearest')
plt.show()
Edit : I want to be able to do this interactively and it appears that the possible duplicate is trying to do this automatically.
The solution could indeed be to use a Slider as in the excellent answer by #hashmuke. In his answer he mentioned that
"The slider is continuous while the layer index is a discrete integer [...]"
This brought me to think about a solution that wouldn't have this restriction and have
a more page-like look and feel.
The outcome is PageSlider. Subclassing Slider it makes use of the slider functionality, but displays the slider in integer steps starting at 1. It takes the number of pages numpages as init argument, but except of that works as Slider seen from the outside. Additionally it also provides a back- and forward button.
An example, similar to the one from #hashmuke, is given below the class.
import matplotlib.widgets
import matplotlib.patches
import mpl_toolkits.axes_grid1
class PageSlider(matplotlib.widgets.Slider):
def __init__(self, ax, label, numpages = 10, valinit=0, valfmt='%1d',
closedmin=True, closedmax=True,
dragging=True, **kwargs):
self.facecolor=kwargs.get('facecolor',"w")
self.activecolor = kwargs.pop('activecolor',"b")
self.fontsize = kwargs.pop('fontsize', 10)
self.numpages = numpages
super(PageSlider, self).__init__(ax, label, 0, numpages,
valinit=valinit, valfmt=valfmt, **kwargs)
self.poly.set_visible(False)
self.vline.set_visible(False)
self.pageRects = []
for i in range(numpages):
facecolor = self.activecolor if i==valinit else self.facecolor
r = matplotlib.patches.Rectangle((float(i)/numpages, 0), 1./numpages, 1,
transform=ax.transAxes, facecolor=facecolor)
ax.add_artist(r)
self.pageRects.append(r)
ax.text(float(i)/numpages+0.5/numpages, 0.5, str(i+1),
ha="center", va="center", transform=ax.transAxes,
fontsize=self.fontsize)
self.valtext.set_visible(False)
divider = mpl_toolkits.axes_grid1.make_axes_locatable(ax)
bax = divider.append_axes("right", size="5%", pad=0.05)
fax = divider.append_axes("right", size="5%", pad=0.05)
self.button_back = matplotlib.widgets.Button(bax, label=ur'$\u25C0$',
color=self.facecolor, hovercolor=self.activecolor)
self.button_forward = matplotlib.widgets.Button(fax, label=ur'$\u25B6$',
color=self.facecolor, hovercolor=self.activecolor)
self.button_back.label.set_fontsize(self.fontsize)
self.button_forward.label.set_fontsize(self.fontsize)
self.button_back.on_clicked(self.backward)
self.button_forward.on_clicked(self.forward)
def _update(self, event):
super(PageSlider, self)._update(event)
i = int(self.val)
if i >=self.valmax:
return
self._colorize(i)
def _colorize(self, i):
for j in range(self.numpages):
self.pageRects[j].set_facecolor(self.facecolor)
self.pageRects[i].set_facecolor(self.activecolor)
def forward(self, event):
current_i = int(self.val)
i = current_i+1
if (i < self.valmin) or (i >= self.valmax):
return
self.set_val(i)
self._colorize(i)
def backward(self, event):
current_i = int(self.val)
i = current_i-1
if (i < self.valmin) or (i >= self.valmax):
return
self.set_val(i)
self._colorize(i)
if __name__ == "__main__":
import numpy as np
from matplotlib import pyplot as plt
num_pages = 23
data = np.random.rand(9, 9, num_pages)
fig, ax = plt.subplots()
fig.subplots_adjust(bottom=0.18)
im = ax.imshow(data[:, :, 0], cmap='viridis', interpolation='nearest')
ax_slider = fig.add_axes([0.1, 0.05, 0.8, 0.04])
slider = PageSlider(ax_slider, 'Page', num_pages, activecolor="orange")
def update(val):
i = int(slider.val)
im.set_data(data[:,:,i])
slider.on_changed(update)
plt.show()
You can either animate the layers as suggested by Andrew's comment or you can manually walk through the the layers using a slider as follow:
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.widgets import Slider
# generate a five layer data
data = np.random.randint(10, size=(5, 5, 5))
# current layer index start with the first layer
idx = 0
# figure axis setup
fig, ax = plt.subplots()
fig.subplots_adjust(bottom=0.15)
# display initial image
im_h = ax.imshow(data[:, :, idx], cmap='hot', interpolation='nearest')
# setup a slider axis and the Slider
ax_depth = plt.axes([0.23, 0.02, 0.56, 0.04])
slider_depth = Slider(ax_depth, 'depth', 0, data.shape[2]-1, valinit=idx)
# update the figure with a change on the slider
def update_depth(val):
idx = int(round(slider_depth.val))
im_h.set_data(data[:, :, idx])
slider_depth.on_changed(update_depth)
plt.show()
The slider is continues while the layer index is discrete integer, I hope that is not a problem. Here is the resulting figure,
I am generating 2D heat map plots of a set of 3D data. I would like to be able to have a mechanism to interactively page through each pane. Below is a simple sample code, I would like to be able to interactively view both panes (ie, z = [0,1]) via a slider bar (or some other means). Is this possible with matplotlib or is this something I'll need to do post processing after generating the image files?
import numpy as np
from matplotlib import pyplot as plt
data = np.random.randint(10, size=(5, 5, 2))
data_slice = np.zeros((5,5))
for i in range(0, 5):
for j in range(0, 5):
data_slice[i][j] = data[i][j][0]
plt.imshow(data_slice, cmap='hot', interpolation='nearest')
plt.show()
Edit : I want to be able to do this interactively and it appears that the possible duplicate is trying to do this automatically.
The solution could indeed be to use a Slider as in the excellent answer by #hashmuke. In his answer he mentioned that
"The slider is continuous while the layer index is a discrete integer [...]"
This brought me to think about a solution that wouldn't have this restriction and have
a more page-like look and feel.
The outcome is PageSlider. Subclassing Slider it makes use of the slider functionality, but displays the slider in integer steps starting at 1. It takes the number of pages numpages as init argument, but except of that works as Slider seen from the outside. Additionally it also provides a back- and forward button.
An example, similar to the one from #hashmuke, is given below the class.
import matplotlib.widgets
import matplotlib.patches
import mpl_toolkits.axes_grid1
class PageSlider(matplotlib.widgets.Slider):
def __init__(self, ax, label, numpages = 10, valinit=0, valfmt='%1d',
closedmin=True, closedmax=True,
dragging=True, **kwargs):
self.facecolor=kwargs.get('facecolor',"w")
self.activecolor = kwargs.pop('activecolor',"b")
self.fontsize = kwargs.pop('fontsize', 10)
self.numpages = numpages
super(PageSlider, self).__init__(ax, label, 0, numpages,
valinit=valinit, valfmt=valfmt, **kwargs)
self.poly.set_visible(False)
self.vline.set_visible(False)
self.pageRects = []
for i in range(numpages):
facecolor = self.activecolor if i==valinit else self.facecolor
r = matplotlib.patches.Rectangle((float(i)/numpages, 0), 1./numpages, 1,
transform=ax.transAxes, facecolor=facecolor)
ax.add_artist(r)
self.pageRects.append(r)
ax.text(float(i)/numpages+0.5/numpages, 0.5, str(i+1),
ha="center", va="center", transform=ax.transAxes,
fontsize=self.fontsize)
self.valtext.set_visible(False)
divider = mpl_toolkits.axes_grid1.make_axes_locatable(ax)
bax = divider.append_axes("right", size="5%", pad=0.05)
fax = divider.append_axes("right", size="5%", pad=0.05)
self.button_back = matplotlib.widgets.Button(bax, label=ur'$\u25C0$',
color=self.facecolor, hovercolor=self.activecolor)
self.button_forward = matplotlib.widgets.Button(fax, label=ur'$\u25B6$',
color=self.facecolor, hovercolor=self.activecolor)
self.button_back.label.set_fontsize(self.fontsize)
self.button_forward.label.set_fontsize(self.fontsize)
self.button_back.on_clicked(self.backward)
self.button_forward.on_clicked(self.forward)
def _update(self, event):
super(PageSlider, self)._update(event)
i = int(self.val)
if i >=self.valmax:
return
self._colorize(i)
def _colorize(self, i):
for j in range(self.numpages):
self.pageRects[j].set_facecolor(self.facecolor)
self.pageRects[i].set_facecolor(self.activecolor)
def forward(self, event):
current_i = int(self.val)
i = current_i+1
if (i < self.valmin) or (i >= self.valmax):
return
self.set_val(i)
self._colorize(i)
def backward(self, event):
current_i = int(self.val)
i = current_i-1
if (i < self.valmin) or (i >= self.valmax):
return
self.set_val(i)
self._colorize(i)
if __name__ == "__main__":
import numpy as np
from matplotlib import pyplot as plt
num_pages = 23
data = np.random.rand(9, 9, num_pages)
fig, ax = plt.subplots()
fig.subplots_adjust(bottom=0.18)
im = ax.imshow(data[:, :, 0], cmap='viridis', interpolation='nearest')
ax_slider = fig.add_axes([0.1, 0.05, 0.8, 0.04])
slider = PageSlider(ax_slider, 'Page', num_pages, activecolor="orange")
def update(val):
i = int(slider.val)
im.set_data(data[:,:,i])
slider.on_changed(update)
plt.show()
You can either animate the layers as suggested by Andrew's comment or you can manually walk through the the layers using a slider as follow:
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.widgets import Slider
# generate a five layer data
data = np.random.randint(10, size=(5, 5, 5))
# current layer index start with the first layer
idx = 0
# figure axis setup
fig, ax = plt.subplots()
fig.subplots_adjust(bottom=0.15)
# display initial image
im_h = ax.imshow(data[:, :, idx], cmap='hot', interpolation='nearest')
# setup a slider axis and the Slider
ax_depth = plt.axes([0.23, 0.02, 0.56, 0.04])
slider_depth = Slider(ax_depth, 'depth', 0, data.shape[2]-1, valinit=idx)
# update the figure with a change on the slider
def update_depth(val):
idx = int(round(slider_depth.val))
im_h.set_data(data[:, :, idx])
slider_depth.on_changed(update_depth)
plt.show()
The slider is continues while the layer index is discrete integer, I hope that is not a problem. Here is the resulting figure,
Regarding to the post Embedding small plots inside subplots in matplotlib, I'm working on this solution, but for some reason, transform is ignored!
I'm in a mistake? Or there is a bug?
import matplotlib.pyplot as plt
import numpy as np
axes = []
x = np.linspace(-np.pi,np.pi)
fig = plt.figure(figsize=(10,10))
subpos = (0,0.6)
for i in range(4):
axes.append(fig.add_subplot(2,2,i))
for axis in axes:
axis.set_xlim(-np.pi,np.pi)
axis.set_ylim(-1,3)
axis.plot(x,np.sin(x))
fig.add_axes([0.5,0.5,0.1,0.1],transform=axis.transAxes)
plt.show()
import matplotlib.pyplot as plt
import numpy as np
def axis_to_fig(axis):
fig = axis.figure
def transform(coord):
return fig.transFigure.inverted().transform(
axis.transAxes.transform(coord))
return transform
def add_sub_axes(axis, rect):
fig = axis.figure
left, bottom, width, height = rect
trans = axis_to_fig(axis)
figleft, figbottom = trans((left, bottom))
figwidth, figheight = trans([width,height]) - trans([0,0])
return fig.add_axes([figleft, figbottom, figwidth, figheight])
x = np.linspace(-np.pi,np.pi)
fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(10,10))
for axis in axes.ravel():
axis.set_xlim(-np.pi, np.pi)
axis.set_ylim(-1, 3)
axis.plot(x, np.sin(x))
subaxis = add_sub_axes(axis, [0.2, 0.6, 0.3, 0.3])
subaxis.plot(x, np.cos(x))
plt.show()
yields