Why is the first arrow not plotted? There is no error after running the script. There should be three arrows but there are only two arrows. Thank you
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.patches as mpatches
import math
import matplotlib.patches as patches
# Plot figure with size
fig, ax = plt.subplots(sharex=True, sharey=True, figsize=(5,5))
# Axis - lim
plt.xlim(-1.5, 1.5)
plt.ylim(-1.5, 1.5)
shift = 0.011
length = 1.3
coord_45 = math.sqrt(1/2)
# Plot an arrow
style="Simple,tail_width=0.5,head_width=4,head_length=8"
kw = dict(arrowstyle=style)
a3 = patches.FancyArrowPatch((0, 0.366618), (0.211427, 0.211427),connectionstyle="arc3,rad=10", **kw)
ax.scatter(0, 0, marker = 'o', s=30)
arrow = mpatches.FancyArrowPatch((0, 0-shift), (0, length), arrowstyle=style)
plt.gca().add_patch(arrow)
arrow = mpatches.FancyArrowPatch((0, 0), (coord_45, -coord_45), arrowstyle=style)
plt.gca().add_patch(arrow)
plt.savefig('file.pdf')
plt.show()
Related
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()
How can I modify this plot to show me the value of each bar upon hovering mouse?
sns.barplot(x = "date", y = "no_of_dogs", data = dogs_adopted_per_day, palette="husl")
plt.show()
You could employ mplcursors as follows:
import matplotlib.pyplot as plt
import mplcursors
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.DataFrame({"date": pd.date_range('20210101', periods=10),
"no_of_dogs": np.random.randint(10, 30, 10)})
fig, ax = plt.subplots(figsize=(15, 5))
sns.barplot(x="date", y="no_of_dogs", data=df, palette="husl", ax=ax)
x_dates = df['date'].dt.strftime('%Y-%m-%d')
ax.set_xticklabels(labels=x_dates)
cursor = mplcursors.cursor(hover=True)
#cursor.connect("add")
def on_add(sel):
x, y, width, height = sel.artist[sel.target.index].get_bbox().bounds
sel.annotation.set(text=f"{x_dates[round(x)]}\n{height:.0f}",
position=(0, 20), anncoords="offset points")
sel.annotation.xy = (x + width / 2, y + height)
plt.show()
How to make the circle clear transparent? The desired result is a black edge and None colour to see the plots behind the circle.
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D # 3d graph
from mpl_toolkits.mplot3d import proj3d # 3d graph
from matplotlib.patches import FancyArrowPatch
from mpl_toolkits.mplot3d import proj3d, art3d
from matplotlib.patches import Circle
# Plot figure
figsize=[5,5]
fig = plt.figure(figsize=figsize)
ax = fig.add_subplot(111, projection='3d')
ax.azim = -57 # y rotation (default=270)
ax.elev = 29 # x rotation (default=0)
# Set limits
ax.set_xlim(-50, 50)
ax.set_ylim(0, 1)
ax.set_zlim(-50, 50)
R = 50
floor_front = Circle((0, 0), R, linewidth=2, edgecolor = 'black', alpha = 0.3) # (x, z), radius
ax.add_patch(floor_front)
art3d.pathpatch_2d_to_3d(floor_front, z=0, zdir="y") # z = corresponds to y
plt.show()
In Circle(), add facecolor="none".
I am having a multicolored line plot and I want to add a color bar under it in the same figure like as shown in the image below, Is it possible?
I have attached a color bar image as a reference which I took from another code.
My intention here is to use the color bar like a legend for each segment of the line in the plot.
Edit-1: I want to have the color bar using a mappable object such as an image, So don't want to create a new subplot for the sole purpose of the color bar.
Any suggestion is welcome. Thanks in Advance.
This is the code for multicolored line plot
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection
Segments=[[[3,1],[6,1]],[[6,2],[9,2]],[[9,3],[12,3]],[[12,4],[15,4]], [[12,4],[15,4]]]
Points_1 = np.concatenate([Segments[:-1], Segments[1:]], axis=1)
lc = LineCollection(Points_1, colors=['r','g','b','y'], linewidths=2)
fig, ax = plt.subplots()
ax.add_collection(lc)
ax.autoscale()
plt.show()
This is a workaround I'am using:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection
import matplotlib.colorbar as mcolorbar
import matplotlib.colors as mcolors
Segments=[[[3,1],[6,1]],[[6,2],[9,2]],[[9,3],[12,3]],[[12,4],[15,4]], [[12,4],[15,4]]]
Points_1 = np.concatenate([Segments[:-1], Segments[1:]], axis=1)
lc = LineCollection(Points_1, colors=['r','g','b','y'], linewidths=2)
fig, ax = plt.subplots(2, 1, gridspec_kw={'height_ratios' : [5,1]})
ax[0].add_collection(lc)
bounds = np.linspace(0, 1, 5)[:-1]
labels = ['Action1', 'Action2', 'Action3', 'Action4']
ax[0].set_xlim([0, 15])
ax[0].set_ylim([0, 10])
cb2 = mcolorbar.ColorbarBase(ax = ax[1], cmap = cmap, orientation = 'horizontal', extendfrac='auto')
cb2.set_ticks(bounds)
cb2.set_ticklabels(labels)
plt.tight_layout()
plt.show()
If you specifically want to avoid subplots, you can use a scalar mappable:
fig, ax = plt.subplots()
ax.add_collection(lc)
ax.autoscale()
cmap = mcolors.ListedColormap(['r','g','b','y'])
sm = plt.cm.ScalarMappable(cmap=cmap)
sm.set_array([]) # this line may be ommitted for matplotlib >= 3.1
cbar = fig.colorbar(sm, ax=ax, orientation='horizontal',aspect=90)
bounds = np.linspace(0, 1, 5)[:-1]
labels = ['Action1', 'Action2', 'Action3', 'Action4']
ax.set_xlim([0, 15])
ax.set_ylim([0, 10])
cbar.set_ticks(bounds)
cbar.set_ticklabels(labels)
plt.tight_layout()
plt.show()
This helped me to get what I asked.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
from matplotlib.collections import LineCollection
Segments=[[[3,1],[6,1]],[[6,2],[9,2]],[[9,3],[12,3]],[[12,4],[15,4]], [[12,4],[15,4]]]
Points_1 = np.concatenate([Segments[:-1], Segments[1:]], axis=1)
lc = LineCollection(Points_1, colors=['r','g','b','y'], linewidths=2)
fig, ax = plt.subplots()
ax.add_collection(lc)
ax.autoscale()
c=[1,2,3,4,5]
labels = ['Action1', 'Action2', 'Action3', 'Action4']
cmap = mcolors.ListedColormap(['r','g','b','y'])
norm = mcolors.BoundaryNorm([1,2,3,4,5],4)
sm = plt.cm.ScalarMappable(norm=norm, cmap=cmap)
sm.set_array([]) # this line may be ommitted for matplotlib >= 3.1
cbar=fig.colorbar(sm, ticks=c, orientation='horizontal')
cbar.set_ticklabels(['Action1', 'Action2', 'Action3', 'Action4'])
plt.show()
I have the following colormap in Python which maps each value to a color. But my question: How can I quantize the values for getting a same color for a specified range?
For example: from 0 until 10 (Green) ,
from 10 until 50 (yellow),
from 50 until 55 (red) , ...
import matplotlib as mpl
import matplotlib
from matplotlib import cm
.
.
.
norm = matplotlib.colors.Normalize(vmin = min,vmax = max, clip = True)
.
.
for i in range(numberMaterials):
step = (max-min)/numberMaterials
value = min + step*i
mat = bpy.data.materials.new("mat" +str(i))
color = cm.jet(norm(value),bytes=True)
It seems you are asking for a BoundaryNorm.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors
cmap = matplotlib.colors.ListedColormap(["limegreen", "gold", "crimson"])
norm = matplotlib.colors.BoundaryNorm([0,10,50,55], 3)
x = np.linspace(0,55)
fig, (ax, ax2) = plt.subplots(ncols=2)
sc = ax.scatter(x,x, c=x, cmap=cmap, norm=norm)
fig.colorbar(sc, ax=ax, spacing="uniform")
sc2 = ax2.scatter(x,x, c=x, cmap=cmap, norm=norm)
fig.colorbar(sc2, ax=ax2, spacing="proportional")
plt.show()