I'm trying to make a graph much like the Multi-Line Tooltip Example. However I want to make two modifications.
First of all, I want to be able to zoom in to a portion of the data. I fixed that using alt.selection(type="interval", encodings=["x"], bind="scales") and a transform_filter. So far no problem.
The problem is that the text labels near the points are overlapping because the lines are close together. Therefore I would like to move the labels to a fixed position within the axes along the top. Is it possible to put the labels at a fixed position within the axes, even when zooming in on the graph (see mockups below)? The problem is that when you zoom in both the x and the y domains change, so the positions of the labels should be expressed as a fraction of the domains.
Another solution I could accept is when the selected value is appended to the legend labels, or some other label outside the plot area.
Mock up of the full view:
Mock up of the zoomed view:
You can control the text position with the x and y encodings. Here is an example of placing text at the top of the axis:
import altair as alt
import pandas as pd
import numpy as np
data = pd.DataFrame({
'x': np.arange(1, 21),
'y': np.random.randint(0, 20, 20),
})
points = alt.Chart(data).mark_point().encode(
x='x',
y='y'
)
text = points.mark_text(baseline='top').encode(
y=alt.value(0), # pixels from top
text='y'
)
points + text
Related
I am unsure how to customize scatterplot marker styles in Plotly scatterplots.
Specifically, I have a column predictions that is 0 or 1 (1 represents an unexpected value) and even though I used the symbol parameter in px.scatter_3d to indicate the unexpected value through varying point shape (diamond for 1 and circle for 0), the difference is very subtle and I want it to be more dramatic. I was envisioning something like below (doesn't need to be exactly this), but something along the lines of the diamond shaped points have a different outline colors or an additional shape/bubble around it. How would I do this?
Additionally, I have a set column which can take on one of two values, set A or set B. I used the color parameter inside px.scatter_3d and made that equal to set so the points are colored according to which set it came from. While it is doing what I asked, I don't want the colors to be blue and red, but any two colors I specify. How would I be able to this (let's say I want the colors to be blue and orange instead)? Thank you so much!
Here is the code I used:
fig = px.scatter_3d(X_combined, x='x', y='y', z='z',
color='set', symbol='predictions', opacity=0.7)
fig.update_traces(marker=dict(size=12,
line=dict(width=5,
color='Black')),
selector=dict(mode='markers'))
You can use multiple go.Scatter3d() statements and gather them in a list to format each and every segment or extreme values more or less exactly as you'd like. This can be a bit more demanding than using px.scatter_3d(), but it will give you more control. The following plot is produced by the snippet below:
Plot:
Code:
import plotly.graph_objects as go
import numpy as np
import pandas as pd
# sample data
t = np.linspace(0, 10, 50)
x, y, z = np.cos(t), np.sin(t), t
# plotly data
data=[go.Scatter3d(x=[x[2]], y=[y[2]], z=[z[2]],mode='markers', marker=dict(size=20), opacity=0.8),
go.Scatter3d(x=[x[26]], y=[y[26]], z=[z[26]],mode='markers', marker=dict(size=30), opacity=0.3),
go.Scatter3d(x=x, y=y, z=z,mode='markers')]
fig = go.Figure(data)
fig.show()
How you identify the different segmens, whether it be max or min values will be entirely up to you. Anyway, I hope this approach will be useful!
Instead of many entries within a legend I'd like to add one text line which describes multiple lines. I'm looking to an automatic way to do this, instead of fiddling around with the coordinates of the according text object.
Right now I'm using a function which takes the coordinates at which the text should start, a list of strings and a list of colors. Then the function assembles the strings using the colors. Is there a way to get the coordinates of an empty legend string (I just plot nothing with an empty label for that)?
I tried something like this:
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
x = np.linspace(0,6.19, 100)
y = np.sin(x)
ax = plt.gca()
p1 = ax.plot(x,y, label="test")
# list of text objects
text = [mpl.text.Text(0,0,'$a=$'), mpl.text.Text(0,0,'$2$')]
# HPacker of text objects
# right way to get them into the legend?
hp = mpl.offsetbox.HPacker(children=text)
lgd = plt.legend([p1,??], ['legend1',??])
plt.show()
Couldn't find out how to add the HPacker to the legend.
Is the HPacker the right way to assemble (text-)objects horizontally for the usage as an item within a legend?
Using matplotlib 2.2.2 with gridspec in Python 3.6.5, I created a huge plot for a research paper with several subplots. The axes objects are stored in a dictionary called axes. This dictionary is passed to the function adjust_xticklabels(), which is supposed to align the first xticklabel slightly to the right and the last xticklabel slightly to the left in each subplot, such that the xticklabels of neighbouring plots dont get in the way of each other. The function is defined as:
def adjust_xticklabels(axes, rate = 0.1):
for ax in axes.values():
left, right = ax.get_xlim() # get boundaries
dist = right-left # get distance
xtl = ax.get_xticklabels()
if len(xtl) > 1:
xtl[0].set_position((left + rate*dist, 0.)) # (x, y), shift right
xtl[-1].set_position((right - rate*dist, 0.)) # shift left
Calling it has no effect. Of course I also tried it with ridiculously high values. However, is has an effect in y-direction, for instance in case of setting xtl[0].set_position((0.3, 0.3)).
A simple reproduction:
ax = plt.subplot(111)
ax.plot(np.arange(10))
xtl = ax.get_xticklabels()
xtl[4].set_position((0.3, 0.3)) # wlog, 4 corresponds to 6
I spent quite a while on trying to figure out if this is a feature or a bug. Did I miss something or is this a bug? Is there any other way to do the same thing?
This is a feature, no bug. The ticklabels are positionned at drawtime to sit at the correct locations according to the ticker in use. This ensures that the label always sits where the corresponding tick is located. If you change the limits, move or zoom the plot, the label always follows those changes.
You are usually not meant to change this location, but you may, by adding a custom transform to it. This is described in
Moving matplotlib xticklabels by pixel value. The general idea is to set a translating transformation on the label. E.g. to translate the second label by 20 pixels to the right,
import matplotlib.transforms as mtrans
# ...
trans = mtrans.Affine2D().translate(20, 0)
label = ax.get_xticklabels()[1]
label.set_transform(label.get_transform()+trans)
I have a pair of lists of numbers representing points in a 2-D space, and I want to represent the y/x ratios for these points as a 1-dimensional heatmap, with a diverging color map centered around 1, or the logs of my ratios, with a diverging color map centered around 0.
How do I do that?
My current attempt (borrowing somewhat from Heatmap in matplotlib with pcolor?):
from matplotlib import numpy as np
import matplotlib.pyplot as plt
# There must be a better way to generate arrays of random values
x_values = [np.random.random() for _ in range(10)]
y_values = [np.random.random() for _ in range(10)]
labels = list("abcdefghij")
ratios = np.asarray(y_values) / np.asarray(x_values)
axis = plt.gca()
# I transpose the array to get the points arranged vertically
heatmap = axis.pcolor(np.log2([ratios]).T, cmap=plt.cm.PuOr)
# Put labels left of the colour cells
axis.set_yticks(np.arange(len(labels)) + 0.5, minor=False)
# (Not sure I get the label order correct...)
axis.set_yticklabels(labels)
# I don't want ticks on the x-axis: this has no meaning here
axis.set_xticks([])
plt.show()
Some points I'm not satisfied with:
The coloured cells I obtain are horizontally-elongated rectangles. I would like to control the width of these cells and obtain a column of cells.
I would like to add a legend for the color map. heatmap.colorbar = plt.colorbar() fails with RuntimeError: No mappable was found to use for colorbar creation. First define a mappable such as an image (with imshow) or a contour set (with contourf).
One important point:
matplotlib/pyplot always leaves me confused: there seems to be a lot of ways to do things and I get lost in the documentation. I never know what would be the "clean" way to do what I want: I welcome suggestions of reading material that would help me clarify my very approximative understanding of these things.
Just 2 more lines:
axis.set_aspect('equal') # X scale matches Y scale
plt.colorbar(mappable=heatmap) # Tells plt where it should find the color info.
Can't answer your final question very well. Part of it is due to we have two branches of doing things in matplotlib: the axis way (axis.do_something...) and the MATLAB clone way plt.some_plot_method. Unfortunately we can't change that, and it is a good feature for people to migrate into matplotlib. As far as the "Clean way" is concerned, I prefer to use whatever produces the shorter code. I guess that is inline with Python motto: Simple is better than complex and Readability counts.
I am looping through a bunch of CSV files containing various measurements.
Each file might be from one of 4 different data sources.
In each file, I merge the data into monthly datasets, that I then plot in a 3x4 grid. After this plot has been saved, the loop moves on and does the same to the next file.
This part I got figured out, however I would like to add a visual clue to the plots, as to what data it is. As far as I understand it (and tried it)
plt.subplot(4,3,1)
plt.hist(Jan_Data,facecolor='Red')
plt.ylabel('value count')
plt.title('January')
does work, however this way, I would have to add the facecolor='Red' by hand to every 12 subplots. Looping through the plots wont work for this situation, since I want the ylabel only for the leftmost plots, and xlabels for the bottom row.
Setting facecolor at the beginning in
fig = plt.figure(figsize=(20,15),facecolor='Red')
does not work, since it only changes the background color of the 20 by 15 figure now, which subsequently gets ignored when I save it to a PNG, since it only gets set for screen output.
So is there just a simple setthecolorofallbars='Red' command for plt.hist(… or plt.savefig(… I am missing, or should I just copy n' paste it to all twelve months?
You can use mpl.rc("axes", color_cycle="red") to set the default color cycle for all your axes.
In this little toy example, I use the with mpl.rc_context block to limit the effects of mpl.rc to just the block. This way you don't spoil the default parameters for your whole session.
import matplotlib as mpl
import matplotlib.pylab as plt
import numpy as np
np.random.seed(42)
# create some toy data
n, m = 2, 2
data = []
for i in range(n*m):
data.append(np.random.rand(30))
# and do the plotting
with mpl.rc_context():
mpl.rc("axes", color_cycle="red")
fig, axes = plt.subplots(n, m, figsize=(8,8))
for ax, d in zip(axes.flat, data):
ax.hist(d)
The problem with the x- and y-labels (when you use loops) can be solved by using plt.subplots as you can access every axis seperately.
import matplotlib.pyplot as plt
import numpy.random
# creating figure with 4 plots
fig,ax = plt.subplots(2,2)
# some data
data = numpy.random.randn(4,1000)
# some titles
title = ['Jan','Feb','Mar','April']
xlabel = ['xlabel1','xlabel2']
ylabel = ['ylabel1','ylabel2']
for i in range(ax.size):
a = ax[i/2,i%2]
a.hist(data[i],facecolor='r',bins=50)
a.set_title(title[i])
# write the ylabels on all axis on the left hand side
for j in range(ax.shape[0]):
ax[j,0].set_ylabel(ylabel[j])
# write the xlabels an all axis on the bottom
for j in range(ax.shape[1]):
ax[-1,j].set_xlabel(xlabels[j])
fig.tight_layout()
All features (like titles) which are not constant can be put into arrays and placed at the appropriate axis.