I am trying to create a rangeSlider that controls the colorbar and bins of a hvplot quadmesh plot of gridded data. Right now I am using cmap and it is wonderful but I need a way to bin and color the data to a 3 color scheme namely,
(min, rangeSlider[0]) = Green labeled Good
(rangeSlider[0], rangeSlider[1]) = Yellow labeled Caution
(rangeSlider[1], max) = Red labeled Dangerous
So I made a couple of attempts but am not sure how to pass a ListedColormap from Matplotlib.colors as well as labels to a "bining" function of the quadmesh hvplot object.
Related
Suppose we have this:
import seaborn as sns
import pandas as pd
import numpy as np
samples = 2**13
data = pd.DataFrame({'Values': list(np.random.normal(size=samples)) + list(np.random.uniform(size=samples)),
'Kind': ['Normal'] * samples + ['Uniform'] * samples})
sns.displot(data, hue='Kind', x='Values', fill=True)
I want my Normal's histogram (or KDE) emphasized. I'd like it in red and non transparent in the background. Uniform should have alpha = .5.
How do I specify these style parameters in a "per hue" manner?
It's possible to do it with two separate histplots on the same Axes, as #Redox suggested. We can basically recreate the same plot, but with fine-grade control over colours and alpha. However I had to explicitly pass the number of bins in to get the same plot as yours. I also needed to define the colour for Uniform otherwise a ghost element would be added to the legend! I used C1, meaning the first default colour.
_, ax = plt.subplots()
sns.histplot(data=data[data.Kind=='Normal'], x="Values", ax=ax, label='Normal', color='tab:red',bins=130,alpha=1)
sns.histplot(data=data[data.Kind=='Uniform'], x="Values", ax=ax, label='Uniform', color='C1',bins=17, alpha=.5)
ax.set_xlabel('')
ax.legend()
Note that if you just want to set the colour without alpha you can already do this on a displot via the palette argument - pass in a dictionary of your unique hue values to colour names. However, the alpha that you pass in must be a scalar. I tried to use this clever answer to set colours as RGBA colours which include alpha, which seems to work with other figure level plots in Seaborn. However, displot overrides this and sets the alpha separately!
How can I draw such a heatmap using the "seaborn.heatmap" function?
The color shades are determined by matrix A and the annotation of each grid is determined by matrix B.
For example, if I get a matrix, I want its color to be displayed according to the z-score of this matrix, but the annotation remains the matrix itself.
I know I should resort to the parameter 'annot_kws', but how exactly should I write the code?
Instead of simply setting annot=True, annot= can be set to a dataframe (or 2D numpy array, or a list of lists) with the same number of rows and columns as the data. That way, the coloring will be applied using the data, and the annotation will come from annot. Seaborn will still take care to use white text for the dark cells and black text for the light ones.
annot_kws= is used to change the text properties, typically the fontsize. But you also could change the font itself, or the alignment if you'd used multiline text.
Here is an example using numbers 1 to 36 as annotation, but the numbers modulo 10 for the coloring. The annot_kws are used to enlarge and rotate the text. (Note that when the annotation are strings, you also need to set the format, e.g. fmt='').
import seaborn as sns
import numpy as np
a = pd.DataFrame({'count': [1, 2, 3]})
matrix_B = np.arange(1, 37).reshape(6, 6) # used for annotations
matrix_A = (matrix_B % 10) # used for coloring
sns.heatmap(data=matrix_A, annot=matrix_B,
annot_kws={'size': 20, 'rotation': 45},
square=True, cbar_kws={'label': 'last digit'})
I'm working with TraMineR and I don't know how to arrange my plot. So basically what i would like to have the legend under the plot and to remove the space between the x and y axis. Any help is welcomed.
The plot:
Sample code:
seqdplot(Activities.seq, with.legend=FALSE)
legend("bottom", legend=attr(Activities.seq, "labels"),
fill=attr(Activities.seq, "cpal"),
inset=-.1, bty="o", xpd=NA, cex=.75,ncol=3)
The family of seqplot functions offers a series of arguments to control the legend as well as the axes. Look at the help page of seqplot (and of plot.stslist.statd for specific seqdplot parameters).
For instance, you can suppress the x-axis with axes=FALSE, and the y-axis with yaxis=FALSE.
To print the legend you can let seqdplot display it automatically using the default with.legend=TRUE option and control it with for examples cex.legend for the font size, ltext for the text. You can also use the ncol argument to set the number of columns in the legend.
The seqplot functions use by default layout to organize the graphic area between the plots and the legend. If you need more fine tuning (e.g. to change the default par(mar=c(5.1,4.1,4.1,2.1)) margins around the plot and the legend), you should create separately the plot(s) and the legend and then organize them yourself using e.g. layout or par(mfrow=...). In that case, the separate graphics should be created by setting with.legend=FALSE, which prevents the display of the legend and disables the automatic use of layout.
The color legend is easiest obtained with seqlegend.
I illustrate with the mvad data that ships with TraMineR. First the default plot with the legend. Note the use of border=NA to suppress the too many vertical black lines.
library(TraMineR)
data(mvad)
mvad.scode <- c("EM", "FE", "HE", "JL", "SC", "TR")
mvad.seq <- seqdef(mvad, 17:86,
states = mvad.scode,
xtstep = 6)
# Default plot with the legend,
seqdplot(mvad.seq, border=NA)
Now, we suppress the x and y axes and modify the display of the legend
seqdplot(mvad.seq, border=NA,
axes=FALSE, yaxis=FALSE, ylab="",
cex.legend=1.3, ncol=6, legend.prop=.11)
Here is how you can control the space between the plot and the x and y axes
seqdplot(mvad.seq, border=NA, yaxis=FALSE, xaxis=FALSE, with.legend=FALSE)
axis(2, line=-1)
axis(1, line=0)
Creating the legend separately and reducing the left, top, and right margins around the legend
op <- par(mar=c(5.1,0.1,0.1,0.1))
seqlegend(mvad.seq, ncol=2, cex=2)
par(op)
The following code:
# in ipython notebook, enable inline plotting with:
# %pylab inline --no-import-all
import matplotlib.pyplot as plt
# create some circles
circle1 = plt.Circle((-.5,0), 1, color='r', alpha=.2)
circle2 = plt.Circle(( .5,0), 1, color='b', alpha=.2)
# add them to the plot (bad form to use ;, but saving space)
# and control the display a bit
ax = plt.gca()
ax.add_artist(circle1); ax.add_artist(circle2)
ax.set_xlim(-2, 2); ax.set_ylim(-2, 2)
ax.set_aspect('equal')
# display it
plt.plot()
Produces the following plot:
I would like to specify the colors of the four regions (1) the background (currently white), (2 and 3) each individual event (the non-overlapping areas, currently blue and red), and (4) the intersection event (currently blended to purple). For example, I might color them red, green, blue, yellow -or- I might give them four different, precisely specified grayscale values (the later is more likely). [The colors will be generated based on characteristics of the underlying data.]
I specifically do not want to use alpha blending to "infer" a color in the intersection. I need to explicitly control the colors of all four regions.
I can think of a few strategies to solve this:
Ask mpl to extract the "primitive" patch objects that make up the three distinctly colored graphical regions (and do something similar to operate on the background) and then color them.
Given the circles, manually compute their intersections and color that intersection (somehow). Going point by point seems ugly.
Thanks!
I'm not 100% sure but I think matplotlib does not have the functionality to intersect polygons. But you could use shapely:
import shapely.geometry as sg
import matplotlib.pyplot as plt
import descartes
# create the circles with shapely
a = sg.Point(-.5,0).buffer(1.)
b = sg.Point(0.5,0).buffer(1.)
# compute the 3 parts
left = a.difference(b)
right = b.difference(a)
middle = a.intersection(b)
# use descartes to create the matplotlib patches
ax = plt.gca()
ax.add_patch(descartes.PolygonPatch(left, fc='b', ec='k', alpha=0.2))
ax.add_patch(descartes.PolygonPatch(right, fc='r', ec='k', alpha=0.2))
ax.add_patch(descartes.PolygonPatch(middle, fc='g', ec='k', alpha=0.2))
# control display
ax.set_xlim(-2, 2); ax.set_ylim(-2, 2)
ax.set_aspect('equal')
plt.show()
I am plotting a histogram using
plt.imshow(hist2d, norm = LogNorm(), cmap = gray)
where hist2d is a matrix of histogram values. This works fine except for elements in hist2d that are zero. In particular, I obtain the following image
but would like the white patches to be black.
Thank you!
Here's an alternative method that does not require you to muck with your data by setting a rgb value for bad pixels.
import copy
data = np.arange(25).reshape((5,5))
my_cmap = copy.copy(matplotlib.cm.get_cmap('gray')) # copy the default cmap
my_cmap.set_bad((0,0,0))
plt.imshow(data,
norm=matplotlib.colors.LogNorm(),
interpolation='nearest',
cmap=my_cmap)
The problem is that bins with 0 can not be properly log normalized so they are flagged as 'bad', which are mapped to differently. The default behavior is to not draw anything on those pixels. You can also specify what color to draw pixels that are over or under the limits of the color map (the default is to draw them as the highest/lowest color).
If you're happy with the colour scaling as is, and simply want the 0 values to be black, I'd simply change the input matrix so that the 0s are replaced by the next smallest value:
import matplotlib.pyplot as plt
import matplotlib.cm, matplotlib.colors
import numpy
hist2d = numpy.arange(9).reshape(3,3)
plt.imshow(numpy.maximum(hist2d, sorted(hist2d.flat)[1]),
interpolation='nearest',
norm = matplotlib.colors.LogNorm(),
cmap = matplotlib.cm.gray)
produces
Was this generated with the matplotlib hist2d function?
All you need to do is go through the matrix and set some arbitrary floor value, then make sure to plot this with fixed limits
for f in hist2d:
f += 1e-3
then when you show the figure, all of the whitespace will now be at the floor value, and will show up on the lognormal plot . However, if you are letting hist2d automatically pick the scaling for you, it will want to use the 1e-3 floor value as the minimum. To avoid this, you need to set vmin and vmax values in hist2d
hist2d(x,y,bins=40, norm=LogNorm(), vmin=1, vmax=1e4)