I have a chart, with 95% confidence intervals as patches. Naturally some of the data points overlap. Because of this, I need the point labels to be dynamically spaced so they are human readable. I have the following code below. As you can see the labels currently overlap. Any suggestions how to get them not to overlap?
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import matplotlib.transforms as transforms
from matplotlib.font_manager import FontProperties
from matplotlib.pyplot import *
mypath = ['1,0.025','1.01,0.05','1.02,0.035','1.03,0.040']
fig = plt.figure()
ax = fig.add_subplot(111)
Distances = []
Confidence_Intervals = []
for line in mypath:
Distances.append(float(line.split(',')[0].strip()))
Confidence_Intervals.append(float(line.split(',')[1].strip()))
ind = np.arange(len(Distances))
data = np.array(Distances)
y_error = np.array(Confidence_Intervals)
circles = []
plt.xlim(-1,1)
plt.ylim(0.8,1.1)
for a in range(len(ind)):
ax.scatter(0, data[a], s=60, color='Black')
trans = transforms.blended_transform_factory(ax.transData, ax.transData)
circles.append(patches.Circle((0,data[a]),y_error[a], transform=trans, facecolor='yellow', alpha=0.5))
fig.set_size_inches(24,12)
for circle in circles:
ax.add_patch(circle)
labels = ['{0}'.format(i) for i in range(len(data))]
for label, x, y in zip(labels, ind, data):
plt.annotate(
label,
xy = (0, y), xytext = (100, 0),
textcoords = 'offset points', ha = 'right', va = 'bottom',
bbox = dict(boxstyle = 'round,pad=0.5', fc = 'yellow', alpha = 0.5),
arrowprops = dict(arrowstyle = '->', connectionstyle = 'arc3,rad=0'))
plt.grid(True)
plt.legend(loc=0, scatterpoints = 1)
plt.ylabel('Pairwise distance (FastTree)')
plt.xlabel('Clade pairing')
plt.tick_params(axis='both', which='minor', labelsize=8)
plt.title('Sample Patch Chart')
axes().set_aspect('equal', 'datalim')
plt.show()
Related
I have a simple plot, where I want to insert image of UAV, but it doesn't show up. I believe that the annotation box is somewhere out of area of plot, but can't figure out where to move it. Currently I want to have it at [2,4], just to test.
Here is my code:
from mpl_toolkits import mplot3d
import numpy as np
import matplotlib.pyplot as plt
import random
from matplotlib.offsetbox import (OffsetImage, AnnotationBbox)
import matplotlib.image as image
fig = plt.figure()
ax = plt.axes(projection="3d")
num_bars = 3
x_pos = random.sample(range(20), num_bars)
y_pos = random.sample(range(20), num_bars)
z_pos = [0] * num_bars
x_size = np.ones(num_bars)
y_size = np.ones(num_bars)
z_size = random.sample(range(20), num_bars)
#ax.bar3d(x_pos, y_pos, z_pos, x_size, y_size, z_size, color='grey')
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('z')
ax.set_xlim(0,20)
ax.set_ylim(0,20)
ax.set_zlim(0,30)
"""
ax.set_xticks([])
ax.set_yticks([])
ax.set_zticks([])
"""
img="./UAV.png"
uav = image.imread(img)
arr_img = plt.imread("./UAV.png", format='png')
imagebox = OffsetImage(arr_img, zoom = .15)
imagebox.image.axes = ax
#ab = AnnotationBbox(imagebox, (5, 10), xybox = (5.0, 10.0), box_alignment=(1, 0))
ab = AnnotationBbox(imagebox, [2., 4.],
xycoords='data',
boxcoords="offset points",
pad=0
)
ax.add_artist(ab)
ax.bar3d(0,0,0,4,4,25,color="grey")
ax.bar3d(16,16,0,4,4,27,color="grey")
ax.bar3d(0,16,0,4,4,23,color="grey")
plt.tight_layout()
plt.show()
I could not find the problem with annotation box, but I have managed to fix this by adding the image to the plot by imshow instead. See the code:
arr_img = plt.imread("./UAV.png", format='png')
newax = fig.add_axes([0.45,0.5,0.2,0.2], anchor='NE', zorder=1)
newax.imshow(arr_img)
newax.patch.set_alpha(0.01)
newax.get_xaxis().set_ticks([])
newax.get_yaxis().set_ticks([])
newax.spines['top'].set_visible(False)
newax.spines['right'].set_visible(False)
newax.spines['bottom'].set_visible(False)
newax.spines['left'].set_visible(False)
output:
I have a problem about putting a radar chart and bar graph in the subplot in Python.
I defined 1 row and 2 columns to put each one into each slot.
I tried to handle with this process but I couldn't.
How can I do that?
Here is my radar function shown below.
def radar_chart(values=[]):
labels=np.array(['Crew',
'Length',
'Wingspan',
'Height',
'WingArea'
]
)
angles=np.linspace(0, 2*np.pi, len(labels), endpoint=False)
#print(angles)
fig=plt.figure(figsize=(6,6))
#plt.suptitle(title, y=1.04)
for v in values:
stats=np.array(ww2aircraft_df[ww2aircraft_df["Name"]==v][labels])[0]
#print(stats)
ax = fig.add_subplot(111, polar=True)
ax.plot(angles, stats, 'o-', linewidth=2, label = v)
ax.fill(angles, stats, alpha=0.25)
ax.set_thetagrids(angles * 180/np.pi, labels)
ax.grid(True)
#plt.legend(loc="upper right",bbox_to_anchor=(1.2,1.0))
ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.10),
fancybox=True, shadow=True, ncol=1, fontsize=13)
Here is my code snippets shown below.
f,a = plt.subplots(1,2,figsize=(24,10))
radar_chart(values=ww2aircraft_df_top_5["Name"])
graph_1 = sns.barplot(data = ww2aircraft_df_top_5,
x = "MaxSpeed",
y = "Name" , ax = a[1])
show_values_on_bars(graph_1, "h", 0.3)
plt.suptitle('Top 5 fastest of WW2 warplane by their features',
fontsize=20,
fontweight="semibold",
)
plt.tight_layout()
plt.savefig('images/image10.png', bbox_inches = "tight")
plt.show()
Possible solution is the following:
The dataset can be found HERE
# pip install matplotlib
# pip install pandas
# pip install seaborn
import csv
import pandas as pd
import numpy as np
from math import pi
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
import seaborn as sns
# read csv to dataframe
df = pd.read_csv('ww2aircraft.csv', sep=';')
# select top-5 rows by 'MaxSpeed' column
df_top5_maxspeed = df.nlargest(5, 'MaxSpeed').reset_index(drop=True)
# convert column values to float type
df_top5_maxspeed['Length'] = df_top5_maxspeed['Length'].astype('float64')
df_top5_maxspeed['Wingspan'] = df_top5_maxspeed['Wingspan'].astype('float64')
# limit dataframe to required columns
df_top5_maxspeed_data = df_top5_maxspeed[["Name","Crew","Length","Wingspan","Height","WingArea","MaxSpeed"]]
df_top5_maxspeed_data
def create_radar_chart(df):
# limit data drame
df = df.iloc[:, :-1]
categories=list(df_top5_maxspeed_data)[1:-1]
N = len(categories)
angles = [n / float(N) * 2 * pi for n in range(N)]
angles += angles[:1]
ax = fig.add_subplot(gs[0, 0], polar=True)
ax.set_theta_offset(pi / 2)
ax.set_theta_direction(-1)
plt.xticks(angles[:-1], categories, size=10)
ax.set_rlabel_position(0)
plt.yticks([10,20,30,40], ["10","20","30","40"], color="grey", size=10)
plt.ylim(0,40)
for row in range(0, len(df.index)):
values=df.loc[row].drop(['Name']).values.flatten().tolist()
values+= values[:1]
ax.plot(angles, values, 'o-', linewidth=2, label = df.loc[row]["Name"])
ax.fill(angles, values, alpha=0.2)
ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.10),
fancybox=False, shadow=False, ncol=1, fontsize=10, frameon=False)
def create_bar_chart(df):
ax = fig.add_subplot(gs[0, 1])
df = df[['Name','MaxSpeed']]
df.plot.bar(x='Name', y='MaxSpeed', ax = ax, legend=False)
plt.xlabel("")
# create plots area
fig = plt.figure(figsize=(15, 5))
gs = GridSpec(nrows=1, ncols=2, width_ratios=[1, 1], wspace=0.1)
fig.suptitle('Top 5 fastest of WW2 warplane by their features', fontsize=16)
# add charts
create_radar_chart(df_top5_maxspeed_data)
create_bar_chart(df_top5_maxspeed_data)
# adjust space between title and charts
plt.subplots_adjust(top=0.85)
When I run the following lines, I get a plot with a large space at the top and the bottom with no bars.
How can I remove this extra space?
import pandas as pd
import numpy as np
import random
import matplotlib.pyplot as plt
from matplotlib.transforms import Affine2D
random.seed(1)
df = pd.DataFrame(np.random.randn(50, 1), columns=["parameter"])
df["standard_error"]= ((df.parameter**2)**0.5)/2
name = "plot"
x = ["A"+str(x) for x in df.index.tolist()]
y1 = df.parameter
yerr1 = df.standard_error
fig, ax = plt.subplots()
fig.set_figheight(len(x))
plt.rc('axes', labelsize=22)
plt.grid(b=True, which='major', color='#666666', linestyle='-', alpha=0.2)
trans1 = Affine2D().translate(-0.1, 0.0) + ax.transData
trans2 = Affine2D().translate(+0.1, 0.0) + ax.transData
er1 = ax.errorbar(y1, x, xerr=yerr1, marker="o", linestyle="none", transform=trans1)
ax.axvline(x=0, color="black")
plt.savefig(name + '.png', bbox_inches='tight')
If you mean the extra space below and above your smallest and largest data points along the y-axis then you can simply use plt.ylim, e.g:
plt.ylim(0, 50)
Which will change the extent of the y-axis to the range 0 - 50. Similarly for the x-axis there's plt.xlim
I have written a code that plot some points and lines on the xy plane. It plots everything for a given value of n. So for different n I get my desired plots. But I want to animate these plots for different values of n, say, for n=1, 2, ..., 100. But I cannot do this animation.
Can anyone here help me to do this? Thank you.. I paste my code here:
My Code
import matplotlib as mpl
mpl.rc('text', usetex = True)
mpl.rc('font', family = 'serif')
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.patches import Circle
fig = plt.subplots()
ax = plt.axes(xlim=(-1.2, 1.2), ylim=(-1.2, 1.2))
plt.gca().set_aspect('equal', adjustable='box')
plt.style.use(['ggplot','dark_background'])
plt.rcParams['figure.figsize'] = (12, 8)
n = 10 #I want to animate this n.
p = 2
for k in range(0,n,1):
theta1 = np.pi + 2*k*np.pi / n
theta2 = np.pi + 2*p*k*np.pi / n
x, y = np.cos(theta1), np.sin(theta1)
x1, y1 = np.cos(theta2), np.sin(theta2)
plt.scatter(x, y, s=50, c='violet', zorder=3)
plt.plot([x,x1], [y,y1], color = 'w')
circle = plt.Circle((0, 0), 1, color='c', fill=False, lw = 1)
ax.add_artist(circle)
#Customize the axes and gridlines:
ax.grid(False)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['bottom'].set_visible(False)
#TickMarks Customization:
ax.set_xticks([])
ax.set_yticks([])
#plt.savefig('nthRoots.png', format='png', dpi=1000,bbox_inches='tight')
plt.show()
Output
Is it possible to animate n over different values?
EDIT: Here I donot have only scatter plots ...so I cannot understand how to do this job using those links..!
My Attempt
#Animation.
import matplotlib as mpl
mpl.rc('text', usetex = True) #for LaTex notation in the Plot
mpl.rc('font', family = 'serif')
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.patches import Circle
from matplotlib import animation, rc
rc('animation', html='html5')
fig = plt.subplots()
ax = plt.axes(xlim=(-1.2, 1.2), ylim=(-1.2, 1.2))
plt.gca().set_aspect('equal', adjustable='box')
plt.style.use(['ggplot','dark_background'])
plt.rcParams['figure.figsize'] = (12, 8)
p = 2
#Plotting Function:
def f(n):
for k in range(0,n,1):
theta1 = np.pi + 2*k*np.pi / n
theta2 = np.pi + 2*p*k*np.pi / n
x, y = np.cos(theta1), np.sin(theta1)
x1, y1 = np.cos(theta2), np.sin(theta2)
plt.scatter(x, y, s=50, c='violet', zorder=3)
plt.plot([x,x1], [y,y1], color = 'w')
circle = plt.Circle((0, 0), 1, color='c', fill=False, lw = 1)
ax.add_artist(circle)
#Customize the axes and gridlines:
ax.grid(False)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['bottom'].set_visible(False)
#TickMarks Customization:
ax.set_xticks([])
ax.set_yticks([])
plt.show()
#Now I try to define a function for animating n in f(n)
def animate(n):
f(n)
anim = animation.FuncAnimation(fig, animate,
frames=100, interval=100, blit=True)
#anim.save('Wave.mp4', writer = 'ffmpeg', fps = 2, dpi=500,extra_args=['-vcodec', 'libx264'])
That's all I had... But this idea didn't work...I think I have to properly define animate(n).
Any suggestion...! thanks.
Several problems in your code (most are unrelated to animations)
rcParams need to be defined before creating the figure
plt.subplots returns a tuple of figure and axes.
The animation must return a sequence of artist objects when blitting is used. You might turn it off though
plt.show() should be called once at the end of the script.
Correcting for those you get
import matplotlib as mpl
mpl.rc('font', family = 'serif')
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.patches import Circle
from matplotlib import animation, rc
plt.rcParams['figure.figsize'] = (12, 8)
plt.style.use(['ggplot','dark_background'])
fig, ax = plt.subplots()
p = 2
#Plotting Function:
def f(n):
ax.clear()
ax.set(xlim=(-1.2, 1.2), ylim=(-1.2, 1.2))
ax.set_aspect('equal', adjustable='box')
for k in range(0,n,1):
theta1 = np.pi + 2*k*np.pi / n
theta2 = np.pi + 2*p*k*np.pi / n
x, y = np.cos(theta1), np.sin(theta1)
x1, y1 = np.cos(theta2), np.sin(theta2)
plt.scatter(x, y, s=50, c='violet', zorder=3)
plt.plot([x,x1], [y,y1], color = 'w')
circle = Circle((0, 0), 1, color='c', fill=False, lw = 1)
ax.add_artist(circle)
#Customize the axes and gridlines:
ax.grid(False)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['bottom'].set_visible(False)
#TickMarks Customization:
ax.set_xticks([])
ax.set_yticks([])
anim = animation.FuncAnimation(fig, f, frames=100, interval=100, blit=False)
plt.show()
I would like to set the colorbar of my plot to a custom height, not necessarily to match the size of the plot. In fact I would like the height of the colorbar PLUS the title on top of it to match the height of the figure.
With
ax3 = divider.append_axes('right', size='10%', pad=0.3)
cb = plt.colorbar(Q, cax=ax3, ticks=[0.0, 3.0, 6.0, 9.0, 12.0, 15.0], format='%.1f')
I managed to have a colorbar with the same height as the plot, which has been asked for many other times, now I would like to shrink it.
Following suggestion provided in other questions I decided to explicitly give the colorbar its own axes with add_axes, after getting the position of the last plot axes with get_position. Here is what I'm trying to do. There are no data and no colorbar in this example, just to show that I'm not getting the result I expected:
from __future__ import unicode_literals
import numpy as np
from scipy.interpolate import griddata
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.patches import Circle
from matplotlib.pylab import cm
import matplotlib.colors as colors
from mpl_toolkits.axes_grid1 import make_axes_locatable, axes_size
matplotlib.rcParams.update({'font.size': 8})
fig = plt.figure()
fig.set_size_inches(6.3,6.3)
ax1 = plt.subplot(111)
divider = make_axes_locatable(ax1)
ax2 = divider.append_axes('right', size='100%', pad=0.3)
axes = [ax1, ax2]
ltypes = ['dashed', 'solid']
xi = np.linspace(-18.125, 18.125, 11)
yi = np.linspace(0, 28, 9)
xv, yv = np.meshgrid(xi, yi)
xcOdd = 0.2
zcOdd = 0.725
xcEven = 0.6
zcEven = 0.725
maskRadius = 0.15
for i in range(2):
ax = axes[i]
ax.set_xlabel('distance [m]')
if i == 0:
ax.set_ylabel('depth [m]')
if i == 1:
ax.set_yticklabels([])
ax.invert_yaxis()
ax.tick_params(direction='in')
ax.set_aspect('equal')
odd = Circle((xcOdd, zcOdd), .15, linewidth=1.2, color='k', fill=False)
even = Circle((xcEven, zcEven), .15, linewidth=1.2, linestyle=ltypes[i], color='k', fill=False)
vmax = 15.
vmin = 0.
norm = matplotlib.colors.Normalize(vmin,vmax, clip=False)
color_map = matplotlib.colors.ListedColormap(plt.cm.Greys(np.linspace(0.25, 1, 5)), "name")
ax.add_patch(odd)
pad = 0.03
width = 0.03
pos = ax2.get_position()
ax3 = fig.add_axes([pos.xmax + pad, pos.ymin, width, 0.7*(pos.ymax-pos.ymin) ])
plt.savefig('prova-vect-paper-test-2.eps', format='eps')
Why is get_position returning the wrong boundingbox?
You need to draw the canvas before obtaining the actual position from .get_position(). This is because due to the equal aspect ratio, the axes changes size and position at draw time.
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.colors as colors
from mpl_toolkits.axes_grid1 import make_axes_locatable
matplotlib.rcParams.update({'font.size': 8})
fig = plt.figure()
fig.set_size_inches(6.3,6.3)
ax1 = plt.subplot(111)
divider = make_axes_locatable(ax1)
ax2 = divider.append_axes('right', size='100%', pad=0.3)
axes = [ax1, ax2]
xi = np.linspace(-18.125, 18.125, 11)
yi = np.linspace(0, 28, 9)
xv, yv = np.meshgrid(xi, yi)
for i in range(2):
ax = axes[i]
ax.set_xlabel('distance [m]')
if i == 0:
ax.set_ylabel('depth [m]')
if i == 1:
ax.set_yticklabels([])
ax.invert_yaxis()
ax.tick_params(direction='in')
ax.set_aspect('equal')
vmax = 15.
vmin = 0.
norm = colors.Normalize(vmin,vmax, clip=False)
color_map = colors.ListedColormap(plt.cm.Greys(np.linspace(0.25, 1, 5)), "name")
im = ax.imshow(yv, cmap=color_map, norm=norm)
pad = 0.03
width = 0.03
fig.canvas.draw()
pos = ax2.get_position()
ax3 = fig.add_axes([pos.xmax + pad, pos.ymin, width, 0.7*(pos.ymax-pos.ymin) ])
fig.colorbar(im, cax=ax3)
plt.show()