I'm working on line plotting a metric for a course module as well as each of its questions within a Jupyter Notebook using %matplotlib notebook. That part is no problem. A module has typically 20-35 questions, so it results in a lot of lines on a chart. Therefore, I am plotting the metric for each question in a low alpha and I want to change the alpha and display the question name when I hover over the line, then reverse those when no longer hovering over the line.
The thing is, I've tried every test version of interactivity from the matplotlib documentation on event handling, as well as those in this question. It seems like the mpl_connect event is never firing, whether I use click or hover.
Here's a test version with a reduced dataset using the solution to the question linked above. Am I missing something necessary to get events to fire?
def update_annot(ind):
x,y = line.get_data()
annot.xy = (x[ind["ind"][0]], y[ind["ind"][0]])
text = "{}, {}".format(" ".join(list(map(str,ind["ind"]))),
" ".join([names[n] for n in ind["ind"]]))
annot.set_text(text)
annot.get_bbox_patch().set_alpha(0.4)
def hover(event):
vis = annot.get_visible()
if event.inaxes == ax:
cont, ind = line.contains(event)
if cont:
update_annot(ind)
annot.set_visible(True)
fig.canvas.draw_idle()
else:
if vis:
annot.set_visible(False)
fig.canvas.draw_idle()
module = 'bd2bc472-ee0d-466f-8557-788cc6de3018'
module_metrics[module] = {
'q_count': 31,
'sequence_pks': [0.5274546300604932,0.5262044653349001,0.5360993905297703,0.5292329279700655,0.5268691588785047,0.5319099014547161,0.5305164319248826,0.5268235294117647,0.573648805381582,0.5647933116581514,0.5669839795681448,0.5646591970121382,0.5663157894736842,0.5646976090014064,0.5659005628517824,0.5693634879925391,0.5728268468888371,0.5668834184858337,0.5687237026647967,0.5795640965549567,0.5877684407096172,0.585690904839841,0.5766899766899767,0.5971341320178529,0.6059972105997211,0.6055516678329834,0.6209865053513262,0.6203121360354065,0.6153666510976179,0.6236909471724459,0.6387654898293196],
'q_pks': {
'0da04f02-4aad-4ac8-91a5-214862b5c0d0': [0.6686046511627907,0.6282051282051282,0.76,0.6746987951807228,0.7092198581560284,0.71875,0.6585365853658537,0.7070063694267515,0.7171052631578947,0.7346938775510204,0.7737226277372263,0.7380952380952381,0.6774193548387096,0.7142857142857143,0.7,0.6962962962962963,0.723404255319149,0.6737588652482269,0.7232704402515723,0.7142857142857143,0.7164179104477612,0.7317073170731707,0.6333333333333333,0.75,0.7217391304347827,0.7017543859649122,0.7333333333333333,0.7641509433962265,0.6869565217391305,0.75,0.794392523364486],
'10bd29aa-3a26-49e6-bc2c-50fd503d7ab5': [0.64375,0.6014492753623188,0.5968992248062015,0.5059523809523809,0.5637583892617449,0.5389221556886228,0.5576923076923077,0.51875,0.4931506849315068,0.5579710144927537,0.577922077922078,0.5467625899280576,0.5362318840579711,0.6095890410958904,0.5793103448275863,0.5159235668789809,0.6196319018404908,0.6143790849673203,0.5035971223021583,0.5897435897435898,0.5857142857142857,0.5851851851851851,0.6164383561643836,0.6054421768707483,0.5714285714285714,0.627906976744186,0.5826771653543307,0.6504065040650406,0.5864661654135338,0.6333333333333333,0.6851851851851852]
}}
suptitle_size = 24
title_size = 18
tick_size = 12
axis_label_size = 15
legend_size = 14
fig, ax = plt.subplots(figsize=(15,8))
fig.suptitle('PK by Sequence Order', fontsize=suptitle_size)
module_name = 'Test'
q_count = module_metrics[module]['q_count']
y_ticks = [0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0]
x_ticks = np.array([x for x in range(0,q_count)])
x_labels = x_ticks + 1
# Plot it
ax.set_title(module_name, fontsize=title_size)
ax.set_xticks(x_ticks)
ax.set_yticks(y_ticks)
ax.set_xticklabels(x_labels, fontsize=tick_size)
ax.set_yticklabels(y_ticks, fontsize=tick_size)
ax.set_xlabel('Sequence', fontsize=axis_label_size)
ax.set_xlim(-0.5,q_count-0.5)
ax.set_ylim(0,1)
ax.grid(which='major',axis='y')
# Output module PK by sequence
ax.plot(module_metrics[module]['sequence_pks'])
# Output PK by sequence for each question
for qid in module_metrics[module]['q_pks']:
ax.plot(module_metrics[module]['q_pks'][qid], alpha=0.15, label=qid)
annot = ax.annotate("", xy=(0,0), xytext=(-20,20),textcoords="offset points", bbox=dict(boxstyle="round", fc="w"), arrowprops=dict(arrowstyle="->"))
annot.set_visible(False)
mpl_id = fig.canvas.mpl_connect('motion_notify_event', hover)
Since there are dozens of modules, I created an ipywidgets dropdown to select the module, which then runs a function to output the chart. Nonetheless, whether running it hardcoded as here or from within the function, mpl_connect never seems to fire.
Here's what this one looks like when run
Related
I am building a control app, and i use a matplotlib.figure graph to represent some data. I made a function that will do the plotting, but the problem is removing the plot. I can't remove the plot from the window, as it just stays there no matter what. I've tried using tk_widget.place_forget(), tk_widget.destroy(), and figure_subplot.remove(), but the window stays there.
def plot_box_1(sx, sy, px, py):
fig = Figure(figsize=(sx/100, sy/100), dpi=100)
pl1 = fig.add_subplot(111)
pl1.plot(y)
pltwidget = FigureCanvasTkAgg(fig, master=window)
pltwidget.draw()
tkplt = pltwidget.get_tk_widget()
tkplt.place(x=px, y=py)
return tkplt, pl1
def b5_updater():
global tab, asdf, current_after
p1 = page[5]["p1"]
p2 = page[5]["p2"]
box_pos = page[5]["box_pos"]
if tab == 5:
if asdf == 100:
asdf = 0
p1['value'] = asdf
p2['value'] = 100 - asdf
y.append(asdf)
if len(y) >= plot_lim:
y.pop(0)
asdf += 1
page[5]["plt_wid"], page[5]["pl1"] = plot_box_1(250, 180, box_pos[2][0] + 25, box_pos[2][1] + 30)
current_after = window.after(20, b5_updater)
else:
window.after_cancel(current_after) if current_after is not None else None
p1.destroy()
p2.destroy()
page[5]["plt_wid"].place_forget()
page[5]["pl1"].remove()
page[5]["plt_wid"].destroy()
print("Closed B5 Successfully")
Tried .remove, .destroy, .place_forget on multiple items including the Canvas in tkinter, the graph still stayed there.
If you want to completely remove the tkinter widget the following works:
pltwidget.get_tk_widget().destroy()
If you just want to clear the figure, use:
fig.clear()
Both options work for me. You could test it in isolation for yourself by trying these options before the return value of def plot_box_1().
I'm trying to create stimuli that consist of 100 small lines in the centre of the screen, with orientations sampled from a Gaussian distribution (please see the image link below):
Orientation stimuli
I've managed to achieve something that almost fits the bill, but this code only works in isolation:
from psychopy import visual, core, event
import numpy as np
from numpy.random import random
import random
Lines = visual.Rect(
win=win, name='Lines',
width=(0.015, 0.0025)[0], height=(0.015, 0.0025)[1],
lineWidth=1, lineColor=[1,1,1], lineColorSpace='rgb',
fillColor=[1,1,1], fillColorSpace='rgb',
opacity=1, depth=-2.0, interpolate=True)
lines_hpos = np.random.uniform(-0.49,0.49,100)
mu = 315
sigma = 15
for i in range(100):
Lines.pos = [lines_hpos[i],np.random.uniform(-0.49,0.49)]
Lines.ori = random.gauss(mu, sigma)
I've tried to manipulate this code so that I can integrate it into the full experiment I'm designing in PsychoPy's experiment builder. I run the below code in the experiment builder's coding window calling 'gdist' and 'loc' as values for the 'Orientation' and 'Position' of the rectangles, respectively:
import random
gdist =[]
loc = []
lines_hpos = np.random.uniform(-0.49,0.49,100)
mu = 90
sigma = 20
for i in range(100):
rloc = [lines_hpos[i],np.random.uniform(-0.49,0.49)]
loc.append(rloc)
gauss = random.gauss(mu, sigma)
gdist.append(gauss)
When I attempt to run the experiment, I get an error return and the experiment fails to start:
File "C:\Users\r02mj20\AppData\Local\PsychoPy3\lib\site-packages\psychopy\visual\image.py", line 238, in __del__
File "C:\Users\r02mj20\AppData\Local\PsychoPy3\lib\site-packages\pyglet\gl\lib.py", line 97, in errcheck
ImportError: sys.meta_path is None, Python is likely shutting down
I'm assuming this has something to do with pyglet not liking the idea of there being 100 rectangles all at once (side note: the script works fine if range(1)). If anyone has any suggestions for how I might fix or work around this problem, I'd be eternally grateful.
i don't see any problem with this idea, except you better use visual.Line instead of Rect, and your units of measure are not described; the key to preserving video memory is BufferImageStim, btw
from psychopy import visual, core, event, monitors
from psychopy.iohub.client import launchHubServer
import random
import numpy as np
MU = 315; SIGMA = 15
num_lines = 100
io = launchHubServer(iohub_config_name='iohub_config.yaml')
display = io.devices.display
mon = monitors.Monitor(name = display.getPsychopyMonitorName())
win = visual.Window([640, 480], units='pix', viewScale = 1.0,
monitor = mon, winType='pyglet',
fullScr = False, waitBlanking = True, useFBO = True, useLights = False,
allowStencil=False, allowGui = True,
screen = display.getIndex(), colorSpace = 'rgb255', color = [128,128,128],
name = 'my_win01')
rects = []
lines_hpos = np.random.uniform(-0.49, 0.49, num_lines)
for i in range(num_lines):
line_rect = visual.Rect(win=win, size=(0.001, 1.0), units='norm',
pos=(0,0), lineWidth=1, lineColor=[1,1,1], fillColor=[1,1,1], opacity=1, depth=-2.0,
name='lines_rect', interpolate=True, autoLog=False, autoDraw=False)
line_rect.pos = [lines_hpos[i], np.random.uniform(-0.49,0.49)]
line_rect.ori = random.gauss(MU, SIGMA)
rects.append(line_rect)
rect_buffer = visual.BufferImageStim(win, buffer='back', stim=rects, sqPower2=False, interpolate=False, name='rect-buffer', autoLog=True)
rect_buffer.draw()
win.flip()
event.waitKeys()
I would like to plot a ggplot2 image using ggplotly
What I am trying to do is to initially plot rectangles of grey fill without any aesthetic mapping, and then in a second step to plot tiles and change colors based on aesthetics. My code is working when I use ggplot but crashes when I try to use ggplotly to transform my graph into interactive
Here is a sample code
library(ggplot2)
library(data.table)
library(plotly)
library(dplyr)
x = rep(c("1", "2", "3"), 3)
y = rep(c("K", "B","A"), each=3)
z = sample(c(NA,"A","L"), 9,replace = TRUE)
df <- data.table(x,y,z)
p<-ggplot(df)+
geom_tile(aes(x=x,y=y),width=0.9,height=0.9,fill="grey")
p<-p+geom_tile(data=filter(df,z=="A"),aes(x=x,y=y,fill=z),width=0.9,height=0.9)
p
But when I type this
ggplotly(p)
I get the following error
Error in [.data.frame(g, , c("fill_plotlyDomain", "fill")) :
undefined columns selected
The versions I use are
> packageVersion("plotly")
1 ‘4.7.1
packageVersion("ggplot2")
1 ‘2.2.1.9000’
##########Edited example for Arthur
p<-ggplot(df)+
geom_tile(aes(x=x,y=y,fill="G"),width=0.9,height=0.9)
p<- p+geom_tile(data=filter(df,z=="A"),aes(x=x,y=y,fill=z),width=0.9,height=0.9)
p<-p+ scale_fill_manual(
guide = guide_legend(title = "test",
override.aes = list(
fill =c("red","white") )
),
values = c("red","grey"),
labels=c("A",""))
p
This works
but ggplotly(p) adds the grey bar labeled G in the legend
The output of the ggplotly function is a list with the plotly class. It gets printed as Plotly graph but you can still work with it as a list. Moreover, the documentation indicates that modifying the list makes it possible to clear all or part of the legend. One only has to understand how the data is structured.
p<-ggplot(df)+
geom_tile(aes(x=x,y=y,fill=z),width=0.9,height=0.9)+
scale_fill_manual(values = c(L='grey', A='red'), na.value='grey')
p2 <- ggplotly(p)
str(p2)
The global legend is here in p2$x$layout$showlegend and setting this to false displays no legend at all.
The group-specific legend appears at each of the 9 p2$x$data elements each time in an other showlegend attribute. Only 3 of them are set to TRUE, corresponding to the 3 keys in the legend. The following loop thus clears all the undesired labels:
for(i in seq_along(p2$x$data)){
if(p2$x$data[[i]]$legendgroup!='A'){
p2$x$data[[i]]$showlegend <- FALSE
}
}
Voilà!
This works here:
ggplot(df)+
geom_tile(aes(x=x,y=y,fill=z),width=0.9,height=0.9)+
scale_fill_manual(values = c(L='grey', A='red'), na.value='grey')
ggplotly(p)
I guess your problem comes from the use of 2 different data sources, df and filter(df,z=="A"), with columns with the same name.
[Note this is not an Answer Yet]
(Putting for reference, as it is beyond the limits for comments.)
The problem is rather complicated.
I just finished debugging the code of plotly. It seems like it's occurring here.
I have opened an issue in GitHub
Here is the minimal code for the reproduction of the problem.
library(ggplot2)
set.seed(1503)
df <- data.frame(x = rep(1:3, 3),
y = rep(1:3, 3),
z = sample(c("A","B"), 9,replace = TRUE),
stringsAsFactors = F)
p1 <- ggplot(df)+
geom_tile(aes(x=x,y=y, fill="grey"), color = "black")
p2 <- ggplot(df)+
geom_tile(aes(x=x,y=y),fill="grey", color = "black")
class(plotly::ggplotly(p1))
#> [1] "plotly" "htmlwidget"
class(plotly::ggplotly(p2))
#> Error in `[.data.frame`(g, , c("fill_plotlyDomain", "fill")): undefined columns selected
I have written a chaco plotting class that plots some data and allows the user to interact with it. I then wanted to make a TraitsUI GUI that has several different instances of this chaco plot so that the user can have several of the plots and interact with them independently.
However, when I try and implement this I seem to get that each of the separate instances of my chaco plot are displaying all the data from all the plots. I have made a very simple GUI below that reproduces the problem.
In the example below I would like each tab to show a container with a single line plot. However, each container seems to plot all the plots that have been plotted in any of the containers. From the documentation here chaco container docs, I think what I have done should work.
I have also tried using the ListEditor view, but this has the same problem.
Am I misunderstanding something about chaco Containers? How can I get each container instance to act independently? Any help would be appreciated.
Thanks!
import enthought.chaco.api as chaco
import enthought.traits.api as traits
import enthought.traits.ui.api as traitsui
from enthought.enable.api import ComponentEditor
import scipy
class BasicPlot(traits.HasTraits):
container = chaco.Plot(padding=(120,20,20,40), bgcolor="white",
use_backbuffer = True,
border_visible = True,
fill_padding = True)
traits_view = traitsui.View(traitsui.Item('container', editor = ComponentEditor(), show_label = False),
width = 500, height = 500,
resizable = True, title = "My line plot")
def __init__(self, n, *args, **kw):
super(BasicPlot, self).__init__(*args, **kw)
xs = scipy.linspace(0, 6.3, 1000)
ys = scipy.sin(n*xs)
plot = chaco.create_line_plot([xs,ys])
self.container.add(plot)
chaco.add_default_grids(plot)
chaco.add_default_axes(plot)
class tabbedPlots(traits.HasTraits):
bp1 = BasicPlot(1)
bp2 = BasicPlot(2)
bpGroup = traitsui.Group(traitsui.Item("bp1", editor = traitsui.InstanceEditor(), style="custom", show_label=False),
traitsui.Item("bp2", editor = traitsui.InstanceEditor(), style="custom", show_label=False), layout="tabbed")
traits_view = traitsui.View(bpGroup,title = "Log File Plots")
class tabbedPlotsList(traits.HasTraits):
bps = traits.List(BasicPlot)
bpGroup = traitsui.Group(
traitsui.Item('bps',style="custom",
editor=traitsui.ListEditor(use_notebook=True, deletable=True,export = 'DockWindowShell', page_name=".name")
,label="logFilePlots", show_label=False)
)
traits_view = traitsui.View(bpGroup,title = "Log File Plots")
def __init__(self, **traitsDict):
super(tabbedPlotsList, **traitsDict)
self.bps = [BasicPlot(n) for n in range(0,8)]
if __name__=="__main__":
gui = tabbedPlots()
gui.configure_traits()
gui2 = tabbedPlotsList()
gui2.configure_traits()
I found the fix to this.
def __init__(self, n, *args, **kw):
super(BasicPlot, self).__init__(*args, **kw)
self.container = chaco.Plot(padding=(120,20,20,40), bgcolor="white",
use_backbuffer = True,
border_visible = True,
fill_padding = True)
xs = scipy.linspace(0, 6.3, 1000)
ys = scipy.sin(n*xs)
plot = chaco.create_line_plot([xs,ys])
self.container.add(plot)
chaco.add_default_grids(plot)
chaco.add_default_axes(plot)
To make it work as desired the container cannot be a class attribute. Instead it must be defined inside the init as self.container(...). (This makes sense)
If this change is made you get the desired functionality.
I can capture data from serial device via pyserial, at this time I can only export data to text file, the text file has format like below, it's have 3 columns
>21 21 0
>
>41 41 0.5
>
>73 73 1
>
....
>2053 2053 5
>
>2084 2084 5.5
>
>2125 2125 6
Now I want to use matplotlib to generate live graph has 2 figure (x,y) x,y are second and third column, first comlumn,'>', and lines don't have data can be remove
thank folks!
============================
Update : today, after follow these guides from
http://www.blendedtechnologies.com/realtime-plot-of-arduino-serial-data-using-python/231
http://eli.thegreenplace.net/2008/08/01/matplotlib-with-wxpython-guis
pyserial - How to read the last line sent from a serial device
now I can live plot with threading but eliben said that this Guis only plot single value each time, that lead to me the very big limitation, beacause my purpose is plotting 2 or 3 column, here is the code was modified from blendedtechnologies
Here is serial handler :
from threading import Thread
import time
import serial
last_received = ''
def receiving(ser):
global last_received
buffer = ''
while True:
buffer = buffer + ser.read(ser.inWaiting())
if '\n' in buffer:
lines = buffer.split('\n') # Guaranteed to have at least 2 entries
last_received = lines[-2]
#If the Arduino sends lots of empty lines, you'll lose the
#last filled line, so you could make the above statement conditional
#like so: if lines[-2]: last_received = lines[-2]
buffer = lines[-1]
class SerialData(object):
def __init__(self, init=50):
try:
self.ser = ser = serial.Serial(
port='/dev/ttyS0',
baudrate=9600,
bytesize=serial.EIGHTBITS,
parity=serial.PARITY_NONE,
stopbits=serial.STOPBITS_ONE,
timeout=0.1,
xonxoff=0,
rtscts=0,
interCharTimeout=None
)
except serial.serialutil.SerialException:
#no serial connection
self.ser = None
else:
Thread(target=receiving, args=(self.ser,)).start()
def next(self):
if not self.ser:
return 100 #return anything so we can test when Arduino isn't connected
#return a float value or try a few times until we get one
for i in range(40):
raw_line = last_received[1:].split(' ').pop(0)
try:
return float(raw_line.strip())
except ValueError:
print 'bogus data',raw_line
time.sleep(.005)
return 0.
def __del__(self):
if self.ser:
self.ser.close()
if __name__=='__main__':
s = SerialData()
for i in range(500):
time.sleep(.015)
print s.next()
For me I modified this segment so it can grab my 1st column data
for i in range(40):
raw_line = last_received[1:].split(' ').pop(0)
try:
return float(raw_line.strip())
except ValueError:
print 'bogus data',raw_line
time.sleep(.005)
return 0.
and generate graph base on these function on the GUI file
from Arduino_Monitor import SerialData as DataGen
def __init__(self):
wx.Frame.__init__(self, None, -1, self.title)
self.datagen = DataGen()
self.data = [self.datagen.next()]
................................................
def init_plot(self):
self.dpi = 100
self.fig = Figure((3.0, 3.0), dpi=self.dpi)
self.axes = self.fig.add_subplot(111)
self.axes.set_axis_bgcolor('black')
self.axes.set_title('Arduino Serial Data', size=12)
pylab.setp(self.axes.get_xticklabels(), fontsize=8)
pylab.setp(self.axes.get_yticklabels(), fontsize=8)
# plot the data as a line series, and save the reference
# to the plotted line series
#
self.plot_data = self.axes.plot(
self.data,
linewidth=1,
color=(1, 1, 0),
)[0]
So my next question is how to realtime grab at least 2 column and passing 2 columns'data to the GUIs that it can generate graph with 2 axis.
self.plot_data.set_xdata(np.arange(len(self.data))) #my 3rd column data
self.plot_data.set_ydata(np.array(self.data)) #my 2nd column data
Well, this reads your string and converts the numbers to floats. I assume you'll be able to adapt this as needed.
import numpy as np
import pylab as plt
str = '''>21 21 0
>
>41 41 0.5
>
>73 73 1
>
>2053 2053 5
>
>2084 2084 5.5
>
>2125 2125 6'''
nums = np.array([[float(n) for n in sub[1:].split(' ') if len(n)>0] for sub in str.splitlines() if len(sub)>1])
fig = plt.figure(0)
ax = plt.subplot(2,1,1)
ax.plot(nums[:,0], nums[:,1], 'k.')
ax = plt.subplot(2,1,2)
ax.plot(nums[:,0], nums[:,2], 'r+')
plt.show()
Here you have an Eli Bendersky's example of how plotting data arriving from a serial port
some time back I had the same problem. I wasted a lot of writing same ting over and over again. so I wrote a python package for it.
https://github.com/girish946/plot-cat
you just have to write the logic for obtaining the data from serial port.
the example is here: https://github.com/girish946/plot-cat/blob/master/examples/test-ser.py