In order to create an Hasse Diagram like the following
One is using the following libraries
library(rPref)
library(Rgraphviz)
One is taking a small sample of one's data
df <- data[1:10,]
Then creating the preferences
pref <- low(time) * low(MAPE)
And the Better-Than-Graph (BTG)
btg <- get_btg(df, pref)
In order to display the labels for the nodes containing relevant values, one is creating the labels as following
labels <- paste0(df$time, "\n", df$MAPE)
However, when one builds the visualization with
plot_btg(df, pref, labels)
One can only see the first label, instead of the two. Here is what one is seeing
Passing use_dot=FALSE solved the problem
plot_btg(df, pref, labels, use_dot = FALSE)
I'm working on a package for Julia with the goal of doing quick plots using Vega-Lite as backend.
As people familiar with Matplotlib know, it is very common to have different sets for vectors, and plot all of them in the same figure, each with it's own label. For example:
x = range(0,10)
y = np.random.rand(10)
w = range(0,5)
z = np.random.rand(5)
plt.plot(x,y,label = 'y')
plt.plot(w,z,label = 'z')
plt.legend()
What I'd like to know is how can I do something similar, but using Vega-Lite (or Altair).
I know that I can do two separate plots and then add one over another. My problem is mainly about how to get the legends to work, since to get a legend, one usually needs another field
such as "color", pointing to another field in the dataframe.
I've seen similar posts, but dealing with the question of posting data from different columns. The answer to this case is basically to use the Fold Transform. But in my question this doesn't quite work, because I'm more interested in starting from two different plots, possibly using two different datasets, so "merging" the datasets is not a good solution.
You can take advantage of the fact that in composite charts, Vega-Lite uses shared scales by default. If you assign the color, shape, strokeDash, etc. to a unique value for each layer, an appropriate legend will be generated automatically.
Here is an example, using Altair to generate the Vega-Lite specification:
import pandas as pd
import numpy as np
import altair as alt
x = np.linspace(0, 10)
df1 = pd.DataFrame({
'x': x,
'y': np.sin(x)
})
df2 = pd.DataFrame({
'x': x,
'y': np.cos(x)
})
chart1 = alt.Chart(df1).transform_calculate(
label='"sine"'
).mark_line().encode(
x='x',
y='y',
color='label:N'
)
chart2 = alt.Chart(df2).transform_calculate(
label='"cosine"'
).mark_line().encode(
x='x',
y='y',
color='label:N'
)
alt.layer(chart1, chart2)
I am trying to create a plot in Octave (using v4.4.1 on Windows) using plotyy and putting the legend outside the plot (because the data covers all the usable space inside the graph). The following MVCE should reproduce the issue fairly well:
% Generate some random data to reproduce the issue
data = rand(1000,10);
data(:,1:8) = data(:,1:8)-0.5;
data(:,9:10) = data(:,9:10)+30;
timedate = linspace(737310,737313,size(data,1));
data_labels={'1';'2';'3';'4';'5';'6';'7';'8';'9';'10'};
% Plot the data
figure('Name','MVCE','Position',[300 200 1000 600])
[ax,h1,h2] = plotyy(timedate,data(:,1:8),timedate,data(:,9:10));
set(h2,'Visible','on');
datetick(ax(1),'x','HH:MM:SS')
datetick(ax(2),'x','HH:MM:SS')
ylim(ax(1),[-1 1])
ylim(ax(2),[20 50])
xlabel('Date & time')
ylabel(ax(1),'Something')
ylabel(ax(2),'Something else')
title('plotyy graph with legend problem')
[hl,hlo] = legend([h1;h2],data_labels,'location','eastoutside');
grid on
This the output of the code using the gnuplot graphics toolkit:
As you can see, the legend does not go outside the plot, and the second y axis is not visible (it looks like part of the plot is actually truncated).
I have tried using the qt and fltk graphics toolkits, which give issues of their own:
With qt graphics toolkit
With fltk graphics toolkit
Can anoybody suggest a fix or at least workaround? Does the same issue also happen in MATLAB or is it Octave-specific?
EDIT
Using the suggestion in Tasos' answer, I managed to almost make it work with gnuplot:
% Plot the data
figure('Name','MVCE','Position',[300 200 1000 600])
[ax,h1,h2] = plotyy(timedate,data(:,1:8),timedate,data(:,9:10));
set(h2,'Visible','on');
datetick(ax(1),'x','HH:MM:SS')
datetick(ax(2),'x','HH:MM:SS')
ylim(ax(1),[-1 1])
ylim(ax(2),[20 50])
ax1Pos = get(ax(1), 'position');
ax2Pos = get(ax(2), 'position');
ax1Pos(3) = ax1Pos(3) * 0.73;
ax2Pos(3) = ax2Pos(3) * 0.73;
set(ax(1), 'position', ax2Pos);
set(ax(2), 'position', ax2Pos);
xlabel('Date & time')
ylabel(ax(1),'Something')
ylabel(ax(2),'Something else')
title('plotyy graph with legend problem')
[hl,hlo] = legend([h1;h2],data_labels,'location','eastoutside');
pos = get(hl,'Position');
pos(1) = 0.9;
set(hl,'Position',pos)
grid on
Which produces:
Apart from the fact that the legend overlays with the second y axis label (which it doesn't on my screen, only when printing to jpg), the problem is that Octave appears to plot two legends on top of each other for some reason: one with the first set of data attached to the first set of axes, and one with the complete set of data, for both axes right on top of the first legend. This is obviously wrong, and trying to set the Visible property of hl to off deletes both legends, not just the one.
UPDATED: deals with both legend placement and OpenGL precision affecting graph.
Regarding the problem of the legend not appearing exactly in the position you want it to, you can manually adjust the position of all axes involved in a figure, to place them exactly where you want.
Regarding the problem of OpenGL being unable to deal with the precision involved when adding small numbers to a large number, plot the graph with only the small numbers involved, and then simply adjust the xticklabels to correspond to the numbers you desire.
Full code below:
% Generate some random data to reproduce the issue
data = rand(1000,10);
data(:,1:8) = data(:,1:8)-0.5;
data(:,9:10) = data(:,9:10)+30;
t_offset = 737310;
timedate = linspace(0,3,size(data,1));
data_labels={'1';'2';'3';'4';'5';'6';'7';'8';'9';'10'};
% Plot the data
figure('Name','MVCE','Position',[300 200 1000 600])
[ax,h1,h2] = plotyy(timedate,data(:,1:8),timedate,data(:,9:10));
set(h2,'Visible','on');
ylim(ax(1),[-1 1])
ylim(ax(2),[20 50])
ylabel(ax(1),'Something')
ylabel(ax(2),'Something else')
title('plotyy graph with legend problem')
[hl,hlo] = legend([h1;h2],data_labels,'location','eastoutside');
set(hl, 'position', get(hl, 'position') .* [0.975, 1, 0.975, 1] )
grid on
ax1Pos = get(ax(1), 'position'); ax2Pos = get(ax(2), 'position');
ax1Pos(3) = ax1Pos(3) * 0.95; ax2Pos(3) = ax2Pos(3) * 0.95;
set(ax(1), 'position', ax2Pos); set(ax(2), 'position', ax2Pos);
XTicks = get(ax(1), 'xtick');
set(ax(1), 'xticklabel', datestr(XTicks + t_offset, 'HH:MM:SS'))
xlabel('Date & time')
set(ax(2), 'xtick', []);
Output:
I'm trying to do 4 plots using for loop.But I'm not sure how to do it.how can I display the plots one by one orderly?or save the figure as png?
Here is my code:
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from astropy.io import fits
import pyregion
import glob
# read in the image
xray_name = glob.glob("*.fits")
for filename in xray_name:
f_xray = fits.open(filename)
#name = file_name[:-len('.fits')]
try:
from astropy.wcs import WCS
from astropy.visualization.wcsaxes import WCSAxes
wcs = WCS(f_xray[0].header)
fig = plt.figure()
ax = plt.subplot(projection=wcs)
fig.add_axes(ax)
except ImportError:
ax = plt.subplot(111)
ax.imshow(f_xray[0].data, cmap="summer", vmin=0., vmax=0.00038, origin="lower")
reg_name=glob.glob("*.reg")
for i in reg_name:
r =pyregion.open(i).as_imagecoord(header=f_xray[0].header)
from pyregion.mpl_helper import properties_func_default
# Use custom function for patch attribute
def fixed_color(shape, saved_attrs):
attr_list, attr_dict = saved_attrs
attr_dict["color"] = "red"
kwargs = properties_func_default(shape, (attr_list, attr_dict))
return kwargs
# select region shape with tag=="Group 1"
r1 = pyregion.ShapeList([rr for rr in r if rr.attr[1].get("tag") == "Group 1"])
patch_list1, artist_list1 = r1.get_mpl_patches_texts(fixed_color)
r2 = pyregion.ShapeList([rr for rr in r if rr.attr[1].get("tag") != "Group 1"])
patch_list2, artist_list2 = r2.get_mpl_patches_texts()
for p in patch_list1 + patch_list2:
ax.add_patch(p)
#for t in artist_list1 + artist_list2:
# ax.add_artist(t)
plt.show()
the aim of the code is to plot a region on fits file image,if there is a way to change the color of the background image to white and the brighter (centeral region) as it is would be okay.Thanks
You are using colormap "summer" with provided limits. It is not clear to me what you want to achieve since the picture you posted looks more or less digital black and white pixelwise.
In matplotlib there are built in colormaps, and all of those have a reversed twin.
'summer' has a reversed twin with 'summer_r'
This can be picked up in the mpl docs at multiple spots, like colormap example, or SO answers like this.
Hope that is what you are looking for. For the future, when posting code like this, try to remove all non relevant portions as well as at minimum provide a description of the data format/type. Best is to also include a small sample of the data and it's structure. A piece of code only works together with a set of data, so only sharing one is only half the problem formulation.
I'm trying to visualize a high-dim point set x (here of dim (6 x 42)) in a series of 2D scatter plots (x[1] vs x[2] etc.) using bokeh. [edit2] See this nice example from scikit-opt as a reference. When x[1] occurs in two plots it should interact with the same range and the plots should rescale simultaneously. I have accomplished this, but I don't get it to scale correctly. Here's a minimal example: [edit2]
import bokeh
import bokeh.io
import numpy as np
import bokeh.plotting
bokeh.io.output_notebook()
# That's my fictional dataset
x = np.random.randn(6, 42)
x[2] *= 10
# Build the pairwise scatter plots
kw = dict(plot_width=165, plot_height=165)
# `ranges` stores the range in each dimension,
# used as both, x- and y-range depending on
# where the variable is.
figs, ranges = {}, {}
for r, row in enumerate(x):
for c, col in enumerate(x):
if r is not c:
fig = bokeh.plotting.figure(
x_range=ranges.get(c, None), y_range=ranges.get(r, None),
**kw)
fig.scatter(x=col, y=row)
fig.xaxis.axis_label = f'Dim {c}'
fig.yaxis.axis_label = f'Dim {r}'
if c not in ranges:
ranges[c] = fig.x_range
if r not in ranges:
ranges[r] = fig.y_range
figs[f'{r}_{c}'] = fig
else:
break
# Setup the plotting layout
plots = [[]]
for r, row in enumerate(x):
for c, col in enumerate(x):
if r is not c:
plots[-1].append(figs[f'{r}_{c}'])
else:
plots.append([])
break
staircase = bokeh.layouts.gridplot(plots, **kw)
bokeh.plotting.show(staircase)
.. into an ipython notebook (>=py3.6), bokeh sets the scale for dim 1, and 2 correctly. Then, it starts to set the scale for the following dimensions as in dim 2. Notice that I scaled dim 2 10-fold to make this point.
Interactively, I can rescale the plot back to optimal settings. However, I'd like to do that by default. What options do I have inside bokeh to rescale? I played a bit with fig.xaxis.bounds, but unsuccessfully. Thanks for your help!
Epilogue:
Following #bigreddot's answer, I added the lines:
for i, X in enumerate(x):
ranges[i].start = X.min()
ranges[i].end = X.max()
to fix the starting ranges. I still think that the behaviour is a bug.
From your code and description I still can't quite tell what you are hoping to accomplish. [1] But I will state that the default DataRange1d ranges that plot's use automatically make space for all renderers, across all plots they are shared by. In this sense, I see exactly what I would expect when I run your code. If you want something different, there are two things you could control:
DataRange1d has a .renderers property. If you only want the "auto" ranging to be over a subset of the renderers, then you can explicitly set this property to the list you want. Renderers are returned by the glyph functions, e.g. fig.scatter
Don't use the "auto" ranges. You can also set the x_range and y_range yourself to be Range1d objects. These have start and end properties that you can set, and these will be the definite bounds of the range, e.g. x-range=Range1d(0, 10)
[1] The ranges are linked in what I would consider an odd way, and I can't tell if that is intended. But that is a result of your looping/python code and not Bokeh.