I need to overlay two sns.catplots
One is of kind='box' and the other is kind='swarm' as follows:
gbox= sns.catplot( x="Emotion",y="Threshold",hue="Group", col = 'Task',
data=indata,palette ["skyblue","salmon"], kind="box", showmeans='True', meanline = 'True', height=6, aspect=0.8, boxprops={'facecolor':'None'}, edgecolor='gray')
sns.catplot( x="Emotion",y="Threshold",hue="Group", col = 'Task',
data=indata, palette=["skyblue","salmon"], kind="swarm",ax=gbox.axes)
I have tried taking the axes from one and feeding into the other catplot, but I receive an error as below. How can I fix this?
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-74-7a05cc88a396> in <module>
17
18 gbox =sns.catplot( x="Emotion",y="Threshold",hue="Group", col = 'Task',
---> 19 data=indata,palette=["skyblue","salmon"],kind="swarm",ax=gbox.axes)
20
21 #plt.show(gbox)
~/anaconda3/lib/python3.6/site-packages/seaborn/categorical.py in catplot(x, y, hue, data, row, col, col_wrap, estimator, ci, n_boot, units, order, hue_order, row_order, col_order, kind, height, aspect, orient, color, palette, legend, legend_out, sharex, sharey, margin_titles, facet_kws, **kwargs)
3753
3754 # Draw the plot onto the facets
-> 3755 g.map_dataframe(plot_func, x, y, hue, **plot_kws)
3756
3757 # Special case axis labels for a count type plot
~/anaconda3/lib/python3.6/site-packages/seaborn/axisgrid.py in map_dataframe(self, func, *args, **kwargs)
818
819 # Draw the plot
--> 820 self._facet_plot(func, ax, args, kwargs)
821
822 # Finalize the annotations and layout
~/anaconda3/lib/python3.6/site-packages/seaborn/axisgrid.py in _facet_plot(self, func, ax, plot_args, plot_kwargs)
836
837 # Draw the plot
--> 838 func(*plot_args, **plot_kwargs)
839
840 # Sort out the supporting information
~/anaconda3/lib/python3.6/site-packages/seaborn/categorical.py in swarmplot(x, y, hue, data, order, hue_order, dodge, orient, color, palette, size, edgecolor, linewidth, ax, **kwargs)
2989 linewidth=linewidth))
2990
-> 2991 plotter.plot(ax, kwargs)
2992 return ax
2993
~/anaconda3/lib/python3.6/site-packages/seaborn/categorical.py in plot(self, ax, kws)
1444 def plot(self, ax, kws):
1445 """Make the full plot."""
-> 1446 self.draw_swarmplot(ax, kws)
1447 self.add_legend_data(ax)
1448 self.annotate_axes(ax)
~/anaconda3/lib/python3.6/site-packages/seaborn/categorical.py in draw_swarmplot(self, ax, kws)
1374 # Set the categorical axes limits here for the swarm math
1375 if self.orient == "v":
-> 1376 ax.set_xlim(-.5, len(self.plot_data) - .5)
1377 else:
1378 ax.set_ylim(-.5, len(self.plot_data) - .5)
AttributeError: 'numpy.ndarray' object has no attribute 'set_xlim'
Thank you for your help!
SJ
In theory, using the .map and .map_dataframe methods should work, but
it fails when you do not want to use the same dimensionality across the plots that you are trying to overlay (for instance, using a different hue parameter for the first and second plot to show data split in different ways but overlaid)
EDIT it sort of works but only when sns.FacetGrid is first initiated independently with col/row argument, and the hue parameter is passed in individual .map_dataframe functions? I did not explore every combination, this is a bit hacky and inconsistently working in anyway.
it is impossible to control the zorder in which the two plots are overlaid (no matter in which order I call g.map(sns.pointplot, x, y1) and g.map(sns.pointplot, x, y2), the order between y1 and y2 will always be the same!!)
EDIT this is at least the case for pointplot, see issue #2339
Ideally, the catplot/lmplot/relplot/displot functions should allow a grid argument (equivalent to the ax argument of the other seaborn functions)!
You cannot pass an ax= parameter to catplot. You will need to create the FacetGrid "by hand".
Something like this: (untested because you did not provide data)
g = sns.FacetGrid(data=indata, hue="Group", col ='Task',
palette=["skyblue","salmon"])
g.map(sns.boxplot, x="Emotion", y="Threshold", showmeans='True', meanline = 'True',
height=6, aspect=0.8, boxprops={'facecolor':'None'}, edgecolor='gray')
g.map(sns.swarmplot, x="Emotion", y="Threshold")
Related
I have a dataframe 'Spreads' where one of the columns is 'HY_OAS'. My goal is to draw a horizontal line (basically representing a range of values for 'HY_OAS') and plot the column mean on that line. In addition, I wanted the x axis min/max to be the min/max for that column and I'd like to include text boxes annotating the min/max. The problem is I'm not sure how to proceed because all I have is the below. Thanks for any and all help. The goal is the second image and the current code is the first image.
fig8 = px.scatter(x=[Spreads['HY_OAS'].mean()], y=[0])
fig8.update_xaxes(visible=True,showticklabels=False,range=[Spreads['HY_OAS'].min(),Spreads['HY_OAS'].max()])
fig8.update_yaxes(visible=True,showticklabels=False, range=[0,0])
Following what you describe and what you have coded
generate some sample data in a dataframe
scatter values along x-axis and use constant for y-axis
add mean marker
format figure
add required annotations
import numpy as np
import plotly.express as px
import pandas as pd
# simulate some data
Spreads = pd.DataFrame({"HY_OAS": np.sin(np.random.uniform(0, np.pi * 2, 50))})
# scatter values along x-axis and and larger point for mean
fig = px.scatter(Spreads, x="HY_OAS", y=np.full(len(Spreads), 0)).add_traces(
px.scatter(x=[Spreads.mean()], y=[0])
.update_traces(marker={"color": "red", "size": 20})
.data
)
# fix up figure config
fig.update_layout(
xaxis_visible=False,
yaxis_visible=False,
showlegend=False,
paper_bgcolor="rgba(0,0,0,0)",
plot_bgcolor="rgba(0,0,0,0)",
)
# finally required annootations
fig.add_annotation(x=Spreads["HY_OAS"].mean(), y=0, text=Spreads["HY_OAS"].mean().round(4))
fig.add_annotation(x=Spreads["HY_OAS"].min(), y=0, text=Spreads["HY_OAS"].min().round(2), showarrow=False, xshift=-20)
fig.add_annotation(x=Spreads["HY_OAS"].max(), y=0, text=Spreads["HY_OAS"].max().round(2), showarrow=False, xshift=20)
straight line
build base figure as follows
then same code to add annotations and configure layout
fig = px.line(x=[Spreads["HY_OAS"].min(), Spreads["HY_OAS"].max()], y=[0,0]).add_traces(
px.scatter(x=[Spreads.mean()], y=[0])
.update_traces(marker={"color": "red", "size": 20})
.data
)
I plotted a scatterplot with seaborn library and I want to change the legend text but dont know how to do that.
example:
The following is iris dataset with species columns encoded in 0/1/2 as per species.
plt.figure(figsize=(8,8))
pl = sns.scatterplot(x='petal_length', y ='petal_width', hue='Species', data=data, s=40,
palette='Set1', legend='full')
I want to change the legends text from [0, 1, 2] to ['setosa', 'versicolor', 'virginica'].
can anybody help.
First, Seaborn (and Matplotlib) usually picks up the labels to put into the legend for hue from the unique values of the array you provide as hue. So as a first step, check that the column Species in your dataframe actually contains the values "setosa", "versicolor", "virginica". If not, one solution is to temporarily map them to other values, for the purpose of plotting:
legend_map = {0: 'setosa',
1: 'versicolor',
2: 'virginica'}
plt.figure(figsize=(8,8))
ax = sns.scatterplot(x=data['petal_length'], y =data['petal_width'], hue=data['species'].map(legend_map),
s=40, palette='Set1', legend='full')
plt.show()
Alternatively, if you want to directly manipulate the plot information and not the underlying data, you can do by accessing the legend names directly:
plt.figure(figsize=(8,8))
ax = sns.scatterplot(x='petal_length', y ='petal_width', hue='species', data=data, s=40,
palette='Set1', legend='full')
l = ax.legend()
l.get_texts()[0].set_text('Species') # You can also change the legend title
l.get_texts()[1].set_text('Setosa')
l.get_texts()[2].set_text('Versicolor')
l.get_texts()[3].set_text('Virginica')
plt.show()
This methodology allows you to also change the legend title, if need be.
Function [Yupper, Ylower] = Segmentation(A)
A=imread('g16.BMP');
AR=A(:,:,1);
[rows, columns] = size(AR);
avgs = mean(AR(50:165,:), 2);
avgs2 = mean(AR(165:315,:), 2);
[~,ind]= max(abs(diff(avgs)));
[~,ind2]= max(abs(diff(avgs2)));
figure, image(AR,'CDataMapping','scaled'); colormap('gray'); hold on;
Yupper=plot([1 size(AR,2)], [ind+50 ind+50], 'r', 'LineWidth', 2);
Ylower=plot([1 size(AR,2)], [ind2+165 ind2+165], 'g', 'LineWidth', 2);
end
Above is code used to segment grey scale images based on large instensity changes,I'm looking to declare the 2 plotted lines as variables so I can carry out future processing. However, If I type in whos , the lines appear as follows in the table;
YL 1x1 112 matlab.graphics.chart.primitive.Line
YU 1x1 112 matlab.graphics.chart.primitive.Line
if I remove the the plot commands I receive errors associated with brackets etc and if I manipulate the code any further it seems to mess up the correctly plotted data, can anyone help?
If what you want is to delete specific data for a figure you can do it accessing the Xdata and Ydata properties of the figure handle. You can do it in Matlab R2014a or older by:
set(YL,'XData',[])
set(YL,'YData',[])
or in Matlab R2014b or newer by:
YL.XData=[];
YL.YData=[];
Example in R2013b:
clear;clc;
y=rand(20,1)*20;
x=1:20;
img=rand(20,20);
figure
hold on
image(img,'CDataMapping','scaled'); colormap('gray');
hp=plot(x,y);
% emulating your code delay
pause(2);
set(hp,'XData',[])
set(hp,'YData',[])
I have a 3D array, of which the first two dimensions are spatial, so say (x,y). The third dimension contains point-specific information.
print H.shape # --> (200, 480, 640) spatial extents (200,480)
Now, by selecting a certain plane in the third dimension, I can display an image with
imdat = H[:,:,100] # shape (200, 480)
img = ax.imshow(imdat, cmap='jet',vmin=imdat.min(),vmax=imdat.max(), animated=True, aspect='equal')
I want to now rotate the cube, so that I switch from (x,y) to (y,x).
H = np.rot90(H) # could also use H.swapaxes(0,1) or H.transpose((1,0,2))
print H.shape # --> (480, 200, 640)
Now, when I call:
imdat = H[:,:,100] # shape (480,200)
img.set_data(imdat)
ax.relim()
ax.autoscale_view(tight=True)
I get weird behavior. The image along the rows displays the data till 200th row, and then it is black until the end of the y-axis (480). The x-axis extends from 0 to 200 and shows the rotated data. Now on, another rotation by 90-degrees, the image displays correctly (just rotated 180 degrees of course)
It seems to me like after rotating the data, the axis limits, (or image extents?) or something is not refreshing correctly. Can somebody help?
PS: to indulge in bad hacking, I also tried to regenerate a new image (by calling ax.imshow) after each rotation, but I still get the same behavior.
Below I include a solution to your problem. The method resetExtent uses the data and the image to explicitly set the extent to the desired values. Hopefully I correctly emulated the intended outcome.
import matplotlib.pyplot as plt
import numpy as np
def resetExtent(data,im):
"""
Using the data and axes from an AxesImage, im, force the extent and
axis values to match shape of data.
"""
ax = im.get_axes()
dataShape = data.shape
if im.origin == 'upper':
im.set_extent((-0.5,dataShape[0]-.5,dataShape[1]-.5,-.5))
ax.set_xlim((-0.5,dataShape[0]-.5))
ax.set_ylim((dataShape[1]-.5,-.5))
else:
im.set_extent((-0.5,dataShape[0]-.5,-.5,dataShape[1]-.5))
ax.set_xlim((-0.5,dataShape[0]-.5))
ax.set_ylim((-.5,dataShape[1]-.5))
def main():
fig = plt.gcf()
ax = fig.gca()
H = np.zeros((200,480,10))
# make distinguishing corner of data
H[100:,...] = 1
H[100:,240:,:] = 2
imdat = H[:,:,5]
datShape = imdat.shape
im = ax.imshow(imdat,cmap='jet',vmin=imdat.min(),
vmax=imdat.max(),animated=True,
aspect='equal',
# origin='lower'
)
resetExtent(imdat,im)
fig.savefig("img1.png")
H = np.rot90(H)
imdat = H[:,:,0]
im.set_data(imdat)
resetExtent(imdat,im)
fig.savefig("img2.png")
if __name__ == '__main__':
main()
This script produces two images:
First un-rotated:
Then rotated:
I thought just explicitly calling set_extent would do everything resetExtent does, because it should adjust the axes limits if 'autoscle' is True. But for some unknown reason, calling set_extent alone does not do the job.
I need to add a shape to a preexisting image generated using a pyplot (plt). The best way I know of to generate basic shapes quickly is using Imagedraw's predefined shapes. The original data has points with corresponding colors in line_holder and colorholder. I need to add a bounding box (or in this case ellipse) to the plot to make it obvious to the user whether the data is in an acceptable range.
import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection
from PIL import Image
...
lines = LineCollection(mpl.line_holder, colors=mpl.colorholder , linestyle='solid')
plt.axes().add_collection(lines)
plt.axes().set_aspect('equal', 'datalim')
plt.axes().autoscale_view(True,True,True)
plt.draw()
plt.show()
I tried inserting this before the show():
image = Image.new('1',(int(ceil(disc/conv))+2,int(ceil(disc/conv))+1), 1)
draw = ImageDraw.Draw(image)
box=(1, 1, int(ceil(disc/conv)), int(ceil(disc/conv))) #create bounding box
draw.ellipse(box, 1, 0) #draw circle in black
but I cannot find a way to then add this ellipse to the pyplot. Does anyone know how one would go about getting the images together? If it is not possible to add an imagedraw object to a pyplot, are there good alternatives for performing this type of operation?
Matplotlib has several patches (shapes) that appear to meet your needs (and remove PIL as a dependency). They are documented here. A helpful example using shapes is here.
To add an ellipse to a plot, you first create a Ellipse patch and then add that patch to the axes you're currently working on. Beware that Circle's (or Ellipse's with equal minor radii) will appear elliptical if your aspect ratio is not equal.
In your snippet you call plt.axes() several times. This is unnecessary, as it is just returning the current axes object. I think it is clearer to keep the axes object and directly operate on it rather than repeatedly getting the same object via plt.axes(). As far as axes() is used in your snippet, gca() does the same thing. The end of my script demonstrates this.
I've also replaced your add_collection() line by a plotting a single line. These essentially do the same thing and allows my snippet to be executed as a standalone script.
import matplotlib.pyplot as plt
import matplotlib as mpl
# set up your axes object
ax = plt.axes()
ax.set_aspect('equal', 'datalim')
ax.autoscale_view(True, True, True)
# adding a LineCollection is equivalent to plotting a line
# this will run as a stand alone script
x = range(10)
plt.plot( x, x, 'x-')
# add and ellipse to the axes
c = mpl.patches.Ellipse( (5, 5), 1, 6, angle=45)
ax.add_patch(c)
# you can get the current axes a few ways
ax2 = plt.axes()
c2 = mpl.patches.Ellipse( (7, 7), 1, 6, angle=-45, color='green')
ax2.add_patch(c2)
ax3 = plt.gca()
c3 = mpl.patches.Ellipse( (0, 2), 3, 3, color='black')
ax3.add_patch(c3)
print id(ax), id(ax2), id(ax3)
plt.show()