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Is there a way to prevent the entire notebook from scrolling, when scrolling over the figure to cycle through the images as shown in this example (and reproduced below): https://matplotlib.org/stable/gallery/event_handling/image_slices_viewer.html
import numpy as np
import matplotlib.pyplot as plt
# Fixing random state for reproducibility
np.random.seed(19680801)
class IndexTracker:
def __init__(self, ax, X):
self.ax = ax
ax.set_title('use scroll wheel to navigate images')
self.X = X
rows, cols, self.slices = X.shape
self.ind = self.slices//2
self.im = ax.imshow(self.X[:, :, self.ind])
self.update()
def on_scroll(self, event):
print("%s %s" % (event.button, event.step))
if event.button == 'up':
self.ind = (self.ind + 1) % self.slices
else:
self.ind = (self.ind - 1) % self.slices
self.update()
def update(self):
self.im.set_data(self.X[:, :, self.ind])
self.ax.set_ylabel('slice %s' % self.ind)
self.im.axes.figure.canvas.draw_idle()
def plot(X):
mpl.rc('image', cmap='gray')
fig, ax = plt.subplots(1, 1)
plot = IndexTracker(ax, X)
fig.canvas.mpl_connect('scroll_event', plot.scroll)
plt.show()
On VS Code, using the,
%matplotlib widget
backend, I call the following function in a new code cell:
X = np.random.rand(20, 20, 40)
plot(X)
I am able to successfully generate an interactive plot in an iPython notebook, whereby scrolling over the figure scrolls through the "image slices". The scroll event only works if I hover the cursor over the plot/figure (as it should), however the entire notebook scrolls as well, thereby moving the cursor out of the figure frame.
Is there a way to prevent the notebook from scrolling when the cursor is hovering over a figure in the cell output?
I'm trying to do something that I'd normally consider trivial but seems to be very difficult in bokeh: Adding a vertical colorbar to a plot and then having the title of the colorbar (a.k.a. the variable behind the colormapping) appear to one side of the colorbar but rotated 90 degrees clockwise from horizontal.
From what I can tell of the bokeh ColorBar() interface (looking at both documentation and using the python interpreter's help() function for this element), this is not possible. In desperation I have added my own Label()-based annotation. This works but is klunky and displays odd behavior when deployed in a bokeh serve situation--that the width of the data window on the plot varies inversely with the length of the title colorbar's title string.
Below I've included a modified version of the bokeh server mpg example. Apologies for its complexity, but I felt this was the best way to illustrate the problem using infrastructure/data that ships with bokeh. For those unfamiliar with bokeh serve, this code snippet needs to saved to a file named main.py that resides in a directory--for the sake of argument let's say CrossFilter2--and in the parent directory of CrossFilter2 one needs to invoke the command
bokeh serve --show CrossFilter2
this will then display in a browser window (localhost:5006/CrossFilter2) and if you play with the color selection widget you will see what I mean, namely that short variable names such as 'hp' or 'mpg' result in a wider data display windows than longer variable names such as 'accel' or 'weight'. I suspect that there may be a bug in how label elements are sized--that their x and y dimensions are swapped--and that bokeh has not understood that the label element has been rotated.
My questions are:
Must I really have to go to this kind of trouble to get a simple colorbar label feature that I can get with little-to-no trouble in matplotlib/plotly?
If I must go through the hassle you can see in my sample code, is there some other way I can do this that avoids the data window width problem?
import numpy as np
import pandas as pd
from bokeh.layouts import row, widgetbox
from bokeh.models import Select
from bokeh.models import HoverTool, ColorBar, LinearColorMapper, Label
from bokeh.palettes import Spectral5
from bokeh.plotting import curdoc, figure, ColumnDataSource
from bokeh.sampledata.autompg import autompg_clean as df
df = df.copy()
SIZES = list(range(6, 22, 3))
COLORS = Spectral5
# data cleanup
df.cyl = df.cyl.astype(str)
df.yr = df.yr.astype(str)
columns = sorted(df.columns)
discrete = [x for x in columns if df[x].dtype == object]
continuous = [x for x in columns if x not in discrete]
quantileable = [x for x in continuous if len(df[x].unique()) > 20]
def create_figure():
xs = df[x.value].tolist()
ys = df[y.value].tolist()
x_title = x.value.title()
y_title = y.value.title()
name = df['name'].tolist()
kw = dict()
if x.value in discrete:
kw['x_range'] = sorted(set(xs))
if y.value in discrete:
kw['y_range'] = sorted(set(ys))
kw['title'] = "%s vs %s" % (y_title, x_title)
p = figure(plot_height=600, plot_width=800,
tools='pan,box_zoom,wheel_zoom,lasso_select,reset,save',
toolbar_location='above', **kw)
p.xaxis.axis_label = x_title
p.yaxis.axis_label = y_title
if x.value in discrete:
p.xaxis.major_label_orientation = pd.np.pi / 4
if size.value != 'None':
groups = pd.qcut(df[size.value].values, len(SIZES))
sz = [SIZES[xx] for xx in groups.codes]
else:
sz = [9] * len(xs)
if color.value != 'None':
coloring = df[color.value].tolist()
cv_95 = np.percentile(np.asarray(coloring), 95)
mapper = LinearColorMapper(palette=Spectral5,
low=cv_min, high=cv_95)
mapper.low_color = 'blue'
mapper.high_color = 'red'
add_color_bar = True
ninety_degrees = pd.np.pi / 2.
color_bar = ColorBar(color_mapper=mapper, title='',
#title=color.value.title(),
title_text_font_style='bold',
title_text_font_size='20px',
title_text_align='center',
orientation='vertical',
major_label_text_font_size='16px',
major_label_text_font_style='bold',
label_standoff=8,
major_tick_line_color='black',
major_tick_line_width=3,
major_tick_in=12,
location=(0,0))
else:
c = ['#31AADE'] * len(xs)
add_color_bar = False
if add_color_bar:
source = ColumnDataSource(data=dict(x=xs, y=ys,
c=coloring, size=sz, name=name))
else:
source = ColumnDataSource(data=dict(x=xs, y=ys, color=c,
size=sz, name=name))
if add_color_bar:
p.circle('x', 'y', fill_color={'field': 'c',
'transform': mapper},
line_color=None, size='size', source=source)
else:
p.circle('x', 'y', color='color', size='size', source=source)
p.add_tools(HoverTool(tooltips=[('x', '#x'), ('y', '#y'),
('desc', '#name')]))
if add_color_bar:
color_bar_label = Label(text=color.value.title(),
angle=ninety_degrees,
text_color='black',
text_font_style='bold',
text_font_size='20px',
x=25, y=300,
x_units='screen', y_units='screen')
p.add_layout(color_bar, 'right')
p.add_layout(color_bar_label, 'right')
return p
def update(attr, old, new):
layout.children[1] = create_figure()
x = Select(title='X-Axis', value='mpg', options=columns)
x.on_change('value', update)
y = Select(title='Y-Axis', value='hp', options=columns)
y.on_change('value', update)
size = Select(title='Size', value='None',
options=['None'] + quantileable)
size.on_change('value', update)
color = Select(title='Color', value='None',
options=['None'] + quantileable)
color.on_change('value', update)
controls = widgetbox([x, y, color, size], width=200)
layout = row(controls, create_figure())
curdoc().add_root(layout)
curdoc().title = "Crossfilter"
You can add a vertical label to the Colorbar by plotting it on a separate axis and adding a title to this axis. To illustrate this, here's a modified version of Bokeh's standard Colorbar example (found here):
import numpy as np
from bokeh.plotting import figure, output_file, show
from bokeh.models import LogColorMapper, LogTicker, ColorBar
from bokeh.layouts import row
plot_height = 500
plot_width = 500
color_bar_height = plot_height + 11
color_bar_width = 180
output_file('color_bar.html')
def normal2d(X, Y, sigx=1.0, sigy=1.0, mux=0.0, muy=0.0):
z = (X-mux)**2 / sigx**2 + (Y-muy)**2 / sigy**2
return np.exp(-z/2) / (2 * np.pi * sigx * sigy)
X, Y = np.mgrid[-3:3:100j, -2:2:100j]
Z = normal2d(X, Y, 0.1, 0.2, 1.0, 1.0) + 0.1*normal2d(X, Y, 1.0, 1.0)
image = Z * 1e6
color_mapper = LogColorMapper(palette="Viridis256", low=1, high=1e7)
plot = figure(x_range=(0,1), y_range=(0,1), toolbar_location=None,
width=plot_width, height=plot_height)
plot.image(image=[image], color_mapper=color_mapper,
dh=[1.0], dw=[1.0], x=[0], y=[0])
Now, to make the Colorbar, create a separate dummy plot, add the Colorbar to the dummy plot and place it next to the main plot. Add the Colorbar label as the title of the dummy plot and center it appropriately.
color_bar = ColorBar(color_mapper=color_mapper, ticker=LogTicker(),
label_standoff=12, border_line_color=None, location=(0,0))
color_bar_plot = figure(title="My color bar title", title_location="right",
height=color_bar_height, width=color_bar_width,
toolbar_location=None, min_border=0,
outline_line_color=None)
color_bar_plot.add_layout(color_bar, 'right')
color_bar_plot.title.align="center"
color_bar_plot.title.text_font_size = '12pt'
layout = row(plot, color_bar_plot)
show(layout)
This gives the following output image:
One thing to look out for is that color_bar_width is set wide enough to incorporate both the Colorbar, its axes labels and the Colorbar label. If the width is set too small, you will get an error and the plot won't render.
As of Bokeh 0.12.10 there is no built in label available for colorbars. In addition to your approach or something like it, another possibility would be a custom extension, though that is similarly not trivial.
Offhand, a colobar label certainly seems like a reasonable thing to consider. Regarding the notion that it ought to be trivially available, if you polled all users about what they consider should be trivially available, there will be thousands of different suggestions for what to prioritize. As is very often the case in the OSS world, there are far more possible things to do, than there are people to do them (less than 3 in this case). So, would first suggest a GitHub Issue to request the feature, and second, if you have the ability, volunteering to help implement it. Your contribution would be valuable and appreciated by many.
I am currently trying to create a digital speedometer. The project has come to a screeching halt due to the problem encountered when trying to control the dial. My variable radial_pos is intended to calculate the exact x,y coordinates of each tick of the speedometer and if the user presses K_UP, radial_pos will increase by 1 to show the next tick of the speedometer. However, I'm afraid its been too many years since I've learned the concept of finding the coordinates of a circle, let alone understand it enough to be able to increment it accordingly. Here is my code so far:
import pygame, sys
from pygame.locals import *
pygame.init()
#Screen Resolution
width = 1080
height = 720
#Instrument Characteristics
color = (255,255,255)
background = (0,0,0)
radius_speedometer = 100
weight_speedometer = radius_speedometer-5
#Window Measurements
screen= pygame.display.set_mode((width,height),0,32)
pos_center_x = width/2
pos_center_y = height/2
#Speedometer Dial Position
pos_speedometer_dial_x = pos_center_x - 60
pos_speedometer_dial_y = pos_center_y +60
#Radial Position (hint: ticks on the clock)
'''
radial_pos = ...
'''
while True:
for event in pygame.event.get():
if event.type==QUIT:
pygame.quit()
sys.exit()
if event.type==KEYDOWN:
if event.key==K_DOWN:
radial_pos=-1
if event.key==K_UP:
radial_pos=+1
if event.type==KEYUP:
if event.key==K_DOWN:
radial_pos=0
if event.key==K_UP:
radial_pos=0
screen.lock()
#Speedometer Ring
pygame.draw.circle(screen, color, (pos_center_x,pos_center_y), radius_speedometer)
pygame.draw.circle(screen, background, (pos_center_x,pos_center_y), weight_speedometer)
#Speedometer Dial
pygame.draw.line(screen, color, (pos_center_x,pos_center_y), (pos_speedometer_dial_x, pos_speedometer_dial_y),5)
screen.unlock()
pygame.display.update()
If anyone has any advice for this particular issue, it would greatly appreciated. Thank you for your time.
It seems I have finally found a solution that works well. Here is the most updated code:
import pygame, sys
from pygame.locals import *
from math import *
pygame.init()
#Screen Resolution
width = 1080
height = 720
#Instrument Characteristics
color = (255,255,255)
background = (0,0,0)
radius_speedometer = 100
weight_speedometer = radius_speedometer-5
#Window Measurements
screen= pygame.display.set_mode((width,height),0,32)
pos_center_x = width/2
pos_center_y = height/2
#Speedometer Dial Position
pos_speedometer_dial_x = pos_center_x - 60
pos_speedometer_dial_y = pos_center_y +60
#Radial Position (hint: ticks on the clock)
radial_pos = 70
move_rad = 0
while True:
for event in pygame.event.get():
if event.type==QUIT:
pygame.quit()
sys.exit()
if event.type==KEYDOWN:
if event.key==K_DOWN:
move_rad=-0.01
if event.key==K_UP:
move_rad=+0.01
if event.type==KEYUP:
if event.key==K_DOWN:
move_rad=-0.05
if event.key==K_UP:
move_rad=-0.005
angle = 2.0*pi*radial_pos/120.0
radial_pos+=move_rad
pos_speedometer_dial_x = pos_center_x + (radius_speedometer-10)*sin(angle)
pos_speedometer_dial_y = pos_center_y - (radius_speedometer-10)*cos(angle)
screen.lock()
#Speedometer Ring
pygame.draw.circle(screen, color, (pos_center_x,pos_center_y), radius_speedometer)
pygame.draw.circle(screen, background, (pos_center_x,pos_center_y), weight_speedometer)
#Speedometer Dial
pygame.draw.line(screen, color, (pos_center_x,pos_center_y), (pos_speedometer_dial_x, pos_speedometer_dial_y),5)
screen.unlock()
pygame.display.update()
It's not perfect (such as no zero yet), however we are now much closer to completing the first instrument of the dash cluster!
I have modified the ImageView example by adding the statement data[:, ::10, :] = 0, which sets every tenth element of the middle dimension to 0. The program now shows horizontal lines. This is consistent with the documentation of the ImageView.setImage function: the default axes dictionary is {'t':0, 'x':1, 'y':2, 'c':3}. However, when I change this to {'t':0, 'x':2, 'y':1, 'c':3}, nothing changes where I would expect to get vertical rows.
So my question is: how can I give the row dimension a higher precedence in PyQtGraph? Of course I can transpose all my arrays myself before passing them to the setImage function but I prefer not to. Especially since both Numpy and Qt use the row/column convention and not X before Y. I don't see why PyQtGraph chooses the latter.
For completeness, find my modified ImageView example below.
import numpy as np
from pyqtgraph.Qt import QtCore, QtGui
import pyqtgraph as pg
app = QtGui.QApplication([])
## Create window with ImageView widget
win = QtGui.QMainWindow()
win.resize(800,800)
imv = pg.ImageView()
win.setCentralWidget(imv)
win.show()
win.setWindowTitle('pyqtgraph example: ImageView')
## Create random 3D data set with noisy signals
img = pg.gaussianFilter(np.random.normal(size=(200, 200)), (5, 5)) * 20 + 100
img = img[np.newaxis,:,:]
decay = np.exp(-np.linspace(0,0.3,100))[:,np.newaxis,np.newaxis]
data = np.random.normal(size=(100, 200, 200))
data += img * decay
data += 2
## Add time-varying signal
sig = np.zeros(data.shape[0])
sig[30:] += np.exp(-np.linspace(1,10, 70))
sig[40:] += np.exp(-np.linspace(1,10, 60))
sig[70:] += np.exp(-np.linspace(1,10, 30))
sig = sig[:,np.newaxis,np.newaxis] * 3
data[:,50:60,50:60] += sig
data[:, ::10, :] = 0 # Make image a-symmetrical
## Display the data and assign each frame a time value from 1.0 to 3.0
imv.setImage(data, xvals=np.linspace(1., 3., data.shape[0]),
axes={'t':0, 'x':2, 'y':1, 'c':3}) # doesn't help
## Start Qt event loop unless running in interactive mode.
if __name__ == '__main__':
import sys
if (sys.flags.interactive != 1) or not hasattr(QtCore, 'PYQT_VERSION'):
QtGui.QApplication.instance().exec_()
Looking through ImageView.py, setImage() parses the axes dictionary and based on presence of 't' it builds the z-axis/frame slider, and that's it. Rearranging the axes seems unimplemented yet.
I want to know how to arrange for the text on a ttk widget (a label or button, say) to resize automatically.
Changing the size of the text is easy, it is just a matter of changing the font in the style. However, hooking it into changes in the size of the window is a little more tricky. Looking on the web I found some hints, but there was nowhere a complete answer was posted.
So, here below is a complete working example posted as an answer to my own question. I hope someone finds it useful. If anyone has further improvements to suggest, I will be delighted to see them!
The example below shows two techniques, one activated by re-sizing the window (see the resize() method, bound to the <Configure> event), and the other by directly changing the size of the font (see the mutate() method).
Other code necessary to get resizing working is the grid configuration code in the __init__() method.
When running the example, there is some interaction between the two methods, but I think in a 'real' situation one technique would be sufficient, so that issue won't arise.
from tkinter import *
from tkinter.ttk import *
class ButtonApp(Frame):
"""Container for the buttons."""
def __init__(self, master=None):
"""Initialize the frame and its children."""
super().__init__(master)
self.createWidgets()
# configure the frame's resize behaviour
master.columnconfigure(0, weight=1)
master.rowconfigure(0, weight=1)
self.grid(sticky=(N,S,E,W))
# configure resize behaviour for the frame's children
self.columnconfigure(0, weight=1)
self.rowconfigure(0, weight=1)
self.rowconfigure(0, weight=1)
# bind to window resize events
self.bind('<Configure>', self.resize)
def createWidgets(self):
"""Make the widgets."""
# this button mutates
self.mutantButton = Button(self, text='Press Me',
style='10.TButton')
self.mutantButton.grid(column=0, row=0, sticky=(N,S,E,W))
self.mutantButton['command'] = self.mutate
# an ordinary quit button for comparison
self.quitButton = Button(self, text='Quit', style='TButton')
self.quitButton.grid(column=0, row=1, sticky=(N,S,E,W))
self.quitButton['command'] = self.quit
def mutate(self):
"""Rotate through the styles by hitting the button."""
style = int(self.mutantButton['style'].split('.')[0])
newStyle = style + 5
if newStyle > 50: newStyle = 10
print('Choosing font '+str(newStyle))
self.mutantButton['style'] = fontStyle[newStyle]
# resize the frame
# get the current geometries
currentGeometry = self._root().geometry()
w, h, x, y = self.parseGeometry(currentGeometry)
reqWidth = self.mutantButton.winfo_reqwidth()
reqHeight = self.mutantButton.winfo_reqheight()
# note assume height of quit button is constant at 20.
w = max([w, reqWidth])
h = 20 + reqHeight
self._root().geometry('%dx%d+%d+%d' % (w, h, x, y))
def parseGeometry(self, geometry):
"""Geometry parser.
Returns the geometry as a (w, h, x, y) tuple."""
# get w
xsplit = geometry.split('x')
w = int(xsplit[0])
rest = xsplit[1]
# get h, x, y
plussplit = rest.split('+')
h = int(plussplit[0])
x = int(plussplit[1])
y = int(plussplit[2])
return w, h, x, y
def resize(self, event):
"""Method bound to the <Configure> event for resizing."""
# get geometry info from the root window.
wm, hm = self._root().winfo_width(), self._root().winfo_height()
# choose a font height to match
# note subtract 30 for the button we are NOT scaling.
# note we assume optimal font height is 1/2 widget height.
fontHeight = (hm - 20) // 2
print('Resizing to font '+str(fontHeight))
# calculate the best font to use (use int rounding)
bestStyle = fontStyle[10] # use min size as the fallback
if fontHeight < 10: pass # the min size
elif fontHeight >= 50: # the max size
bestStyle = fontStyle[50]
else: # everything in between
bestFitFont = (fontHeight // 5) * 5
bestStyle = fontStyle[bestFitFont]
# set the style on the button
self.mutantButton['style'] = bestStyle
root = Tk()
root.title('Alice in Pythonland')
# make a dictionary of sized font styles in the range of interest.
fontStyle = {}
for font in range(10, 51, 5):
styleName = str(font)+'.TButton'
fontName = ' '.join(['helvetica', str(font), 'bold'])
fontStyle[font] = styleName
Style().configure(styleName, font=fontName)
# run the app
app = ButtonApp(master=root)
app.mainloop()
root.destroy()