I want to make a scatterplot (using matplotlib) where the points are shaded according to a third variable. I've got very close with this:
plt.scatter(w, M, c=p, marker='s')
where w and M are the data points and p is the variable I want to shade with respect to.
However I want to do it in greyscale rather than colour. Can anyone help?
There's no need to manually set the colors. Instead, specify a grayscale colormap...
import numpy as np
import matplotlib.pyplot as plt
# Generate data...
x = np.random.random(10)
y = np.random.random(10)
# Plot...
plt.scatter(x, y, c=y, s=500) # s is a size of marker
plt.gray()
plt.show()
Or, if you'd prefer a wider range of colormaps, you can also specify the cmap kwarg to scatter. To use the reversed version of any of these, just specify the "_r" version of any of them. E.g. gray_r instead of gray. There are several different grayscale colormaps pre-made (e.g. gray, gist_yarg, binary, etc).
import matplotlib.pyplot as plt
import numpy as np
# Generate data...
x = np.random.random(10)
y = np.random.random(10)
plt.scatter(x, y, c=y, s=500, cmap='gray')
plt.show()
In matplotlib grey colors can be given as a string of a numerical value between 0-1.
For example c = '0.1'
Then you can convert your third variable in a value inside this range and to use it to color your points.
In the following example I used the y position of the point as the value that determines the color:
from matplotlib import pyplot as plt
x = [1, 2, 3, 4, 5, 6, 7, 8, 9]
y = [125, 32, 54, 253, 67, 87, 233, 56, 67]
color = [str(item/255.) for item in y]
plt.scatter(x, y, s=500, c=color)
plt.show()
Sometimes you may need to plot color precisely based on the x-value case. For example, you may have a dataframe with 3 types of variables and some data points. And you want to do following,
Plot points corresponding to Physical variable 'A' in RED.
Plot points corresponding to Physical variable 'B' in BLUE.
Plot points corresponding to Physical variable 'C' in GREEN.
In this case, you may have to write to short function to map the x-values to corresponding color names as a list and then pass on that list to the plt.scatter command.
x=['A','B','B','C','A','B']
y=[15,30,25,18,22,13]
# Function to map the colors as a list from the input list of x variables
def pltcolor(lst):
cols=[]
for l in lst:
if l=='A':
cols.append('red')
elif l=='B':
cols.append('blue')
else:
cols.append('green')
return cols
# Create the colors list using the function above
cols=pltcolor(x)
plt.scatter(x=x,y=y,s=500,c=cols) #Pass on the list created by the function here
plt.grid(True)
plt.show()
A pretty straightforward solution is also this one:
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8,8))
p = ax.scatter(x, y, c=y, cmap='cmo.deep')
fig.colorbar(p,ax=ax,orientation='vertical',label='labelname')
Related
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 want to prepare a grayscale figure with multiple, overlapping scatter plots. A simplified version of my code is as follows:
for object in list_of_objects:
plt.scatter(object.x,object.y,marker=object.marker,label=object.label)
plt.legend()
plt.show()
The problem is that the legend appears in non-grayscale color, so is there any way to force it to use grayscale colors? (i.e. a different grayscale color for the data of each object)
Matplotlib
If you need to use matplotlib, then here is a sample of how you can achieve this. Assuming there are 5 groups that you want to add here.
import numpy as np
import matplotlib.pyplot as plt
x = np.random.rand(100)
y = np.random.rand(100)
groups = [1, 2, 3, 4, 5] * 20
plt.scatter(x, y, c=groups, cmap='Greys')
plt.colorbar(ticks=[1, 2, 3, 4, 5])
plt.show()
Output graph:
Seaborn
If you are able to use seaborn, it is simpler...
You can use palette = 'gray' and hue=<your group> to give it different shades of gray.
Sample code:
tips = sns.load_dataset("tips")
sns.scatterplot(data=tips, x="total_bill", y="tip", hue="size", palette="gray")
Will give this pic
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 am trying to make a scatter plot for my data using matplotlib.pyplot (which is imported as plt) and using the following command:
fig = plt.figure(1, figsize = (10, 6))
plt.figure()
plt.scatter(X_train_reduced[:, 0], X_train_reduced[:, 1], c = y_train, cmap = plt.cm.Paired , linewidths=10)
but it gives this error:
ValueError: 'c' argument must be a mpl color, a sequence of mpl colors or a sequence of numbers, not sample group
when I remove this argument:
c = y_train
it returns the plot but the same color for all groups (I have 2 groups).
do you know how to fix it?
I need to draw many such rows (for a0 .. a128) in a single window. I've searched in FacetGrid, PairGrid and all over around but couldn't find. Only regplot has similar argument ax but it doesn't plot histograms. My data is 128 real valued features with label column [0, 1]. I need the graphs to be shown from my Python code as a separate application on Linux.
Also, it there a way to scale this histogram to show relative values on Y such that the right curve is not skewed?
g = sns.FacetGrid(df, col="Result")
g.map(plt.hist, "a0", bins=20)
plt.show()
Just a simple example using matplotlib. The code is not optimized (ugly, but simple plot-indexing):
import numpy as np
import matplotlib.pyplot as plt
N = 5
data = np.random.normal(size=(N*N, 1000))
f, axarr = plt.subplots(N, N) # maybe you want sharex=True, sharey=True
pi = [0,0]
for i in range(data.shape[0]):
if pi[1] == N:
pi[0] += 1 # next row
pi[1] = 0 # first column again
axarr[pi[0], pi[1]].hist(data[i], normed=True) # i was wrong with density;
# normed=True should be used
pi[1] += 1
plt.show()
Output: