I am trying to draw a circle with matplotlib, with a diameter, say, of 2 inches and a border of 10 pixels, and I want to save it in a file. This is my code:
import matplotlib.pyplot as plt
from matplotlib import patches
path = 'test.png'
fig1 = plt.figure()
fig1.dpi = 100
fig1.set_size_inches(2, 2)
ax1 = fig1.add_subplot(111, aspect='equal')
ax1.axes.get_xaxis().set_visible(False)
ax1.axes.get_yaxis().set_visible(False)
ax1.add_patch(patches.Circle((0.5, 0.5),
radius=0.5,
color='k', linewidth=10, fill=False))
fig1.tight_layout()
fig1.savefig(path, bbox_inches='tight', pad_inches=0)
and this is what I get:
As you can see, part of the border is out of the picture.
In fact, even doing something much simpler, I get similar results:
import matplotlib.pyplot as plt
from matplotlib import patches
fig1 = plt.figure()
ax1 = fig1.add_subplot(111, aspect='equal')
ax1.add_patch(patches.Circle((0.5, 0.5),
radius=0.5,
color='k', linewidth=10, fill=False))
plt.show()
so, I can't understand where is the problem.
What am I doing wrong?
Adding a patch won't automatically adjust the axes limits. You have to call ax1.autoscale_view() to adjust the limits to the content.
fig1 = plt.figure()
ax1 = fig1.add_subplot(111, aspect='equal')
ax1.add_patch(patches.Circle((0.5, 0.5),
radius=0.5,
color='k', linewidth=10, fill=False))
ax1.autoscale_view()
The limits are small, by default take the minimum position and maximum of all points without considering the thickness, I recommend that you set the limits a little bigger. You must be {axes}.set_xlim() and {axes}.set_ylim()
import matplotlib.pyplot as plt
from matplotlib import patches
path = 'test.png'
fig1 = plt.figure()
fig1.dpi = 100
fig1.set_size_inches(2, 2)
ax1 = fig1.add_subplot(111, aspect='equal')
ax1.axes.get_xaxis().set_visible(False)
ax1.axes.get_yaxis().set_visible(False)
ax1.add_patch(patches.Circle((0.5, 0.5),
radius=0.5,
color='k', linewidth=10, fill=False))
ax1.set_xlim([-0.1, 1.1])
ax1.set_ylim([-0.1, 1.1])
fig1.tight_layout()
fig1.savefig(path, bbox_inches='tight', pad_inches=0)
Related
I would like to show the topmost minor gridline (at 2.25) in its full width without adjusting the limits manually. How can I achieve this?
import matplotlib.pyplot as plt
import numpy as np
import matplotlib as mpl
t = np.arange(0.0, 2.0, 0.01)
s = 1 + np.sin(2 * np.pi * t)
fig, ax = plt.subplots(constrained_layout=True)
ax.yaxis.set_tick_params(which='minor', width=5)
ax.plot(t, s)
ax.yaxis.set_major_locator(mpl.ticker.MultipleLocator(0.50))
ax.yaxis.set_minor_locator(mpl.ticker.MultipleLocator(0.25))
ax.grid(linewidth=5, axis='y', which='both')
ax.set_ylim(0, 2.25)
plt.show()
Adjusting edge gridline visibility in matplotlib
There are a couple ways to accomplish what you're looking for. I think the best method would be to hide the top and right spines. Alternatively, ax.grid takes Line2D args which includes clip_on. Setting clip_on=False has the intended effect of making the ax box not clip the line - but it does result in the top spine going through the grid line (which is not very attractive in my opinion). Lastly, as you mention, you could make a minor increase in y lim (to 2.26 or 2.27) and that would also make the top grid line more visible.
import matplotlib.pyplot as plt
import numpy as np
import matplotlib as mpl
t = np.arange(0.0, 2.0, 0.01)
s = 1 + np.sin(2 * np.pi * t)
fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(15,12))
axes = axes.flatten()
ax = axes[0]
ax.yaxis.set_tick_params(which='minor', width=5)
ax.plot(t, s)
ax.yaxis.set_major_locator(mpl.ticker.MultipleLocator(0.50))
ax.yaxis.set_minor_locator(mpl.ticker.MultipleLocator(0.25))
ax.grid(linewidth=5, axis='y', which='both', clip_on=False)
[ax.spines[s].set_visible(False) for s in ['top', 'right']]
ax.set_ylim(0, 2.25)
ax.set_title('Hide top/right spines', fontsize=16, fontweight='bold')
ax = axes[1]
ax.yaxis.set_tick_params(which='minor', width=5)
ax.plot(t, s)
ax.yaxis.set_major_locator(mpl.ticker.MultipleLocator(0.50))
ax.yaxis.set_minor_locator(mpl.ticker.MultipleLocator(0.25))
ax.grid(linewidth=5, axis='y', which='both', zorder=3, clip_on=False)
ax.set_ylim(0, 2.25)
ax.set_title('Make clip_on=False', fontsize=16, fontweight='bold')
ax = axes[2]
ax.yaxis.set_tick_params(which='minor', width=5)
ax.plot(t, s)
ax.yaxis.set_major_locator(mpl.ticker.MultipleLocator(0.50))
ax.yaxis.set_minor_locator(mpl.ticker.MultipleLocator(0.25))
ax.grid(linewidth=5, axis='y', which='both')
ax.set_ylim(0, 2.265)
ax.set_title('Increase ylim to 2.265', fontsize=16, fontweight='bold')
axes[3].axis('off')
I am using secondary y-axis and cmap color but when I plot together the color bar cross to my plot
here is my code
fig,ax1=plt.subplots()
ax1 = df_Combine.plot.scatter('Parameter2', 'NPV (MM €)', marker='s', s=500, ylim=(-10,60), c='Lifetime1 (a)', colormap='jet_r', vmin=0, vmax=25, ax=ax1)
graph.axhline(0, color='k')
plt.xticks(rotation=90)
ax2 = ax1.twinx()
ax2.plot(df_Combine_min_select1["CumEnergy1 (kWH)"])
plt.show()
and here is my plotting
anyone can help how to solve this issue?
Thank you
When you let pandas automatically create a colorbar, you don't have positioning options. Therefore, you can create the colorbar in a separate step and provide the pad= parameter to set a wider gap. Default, pad is 0.05, meaning 5% of the width of the subplot.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
fig, ax1 = plt.subplots()
df_Combine = pd.DataFrame({'Parameter2': np.random.rand(10) * 10,
'NPV (MM €)': np.random.rand(10),
'Lifetime1 (a)': np.random.rand(10) * 25,
})
ax1 = df_Combine.plot.scatter('Parameter2', 'NPV (MM €)', marker='s', s=500, ylim=(-10, 60), c='Lifetime1 (a)',
colormap='jet_r', vmin=0, vmax=25, ax=ax1, colorbar=False)
plt.colorbar(ax1.collections[0], ax=ax1, pad=0.1)
ax2 = ax1.twinx()
ax2.plot(np.random.rand(10))
plt.show()
My goal is to create plot with four subplots, where the bottom two are really just empty boxes where I will display some text. Unfortunately, all of my efforts to remove the y and x axis tick marks and labels have failed. I'm still new to matplotlib so I'm sure there's something simple that I'm missing. Here's what I'm trying and what I get:
import matplotlib.pyplot as plt
fig, axes = plt.subplots(2, 2, sharex=False, sharey=True, figsize=(6,6))
fig.add_subplot(111, frameon=False)
plt.tick_params(labelcolor='none', top=False, bottom=False, left=False, right=False)
plt.title('Neuron Length')
plt.xlabel('Strain')
plt.ylabel('Neuron Length (um)')
aIP = fig.add_subplot(223, frameon=False)
aIP.annotate('Big Axes \nGridSpec[1:, -1]', (0.1, 0.5),
xycoords='axes fraction', va='center')
# First approach
aIP.axes.xaxis.set_ticks([])
aIP.axes.yaxis.set_ticks([])
# Second approach
ax = plt.gca()
ax.axes.yaxis.set_visible(False)
plt.show()
This is achieved by using plt.subplots() to draw four of them and remove the bottom left frame.
import matplotlib.pyplot as plt
import numpy as np
t = np.linspace(-np.pi, np.pi, 1000)
x1 = np.sin(2*t)
x2 = np.cos(2*t)
x3 = x1 + x2
fig,axes = plt.subplots(nrows=2,ncols=2,figsize=(6,6), sharex=True, sharey=True)
axes[0,0].plot(t, x1, linewidth=2)
axes[0,1].plot(t, x2, linewidth=2)
axes[1,1].plot(t, x3, linewidth=2)
axes[1,0].axis('off') # off
axes[1,0].annotate('Big Axes \nGridSpec[1:, -1]', (0.1, 0.5), xycoords='axes fraction', va='center')
fig.suptitle('Neuron Length')
for ax in axes.flat:
ax.set(xlabel='Strain', ylabel='Neuron Length (um)')
plt.show()
How do I get a figure to have a 1:1 aspect ratio? I currently have the following figure
import matplotlib.pyplot as plt
circle1 = plt.Circle((0.5, 0.5), 0.2, color='r')
fig, ax = plt.subplots()
ax.add_artist(circle1)
But the x-axis is bigger than the y-axis. I tried using the command I found here :
import matplotlib.pyplot as plt
circle1 = plt.Circle((0.5, 0.5), 0.2, color='r')
fig, ax = plt.subplots()
ax.add_artist(circle1)
plt.axes().set_aspect('equal', 'datalim')
but then the circle I drew disappears.
How can I set an equal aspect ratio?
Add the aspect kw to your fig, ax statement:
fig, ax = plt.subplots(subplot_kw={'aspect': 1})
If you want to change the aspect ratio of your ax at any time rather than when it's created, you can call:
ax.set_aspect("equal")
as documented here.
I'm trying to figure out how to append an axes to my polar projection. The newly added axes is supposed to wrap around the original polar axes like a ring.
For that purpose I tried to use append_axes from a divider created via make_axes_locatable on a polar matplotlib projection ax.
However, there's no option for "outer" or anything that would resemble polar projections with the append_axes arguments.
Instead of a ring around the axes, I get a new axes just below the original one (see picture).
Is there any alternative that would allow creating an axes of ring shape around an existing polar axes?
Note, I don't want to add them on the same ax because the scales may be different.
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
plt.style.use("seaborn-white")
def synthesize(polar=False):
fig = plt.figure()
ax = fig.add_subplot(111, polar=polar)
t = np.linspace(0,2*np.pi)
r_sin = np.sin(t)
r_cos = np.cos(t)
for r in [r_sin, r_cos]:
ax.scatter(t, r, c=r, s=t*100, edgecolor="white", cmap=plt.cm.magma_r)
ax.scatter(t, -r, c=r, s=t*100, edgecolor="white", cmap=plt.cm.magma_r)
ax.set_title("polar={}".format(polar),fontsize=15)
ax.set_xticklabels([])
return fig, ax, t
# Rectilinear
fig, ax, t = synthesize(polar=False)
# Here are the plot dimensions in response to the answer below
xlim = ax.get_xlim()
ylim = ax.get_ylim()
rlim = (ax.get_rmin(), ax.get_rmax())
print(xlim, ylim, rlim)
(0.0, 6.283185307179586) (0.0, 2.2437621373846617) (0.0, 2.2437621373846617)
# Encircling axis
divider = make_axes_locatable(ax)
ax_below = divider.append_axes("bottom", size="32.8%", pad=0.1)
ax_below.scatter(t, np.tan(t), c="black", edgecolor="white")
# Polar
fig, ax, t = synthesize(polar=True)
divider = make_axes_locatable(ax)
ax_below = divider.append_axes("bottom", size="32.8%", pad=0.1)
ax_below.scatter(t, np.tan(t), c="black", edgecolor="white")
You probably can do something by tunning set_rmax and set_rorigin like this:
import numpy as np
import matplotlib.pyplot as plt
plt.style.use("seaborn-white")
def synthesize(polar=False):
fig = plt.figure()
ax = fig.add_subplot(111, polar=polar)
ax.set_rmax(30)
t = np.linspace(0,2*np.pi)
r_sin = np.sin(t)
r_cos = np.cos(t)
for r in [r_sin, r_cos]:
ax.scatter(t, r, c=r, s=t*100, edgecolor="white", cmap=plt.cm.magma_r)
ax.scatter(t, -r, c=r, s=t*100, edgecolor="white", cmap=plt.cm.magma_r)
ax.set_title("polar={}".format(polar),fontsize=15)
ax.set_xticklabels([])
return fig, ax, t
# Polar
fig, ax, t = synthesize(polar=True)
ax_below = fig.add_subplot(111, polar=True, frameon=True)
# ax_below = divider.append_axes("bottom", size="32.8%", pad=0.1)
ax_below.scatter(t, np.tan(t), c="black", edgecolor="white")
ax_below.set_rorigin(-75)
ax_below.set_rmin(-25)
plt.show()