Related
I have a 2D array representing the efficiency of a process for a given set of parameters A and B. The parameter A along the columns changes periodically, starting from 0 to 225 with increment one. The problem is with the rows where the parameter was changed in the following order:
[16 ,18 ,20 ,21 ,22 ,23 ,24 ,25 ,26 ,27 ,28 ,29 ,30 ,31 ,32 ,33 ,35 ,40 ,45 ,50 ,55 ,60 ,65 ,70 ,75 ,80 ,85 ,90 ,95 ,100 ,105 ,110 ,115 ,120 ,125]
So even though the rows increase with increment one, they represent a non-uniform increment of the parameter B. What I need is to showcase the values of the parameter B on the y-axis. Using axes.set_yticks() does not give me what I am looking for, and I do understand why but I do not know how to solve it.
A minimum example:
# Define parameter B values
parb_increment = [16, 18, 20] + list(range(21,34)) + list(range(35,126,5))
print(len(parb_increment))
print(x.shape)
# Figure and axes
figure, axes = plt.subplots(figsize=(10, 8))
# Plotting
im = axes.imshow(x, aspect='auto',
origin="lower",
cmap='Blues',
interpolation='none',
extent=(0, x.shape[1], 0, parb_increment[-1]))
# Unsuccessful trial for yticks
axes.set_yticks(parb_increment, labels=parb_increment)
# Colorbar
cb = figure.colorbar(im, ax=axes)
The previous code gives the figure and output below, and you can see how the ticks are not only misplaced but also start from an incorrect position.
35
(35, 225)
The item that controls the width/height of each pixel is aspect. Unfortunately you can't make it variable. The aspect won't change even if you modify/update y-axis ticks. That's why in your example ticks are mis-aligned with the rows of pixels.
Therefore, the solution to your problem is to duplicate those rows that increment non-uniformly.
See example below:
import numpy as np
import matplotlib.pyplot as plt
# Generate fake data
x = np.random.random((3, 4))
# Create uniform x-ticks and non-uniform y-ticks
x_increment = np.arange(0, x.shape[1]+1, 1)
y_increment = np.arange(0, x.shape[0]+1, 1) * np.arange(0, x.shape[0]+1, 1)
# Plot the data
fig, ax = plt.subplots(figsize=(6, 10))
img = ax.imshow(
x,
extent=(
0, x.shape[1], 0, y_increment[-1]
)
)
fig.colorbar(img, ax=ax)
ax.set_xlim(0, x.shape[1])
ax.set_xticks(x_increment)
ax.set_ylim(0, y_increment[-1])
ax.set_yticks(y_increment);
This replicates your problem and produces the following outcome.
The solution
First, determine the number of repeats of each row in the array:
nr_of_repeats_per_row =np.diff(y_increment)
nr_of_repeats_per_row = nr_of_repeats_per_row[::-1]
You need to reverse the order as the top row in the image is the first row in the array and y_increments provide the difference between rows starting from the last row in the array.
Now you can repeat each row in the array a specific number of times:
x_extended = np.repeat(x, nr_of_repeats_per_row, axis=0)
Replot with the x_extended:
fig, ax = plt.subplots(figsize=(6, 10))
img = ax.imshow(
x_extended,
extent=(
0, x.shape[1], 0, y_increment[-1]
),
interpolation="none"
)
fig.colorbar(img, ax=ax)
ax.set_xlim(0, x.shape[1])
ax.set_xticks(x_increment)
ax.set_ylim(0, y_increment[-1])
ax.set_yticks(y_increment);
And you should get this.
This question already has answers here:
How to add value labels on a bar chart
(7 answers)
Closed 8 months ago.
I have a dataframe that looks like:
User A B C
ABC 100 121 OPEN
BCD 200 255 CLOSE
BCD 500 134 OPEN
DEF 600 125 CLOSE
ABC 900 632 OPEN
ABC 150 875 CLOSE
DEF 690 146 OPEN
I am trying to display a countplot on column 'User'. The code is as follows:
fig, ax1 = plt.subplots(figsize=(20,10))
graph = sns.countplot(ax=ax1,x='User', data=df)
graph.set_xticklabels(graph.get_xticklabels(),rotation=90)
for p in graph.patches:
height = p.get_height()
graph.text(p.get_x()+p.get_width()/2., height + 0.1,
'Hello',ha="center")
The output looks like:
However, I want to replace string 'Hello' with the value_counts of column 'User'. When I add the code to add label to graph :
for p in graph.patches:
height = p.get_height()
graph.text(p.get_x()+p.get_width()/2., height + 0.1,
df['User'].value_counts(),ha="center")
I get the output as:
New in matplotlib 3.4.0
We can now automatically annotate bar plots with the built-in Axes.bar_label, so all we need to do is access/extract the seaborn plot's Axes.
Seaborn offers several ways to plot counts, each with slightly different count aggregation and Axes handling:
seaborn.countplot (most straightforward)
This automatically aggregates counts and returns an Axes, so just directly label ax.containers[0]:
ax = sns.countplot(x='User', data=df)
ax.bar_label(ax.containers[0])
seaborn.catplot (kind='count')
This plots a countplot onto a facet grid, so extract the Axes from the grid before labeling ax.containers[0]:
g = sns.catplot(x='User', kind='count', data=df)
for ax in g.axes.flat:
ax.bar_label(ax.containers[0])
seaborn.barplot
This returns an Axes but does not aggregate counts, so first compute Series.value_counts before labeling ax.containers[0]:
counts = df['User'].value_counts().rename_axis('user').reset_index(name='count')
ax = sns.barplot(x='user', y='count', data=counts)
ax.bar_label(ax.containers[0])
If you are using hue:
hue plots will contain multiple bar containers, so ax.containers will need to be iterated:
ax = sns.countplot(x='User', hue='C', data=df)
for container in ax.containers:
ax.bar_label(container)
df['User'].value_counts() will return a Series containing counts of unique values of the column User.
Without analyzing in much detail your code, you could correct it by indexing the result of value_counts with a counter:
fig, ax1 = plt.subplots(figsize=(20,10))
graph = sns.countplot(ax=ax1,x='User', data=df)
graph.set_xticklabels(graph.get_xticklabels(),rotation=90)
i=0
for p in graph.patches:
height = p.get_height()
graph.text(p.get_x()+p.get_width()/2., height + 0.1,
df['User'].value_counts()[i],ha="center")
i += 1
With your sample data, it produces the following plot:
As suggested by #ImportanceOfBeingErnest, the following code produces the same output with simpler code, using the height variable itself instead of the value_counts indexed:
fig, ax1 = plt.subplots(figsize=(20,10))
graph = sns.countplot(ax=ax1,x='User', data=df)
graph.set_xticklabels(graph.get_xticklabels(),rotation=90)
for p in graph.patches:
height = p.get_height()
graph.text(p.get_x()+p.get_width()/2., height + 0.1,height ,ha="center")
other solution
#data
labels=data['Sistema Operativo'].value_counts().index
values=data['Sistema Operativo'].value_counts().values
plt.figure(figsize = (15, 8))
ax = sns.barplot(x=labels, y=values)
for i, p in enumerate(ax.patches):
height = p.get_height()
ax.text(p.get_x()+p.get_width()/2., height + 0.1, values[i],ha="center")
Chart Image
Note: This solution does not try to show the count on top of the bar. Instead, this simple solution will print the values inside the bar. This may be an elegant solution for some occasions.
import seaborn as sns
ax=sns.countplot(x=df['category'], data=df);
for p in ax.patches:
ax.annotate(f'\n{p.get_height()}', (p.get_x()+0.2, p.get_height()), ha='center', va='top', color='white', size=18)
I'm trying to draw with matplotlib two average vertical line for every overlapping histograms using a loop. I have managed to draw the first one, but I don't know how to draw the second one. I'm using two variables from a dataset to draw the histograms. One variable (feat) is categorical (0 - 1), and the other one (objective) is numerical. The code is the following:
for chas in df[feat].unique():
plt.hist(df.loc[df[feat] == chas, objective], bins = 15, alpha = 0.5, density = True, label = chas)
plt.axvline(df[objective].mean(), linestyle = 'dashed', linewidth = 2)
plt.title(objective)
plt.legend(loc = 'upper right')
I also have to add to the legend the mean and standard deviation values for each histogram.
How can I do it? Thank you in advance.
I recommend you using axes to plot your figure. Pls see code below and the artist tutorial here.
import numpy as np
import matplotlib.pyplot as plt
# Fixing random state for reproducibility
np.random.seed(19680801)
mu1, sigma1 = 100, 8
mu2, sigma2 = 150, 15
x1 = mu1 + sigma1 * np.random.randn(10000)
x2 = mu2 + sigma2 * np.random.randn(10000)
fig, ax = plt.subplots(1, 1, figsize=(7.2, 7.2))
# the histogram of the data
lbs = ['a', 'b']
colors = ['r', 'g']
for i, x in enumerate([x1, x2]):
n, bins, patches = ax.hist(x, 50, density=True, facecolor=colors[i], alpha=0.75, label=lbs[i])
ax.axvline(bins.mean())
ax.legend()
Before I start I want to say that I've tried follow this and this post on the same problem however they are doing it with imshow heatmaps unlike 2d histogram like I'm doing.
Here is my code(the actual data has been replaced by randomly generated data but the gist is the same):
import matplotlib.pyplot as plt
import numpy as np
def subplots_hist_2d(x_data, y_data, x_labels, y_labels, titles):
fig, a = plt.subplots(2, 2)
a = a.ravel()
for idx, ax in enumerate(a):
image = ax.hist2d(x_data[idx], y_data[idx], bins=50, range=[[-2, 2],[-2, 2]])
ax.set_title(titles[idx], fontsize=12)
ax.set_xlabel(x_labels[idx])
ax.set_ylabel(y_labels[idx])
ax.set_aspect("equal")
cb = fig.colorbar(image[idx])
cb.set_label("Intensity", rotation=270)
# pad = how big overall pic is
# w_pad = how separate they're left to right
# h_pad = how separate they're top to bottom
plt.tight_layout(pad=-1, w_pad=-10, h_pad=0.5)
x1, y1 = np.random.uniform(-2, 2, 10000), np.random.uniform(-2, 2, 10000)
x2, y2 = np.random.uniform(-2, 2, 10000), np.random.uniform(-2, 2, 10000)
x3, y3 = np.random.uniform(-2, 2, 10000), np.random.uniform(-2, 2, 10000)
x4, y4 = np.random.uniform(-2, 2, 10000), np.random.uniform(-2, 2, 10000)
x_data = [x1, x2, x3, x4]
y_data = [y1, y2, y3, y4]
x_labels = ["x1", "x2", "x3", "x4"]
y_labels = ["y1", "y2", "y3", "y4"]
titles = ["1", "2", "3", "4"]
subplots_hist_2d(x_data, y_data, x_labels, y_labels, titles)
And this is what it's generating:
So now my problem is that I could not for the life of me make the colorbar apply for all 4 of the histograms. Also for some reason the bottom right histogram seems to behave weirdly compared with the others. In the links that I've posted their methods don't seem to use a = a.ravel() and I'm only using it here because it's the only way that allows me to plot my 4 histograms as subplots. Help?
EDIT:
Thomas Kuhn your new method actually solved all of my problem until I put my labels down and tried to use plt.tight_layout() to sort out the overlaps. It seems that if I put down the specific parameters in plt.tight_layout(pad=i, w_pad=0, h_pad=0) then the colorbar starts to misbehave. I'll now explain my problem.
I have made some changes to your new method so that it suits what I want, like this
def test_hist_2d(x_data, y_data, x_labels, y_labels, titles):
nrows, ncols = 2, 2
fig, axes = plt.subplots(nrows, ncols, sharex=True, sharey=True)
##produce the actual data and compute the histograms
mappables=[]
for (i, j), ax in np.ndenumerate(axes):
H, xedges, yedges = np.histogram2d(x_data[i][j], y_data[i][j], bins=50, range=[[-2, 2],[-2, 2]])
ax.set_title(titles[i][j], fontsize=12)
ax.set_xlabel(x_labels[i][j])
ax.set_ylabel(y_labels[i][j])
ax.set_aspect("equal")
mappables.append(H)
##the min and max values of all histograms
vmin = np.min(mappables)
vmax = np.max(mappables)
##second loop for visualisation
for ax, H in zip(axes.ravel(), mappables):
im = ax.imshow(H,vmin=vmin, vmax=vmax, extent=[-2,2,-2,2])
##colorbar using solution from linked question
fig.colorbar(im,ax=axes.ravel())
plt.show()
# plt.tight_layout
# plt.tight_layout(pad=i, w_pad=0, h_pad=0)
Now if I try to generate my data, in this case:
phi, cos_theta = get_angles(runs)
detector_x1, detector_y1, smeared_x1, smeared_y1 = detection_vectorised(1.5, cos_theta, phi)
detector_x2, detector_y2, smeared_x2, smeared_y2 = detection_vectorised(1, cos_theta, phi)
detector_x3, detector_y3, smeared_x3, smeared_y3 = detection_vectorised(0.5, cos_theta, phi)
detector_x4, detector_y4, smeared_x4, smeared_y4 = detection_vectorised(0, cos_theta, phi)
Here detector_x, detector_y, smeared_x, smeared_y are all lists of data point
So now I put them into 2x2 lists so that they can be unpacked suitably by my plotting function, as such:
data_x = [[detector_x1, detector_x2], [detector_x3, detector_x4]]
data_y = [[detector_y1, detector_y2], [detector_y3, detector_y4]]
x_labels = [["x positions(m)", "x positions(m)"], ["x positions(m)", "x positions(m)"]]
y_labels = [["y positions(m)", "y positions(m)"], ["y positions(m)", "y positions(m)"]]
titles = [["0.5m from detector", "1.0m from detector"], ["1.5m from detector", "2.0m from detector"]]
I now run my code with
test_hist_2d(data_x, data_y, x_labels, y_labels, titles)
with just plt.show() turned on, it gives this:
which is great because data and visual wise, it is exactly what I want i.e. the colormap corresponds to all 4 histograms. However, since the labels are overlapping with the titles, I thought I would just run the same thing but this time with plt.tight_layout(pad=a, w_pad=b, h_pad=c) hoping that I would be able to adjust the overlapping labels problem. However this time it doesn't matter how I change the numbers a, b and c, I always get my colorbar lying on the second column of graphs, like this:
Now changing a only makes the overall subplots bigger or smaller, and the best I could do was to adjust it with plt.tight_layout(pad=-10, w_pad=-15, h_pad=0), which looks like this
So it seems that whatever your new method is doing, it made the whole plot lost its adjustability. Your solution, as wonderful as it is at solving one problem, in return, created another. So what would be the best thing to do here?
Edit 2:
Using fig, axes = plt.subplots(nrows, ncols, sharex=True, sharey=True, constrained_layout=True) along with plt.show() gives
As you can see there's still a vertical gap between the columns of subplots for which not even using plt.subplots_adjust() can get rid of.
Edit:
As has been noted in the comments, the biggest problem here is actually to make the colorbar for many histograms meaningful, as ax.hist2d will always scale the histogram data it receives from numpy. It may therefore be best to first calculated the 2d histogram data using numpy and then use again imshow to visualise it. This way, also the solutions of the linked question can be applied. To make the problem with the normalisation more visible, I put some effort into producing some qualitatively different 2d histograms using scipy.stats.multivariate_normal, which shows how the height of the histogram can change quite dramatically even though the number of samples is the same in each figure.
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import gridspec as gs
from scipy.stats import multivariate_normal
##opening figure and axes
nrows=3
ncols=3
fig, axes = plt.subplots(nrows,ncols)
##generate some random data for the distributions
means = np.random.rand(nrows,ncols,2)
sigmas = np.random.rand(nrows,ncols,2)
thetas = np.random.rand(nrows,ncols)*np.pi*2
##produce the actual data and compute the histograms
mappables=[]
for mean,sigma,theta in zip( means.reshape(-1,2), sigmas.reshape(-1,2), thetas.reshape(-1)):
##the data (only cosmetics):
c, s = np.cos(theta), np.sin(theta)
rot = np.array(((c,-s), (s, c)))
cov = rot#np.diag(sigma)#rot.T
rv = multivariate_normal(mean,cov)
data = rv.rvs(size = 10000)
##the 2d histogram from numpy
H,xedges,yedges = np.histogram2d(data[:,0], data[:,1], bins=50, range=[[-2, 2],[-2, 2]])
mappables.append(H)
##the min and max values of all histograms
vmin = np.min(mappables)
vmax = np.max(mappables)
##second loop for visualisation
for ax,H in zip(axes.ravel(),mappables):
im = ax.imshow(H,vmin=vmin, vmax=vmax, extent=[-2,2,-2,2])
##colorbar using solution from linked question
fig.colorbar(im,ax=axes.ravel())
plt.show()
This code produces a figure like this:
Old Answer:
One way to solve your problem is to generate the space for your colorbar explicitly. You can use a GridSpec instance to define how wide your colorbar should be. Below your subplots_hist_2d() function with a few modifications. Note that your use of tight_layout() shifted the colorbar into a funny place, hence the replacement. If you want the plots closer to each other, I'd rather recommend to play with the aspect ratio of the figure.
def subplots_hist_2d(x_data, y_data, x_labels, y_labels, titles):
## fig, a = plt.subplots(2, 2)
fig = plt.figure()
g = gs.GridSpec(nrows=2, ncols=3, width_ratios=[1,1,0.05])
a = [fig.add_subplot(g[n,m]) for n in range(2) for m in range(2)]
cax = fig.add_subplot(g[:,2])
## a = a.ravel()
for idx, ax in enumerate(a):
image = ax.hist2d(x_data[idx], y_data[idx], bins=50, range=[[-2, 2],[-2, 2]])
ax.set_title(titles[idx], fontsize=12)
ax.set_xlabel(x_labels[idx])
ax.set_ylabel(y_labels[idx])
ax.set_aspect("equal")
## cb = fig.colorbar(image[-1],ax=a)
cb = fig.colorbar(image[-1], cax=cax)
cb.set_label("Intensity", rotation=270)
# pad = how big overall pic is
# w_pad = how separate they're left to right
# h_pad = how separate they're top to bottom
## plt.tight_layout(pad=-1, w_pad=-10, h_pad=0.5)
fig.tight_layout()
Using this modified function, I get the following output:
I am trying to make a 2x2 subplot, with each of the inner subplots consisting of two x axes and two y axes; the first xy correspond to a linear scale and the second xy correspond to a logarithmic scale. Before assuming this question has been asked before, the matplotlib docs and examples show how to do multiple scales for either x or y but not both. This post on stackoverflow is the closest thing to my question, and I have attempted to use this idea to implement what I want. My attempt is below.
Firstly, we initialize data, ticks, and ticklabels. The idea is that the alternate scaling will have the same tick positions with altered ticklabels to reflect the alternate scaling.
import numpy as np
import matplotlib.pyplot as plt
# xy data (global)
X = np.linspace(5, 13, 9, dtype=int)
Y = np.linspace(7, 12, 9)
# xy ticks for linear scale (global)
dtick = dict(X=X, Y=np.linspace(7, 12, 6, dtype=int))
# xy ticklabels for linear and logarithmic scales (global)
init_xt = 2**dtick['X']
dticklabel = dict(X1=dtick['X'], Y1=dtick['Y']) # linear scale
dticklabel['X2'] = ['{}'.format(init_xt[idx]) if idx % 2 == 0 else '' for idx in range(len(init_xt))] # log_2 scale
dticklabel['Y2'] = 2**dticklabel['Y1'] # log_2 scale
Borrowing from the linked SO post, I will plot the same thing in each of the 4 subplots. Since similar methods are used for both scalings in each subplot, the method is thrown into a for-loop. But we need the row number, column number, and plot number for each.
# 2x2 subplot
# fig.add_subplot(row, col, pnum); corresponding iterables = (irows, icols, iplts)
irows = (1, 1, 2, 2)
icols = (1, 2, 1, 2)
iplts = (1, 2, 1, 2)
ncolors = ('red', 'blue', 'green', 'black')
Putting all of this together, the function to output the plot is below:
def initialize_figure(irows, icols, iplts, ncolors, figsize=None):
""" """
fig = plt.figure(figsize=figsize)
for row, col, pnum, color in zip(irows, icols, iplts, ncolors):
ax1 = fig.add_subplot(row, col, pnum) # linear scale
ax2 = fig.add_subplot(row, col, pnum, frame_on=False) # logarithmic scale ticklabels
ax1.plot(X, Y, '-', color=color)
# ticks in same positions
for ax in (ax1, ax2):
ax.set_xticks(dtick['X'])
ax.set_yticks(dtick['Y'])
# remove xaxis xtick_labels and labels from top row
if row == 1:
ax1.set_xticklabels([])
ax2.set_xticklabels(dticklabel['X2'])
ax1.set_xlabel('')
ax2.set_xlabel('X2', color='gray')
# initialize xaxis xtick_labels and labels for bottom row
else:
ax1.set_xticklabels(dticklabel['X1'])
ax2.set_xticklabels([])
ax1.set_xlabel('X1', color='black')
ax2.set_xlabel('')
# linear scale on left
if col == 1:
ax1.set_yticklabels(dticklabel['Y1'])
ax1.set_ylabel('Y1', color='black')
ax2.set_yticklabels([])
ax2.set_ylabel('')
# logarithmic scale on right
else:
ax1.set_yticklabels([])
ax1.set_ylabel('')
ax2.set_yticklabels(dticklabel['Y2'])
ax2.set_ylabel('Y2', color='black')
ax1.tick_params(axis='x', colors='black')
ax1.tick_params(axis='y', colors='black')
ax2.tick_params(axis='x', colors='gray')
ax2.tick_params(axis='y', colors='gray')
ax1.xaxis.tick_bottom()
ax1.yaxis.tick_left()
ax1.xaxis.set_label_position('top')
ax1.yaxis.set_label_position('right')
ax2.xaxis.tick_top()
ax2.yaxis.tick_right()
ax2.xaxis.set_label_position('top')
ax2.yaxis.set_label_position('right')
for ax in (ax1, ax2):
ax.set_xlim([4, 14])
ax.set_ylim([6, 13])
fig.tight_layout()
plt.show()
plt.close(fig)
Calling initialize_figure(irows, icols, iplts, ncolors) produces the figure below.
I am applying the same xlim and ylim so I do not understand why the subplots are all different sizes. Also, the axis labels and axis ticklabels are not in the specified positions (since fig.add_subplot(...) indexing starts from 1 instead of 0.
What is my mistake and how can I achieve the desired result?
(In case it isn't clear, I am trying to put the xticklabels and xlabels for the linear scale on the bottom row, the xticklabels and xlabels for the logarithmic scale on the top row, the 'yticklabelsandylabelsfor the linear scale on the left side of the left column, and the 'yticklabels and ylabels for the logarithmic scale on the right side of the right column. The color='black' kwarg corresponds to the linear scale and the color='gray' kwarg corresponds to the logarithmic scale.)
The irows and icols lists inn the code do not serve any purpose. To create 4 subplots in a 2x2 grid you would loop over the range(1,5),
for pnum in range(1,5):
ax1 = fig.add_subplot(2, 2, pnum)
This might not be the only problem in the code, but as long as the subplots aren't created correctly it's not worth looking further down.