I am looking for a way to remove grid lines from the axes of a plot, but unfortunately, I've not come up to a solution for this issue and neither found it anywhere else.
Is there a way to remove certain grid lines or choose which grid lines to plot without having to rely on the automatic function?
I've coded a quick example outputting a plot for illustration below and would be glad for any help.
import matplotlib.pyplot as plt
import numpy as np
def linear(x, a, b):
return a*x+b
x = np.linspace(0, 1, 20)
y = linear(x, a=1, b=2)
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(10, 6))
ax.plot(x, y, color='darkred')
ax.set_xlim(0, 1)
ax.set_ylim(2, 3)
ax.grid(which='major', axis='y', linestyle='--', color='grey', linewidth=3)
plt.savefig("Testplot.pdf", format='pdf')
The major gridlines appear at positions of the major ticks. You can set any individual gridline invisible. E.g. to set the fifth gridline off,
ax.yaxis.get_major_ticks()[5].gridline.set_visible(False)
Here is a proposition with ticks and horizontal lines. The idea is to specify the ticks (not really necessary, but why not), and then to draw horizontal dashes lines where you want your grid.
import matplotlib.pyplot as plt
import numpy as np
def linear(x, a, b):
return a*x+b
x = np.linspace(0, 1, 20)
y = linear(x, a=1, b=2)
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(10, 6))
ax.plot(x, y, color='darkred')
ax.set_xlim(0, 1)
ax.set_ylim(2, 3)
yticks = np.arange(2, 3, 0.2)
grid_lines = np.arange(2.2, 3, 0.2)
ax.set_yticks(yticks)
for grid in grid_lines:
ax.axhline(grid, linestyle='--', color='grey', linewidth=3)
Output:
Why did I include the yticks? Well you could design a function which takes in input the yticks and return the position of the grid lines accordingly. I think it could be handy depending on your needs. Good luck!
Related
Imagine you have a pyplot with a vertical line (matplotlib.axes.Axes.axvline) at a specific location x. Now I would like to have a text like "COG" on the x-axis at x, like as if it was a tick. It can be either on the visible or non-visible axis or both.
However,
ticks already exist (given array)
shared x-axis for subplots, only lowest visible
I though about using normal text (matplotlib.pyplot.text), but
it would be inside the subplot
it would not be in xaxis relation (at least I didn't find a working way so far)
I feel like manually editing the ticks to add a single item is a not so nice workaround..
Thanks ahead!
Here is an example graph of subplots from matplotlib:
import matplotlib.pyplot as plt
import numpy as np
# Some example data to display
x = np.linspace(0, 2 * np.pi, 400)
y = np.sin(x ** 2)
fig, axs = plt.subplots(2, sharex=True)
fig.suptitle('Vertically stacked subplots')
axs[0].plot(x, y)
axs[1].plot(x, -y)
To add the elements you wanted, you can just use axvline and text; Text elements can be outside of the boundaries of the graph (and in fact the tick labels are Text).
#continued from above:
axs[0].xaxis.set_visible(False)
axs[0].axvline(4.5, color='red')
axs[0].text(4.5, -.05, 'COG', color='red', transform=axs[0].get_xaxis_transform(),
ha='center', va='top')
axs[1].axvline(4.5, color='red')
axs[1].text(4.5, -.05, 'COG', color='red', transform=axs[1].get_xaxis_transform(),
ha='center', va='top')
You can instead add another tick and change its color:
#again, continued from the first code block
axs[0].xaxis.set_visible(False)
axs[0].axvline(4.5, color='red')
axs[0].text(4.5, -.05, 'COG', color='red', transform=axs[0].get_xaxis_transform(),
ha='center', va='top')
ticks = [0, 1, 2, 3, 4, 4.5, 5, 6]
labels = [0, 1, 2, 3, 4, "COG", 5, 6]
axs[1].axvline(4.5, color='red')
axs[1].set_xticks(ticks)
axs[1].set_xticklabels(labels)
axs[1].get_xticklabels()[5].set_color('red')
But, if you don't want ticks on the top graph, then it seems like adding Text (as in the first example) is simplest. Additionally, manually setting the ticks in the second example seems more verbose, and there's the issue of selecting the tick you want to change (I index with axs[1].get_xticklabels()[5] here, but with more ticks/nastier numbers you might need something smarter). So I prefer the first approach better than this, but it might be useful in some cases (like if you want your line to occur on an existing tick).
Using Toms´s first example leads to the desired outcome.
Additionally, for the case of an overlapping text on the label, I searched for neighboring tick labels and set their transparency != 1. Thus, the text "cog" is always visible.
import matplotlib.pyplot as plt
import numpy as np
xV = 4.5
dxV = 1/4 # best 1/4 of the spacing between two ticks
# Some example data to display
x = np.linspace(0, 2 * np.pi, 400)
y = np.sin(x ** 2)
fig, axs = plt.subplots(2, sharex=True)
fig.suptitle('Vertically stacked subplots')
axs[0].plot(x, y)
axs[0].xaxis.set_visible(False)
axs[0].axvline(xV, color='red')
axs[0].text(xV, -.05, 'COG', color='red', transform=axs[0].get_xaxis_transform(),
ha='center', va='top')
axs[1].plot(x, -y)
axs[1].axvline(xV, color='red')
axs[1].text(xV, -.05, 'COG', color='red', transform=axs[1].get_xaxis_transform(),
ha='center', va='top')
# Change Transparency if too close
xticks = axs[1].xaxis.get_major_ticks()
values = axs[1].get_xticks()
# values = axs[1].xaxis.get_major_locator()()
pos = np.where(np.logical_and( (xV-dxV) <= values, (xV+dxV) >= values))[0]
if pos.size > 0:
dist = np.abs(values[pos]-xV)
pos = pos[dist.argmin()]
xticks[pos].label1.set_alpha(0.5)
plt.show()
I'm trying to make a subplot with three plots next to each other, and then a colorbar on the right side of the last plot (see figure).
I'm doing it with this code:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import rcParams
from mpl_toolkits.axes_grid1 import make_axes_locatable
x = np.linspace(1, 100, 100)
y = np.linspace(0.1, 10, 100)
z = x[:, np.newaxis] + y[np.newaxis, :]
fig, ax = plt.subplots(1, 3, figsize=(12, 4))
ax[0].contourf(x, y, z)
ax[0].set_xlabel('x')
ax[0].set_ylabel('y')
ax[1].contourf(x, y, z)
ax[1].set_xlabel('x')
ax[1].set_ylabel('y')
plt.contourf(x, y, z)
ax[2].set_xlabel('x')
ax[2].set_ylabel('y')
divider = make_axes_locatable(plt.gca())
cax = divider.append_axes("right", "10%", pad="3%")
plt.colorbar(cax=cax)
plt.tight_layout()
plt.show()
My problem is that 1) I don't think the first two plots are completely square (which I would like them to be), 2) the last plot that includes the colorbar is smaller in width than the two others. Is there some easy trick to fix this, or do I manually have to go in and give one a little more padding than the other an so on.
If you don't want the subplot to eat into the third axes, already create an extra axes for it when you make the subplots.
To make the plots square, you need to set the aspect ratio: axes.set_aspect(10).
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(1, 100, 100)
y = np.linspace(0.1, 10, 100)
z = x[:, np.newaxis] + y[np.newaxis, :]
gridspec = {'width_ratios': [1, 1, 1, 0.1]}
fig, ax = plt.subplots(1, 4, figsize=(12, 4), gridspec_kw=gridspec)
ax[0].contourf(x, y, z)
ax[0].set_xlabel('x')
ax[0].set_ylabel('y')
ax[1].contourf(x, y, z)
ax[1].set_xlabel('x')
ax[1].set_ylabel('y')
plt.sca(ax[2])
plt.contourf(x, y, z)
ax[2].set_xlabel('x')
ax[2].set_ylabel('y')
for axes in ax[:3]:
axes.set_aspect(10)
cax = ax[3]
plt.colorbar(cax=cax)
plt.tight_layout()
plt.show()
I am struggling with matplotlib and padding on the x-axis together with a logarithmic scale (see the first picture).
Without a logarithmic scale, the padding applies nicely (see the second one).
Any suggestations how to get a padding between plot lines and the axis line in the bottom left corner so that one can see the points on the line?
Thanks.
The code:
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.pyplot import *
from matplotlib.ticker import ScalarFormatter
style.use('fivethirtyeight')
fig, ax = plt.subplots()
T = np.array([2**x for x in range(0,7+1)])
opt1 = np.array([x for x in range(0,7+1)])
opt2 = np.array([x*2 for x in range(0,7+1)])
opt3 = np.array([x*4 for x in range(0,7+1)])
ax.grid(True)
xlabel("#nodes")
ylabel("time(s)")
legend(loc="best")
title(r"Node start times")
plt.xticks([2**x for x in range(0,7+1)])
plt.plot(T,opt1,"o-", label="opt1")
plt.plot(T,opt2, "s-", label="opt2")
plt.plot(T,opt3, "d-", label="opt2")
plt.legend(loc="upper left")
# This should be called after all axes have been added
plt.tight_layout()
plt.margins(0.05, 0.05)
# 1, 2, 4, ...
ax.set_xscale('log', basex=2)
ax.xaxis.set_major_formatter(matplotlib.ticker.FormatStrFormatter("%d"))
plt.show()
#savefig("plot_1.pdf")
This does not address your padding issue, but you could use clip_on=False to prevent the points from being cut off. It seems you also need to make sure they're above the axes using zorder
plt.plot(T,opt1,"o-", label="opt1", clip_on=False, zorder=10)
plt.plot(T,opt2, "s-", label="opt2", clip_on=False, zorder=10)
plt.plot(T,opt3, "d-", label="opt2", clip_on=False, zorder=10)
I have two graphs to where both have the same x-axis, but with different y-axis scalings.
The plot with regular axes is the data with a trend line depicting a decay while the y semi-log scaling depicts the accuracy of the fit.
fig1 = plt.figure(figsize=(15,6))
ax1 = fig1.add_subplot(111)
# Plot of the decay model
ax1.plot(FreqTime1,DecayCount1, '.', color='mediumaquamarine')
# Plot of the optimized fit
ax1.plot(x1, y1M, '-k', label='Fitting Function: $f(t) = %.3f e^{%.3f\t} \
%+.3f$' % (aR1,kR1,bR1))
ax1.set_xlabel('Time (sec)')
ax1.set_ylabel('Count')
ax1.set_title('Run 1 of Cesium-137 Decay')
# Allows me to change scales
# ax1.set_yscale('log')
ax1.legend(bbox_to_anchor=(1.0, 1.0), prop={'size':15}, fancybox=True, shadow=True)
Now, i'm trying to figure out to implement both close together like the examples supplied by this link
http://matplotlib.org/examples/pylab_examples/subplots_demo.html
In particular, this one
When looking at the code for the example, i'm a bit confused on how to implant 3 things:
1) Scaling the axes differently
2) Keeping the figure size the same for the exponential decay graph but having a the line graph have a smaller y size and same x size.
For example:
3) Keeping the label of the function to appear in just only the decay graph.
Any help would be most appreciated.
Look at the code and comments in it:
import matplotlib.pyplot as plt
import numpy as np
from matplotlib import gridspec
# Simple data to display in various forms
x = np.linspace(0, 2 * np.pi, 400)
y = np.sin(x ** 2)
fig = plt.figure()
# set height ratios for subplots
gs = gridspec.GridSpec(2, 1, height_ratios=[2, 1])
# the first subplot
ax0 = plt.subplot(gs[0])
# log scale for axis Y of the first subplot
ax0.set_yscale("log")
line0, = ax0.plot(x, y, color='r')
# the second subplot
# shared axis X
ax1 = plt.subplot(gs[1], sharex = ax0)
line1, = ax1.plot(x, y, color='b', linestyle='--')
plt.setp(ax0.get_xticklabels(), visible=False)
# remove last tick label for the second subplot
yticks = ax1.yaxis.get_major_ticks()
yticks[-1].label1.set_visible(False)
# put legend on first subplot
ax0.legend((line0, line1), ('red line', 'blue line'), loc='lower left')
# remove vertical gap between subplots
plt.subplots_adjust(hspace=.0)
plt.show()
Here is my solution:
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0, 2 * np.pi, 400)
y = np.sin(x ** 2)
fig, (ax1,ax2) = plt.subplots(nrows=2, sharex=True, subplot_kw=dict(frameon=False)) # frameon=False removes frames
plt.subplots_adjust(hspace=.0)
ax1.grid()
ax2.grid()
ax1.plot(x, y, color='r')
ax2.plot(x, y, color='b', linestyle='--')
One more option is seaborn.FacetGrid but this requires Seaborn and Pandas libraries.
Here are some adaptions to show how the code could work to add a combined legend when plotting a pandas dataframe. ax=ax0 can be used to plot on a given ax and ax0.get_legend_handles_labels() gets the information for the legend.
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
dates = pd.date_range('20210101', periods=100, freq='D')
df0 = pd.DataFrame({'x': np.random.normal(0.1, 1, 100).cumsum(),
'y': np.random.normal(0.3, 1, 100).cumsum()}, index=dates)
df1 = pd.DataFrame({'z': np.random.normal(0.2, 1, 100).cumsum()}, index=dates)
fig, (ax0, ax1) = plt.subplots(nrows=2, sharex=True, gridspec_kw={'height_ratios': [2, 1], 'hspace': 0})
df0.plot(ax=ax0, color=['dodgerblue', 'crimson'], legend=False)
df1.plot(ax=ax1, color='limegreen', legend=False)
# put legend on first subplot
handles0, labels0 = ax0.get_legend_handles_labels()
handles1, labels1 = ax1.get_legend_handles_labels()
ax0.legend(handles=handles0 + handles1, labels=labels0 + labels1)
# remove last tick label for the second subplot
yticks = ax1.get_yticklabels()
yticks[-1].set_visible(False)
plt.tight_layout()
plt.show()
Here's some code that does scatter plot of a number of different series using matplotlib and then adds the line y=x:
import numpy as np, matplotlib.pyplot as plt, matplotlib.cm as cm, pylab
nseries = 10
colors = cm.rainbow(np.linspace(0, 1, nseries))
all_x = []
all_y = []
for i in range(nseries):
x = np.random.random(12)+i/10.0
y = np.random.random(12)+i/5.0
plt.scatter(x, y, color=colors[i])
all_x.extend(x)
all_y.extend(y)
# Could I somehow do the next part (add identity_line) if I haven't been keeping track of all the x and y values I've seen?
identity_line = np.linspace(max(min(all_x), min(all_y)),
min(max(all_x), max(all_y)))
plt.plot(identity_line, identity_line, color="black", linestyle="dashed", linewidth=3.0)
plt.show()
In order to achieve this I've had to keep track of all the x and y values that went into the scatter plot so that I know where identity_line should start and end. Is there a way I can get y=x to show up even if I don't have a list of all the points that I plotted? I would think that something in matplotlib can give me a list of all the points after the fact, but I haven't been able to figure out how to get that list.
You don't need to know anything about your data per se. You can get away with what your matplotlib Axes object will tell you about the data.
See below:
import numpy as np
import matplotlib.pyplot as plt
# random data
N = 37
x = np.random.normal(loc=3.5, scale=1.25, size=N)
y = np.random.normal(loc=3.4, scale=1.5, size=N)
c = x**2 + y**2
# now sort it just to make it look like it's related
x.sort()
y.sort()
fig, ax = plt.subplots()
ax.scatter(x, y, s=25, c=c, cmap=plt.cm.coolwarm, zorder=10)
Here's the good part:
lims = [
np.min([ax.get_xlim(), ax.get_ylim()]), # min of both axes
np.max([ax.get_xlim(), ax.get_ylim()]), # max of both axes
]
# now plot both limits against eachother
ax.plot(lims, lims, 'k-', alpha=0.75, zorder=0)
ax.set_aspect('equal')
ax.set_xlim(lims)
ax.set_ylim(lims)
fig.savefig('/Users/paul/Desktop/so.png', dpi=300)
Et voilà
In one line:
ax.plot([0,1],[0,1], transform=ax.transAxes)
No need to modify the xlim or ylim.
Starting with matplotlib 3.3 this has been made very simple with the axline method which only needs a point and a slope. To plot x=y:
ax.axline((0, 0), slope=1)
You don't need to look at your data to use this because the point you specify (i.e. here (0,0)) doesn't actually need to be in your data or plotting range.
If you set scalex and scaley to False, it saves a bit of bookkeeping. This is what I have been using lately to overlay y=x:
xpoints = ypoints = plt.xlim()
plt.plot(xpoints, ypoints, linestyle='--', color='k', lw=3, scalex=False, scaley=False)
or if you've got an axis:
xpoints = ypoints = ax.get_xlim()
ax.plot(xpoints, ypoints, linestyle='--', color='k', lw=3, scalex=False, scaley=False)
Of course, this won't give you a square aspect ratio. If you care about that, go with Paul H's solution.