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I am trying to get rid of these purple points on the picture below. Here is my code:
p_values = [0., 0.05, 0.25, 0.5, 1, 1.5, 2, 5, 10, np.inf]
xx, yy = np.meshgrid(np.linspace(-3, 3, num = 101),
np.linspace(-3, 3, num = 101))
fig, axes = plt.subplots(ncols = (len(p_values) + 1) // 2,
nrows = 2, figsize = (16, 7))
for p, ax in zip(p_values, axes.flat):
### BEGIN Solution (do not delete this comment)
z = np.linalg.norm([xx, yy], ord = p, axis = 0)
ax.contourf(yy, xx, z, 25, cmap = 'coolwarm')
ax.contour(yy, xx, z, [1], colors = 'fuchsia', linewidths = 3)
ax.set_title(f'p = {p}')
ax.legend([f'$x: |x|_{{{p}}} = 1$']);
### END Solution (do not delete this comment)
plt.show()
Which parameters should be specified in ax.legend() in order to plot the graph clear.
You could create the legend using an explicit handle. In this case the fuchsia colored line is stored as the last element of ax.collections. Creating the legend with only labels, when there were no "handles with labels" set, could be the cause of the weird purple dots.
import matplotlib.pyplot as plt
import numpy as np
p_values = [0., 0.05, 0.25, 0.5, 1, 1.5, 2, 5, 10, np.inf]
xx, yy = np.meshgrid(np.linspace(-3, 3, num=101),
np.linspace(-3, 3, num=101))
fig, axes = plt.subplots(ncols=(len(p_values) + 1) // 2,
nrows=2, figsize=(16, 7))
cmap = plt.get_cmap('magma').copy()
cmap.set_extremes(over='green', under='black', bad='turquoise')
for p, ax in zip(p_values, axes.flat):
### BEGIN Solution (do not delete this comment)
z = np.linalg.norm([xx, yy], ord=p, axis=0)
cnt = ax.contourf(yy, xx, z, 25, cmap='coolwarm')
ax.contour(yy, xx, z, [1], colors='fuchsia', linewidths=3)
ax.set_title(f'p = {p}')
ax.legend(handles=[ax.collections[-1]], labels=[f'$x: |x|_{{{p}}} = 1$'])
plt.colorbar(cnt, ax=ax)
### END Solution (do not delete this comment)
plt.tight_layout()
plt.show()
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 am plotting my pandas data using matplotlib, My plot looks like this:
There are four classes in the dataset. I want to color the backgroud area for each class, something like this
My matplotlib code looks like this:
import pandas as pd
df = pd.read_csv('normalized.csv')
fig = plt.figure(figsize=(8,8))
plt.scatter(df['p1'], df['p2'], c= list(df['cs']), alpha=0.9)
plt.show()
I also tried sns for this:
import pandas as pd
df = pd.read_csv('normalized.csv')
sn.FacetGrid(df, hue="cs", size = 8).map(plt.scatter, "p1", "p2").add_legend()
plt.show()
How I can fill the backgroud area for four classes in any of module?
A filled contour could serve as background:
import numpy as np
import matplotlib.pyplot as plt
N = 100
M = 4
points = np.random.normal(np.tile(np.random.uniform(1, 10, 2 * M), N)).reshape(-1, 2)
group = np.tile(np.arange(M), N)
fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(14, 5), sharey=True, sharex=True)
cmap = plt.cm.get_cmap('tab10', 4)
ax1.scatter(points[:, 0], points[:, 1], c=group, cmap=cmap)
ax2.scatter(points[:, 0], points[:, 1], c=group, cmap=cmap)
ax2.tricontourf(points[:, 0], points[:, 1], group, levels=np.arange(-0.5, 4), zorder=0, cmap=cmap, alpha=0.3)
plt.show()
Note that the contour plot also creates some narrow zones of inbetween values, because it only looks at numeric values and supposes that between a zone 0 and a zone 2 there must exist some small zone 1.
A bit more involved approach uses a nearest neighbor fit:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn import neighbors
N = 100
M = 4
points = np.random.normal(np.tile(np.random.uniform(1, 10, 2 * M), N)).reshape(-1, 2)
groups = np.tile(np.arange(M), N)
fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(14, 5), sharey=True, sharex=True)
cmap = ListedColormap(['orange', 'cyan', 'cornflowerblue', 'crimson'])
ax1.scatter(points[:, 0], points[:, 1], c=groups, cmap=cmap)
ax2.scatter(points[:, 0], points[:, 1], c=groups, cmap=cmap)
clf = neighbors.KNeighborsClassifier(10)
clf.fit(points, groups)
x_min, x_max = points[:, 0].min() - 1, points[:, 0].max() + 1
y_min, y_max = points[:, 1].min() - 1, points[:, 1].max() + 1
xx, yy = np.meshgrid(np.linspace(x_min, x_max, 50),
np.linspace(y_min, y_max, 50))
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape)
ax2.imshow(Z, extent=[x_min, x_max, y_min, y_max], cmap=cmap, alpha=0.3, aspect='auto', origin='lower')
plt.show()
If you don't need to fill the space and do not bother about areas overlap (your data points show some overlap) then you can try to fill out the convex hull defined by each subset.
import matplotlib.pyplot as plt
import numpy as np
from scipy.spatial import ConvexHull
N = 100
points = [np.random.normal(np.tile(np.random.uniform(1, 5, 2), N)).reshape(-1, 2) for i in range(4)]
colors = ['r', 'g', 'b', 'k']
for k in range(4):
hull = ConvexHull(points[k])
plt.plot(points[k][:,0], points[k][:,1], '.', color = colors[k])
plt.fill(points[k][hull.vertices,0], points[k][hull.vertices,1], color = colors[k], alpha=0.3)
stack.imgur.com/2562R.png
I want to remove the (ticks) axis with numbers to the right of the colorbar. I am using matplotlib with python as follows:
f = plt.figure()
ax = f.add_subplot(1,1,1)
i = ax.imshow(mat, cmap= 'gray')
cbar = f.colorbar(i)
If you just want to remove the ticks but keep the ticklabels, you can set the size of the ticks to be 0 as following
f = plt.figure()
ax = f.add_subplot(1,1,1)
mat = np.arange(100).reshape((10, 10))
i = ax.imshow(mat, cmap= 'viridis')
cbar = f.colorbar(i)
cbar.ax.tick_params(size=0)
If you want to remove both, the ticks and the labels, you can use set_ticks([]) by passing an empty list.
cbar.set_ticks([])
Another option is to provided a formatter or locator. Here two combinations of:
a formatter which sets any value to an empty sting ('')
a locator that doesn't place a tick.
See the official matplotlib docs for more formatters or locators.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker
from mpl_toolkits.axes_grid1 import make_axes_locatable
fig, ax = plt.subplots(ncols=1)
mat = np.arange(100).reshape((10, 10))
cs = ax.imshow(mat, cmap= 'viridis')
divider = make_axes_locatable(ax)
dvider_kwargs = dict(position="right", size="15%", pad=0.5)
fig.colorbar(cs,
cax=divider.append_axes(**dvider_kwargs),
format = matplotlib.ticker.FuncFormatter(lambda x, pos: ''),
ticks = matplotlib.ticker.FixedLocator([]))
fig.colorbar(cs,
cax=divider.append_axes(**dvider_kwargs),
format = matplotlib.ticker.FuncFormatter(lambda x, pos: ''))
fig.colorbar(cs,
cax=divider.append_axes(**dvider_kwargs))
plt.tight_layout()
With make_axes_locatable and cax=divider.append_axes the colorbars have all the same size.
Another example
# gen data
n = 100000
bins = np.arange(-10, 10, .1)
value = np.random.normal(loc=20.0, scale=10.0, size=n)
samples0 = np.random.multivariate_normal([-2, 0], [[1, 0], [0, 1]], n)
samples1 = np.random.multivariate_normal([4, 4], [[1, -.9], [-.9, 1]], n)
samples2 = np.random.multivariate_normal([4, -4], [[1, .6], [.6, 1]], n)
h0, e = np.histogramdd(samples0, bins=[bins, bins], density=True)
h1, e = np.histogramdd(samples1, bins=[bins, bins], density=True)
h2, e = np.histogramdd(samples2, bins=[bins, bins], density=True)
# create figure
fig, ax = plt.subplots(ncols=1, figsize=(3,2))
kwargs = dict(vmin=0, vmax=.3)
cs0 = plt.pcolormesh(e[0][:-1], e[1][:-1], np.ma.masked_equal(h0, 0), cmap='Blues', **kwargs)
cs1 = plt.pcolormesh(e[0][:-1], e[1][:-1], np.ma.masked_equal(h1, 0), cmap='Greens', **kwargs)
cs2 = plt.pcolormesh(e[0][:-1], e[1][:-1], np.ma.masked_equal(h2, 0), cmap='Reds', **kwargs)
# create colorbars
divider = make_axes_locatable(ax)
divider_kwargs = dict(position="right", size="5%", pad=0.1)
fig.colorbar(cs0, extend='max',
cax=divider.append_axes(**divider_kwargs),
format = matplotlib.ticker.FuncFormatter(lambda x, pos: ''))
fig.colorbar(cs1, extend='max',
cax=divider.append_axes(**divider_kwargs),
format = matplotlib.ticker.FuncFormatter(lambda x, pos: ''))
fig.colorbar(cs2, extend='max',
cax=divider.append_axes(**divider_kwargs),
label='PDF')
# tune plot
ax.set_aspect('equal')
# ax.grid()
plt.tight_layout()
Here I tried to add the polar plot on top of the Cartesian grid,but what I got instead was 2 separate figures(one polar another Cartesian),I want this polar figure to be embedded in the Cartesian plot. Also I have used some of the code previously available as I am new to matplotlib.
from pylab import *
import matplotlib.pyplot as plt
x = [0,10,-3,-10]
y = [0,10,1,-10]
color=['w','w','w','w']
fig = plt.figure()
ax1 = fig.add_subplot(111)
scatter(x,y, s=100 ,marker='.', c=color,edgecolor='w')
circle1=plt.Circle((0,0),5,color='r',fill=False)
circle_min=plt.Circle((0,0),4.5,color='g',fill=False)
circle_max=plt.Circle((0,0),5.445,color='b',fill=False)
fig = plt.gcf()
fig.gca().add_artist(circle1)
fig.gca().add_artist(circle_min)
fig.gca().add_artist(circle_max)
left,right = ax1.get_xlim()
low,high = ax1.get_ylim()
arrow( left, 0, right -left, 0, length_includes_head = True, head_width = 0.15 )
arrow( 0, low, 0, high-low, length_includes_head = True, head_width = 0.15 )
grid()
fig = plt.figure()
ax2 = fig.add_subplot(111)
scatter(x,y, s=100 ,marker='.', c=color,edgecolor='w')
circle2=plt.Circle((0,0),5,color='r',fill=False)
circle_min=plt.Circle((0,0),4.5,color='g',fill=False)
circle_max=plt.Circle((0,0),5.445,color='b',fill=False)
fig = plt.gcf()
fig.gca().add_artist(circle2)
fig.gca().add_artist(circle_min)
fig.gca().add_artist(circle_max)
left,right = ax2.get_xlim()
low,high = ax2.get_ylim()
arrow( left, 0, right -left, 0, length_includes_head = True, head_width = 0.15 )
arrow( 0, low, 0, high-low, length_includes_head = True, head_width = 0.15 )
import numpy as np
import matplotlib.pyplot as plt
theta = np.linspace(-np.pi, np.pi, 100)
r1 = 1 - np.sin(3*theta)
r2 = 1 + np.cos(theta)
ax = plt.subplot(111, polar=True, # add subplot in polar coordinates
axisbg='Azure') # background colour
ax.set_rmax(2.2) # r maximum value
ax.grid(True) # add the grid
ax.plot(theta, r1,
color='Tomato', # line colour
ls='--', # line style
lw=3, # line width
label='a 3-fold curve') # label
ax.plot(theta, r2,
color='purple',
linewidth=3,
ls = '-',
label = 'a cardioid')
ax.legend(loc="lower right") # legend location
titlefont = {
'family' : 'serif',
'color' : 'black',
'weight' : 'bold',
'size' : 16,
}
ax.set_title("A plot in polar coordinates", # title
va='bottom', # some space below the title
fontdict = titlefont # set the font properties
)
grid()
show()
#I am getting a separate Cartesian image + a polar image while what I need is both the things in a single image
import matplotlib.pyplot as plt
import numpy as np
#########################################
color=['w','w','w','w']
theta = np.linspace(-np.pi, np.pi, 100)
fig = plt.figure()# initializing the figure
rect = [0.1, 0.1, 0.8, 0.8]# setting the axis limits in [left, bottom, width, height]
ax_carthesian = fig.add_axes(rect)# the carthesian axis:
ax_polar = fig.add_axes(rect, polar=True, frameon=False)# the polar axis:
#########################################
ax_carthesian.add_artist(plt.Circle((0.5,0.5),5/15,color='r',fill=False))
ax_carthesian.add_artist(plt.Circle((0.5,0.5),4.5/15,color='g',fill=False))
ax_carthesian.add_artist(plt.Circle((0.5,0.5),5.445/15,color='b',fill=False))
ax_polar.plot(theta, 1 - np.sin(3*theta), color='Tomato',ls='--',lw=1, label='a 3-fold curve')
ax_polar.plot(theta, 1 + np.cos(theta), color='purple',linewidth=1,ls = '-',label = 'a cardioid')
plt.show()
I am not used to matplotlib but I reduced your code to his minimum to better understand it and make it look less redudant. look at what I get:
import pylab
import matplotlib.pyplot as plt
import numpy as np
#########################################
x = [0,10,-3,-10]
y = [0,10,1,-10]
color=['w','w','w','w']
theta = np.linspace(-np.pi, np.pi, 100)
#########################################
pylab.scatter(x,y, s=100 ,marker='.', c=color,edgecolor='w')
plt.gcf().gca().add_artist(plt.Circle((0,0),5,color='r',fill=False))
plt.gcf().gca().add_artist(plt.Circle((0,0),4.5,color='g',fill=False))
plt.gcf().gca().add_artist(plt.Circle((0,0),5.445,color='b',fill=False))
plt.figure().add_subplot(111)
ax = plt.subplot(111, polar=True,axisbg='Azure')
ax.plot(theta, 1 - np.sin(3*theta),color='Tomato',ls='--',lw=3,label='a 3-fold curve')
ax.plot(theta, 1 + np.cos(theta),color='purple',linewidth=3,ls = '-',label = 'a cardioid')
pylab.show()
it is nearly the same result...