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Hi I a have a data set which I project onto a sphere such that the magnitude of the data, as a function of theta and phi, is shown using a colour spectrum (which uses "ax.plot_surface", "plt.colorbar" and "facecolors"). My query is that at this stage I am limited to "cm.hot" and "cm.jet". Does anyone know of any other colour schemes which are available for this purpose. Please see my code and the figures below
Code:
from numpy import*
import math
import matplotlib.pyplot as plt
from matplotlib import cm
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import matplotlib.cm as cm
#theta inclination angle
#phi azimuthal angle
n_theta = 100 #number of values for theta
n_phi = 100 #number of values for phi
r = 1 #radius of sphere
theta, phi = np.mgrid[0: pi:n_theta*1j,-pi:pi:n_phi*1j ]
x = r*np.sin(theta)*np.cos(phi)
y = r*np.sin(theta)*np.sin(phi)
z = r*np.cos(theta)
inp = []
f = open("data.dat","r")
for line in f:
i = float(line.split()[0])
j = float(line.split()[1])
val = float(line.split()[2])
inp.append([i, j, val])
inp = np.array(inp)
#reshape the input array to the shape of the x,y,z arrays.
c = inp[:,2].reshape((n_phi,n_theta))
#Set colours and render
fig = plt.figure(figsize=(10, 8))
ax = fig.add_subplot(111, projection='3d')
#use facecolors argument, provide array of same shape as z
# cm.<cmapname>() allows to get rgba color from array.
# array must be normalized between 0 and 1
surf = ax.plot_surface(
x,y,z, rstride=1, cstride=1, facecolors=cm.jet(c), alpha=0.9, linewidth=1, shade=False)
ax.set_xlim([-2.0,2.0])
ax.set_ylim([-2.0,2.0])
ax.set_zlim([-2,2])
ax.set_aspect("equal")
plt.title('Plot with cm.jet')
#Label axis.
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
#Creates array for colorbar from 0 to 1.
a = array( [1.0, 0.5, 0.0])
#Creates colorbar
m = cm.ScalarMappable(cmap=cm.jet)
m.set_array(a)
plt.colorbar(m)
plt.savefig('facecolor plots')
f.close()
plt.show()
The following is a list of colormaps provided directly by matplotlib. It's taken from the Colormap reference example.
cmaps = [('Perceptually Uniform Sequential', [
'viridis', 'plasma', 'inferno', 'magma', 'cividis']),
('Sequential', [
'Greys', 'Purples', 'Blues', 'Greens', 'Oranges', 'Reds',
'YlOrBr', 'YlOrRd', 'OrRd', 'PuRd', 'RdPu', 'BuPu',
'GnBu', 'PuBu', 'YlGnBu', 'PuBuGn', 'BuGn', 'YlGn']),
('Sequential (2)', [
'binary', 'gist_yarg', 'gist_gray', 'gray', 'bone', 'pink',
'spring', 'summer', 'autumn', 'winter', 'cool', 'Wistia',
'hot', 'afmhot', 'gist_heat', 'copper']),
('Diverging', [
'PiYG', 'PRGn', 'BrBG', 'PuOr', 'RdGy', 'RdBu',
'RdYlBu', 'RdYlGn', 'Spectral', 'coolwarm', 'bwr', 'seismic']),
('Qualitative', [
'Pastel1', 'Pastel2', 'Paired', 'Accent',
'Dark2', 'Set1', 'Set2', 'Set3',
'tab10', 'tab20', 'tab20b', 'tab20c']),
('Miscellaneous', [
'flag', 'prism', 'ocean', 'gist_earth', 'terrain', 'gist_stern',
'gnuplot', 'gnuplot2', 'CMRmap', 'cubehelix', 'brg', 'hsv',
'gist_rainbow', 'rainbow', 'jet', 'nipy_spectral', 'gist_ncar'])]
To easily view them all you may e.g. use the following 3D colormap viewer (written in PyQt5).
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
from PyQt5 import QtGui, QtCore, QtWidgets
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.figure import Figure
import sys
class MainWindow(QtWidgets.QMainWindow):
def __init__(self):
QtWidgets.QMainWindow.__init__(self)
self.main_widget = QtWidgets.QWidget(self)
self.fig = Figure()
self.canvas = FigureCanvas(self.fig)
self.ax = self.fig.add_subplot(111, projection=Axes3D.name)
u = np.linspace(0, 2 * np.pi, 100)
v = np.linspace(0, np.pi, 100)
x = 10 * np.outer(np.cos(u), np.sin(v))
y = 10 * np.outer(np.sin(u), np.sin(v))
z = 10 * np.outer(np.ones(np.size(u)), np.cos(v))
# Plot the surface
self.surf = self.ax.plot_surface(x, y, z, cmap="YlGnBu")
self.cb = self.fig.colorbar(self.surf)
self.canvas.setSizePolicy(QtWidgets.QSizePolicy.Expanding,
QtWidgets.QSizePolicy.Expanding)
self.canvas.updateGeometry()
self.dropdown1 = QtWidgets.QComboBox()
items = []
for cats in cmaps:
items.extend(cats[1])
self.dropdown1.addItems(items)
self.dropdown1.currentIndexChanged.connect(self.update)
self.label = QtWidgets.QLabel("A plot:")
self.layout = QtWidgets.QGridLayout(self.main_widget)
self.layout.addWidget(QtWidgets.QLabel("Select Colormap"))
self.layout.addWidget(self.dropdown1)
self.layout.addWidget(self.canvas)
self.setCentralWidget(self.main_widget)
self.show()
self.update()
def update(self):
self.surf.set_cmap(self.dropdown1.currentText())
self.fig.canvas.draw_idle()
if __name__ == '__main__':
app = QtWidgets.QApplication(sys.argv)
win = MainWindow()
sys.exit(app.exec_())
My requirement is to plot the data in polar graph. However I need to keep polar graph in particular angle to looks like "V" shape and data need to plotted in between the particular angle.
In python I don't find a solution to keep the polar graph in particular angle, Example : Graph should be display in between -60 to 60 degree radius. To achieve that I have looked into couple of existing examples and creating required polar graph with FloatingSubplot functions. However I am hitting the issue , when we try to use along with function animation function with blit=True. Error message is displayed is "AttributeError: draw_artist can only be used after an initial draw which caches the render"
Here is my code.
#
import matplotlib
matplotlib.use('Qt4Agg')
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import style
import matplotlib.animation as animation
import mpl_toolkits.axisartist.floating_axes as floating_axes
from matplotlib.transforms import Affine2D
from matplotlib.projections import PolarAxes
from mpl_toolkits.axisartist import angle_helper
from mpl_toolkits.axisartist.grid_finder import MaxNLocator, DictFormatter
from mpl_toolkits.axisartist.floating_axes import GridHelperCurveLinear, FloatingSubplot
plt.close('all')
fig = plt.figure('Practice', dpi=100) # To set the fig title as pratice
ax1 = fig.add_subplot(2, 2, 1) # subplot for 1st plot
plt.ion()
ax1.grid(True)
def fractional_polar_axes(f, thlim=(0, 120), rlim=(0, 20), step=(30, 0.25),
thlabel='theta', rlabel='r', ticklabels=True, theta_offset=0, rlabels=None):
'''Return polar axes that adhere to desired theta (in deg) and r limits. steps for theta
and r are really just hints for the locators.'''
th0, th1 = thlim # deg
r0, r1 = rlim
thstep, rstep = step
tr_rotate = Affine2D().translate(theta_offset, 0)
# scale degrees to radians:
tr_scale = Affine2D().scale(np.pi / 180., 1.)
# pa = axes(polar="true") # Create a polar axis
pa = PolarAxes
tr = tr_rotate + tr_scale + pa.PolarTransform()
theta_grid_locator = angle_helper.LocatorDMS((th1 - th0) // thstep)
r_grid_locator = MaxNLocator((r1 - r0) // rstep)
theta_tick_formatter = angle_helper.FormatterDMS()
if rlabels:
rlabels = DictFormatter(rlabels)
grid_helper = GridHelperCurveLinear(tr,
extremes=(th0, th1, r0, r1),
grid_locator1=theta_grid_locator,
grid_locator2=r_grid_locator,
tick_formatter1=theta_tick_formatter,
tick_formatter2=rlabels)
a = FloatingSubplot(f, 222, grid_helper=grid_helper)
# a = Subplot(f,753, grid_helper=grid_helper)
# f.add_subplot(7,5,(3,34))
f.add_subplot(a)
# adjust x axis (theta):
print(a)
a.axis["bottom"].set_visible(False)
a.axis["top"].set_axis_direction("bottom") # tick direction
a.axis["top"].toggle(ticklabels=ticklabels, label=bool(thlabel))
a.axis["top"].major_ticklabels.set_axis_direction("top")
a.axis["top"].label.set_axis_direction("top")
a.axis["top"].major_ticklabels.set_pad(10)
# adjust y axis (r):
a.axis["left"].set_axis_direction("bottom") # tick direction
a.axis["right"].set_axis_direction("top") # tick direction
a.axis["left"].toggle(ticklabels=True, label=bool(rlabel))
# add labels:
a.axis["top"].label.set_text(thlabel)
a.axis["left"].label.set_text(rlabel)
# create a parasite axes whose transData is theta, r:
auxa = a.get_aux_axes(tr)
print(auxa)
# make aux_ax to have a clip path as in a?:
auxa.patch = a.patch
# this has a side effect that the patch is drawn twice, and possibly over some other
# artists. So, we decrease the zorder a bit to prevent this:
a.patch.zorder = -2
# add sector lines for both dimensions:
thticks = grid_helper.grid_info['lon_info'][0]
rticks = grid_helper.grid_info['lat_info'][0]
print(grid_helper.grid_info['lat_info'])
for th in thticks[1:-1]: # all but the first and last
auxa.plot([th, th], [r0, r1], ':', c='grey', zorder=-1, lw=0.5)
for ri, r in enumerate(rticks):
# plot first r line as axes border in solid black only if it isn't at r=0
if ri == 0 and r != 0:
ls, lw, color = 'solid', 1, 'black'
else:
ls, lw, color = 'dashed', 0.5, 'grey'
# From http://stackoverflow.com/a/19828753/2020363
auxa.add_artist(plt.Circle([0, 0], radius=r, ls=ls, lw=lw, color=color, fill=False,
transform=auxa.transData._b, zorder=-1))
return auxa
def animate(i):
global loopcount, th, r
th = th+.1
r = r+.1
datapoints.set_offsets(np.vstack((th,r)).T)
#print("in animate")
return datapoints,
if __name__ == '__main__':
r_locs = [0,5,10, 15, 20]
r_labels = ['0', '5', '10', '15', '20']
r_ticks = {loc: label for loc, label in zip(r_locs, r_labels)}
a1 = fractional_polar_axes(fig, thlim=(-60, 60), step=(20, 5),
theta_offset=90, rlabels=r_ticks)
th= 20
r=10
a1.scatter(th,r , c = 'r', alpha = 0.5, linewidths = '.2', s = 20) # plotting the line at thetha 20 and radius 10
datapoints = a1.scatter([], [], c='b', alpha = 0.5, linewidths = '.2', s = 20) # creating scatter line with given instruction,
ani = animation.FuncAnimation(fig, animate, frames=30, interval=20, blit=True)
plt.show(block=True)
#
"""
Above code is working perfectly fine with blit=False and also same solution working fine with line and scatter plotting in normal graph.
Please someone help me to resolve the issue.
"""
I am new to Matplotlib. Based on my code in following, I wanted to update the data,title,xlabel,ylabel at same time. However, the title and labels did not been updated, but data did.Someone can give me a solution? That will help me a lot.Thank you.
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
def updata(frame_number):
current_index = frame_number % 3
a = [[1,2,3],[4,5,6],[7,8,9]]
idata['position'][:,0] = np.asarray(a[current_index])
idata['position'][:,1] = np.asarray(a[current_index])
scat.set_offsets(idata['position'])
ax.set_xlabel('The Intensity of Image1')
ax.set_ylabel('The Intensity of Image2')
ax.set_title("For Dataset %d" % current_index)
fig = plt.figure(figsize=(5,5))
ax = fig.add_axes([0,0,1,1])
idata = np.zeros(3,dtype=[('position',float,2)])
ax.set_title(label='lets begin',fontdict = {'fontsize':12},loc='center')
scat = ax.scatter(idata['position'][:,0],idata['position'][:,1],s=10,alpha=0.3,edgecolors='none')
animation = FuncAnimation(fig,updata,interval=2000)
plt.show()
Running the code, I see an empty window. The reason is that the axes span the complete figure (fig.add_axes([0,0,1,1])). In order to see the title and labels, you would need to make the axes smaller than the figure, e.g. by
ax = fig.add_subplot(111)
Also, the scale of the axes is not defined, so the animation will happen outside the axes limits. You can use ax.set_xlim and ax.set_ylim to prevent that.
Here is a complete running code:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
def updata(frame_number):
current_index = frame_number % 3
a = [[1,2,3],[4,5,6],[7,8,9]]
idata['position'][:,0] = np.asarray(a[current_index])
idata['position'][:,1] = np.asarray(a[current_index])
scat.set_offsets(idata['position'])
ax.set_xlabel('The Intensity of Image1')
ax.set_ylabel('The Intensity of Image2')
ax.set_title("For Dataset %d" % current_index)
fig = plt.figure(figsize=(5,5))
ax = fig.add_subplot(111)
idata = np.zeros(3,dtype=[('position',float,2)])
ax.set_title(label='lets begin',fontdict = {'fontsize':12},loc='center')
scat = ax.scatter(idata['position'][:,0],idata['position'][:,1],
s=25,alpha=0.9,edgecolors='none')
ax.set_xlim(0,10)
ax.set_ylim(0,10)
animation = FuncAnimation(fig,updata,frames=50,interval=600)
plt.show()
I am new to matplotlib animation and am trying to animate a scatter plot where points moving towards the right will turn red gradually while points moving towards the left will turn blue gradually. The code doesn't work perfectly as it doesn't change the color of the points gradually. When I pause the animation and maximize it, the gradual change in color suddenly appears, when I play it, it is again the same. Here is the animation link. The final image should be something like this:
But the animation doesn't show gradual change of colors as you can see in the video.
Here is the code, I'd really appreciate your help. Thanks
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import numpy as np
import pandas as pd
class AnimatedScatter(object):
"""An animated scatter plot using matplotlib.animations.FuncAnimation."""
def __init__(self, numpoints=5):
self.numpoints = numpoints
self.stream = self.data_stream()
# Setup the figure and axes...
self.fig, self.ax = plt.subplots()
# Then setup FuncAnimation.
self.ani = animation.FuncAnimation(self.fig, self.update, interval=500,
init_func=self.setup_plot, blit=True,repeat=False)
self.fig.canvas.mpl_connect('button_press_event',self.onClick)
#self.ani.save("animation.mp4")
def setup_plot(self):
"""Initial drawing of the scatter plot."""
t=next(self.stream)
x, y, c = t[:,0],t[:,1],t[:,2]
self.scat = self.ax.scatter(x, y, c=c, s=50, animated=True)
self.ax.axis([-15, 15, -10, 10])
# For FuncAnimation's sake, we need to return the artist we'll be using
# Note that it expects a sequence of artists, thus the trailing comma.
return self.scat,
def data_stream(self):
#f=pd.read_csv("crc_viz.csv")
columns = ['TbyN','CbyS']
#f=f[['TbyN','CbyS']]
index=range(1,self.numpoints+1)
x=10*(np.ones((self.numpoints,1))-2*np.random.random((self.numpoints,1)))
y = 5*(np.ones((self.numpoints,1))-2*np.random.random((self.numpoints,1)))
f=np.column_stack((x,y))
f=pd.DataFrame(f,columns=columns)
print f
f['new_cbys'] = f['CbyS']
f['new_cbys'][f['new_cbys']<0] = -1
f['new_cbys'][f['new_cbys']>0] = 1
f=f[:self.numpoints]
cbys=np.array(list(f['CbyS']))
sign = np.array(list(f['new_cbys']))
x = np.array([0]*self.numpoints)
y = np.array(f['TbyN'])
c = np.array([0.5]*self.numpoints)
t = [(255,0,0) for i in range(self.numpoints)]
data=np.column_stack((x,y,c))
x = data[:, 0]
c = data[:,2]
while True:
#print xy
#print cbys
if not pause:
for i in range(len(x)):
if sign[i]==1:
if x[i]<cbys[i]-0.1:
x[i]+=0.1
c[i]+=0.05
else:
x[i]=cbys[i]
elif sign[i]==-1:
if x[i]>cbys[i]+0.1:
x[i]-=0.1
c[i]-=0.05
else:
x[i]=cbys[i]
print c
#print data
#print c
yield data
def onClick(self,event):
global pause
pause ^=True
def update(self, i):
"""Update the scatter plot."""
data = next(self.stream)
print data[:,2]
# Set x and y data...
self.scat.set_offsets(data[:, :2])
# Set colors..
self.scat.set_array(data[:,2])
return self.scat,
def save(self):
plt.rcParams['animation.ffmpeg_path'] = 'C:\\ffmpeg\\bin\\ffmpeg.exe'
self.mywriter = animation.FFMpegWriter()
self.ani.save("myMovie.mp4",writer=self.mywriter)
self.show()
def show(self):
#mng = plt.get_current_fig_manager()
#mng.window.state('zoomed')
plt.show()
pause = False
if __name__ == '__main__':
a = AnimatedScatter(10)
a.show()
#a.save()
The problem you have is that the scatter plot is redrawn in every iteration, renormalizing the colors to the minimal and maximal value of c. So even at the start there will be a dot coresponding to the minmal and maximal color in the colormap already.
The solution would be to use a color normalization which is absolute from the start. The easiest way to do this is using the vmin and vmax keyword arguments.
ax.scatter(x, y, c=c, vmin=-1.5, vmax=2)
(This means that a value of c=-1.5 is the lowest color in the colormap and c=2 corresponds to the highest.)
Now it may be a bit hard to find the appropriate values, as the values are constantly changing in an infinite loop, so you need to find out appropriate values yourself depending on the use case.
I'm attempting to make use of twinx() to create a bar/line combo graph with the line visible on top of the bar. Currently this is how it appears:
I also need the line chart to be plotted on the left vertical axis (ax) and the bar on the right (ax2) as it currently is. If I plot the line on the second axis it does appear on top, but obviously it appears on the wrong axis (right)
Here's my code:
self.ax2=ax.twinx()
df[['Opportunities']].plot(kind='bar', stacked=False, title=get_title, color='grey', ax=self.ax2, grid=False)
ax.plot(ax.get_xticks(),df[['Percentage']].values, linestyle='-', marker='o', color='k', linewidth=1.0)
lines, labels = ax.get_legend_handles_labels()
lines2, labels2 = self.ax2.get_legend_handles_labels()
ax.legend(lines + lines2, labels + labels2, loc='lower right')
Also having trouble with the labels, but one thing at a time.
It appears, by default, that the artists are drawn on ax first, then the
artists on the twin axes ax2 on top. So since in your code the line plot was drawn on ax and the bar plot on ax2, the bar plot sits on top of (and obscures) the line.
(I thought I could change this by specifying zorder, but that attempt did not
work... )
So one way to solve the problem is to use ax to draw the bar plot and ax2 to draw the line. That will place the line on top of the bars. It will also, by default, place the ytick labels for ax (the bar plot) on the left, and the ytick labels for ax2 (the line) on the right. However, you can use
ax.yaxis.set_ticks_position("right")
ax2.yaxis.set_ticks_position("left")
to swap the location of the left and right ytick labels.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import pandas as pd
np.random.seed(2015)
N = 16
df = pd.DataFrame({'Opportunities': np.random.randint(0, 30, size=N),
'Percentage': np.random.randint(0, 100, size=N)},
index=pd.date_range('2015-3-15', periods=N, freq='B').date)
fig, ax = plt.subplots()
df[['Opportunities']].plot(kind='bar', stacked=False, title='get_title',
color='grey', ax=ax, grid=False)
ax2 = ax.twinx()
ax2.plot(ax.get_xticks(), df[['Percentage']].values, linestyle='-', marker='o',
color='k', linewidth=1.0, label='percentage')
lines, labels = ax.get_legend_handles_labels()
lines2, labels2 = ax2.get_legend_handles_labels()
ax.legend(lines + lines2, labels + labels2, loc='best')
ax.yaxis.set_ticks_position("right")
ax2.yaxis.set_ticks_position("left")
fig.autofmt_xdate()
plt.show()
yields
Alternatively, the zorder of the axes can be set so as to draw ax above ax2. Paul Ivanov shows how:
ax.set_zorder(ax2.get_zorder()+1) # put ax in front of ax2
ax.patch.set_visible(False) # hide the 'canvas'
ax2.patch.set_visible(True) # show the 'canvas'
Thus,
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import pandas as pd
np.random.seed(2015)
N = 16
df = pd.DataFrame({'Opportunities': np.random.randint(0, 30, size=N),
'Percentage': np.random.randint(0, 100, size=N)},
index=pd.date_range('2015-3-15', periods=N, freq='B').date)
fig, ax = plt.subplots()
ax2 = ax.twinx()
df[['Opportunities']].plot(kind='bar', stacked=False, title='get_title',
color='grey', ax=ax2, grid=False)
ax.plot(ax.get_xticks(), df[['Percentage']].values, linestyle='-', marker='o',
color='k', linewidth=1.0, label='percentage')
lines, labels = ax.get_legend_handles_labels()
lines2, labels2 = ax2.get_legend_handles_labels()
ax.legend(lines + lines2, labels + labels2, loc='best')
ax.set_zorder(ax2.get_zorder()+1) # put ax in front of ax2
ax.patch.set_visible(False) # hide the 'canvas'
ax2.patch.set_visible(True) # show the 'canvas'
fig.autofmt_xdate()
plt.show()
yields the same result without having to swap the roles played by ax and ax2.