I'm trying to change a colorbar attached to a scatter plot so that the minimum and maximum of the colorbar are the minimum and maximum of the data, but I want the data to be centred at zero as I'm using a colormap with white at zero. Here is my example
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0, 1, 61)
y = np.linspace(0, 1, 61)
C = np.linspace(-10, 50, 61)
M = np.abs(C).max() # used for vmin and vmax
fig, ax = plt.subplots(1, 1, figsize=(5,3), dpi=150)
sc=ax.scatter(x, y, c=C, marker='o', edgecolor='k', vmin=-M, vmax=M, cmap=plt.cm.RdBu_r)
cbar=fig.colorbar(sc, ax=ax, label='$R - R_0$ (mm)')
ax.set_xlabel('x')
ax.set_ylabel('y')
As you can see from the attached figure, the colorbar goes down to -M, where as I want the bar to just go down to -10, but if I let vmin=-10 then the colorbar won't be zerod at white. Normally, setting vmin to +/- M when using contourf the colorbar automatically sorts to how I want. This sort of behaviour is what I expect when contourf uses levels=np.linspace(-M,M,61) rather than setting it with vmin and vmax with levels=62. An example showing the default contourf colorbar behaviour I want in my scatter example is shown below
plt.figure(figsize=(6,5), dpi=150)
plt.contourf(x, x, np.reshape(np.linspace(-10, 50, 61*61), (61,61)),
levels=62, vmin=-M, vmax=M, cmap=plt.cm.RdBu_r)
plt.colorbar(label='$R - R_0$ (mm)')
Does anyone have any thoughts? I found this link which I thought might solve the problem, but when executing the cbar.outline.set_ydata line I get this error AttributeError: 'Polygon' object has no attribute 'set_ydata' .
EDIT a little annoyed that someone has closed this question without allowing me to clarify any questions they might have, as none of the proposed solutions are what I'm asking for.
As for Normalize.TwoSlopeNorm, I do not want to rescale the smaller negative side to use the entire colormap range, I just want the colorbar attached to the side of my graph to stop at -10.
This link also does not solve my issue, as it's the TwoSlopeNorm solution again.
After changing the ylim of the colorbar, the rectangle formed by the surrounding spines is too large. You can make this outline invisible. And then add a new rectangular border:
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0, 1, 61)
y = np.linspace(0, 1, 61)
C = np.linspace(-10, 50, 61)
M = np.abs(C).max() # used for vmin and vmax
fig, ax = plt.subplots(1, 1, figsize=(5, 3), dpi=150)
sc = ax.scatter(x, y, c=C, marker='o', edgecolor='k', vmin=-M, vmax=M, cmap=plt.cm.RdBu_r)
cbar = fig.colorbar(sc, ax=ax, label='$R - R_0$ (mm)')
cb_ymin = C.min()
cb_ymax = C.max()
cb_xmin, cb_xmax = cbar.ax.get_xlim()
cbar.ax.set_ylim(cb_ymin, cb_ymax)
cbar.outline.set_visible(False) # hide the surrounding spines, which are too large after set_ylim
cbar.ax.add_patch(plt.Rectangle((cb_xmin, cb_ymin), cb_xmax - cb_xmin, cb_ymax - cb_ymin,
fc='none', ec='black', clip_on=False))
plt.show()
Another approach until v3.5 is released is to make a custom colormap that does what you want (see also https://matplotlib.org/stable/tutorials/colors/colormap-manipulation.html#sphx-glr-tutorials-colors-colormap-manipulation-py)
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.cm as cm
from matplotlib.colors import ListedColormap
fig, axs = plt.subplots(2, 1)
X = np.random.randn(32, 32) + 2
pc = axs[0].pcolormesh(X, vmin=-6, vmax=6, cmap='RdBu_r')
fig.colorbar(pc, ax=axs[0])
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.cm as cm
from matplotlib.colors import ListedColormap
fig, axs = plt.subplots(2, 1)
X = np.random.randn(32, 32) + 2
pc = axs[0].pcolormesh(X, vmin=-6, vmax=6, cmap='RdBu_r')
fig.colorbar(pc, ax=axs[0])
def keep_center_colormap(vmin, vmax, center=0):
vmin = vmin - center
vmax = vmax - center
dv = max(-vmin, vmax) * 2
N = int(256 * dv / (vmax-vmin))
RdBu_r = cm.get_cmap('RdBu_r', N)
newcolors = RdBu_r(np.linspace(0, 1, N))
beg = int((dv / 2 + vmin)*N / dv)
end = N - int((dv / 2 - vmax)*N / dv)
newmap = ListedColormap(newcolors[beg:end])
return newmap
newmap = keep_center_colormap(-2, 6, center=0)
pc = axs[1].pcolormesh(X, vmin=-2, vmax=6, cmap=newmap)
fig.colorbar(pc, ax=axs[1])
plt.show()
The issue I would like you to figure out is about the coordinantes appearence on matplotlib graph with a double y axis. First of all a code on Jupyter Notebook which draws a graph with two lines and only one y axis (for some unknown reasons I have to run it two times in order to make it working correctly)
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.mlab as mlab
from IPython.display import display
from IPython.core.display import display, HTML #display multiple output on a cell
display(HTML("<style>.container { width:100% !important; }</style>")) # improve cells horizontal size
from IPython.core.interactiveshell import InteractiveShell # It saves you having to repeatedly type "Display"
InteractiveShell.ast_node_interactivity = "all"
%matplotlib notebook
x = np.arange(0, 10, 0.01)
y1 = np.sin(np.pi*x)/(np.pi*x)
y2 = abs(np.tan(0.1*np.pi*x))
plt.figure()
plt.plot(x, y1)
plt.plot(x, y2)
plt.ylim(0, 3)
plt.grid()
plt.show()
The present figure provides the two lines with cursor coordinates on the right bottom part of the graph.
The following code
import pandas as pd
import os
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.mlab as mlab
from IPython.display import display
from IPython.core.display import display, HTML #display multiple output on a cell
display(HTML("<style>.container { width:100% !important; }</style>")) # improve cells horizontal size
from IPython.core.interactiveshell import InteractiveShell # It saves you having to repeatedly type "Display"
InteractiveShell.ast_node_interactivity = "all"
%matplotlib notebook
x = np.arange(0, 10, 0.01)
y1 = np.sin(np.pi*x)/(np.pi*x)
y2 = abs(np.tan(0.1*np.pi*x))
# Create some mock data
fig, ax1 = plt.subplots()
plt.grid()
color = 'tab:red'
ax1.set_xlabel('Time (days from 24 February)')
ax1.set_ylabel('Death cases/Intensive care', color=color)
#ax1.set_xlim(0, 15)
#ax1.set_ylim(0, 900)
ax1.plot(x, y1, '-', color=color, label = 'Left hand scale')
ax1.tick_params(axis='y', labelcolor=color)
ax1.legend(loc = 'upper left')
ax2 = ax1.twinx()
color = 'tab:blue'
ax2.set_ylabel('Total cases/currently positive', color=color) # we already handled the x-label with ax1
ax2.plot(x, y2, '-', color=color, label = 'Right hand scale')
ax2.set_ylim(0, 20)
ax2.tick_params(axis='y', labelcolor=color)
ax2.legend(loc = 'lower right')
fig.tight_layout()
plt.show()
Shows the following graph
Which shows a graph with TWO y scales, one red on the left side and one blue on the right side. The problem here is that in the left bottom side of the picture there are the cursor coordinates related to the right scale and nothing about the left one. Is there a way to show up both the two scales?
Depending on your precise needs, mplcursors seems helpful. Mplcursors allows a lot of ways to customize, for example you can show both y-values together with the current x. Or you could suppress the annotation and only write in the status bar.
Setting hover=True constantly displays the plotted values when the mouse hovers over a curve. Default, the values would only be displayed when clicking.
import matplotlib.pyplot as plt
import numpy as np
import mplcursors
# Create some test data
x = np.arange(0, 10, 0.01)
y1 = np.sin(np.pi * x) / (np.pi * x)
y2 = abs(np.tan(0.1 * np.pi * x))
fig, ax1 = plt.subplots()
plt.grid()
color = 'tab:red'
ax1.set_xlabel('Time (days from 24 February)')
ax1.set_ylabel('Death cases/Intensive care', color=color)
lines1 = ax1.plot(x, y1, '-', color=color, label='Left hand scale')
ax1.tick_params(axis='y', labelcolor=color)
ax1.legend(loc='upper left')
ax2 = ax1.twinx()
color = 'tab:blue'
ax2.set_ylabel('Total cases/currently positive', color=color) # we already handled the x-label with ax1
lines2 = ax2.plot(x, y2, '-', color=color, label='Right hand scale')
ax2.set_ylim(0, 20)
ax2.tick_params(axis='y', labelcolor=color)
ax2.legend(loc='lower right')
cursor1 = mplcursors.cursor(lines1, hover=True)
cursor2 = mplcursors.cursor(lines2, hover=True)
fig.tight_layout()
plt.show()
How to set ticks to be on the opposite axes please? When I drop it the axes changes but not in this way. Thank you
import matplotlib as mpl
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import matplotlib.pyplot as plt
mpl.rcParams['legend.fontsize'] = 10
fig = plt.figure()
ax = fig.gca(projection='3d')
theta = np.linspace(-4 * np.pi, 4 * np.pi, 100)
z = np.linspace(-2, 2, 100)
r = z**2 + 1
x = r * np.sin(theta)
y = r * np.cos(theta)
ax.plot(x, y, z, label='parametric curve')
ax.legend()
plt.show()
How to set linewidth of axis in 3d plot in python? Is it somehow possible with mpl.rcParams?
Code:
import matplotlib as mpl
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import matplotlib.pyplot as plt
mpl.rcParams['legend.fontsize'] = 10
fig = plt.figure()
ax = fig.gca(projection='3d')
theta = np.linspace(-4 * np.pi, 4 * np.pi, 100)
z = np.linspace(-2, 2, 100)
r = z**2 + 1
x = r * np.sin(theta)
y = r * np.cos(theta)
ax.plot(x, y, z, label='parametric curve')
ax.legend()
plt.show()
Try this:
ax.plot_surface(X, Y, Z, linewidth=1)
I am using the following codes to plot several data points (xi, yi)
import numpy as np
import matplotlib.pyplot as plt
xi = np.array(data_df[['col_A']])
yi = np.array(data_df[['col_B']])
plt.figure()
plt.plot(xi, yi)
x = np.linspace(0, 30, 30)
y= np.exp(x*0.16)
plt.plot(x, y)
plt.show()
I want the plot to look like this:
Thanks!
User subplots to plot more than 1 plots in 1 figure.You need to call plt.show() only once.
import numpy as np
import matplotlib.pyplot as plt
xi = np.array(data_df[['col_A']])
yi = np.array(data_df[['col_B']])
plt.figure()
plt.subplot(2,1,1)
plt.plot(xi, yi)
plt.subplot(2,1,2)
x = np.linspace(0, 30, 30)
y= np.exp(x*0.16)
plt.plot(x, y)
plt.show()