I am trying to get a subplot using matplotlib, with number of subplots calculated in runtime (as pnum varies in the example below)
pnum = len(args.m)
f, (ax1, ax2) = plt.subplots(pnum, sharex=True, sharey=True)
ax1.plot(x,ptp, "#757578",label="Total")
ax2.fill_between(x,dxyp,facecolor="C0", label="$d_{xy}$")
This example, obviously, only work, when pnum=2. So, I need to do some thing else.
I have checked the accepted answer of this question, but this is plotting same thing in all the plots.
To create a dynamic number of subplots you may decide not to specify the axes individually, but as an axes array
pnum = len(args.m)
fig, ax_arr = plt.subplots(pnum, sharex=True, sharey=True)
ax_arr is then a numpy array.
You can then do something for each axes in a loop.
for ax in ax_arr.flatten():
ax.plot(...)
Related
I am trying to use matplotlib with gridspec to create a subplot such that the axes are arranged to look similar to the figure below; the figure was taken from this unrelated question.
My attempt at recreating this axes arrangement is below. Specifically, my problem is that the axes are not properly aligned. For example, the axis object for the blue histogram is taller than the axis object for the image with various shades of green; the orange histogram seems to properly align in terms of width, but I attribute this to luck. How can I properly align these axes? Unlike the original figure, I would like to add/pad extra empty space between axes such that there borders do not intersect; the slice notation in the code below does this by adding a blank row/column. (In the interest of not making this post longer than it has to be, I did not make the figures "pretty" by playing with axis ticks and the like.)
Unlike the original picture, the axes are not perfectly aligned. Is there a way to do this without using constrained layout? By this, I mean some derivative of fig, ax = plt.subplots(constrained_layout=True)?
The MWE code to recreate my figure is below; note that there was no difference between ax.imshow(...) and ax.matshow(...).
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import Normalize
## initialize figure and axes
fig = plt.figure()
gs = fig.add_gridspec(6, 6, hspace=0.2, wspace=0.2)
ax_bottom = fig.add_subplot(gs[4:, 2:])
ax_left = fig.add_subplot(gs[:4, :2])
ax_big = fig.add_subplot(gs[:4, 2:])
## generate data
x = np.random.normal(loc=50, scale=10, size=100)
y = np.random.normal(loc=500, scale=50, size=100)
## get singular histograms
x_counts, x_edges = np.histogram(x, bins=np.arange(0, 101, 5))
y_counts, y_edges = np.histogram(y, bins=np.arange(0, 1001, 25))
x_mids = (x_edges[1:] + x_edges[:-1]) / 2
y_mids = (y_edges[1:] + y_edges[:-1]) / 2
## get meshed histogram
sample = np.array([x, y]).T
xy_counts, xy_edges = np.histogramdd(sample, bins=(x_edges, y_edges))
## subplot histogram of x
ax_bottom.bar(x_mids, x_counts,
width=np.diff(x_edges),
color='darkorange')
ax_bottom.set_xlim([x_edges[0], x_edges[-1]])
ax_bottom.set_ylim([0, np.max(x_counts)])
## subplot histogram of y
ax_left.bar(y_mids, y_counts,
width=np.diff(y_edges),
color='steelblue')
ax_left.set_xlim([y_edges[0], y_edges[-1]])
ax_left.set_ylim([0, np.max(y_counts)])
## subplot histogram of xy-mesh
ax_big.imshow(xy_counts,
cmap='Greens',
norm=Normalize(vmin=np.min(xy_counts), vmax=np.max(xy_counts)),
interpolation='nearest',
origin='upper')
plt.show()
plt.close(fig)
EDIT:
One can initialize the axes by explicitly setting width_ratios and height_ratios per row/column; this is shown below. This doesn't affect the output, but maybe I'm using it incorrectly?
## initialize figure and axes
fig = plt.figure()
gs = gridspec.GridSpec(ncols=6, nrows=6, figure=fig, width_ratios=[1]*6, height_ratios=[1]*6)
ax_bottom = fig.add_subplot(gs[4:, 2:])
ax_left = fig.add_subplot(gs[:4, :2])
ax_big = fig.add_subplot(gs[:4, 2:])
The problem is with imshow, which resizes the axes automatically to maintain a square pixel aspect.
You can prevent this by calling:
ax_big.imshow(..., aspect='auto')
I have a plot that looks as follows:
I want to put labels for both the lineplot and the markers in red. However the legend is not appearning because its the plot is taking out its space.
Update
it turns out I cannot put several strings in plt.legend()
I made the figure bigger by using the following:
fig = plt.gcf()
fig.set_size_inches(18.5, 10.5)
However now I have only one label in the legend, with the marker appearing on the lineplot while I rather want two: one for the marker alone and another for the line alone:
Updated code:
plt.plot(range(len(y)), y, '-bD', c='blue', markerfacecolor='red', markeredgecolor='k', markevery=rare_cases, label='%s' % target_var_name)
fig = plt.gcf()
fig.set_size_inches(18.5, 10.5)
# changed this over here
plt.legend()
plt.savefig(output_folder + fig_name)
plt.close()
What you want to do (have two labels for a single object) is not completely impossible but it's MUCH easier to plot separately the line and the rare values, e.g.
# boilerplate
import numpy as np
import matplotlib.pyplot as plt
# synthesize some data
N = 501
t = np.linspace(0, 10, N)
s = np.sin(np.pi*t)
rare = np.zeros(N, dtype=bool); rare[:20]=True; np.random.shuffle(rare)
plt.plot(t, s, label='Curve')
plt.scatter(t[rare], s[rare], label='rare')
plt.legend()
plt.show()
Update
[...] it turns out I cannot put several strings in plt.legend()
Well, you can, as long as ① the several strings are in an iterable (a tuple or a list) and ② the number of strings (i.e., labels) equals the number of artists (i.e., thingies) in the plot.
plt.legend(('a', 'b', 'c'))
I'm trying to create 2 distplots that overlap on same axes, but they seem to be offset. How can I adjust this so that their overlapping is exact? Please see the image link below for the issue I have.
plt.figure(figsize=(10,8))
ax1 = sns.distplot(loans['fico'][loans['credit.policy']==1], bins= 10, kde=False, hist_kws=dict(edgecolor='k', lw=1))
ax2 = sns.distplot(loans['fico'][loans['credit.policy']==0], bins= 10, color='Red', kde=False, hist_kws=dict(edgecolor='k', lw=1))
ax1.set_xlim([600, 850])
ax2.set_xlim([600, 850])
Problematic result
The plots aren't lining up because Seaborn (well, Matplotlib behind the scenes) is working out the best way to give you ten bins for each set of data you pass to it. But the two sets might not have the same range.
You can provide a sequence as the bins argument, which defines the edges of the bins. Assuming you have numpy available you can use its linspace function to easily create this sequence from the smallest and largest values in your data.
plt.figure(figsize(10,8))
bins = np.linspace(min(loans['fico']), max(loans['fico']), num=11)
ax1 = sns.distplot(loans['fico'][loans['credit.policy']==1], bins=bins,
kde=False, hist_kws=dict(edgecolor='k', lw=1))
ax2 = sns.distplot(loans['fico'][loans['credit.policy']==0], bins=bins,
color='Red', kde=False, hist_kws=dict(edgecolor='k', lw=1))
And then you shouldn't need to set the x limits.
An example with some randomly generated values:
python 3.6 om mac
matplotlib 2.1.0
using matplotlib.pyplot (as plt)
Let's say i have a few plt.figures() that i appended into a list called figures as objects. When in command line i do: figures[0]it produces the plot for the index 0 of the list figures.
However, how can i arrange to have all the plots in figures to be in a subplot.
# Pseudo code:
plt.figure()
for i, fig in enumerate(figures): # figures contains the plots
plt.subplot(2, 2, i+1)
fig # location i+1 of the subplot is filled with the fig plot element
So as a result, i would a 2 by 2 grid that contains each plot found in figures.
hoping this makes sense.
A figure is a figure. You cannot have a figure inside a figure. The usual approach is to create a figure, create one or several subplots, plot something in the subplots.
In case it may happen that you want to plot something in different axes or figures, it might make sense to wrap the plotting in a function which takes the axes as argument.
You could then use this function to plot to an axes of a new figure or to plot to an axes of a figure with many subplots.
import numpy as np
import matplotlib.pyplot as plt
def myplot(ax, data_x, data_y, color="C0"):
ax.plot(data_x, data_y, color=color)
ax.legend()
x = np.linspace(0,10)
y = np.cumsum(np.random.randn(len(x),4), axis=0)
#create 4 figures
for i in range(4):
fig, ax = plt.subplots()
myplot(ax, x, y[:,i], color="C{}".format(i))
# create another figure with each plot as subplot
fig, ax = plt.subplots(2,2)
for i in range(4):
myplot(ax.flatten()[i], x, y[:,i], color="C{}".format(i))
plt.show()
fig, ax = plt.subplots()
ax = fig.add_subplot(111)
ax.scatter(X[1],y)
y_projection = X.dot(theta_after)
ax.plot(X[1], y_projection)
plt.show()
Above is my code. What I'm trying to do is basically fitting a line to the data. I use gradient descent method to find the suitable theta.
The problem I came across is that the code above created two x-axis and y-axis and that they were overlapping on each other
This is the result generated from the above code. I'm not allowed to embed a pic now, please click on this to open the pic.
X - is a 97*2 matrix in which the first column is all 1.
You are creating an extra Axes with your second line. Just remove the following line:
ax = fig.add_subplot(111)
You already have an Axes when you run fig, ax = plt.subplots()