I have a subplot that plots a line (x,y) and a particular point (xx,yy). I want to highligh (xx,yy), so I've plotted it with scatter. However, even if I order it after the original plot, the new point still shows up behind the original line. How can I fix this? MWE below.
x = 1:10
y = 1:10
xx = 5
yy = 5
fig, ax = subplots()
ax[:plot](x,y)
ax[:scatter](xx,yy, color="red", label="h_star", s=100)
legend()
xlabel("x")
ylabel("y")
title("test")
grid("on")
You can change which plots are displayed on top of each other with the argument zorder. The matplotlib example shown here gives a brief explanation:
The default drawing order for axes is patches, lines, text. This
order is determined by the zorder attribute. The following defaults
are set
Artist Z-order
Patch / PatchCollection 1
Line2D / LineCollection 2
Text 3
You can change the order for individual artists by setting the zorder.
Any individual plot() call can set a value for the zorder of that
particular item.
A full example based on the code in the question, using python is shown below:
import matplotlib.pyplot as plt
x = range(1,10)
y = range(1,10)
xx = 5
yy = 5
fig, ax = plt.subplots()
ax.plot(x,y)
# could set zorder very high, say 10, to "make sure" it will be on the top
ax.scatter(xx,yy, color="red", label="h_star", s=100, zorder=3)
plt.legend()
plt.xlabel("x")
plt.ylabel("y")
plt.title("test")
plt.grid("on")
plt.show()
Which gives:
Related
This question already has answers here:
Annotate bars with values on Pandas bar plots
(4 answers)
Closed 1 year ago.
I would like to create an annotation to a bar chart that compares the value of the bar to two reference values. An overlay such as shown in the picture, a kind of staff gauge, is possible, but I'm open to more elegant solutions.
The bar chart is generated with the pandas API to matplotlib (e.g. data.plot(kind="bar")), so a plus would be if the solution is playing nicely with that.
You may use smaller bars for the target and benchmark indicators. Pandas cannot annotate bars automatically, but you can simply loop over the values and use matplotlib's pyplot.annotate instead.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
a = np.random.randint(5,15, size=5)
t = (a+np.random.normal(size=len(a))*2).round(2)
b = (a+np.random.normal(size=len(a))*2).round(2)
df = pd.DataFrame({"a":a, "t":t, "b":b})
fig, ax = plt.subplots()
df["a"].plot(kind='bar', ax=ax, legend=True)
df["b"].plot(kind='bar', position=0., width=0.1, color="lightblue",legend=True, ax=ax)
df["t"].plot(kind='bar', position=1., width=0.1, color="purple", legend=True, ax=ax)
for i, rows in df.iterrows():
plt.annotate(rows["a"], xy=(i, rows["a"]), rotation=0, color="C0")
plt.annotate(rows["b"], xy=(i+0.1, rows["b"]), color="lightblue", rotation=+20, ha="left")
plt.annotate(rows["t"], xy=(i-0.1, rows["t"]), color="purple", rotation=-20, ha="right")
ax.set_xlim(-1,len(df))
plt.show()
There's no direct way to annotate a bar plot (as far as I am aware) Some time ago I needed to annotate one so I wrote this, perhaps you can adapt it to your needs.
import matplotlib.pyplot as plt
import numpy as np
ax = plt.subplot(111)
ax.set_xlim(-0.2, 3.2)
ax.grid(b=True, which='major', color='k', linestyle=':', lw=.5, zorder=1)
# x,y data
x = np.arange(4)
y = np.array([5, 12, 3, 7])
# Define upper y limit leaving space for the text above the bars.
up = max(y) * .03
ax.set_ylim(0, max(y) + 3 * up)
ax.bar(x, y, align='center', width=0.2, color='g', zorder=4)
# Add text to bars
for xi, yi, l in zip(*[x, y, list(map(str, y))]):
ax.text(xi - len(l) * .02, yi + up, l,
bbox=dict(facecolor='w', edgecolor='w', alpha=.5))
ax.set_xticks(x)
ax.set_xticklabels(['text1', 'text2', 'text3', 'text4'])
ax.tick_params(axis='x', which='major', labelsize=12)
plt.show()
I've two plots generated using matplotlib. The first represents my backround and the second a group of points which I want to show. Is there a way to overlap the two plots?
background:
import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize = (10,10))
grid_duomo = gpd.read_file('/content/Griglia_2m-SS.shp')
grid_duomo.to_crs(epsg=32632).plot(ax=ax, color='lightgrey')
points:
fig = plt.figure(figsize=(10, 10))
ids = traj_collection_df_new_app['id'].unique()
for id_ in ids:
self_id = traj_collection_df_new_app[traj_collection_df_new_app['id'] == id_]
plt.plot(
self_id['lon'],
self_id['lat'],
# markers= 'o',
# markersize=12
)
plt.plot() will always take the most recent axis found by matplotlib and use it for plotting.
Its practically the same as plt.gca().plot() where plt.gca() stands for "get current axis".
To get full control over which axis is used, you should do something like this:
(the zorder argument is used to set the "vertical stacking" of the artists, e.g. zorder=2 will be plotted on top of zorder=1)
f = plt.figure() # create a figure
ax = f.add_subplot( ... ) # create an axis in the figure f
ax.plot(..., zorder=0)
grid_duomo.plot(ax=ax, ..., zorder=1)
# you can then continue to add more axes to the same figure using
# f.add_subplot() or f.add_axes()
(if this is unclear, maybe check the quick_start guide of matplotlib? )
I have 3 lists to plot as curves. But every time I run the same plt lines, even with the ax.legend(loc='lower right', handles=[line1, line2, line3]), these 3 lists jumps randomly in the legend like below. Is it possible to fix their sequences and the colors for the legend as well as the curves in the plot?
EDIT:
My code is as below:
def plot_with_fixed_list(n, **kwargs):
np.random.seed(0)
fig, ax1 = plt.subplots()
my_handles = []
for key, values in kwargs.items():
value_name = key
temp, = ax1.plot(np.arange(1, n+ 1, 1).tolist(), values, label=value_name)
my_handles.append(temp)
ax1.legend(loc='lower right', handles=my_handles)
ax1.grid(True, which='both')
plt.show()
plot_with_fixed_list(300, FA_Hybrid=fa, BP=bp, Ssym_Hybrid=ssym)
This nondeterminism bug resides with python==3.5, matplotlib==3.0.0. After I updated to python==3.6, matplotlib==3.3.2, problem solved.
I am trying to plot multiple different plots on a single matplotlib figure with in a for loop. At the moment it is all good in matlab as shown in the picture below and then am able to save the figure as a video frame. Here is a link of a sample video generated in matlab for 10 frames
In python, tried it as below
import matplotlib.pyplot as plt
for frame in range(FrameStart,FrameEnd):#loop1
# data generation code within a for loop for n frames from source video
array1 = np.zeros((200, 3800))
array2 = np.zeros((19,2))
array3 = np.zeros((60,60))
for i in range(len(array2)):#loop2
#generate data for arrays 1 to 3 from the frame data
#end loop2
plt.subplot(6,1,1)
plt.imshow(DataArray,cmap='gray')
plt.subplot(6, 1, 2)
plt.bar(data2D[:,0], data2D[:,1])
plt.subplot(2, 2, 3)
plt.contourf(mapData)
# for fourth plot, use array2[3] and array2[5], plot it as shown and keep the\is #plot without erasing for next frame
not sure how to do the 4th axes with line plots. This needs to be there (done using hold on for this axis in matlab) for the entire sequence of frames processing in the for loop while the other 3 axes needs to be erased and updated with new data for each frame in the movie. The contour plot needs to be square all the time with color bar on the side. At the end of each frame processing, once all the axes are updated, it needs to be saved as a frame of a movie. Again this is easily done in matlab, but not sure in python.
Any suggestions
thanks
I guess you need something like this format.
I have used comments # in code to answer your queries. Please check the snippet
import matplotlib.pyplot as plt
fig=plt.figure(figsize=(6,6))
ax1=fig.add_subplot(311) #3rows 1 column 1st plot
ax2=fig.add_subplot(312) #3rows 1 column 2nd plot
ax3=fig.add_subplot(325) #3rows 2 column 5th plot
ax4=fig.add_subplot(326) #3rows 2 column 6th plot
plt.show()
To turn off ticks you can use plt.axis('off'). I dont know how to interpolate your format so left it blank . You can adjust your figsize based on your requirements.
import numpy as np
from numpy import random
import matplotlib.pyplot as plt
fig=plt.figure(figsize=(6,6)) #First is width Second is height
ax1=fig.add_subplot(311)
ax2=fig.add_subplot(312)
ax3=fig.add_subplot(325)
ax4=fig.add_subplot(326)
#Bar Plot
langs = ['C', 'C++', 'Java', 'Python', 'PHP']
students = [23,17,35,29,12]
ax2.bar(langs,students)
#Contour Plot
xlist = np.linspace(-3.0, 3.0, 100)
ylist = np.linspace(-3.0, 3.0, 100)
X, Y = np.meshgrid(xlist, ylist)
Z = np.sqrt(X**2 + Y**2)
cp = ax3.contourf(X, Y, Z)
fig.colorbar(cp,ax=ax3) #Add a colorbar to a plot
#Multiple line plot
x = np.linspace(-1, 1, 50)
y1 = 2*x + 1
y2 = 2**x + 1
ax4.plot(x, y2)
ax4.plot(x, y1, color='red',linewidth=1.0)
plt.tight_layout() #Make sures plots dont overlap
plt.show()
I have a bar chart, the x-axis is (1,2,3...12).
so my bar chart is something like this:
how can I change:
1---> -6month
2---> -1 year
3--->-1.5 year
.
.
.
while showing?
my code to plot is:
dffinal = df[['6month','final-formula','Question Text','numPatients6month']].drop_duplicates().sort_values(['6month'])
df = dffinal.drop('numPatients6month', 1).groupby(['6month','Question Text']).sum().unstack('Question Text')
df.columns = df.columns.droplevel()
ax=df.plot(kind='bar', stacked=True)
import matplotlib.pyplot as plt
ax2 = ax.twinx()
plt.xticks(fontsize=8, rotation=45)
#ax2.spines['right'].set_position(('axes', 1.0))
dffinal.plot(ax=ax2,x='6month', y='numPatients6month',visible=False)
plt.title('Cognitive Impairement-Stack bar')
plt.show()
I have two df as I have two y-axis.
I tried to use replace:
dffinal['6month'].replace(1, '-6 month',inplace=True)
dffinal['6month'].replace(2, '-1 year',inplace=True)
but it just did not worked .
Thanks:)
The command plt.xticks should take care of it. Depending on whether the counting of the x axis starts from 0 (as default) or from 1 (as your plot implies) you could try:
# If x starts from 0
plt.xticks(range(12), ['-6month','-1 year',...], fontsize=8, rotation=90)
or
# If x starts from 1
plt.xticks(range(1,13), ['-6month','-1 year',...], fontsize=8, rotation=90)
In both cases replacing ['-6month','-1 year',...] by the 12 elements list of the labels you want.