I'm wondering why my matlibplot is currently graphing inside a button as shown below:
Code is shown below
from gui export Index
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.widgets import Button
fig, ax = plt.subplots()
plt.subplots_adjust(buttom-0.2)
pos_trim = plt.axes([0.20, 0.05, .1, 0.075])
b_trim = Button(pos_trim, 'Trim', hoverclor='0.25')
callback = Index(c)
b_trim.on_clicked(callback.autoTrim) # from gui.index
freqs = np.arange(2,20,3)
t = np.arange(0.0, 1.0, 0.05)
s = np.sin(2*np.pi*freqs[0]*t)
l, = plt.plot(t,s,lw=2)
plt.show()
Index class:
class Index:
def __init__(self, chart):
self.__chart = chart
def autoTrim(self, event):
print "Autotrim"
#self.__chart.autoTrim()
plt.draw()
Try changing your line where you plot the data to:
l, = ax.plot(t,s,lw=2)
That is, plot to the axes you created for the plot (ax). Otherwise, you're plotting to the last axes you created, which in this case, is the button.
Related
Code below, I want to write a code that updates its function by cearing the axes and assigning new plots. It works when I only have one plot, but doesnt when I use subplots()...
Thanks
import numpy as np
import matplotlib.pyplot as plt
import time
fig , ax = plt.subplots(1,2)
x = np.linspace(0,10,10)
alpha = 0.70
def sin(x):
return np.sin(alpha*x)
def lin(x):
return alpha*x
for i in range(5):
ax[0].clear()
ax[1].clear()
ax[0].plot(x,lin(x), marker='o', label = str(i))
ax[1].plot(x,sin(x), marker='o')
fig.legend()
plt.show()
alpha = alpha**2
time.sleep(0.5)
This code displays a moving graph with two lines, and the data is saved to a CSV file with the code that makes the data. I have tried to create a canvas class using the pyqt5 imports, but I am struggling with where exactly to put the matplotlib code.
from itertools import count
import matplotlib.pyplot as plt
import pandas as pd
from matplotlib.animation import FuncAnimation
plt.style.use('fivethirtyeight')
x_vals = []
y_vals = []
index = count()
def animate(i):
data = pd.read_csv("C:/Users/Khata/PycharmProjects/LiveData1/venv/data.csv")
x = data['x_value']
y1 = data['total_1']
y2 = data['total_2']
plt.cla()
plt.plot(x, y1, label='Channel 1')
plt.plot(x, y2, label='Channel 2')
plt.legend(loc='upper left')
plt.tight_layout()
ani = FuncAnimation(plt.gcf(), animate, interval=1000)
plt.tight_layout()
plt.show()
I created a bar chart using the following code :
%matplotlib notebook
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import pandas as pd
import numpy as np
import scipy.stats
from matplotlib.widgets import Slider, Button, RadioButtons
np.random.seed(12345)
df = pd.DataFrame([np.random.normal(32000,200000,3650),
np.random.normal(43000,100000,3650),
np.random.normal(43500,140000,3650),
np.random.normal(48000,70000,3650)],
index=[1992,1993,1994,1995])
x = df.T.agg(lambda z : z.mean())
def mean_IC(data, confidence=0.95):
n = len(data)
m, se = data.mean(), scipy.stats.sem(data)
h = se * scipy.stats.t.ppf((1 + confidence) / 2., n-1)
return m, m-h, m+h
plt.subplots_adjust(right=0.8)
yerr1 = np.array([[mean_IC(df.iloc[i])[0] - mean_IC(df.iloc[i])[1] for i in range(len(x.index))],
[mean_IC(df.iloc[i])[2] - mean_IC(df.iloc[i])[0] for i in range(len(x.index))]])
ax = plt.bar(range(len(x.index)), x, align='center', yerr=yerr1, width=1, edgecolor='black')
_=plt.xticks(range(len(x.index)), x.index)
#plt.bar(x.index.astype(str), x, align='center')
ax = plt.gca()
def step1(event):
plt.sca(ax)
plt.cla()
plt.bar(range(len(x.index)), x, align='center', yerr=yerr1, width=1, edgecolor='black')
plt.xticks(range(len(x.index)), x.index)
plt.axhline(y=event.ydata, color='black', linestyle='--', linewidth=1)
# tell mpl_connect we want to pass a 'button_press_event' into step1 when the event is detected
plt.gcf().canvas.mpl_connect('button_press_event', step1)
def step2(event) :
axcolor = 'lightgoldenrodyellow'
plt.axes([0.83, 0.7, 0.15, 0.25], facecolor=axcolor)
ax1 = plt.gca()
ax1.cla()
plt.axes([0.83, 0.7, 0.15, 0.25], facecolor=axcolor)
plt.gca().get_xaxis().set_visible(False)
plt.gca().get_yaxis().set_visible(False)
plt.text(0.5, 0.5,'Currently\nchoosing\n y=\n{:.4f}'.format(event.ydata), family='serif', ha='center', wrap=True)
button_ax = plt.axes([0.855, 0.73, 0.1, 0.055])
button = Button(button_ax, 'Continue', color='grey')
plt.gcf().canvas.mpl_connect('button_press_event', step2)
What I'm trying to do is to draw a horizontal line in the bar chart depending on where the user clicks, and then have the y value printed in the new axes object that I added. The problem is I want this to happen only when the user clicks in the actual figure of the bar chart. Instead, what happens is that when you click outside the figure or in the axes object on the right, it also takes into account this as an event. So how can I make the callback function only work under a condition ? I tried many things but nothing seems to work ..
I'm trying to get the RectangleSelector form matplotlib.widgets to work with PySimpleGUI.
I'm basing my test code on the RectangleSelector demo shown in the accepted answer on this question.
I'm getting the plot to show in PySimpleGUI but it's not interactive. Is it even possible in PySimpleGUI to have interactive matplotlib widgets?
import PySimpleGUI as sg
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
from matplotlib.widgets import RectangleSelector
import matplotlib
matplotlib.use('TkAgg')
xdata = np.linspace(0,9*np.pi, num=301)
ydata = np.sin(xdata)
fig, ax = plt.subplots()
line, = ax.plot(xdata, ydata)
def draw_figure(canvas, figure):
figure_canvas_agg = FigureCanvasTkAgg(figure, canvas)
figure_canvas_agg.draw()
figure_canvas_agg.get_tk_widget().pack(side="top", fill="both", expand=1)
return figure_canvas_agg
def line_select_callback(eclick, erelease):
x1, y1 = eclick.xdata, eclick.ydata
x2, y2 = erelease.xdata, erelease.ydata
rect = plt.Rectangle( (min(x1,x2),min(y1,y2)), np.abs(x1-x2), np.abs(y1-y2) )
ax.add_patch(rect)
rs = RectangleSelector(ax, line_select_callback,
drawtype='box', useblit=False, button=[1],
minspanx=5, minspany=5, spancoords='pixels',
interactive=True)
layout = [[sg.Canvas(key="-CANVAS-")]]
window = sg.Window('test', layout, finalize=True, element_justification='center', font='Helvetica 16')
draw_figure(window["-CANVAS-"].TKCanvas, fig)
event, values = window.read()
Edit: Thanks to MikeyB for the pointer, I now have the following code, which shows an interactive plot, but it's still not possible to draw rectangles. The callback function doesn't seem to be firing. New code below:
import PySimpleGUI as sg
import numpy as np
from matplotlib.widgets import RectangleSelector
import matplotlib.figure as figure
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2Tk
# instantiate matplotlib figure
fig = figure.Figure()
ax = fig.add_subplot(111)
DPI = fig.get_dpi()
fig.set_size_inches(505 * 2 / float(DPI), 707 / float(DPI))
# ------------------------------- This is to include a matplotlib figure in a Tkinter canvas
def draw_figure_w_toolbar(canvas, fig, canvas_toolbar):
if canvas.children:
for child in canvas.winfo_children():
child.destroy()
if canvas_toolbar.children:
for child in canvas_toolbar.winfo_children():
child.destroy()
figure_canvas_agg = FigureCanvasTkAgg(fig, master=canvas)
figure_canvas_agg.draw()
toolbar = Toolbar(figure_canvas_agg, canvas_toolbar)
toolbar.update()
figure_canvas_agg.get_tk_widget().pack(side='right', fill='both', expand=1)
def line_select_callback(eclick, erelease):
x1, y1 = eclick.xdata, eclick.ydata
x2, y2 = erelease.xdata, erelease.ydata
rect = plt.Rectangle( (min(x1,x2),min(y1,y2)), np.abs(x1-x2), np.abs(y1-y2) )
print(rect)
ax.add_patch(rect)
class Toolbar(NavigationToolbar2Tk):
def __init__(self, *args, **kwargs):
super(Toolbar, self).__init__(*args, **kwargs)
# ------------------------------- PySimpleGUI CODE
layout = [
[sg.B('start', key='start')],
[sg.Canvas(key='controls_cv')],
[sg.Column(
layout=[
[sg.Canvas(key='fig_cv',
# it's important that you set this size
size=(500 * 2, 700)
)]
],
background_color='#DAE0E6',
pad=(0, 0)
)],
]
window = sg.Window('Test', layout)
while True:
event, values = window.read()
print(event, values)
if event == sg.WIN_CLOSED:
break
elif event == 'start':
x = np.linspace(0, 2 * np.pi)
y = np.sin(x)
line, = ax.plot(x, y)
rs = RectangleSelector(ax, line_select_callback,
drawtype='box', useblit=False, button=[1],
minspanx=5, minspany=5, spancoords='pixels',
interactive=True)
draw_figure_w_toolbar(window['fig_cv'].TKCanvas, fig, window['controls_cv'].TKCanvas)
window.close()
Is it even possible in PySimpleGUI to have interactive matplotlib widgets?
Yes.
The demo program on the project's GitHub shows how to make an interactive Matplotlib drawing.
https://github.com/PySimpleGUI/PySimpleGUI/blob/master/DemoPrograms/Demo_Matplotlib_Embedded_Toolbar.py
You need to embed the controls into the window.
you need to add
fig.canvas.draw()
to your callback-function if you want the plot to be updated after the callback has triggered!
here's an updated version of your code that works just fine:
import PySimpleGUI as sg
import numpy as np
from matplotlib.widgets import RectangleSelector
import matplotlib.figure as figure
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2Tk
import matplotlib.pyplot as plt
# instantiate matplotlib figure
fig = figure.Figure()
ax = fig.add_subplot(111)
DPI = fig.get_dpi()
fig.set_size_inches(505 * 2 / float(DPI), 707 / float(DPI))
# ------------------------------- This is to include a matplotlib figure in a Tkinter canvas
def draw_figure_w_toolbar(canvas, fig, canvas_toolbar):
if canvas.children:
for child in canvas.winfo_children():
child.destroy()
if canvas_toolbar.children:
for child in canvas_toolbar.winfo_children():
child.destroy()
figure_canvas_agg = FigureCanvasTkAgg(fig, master=canvas)
figure_canvas_agg.draw()
toolbar = Toolbar(figure_canvas_agg, canvas_toolbar)
toolbar.update()
figure_canvas_agg.get_tk_widget().pack(side='right', fill='both', expand=1)
def line_select_callback(eclick, erelease):
x1, y1 = eclick.xdata, eclick.ydata
x2, y2 = erelease.xdata, erelease.ydata
rect = plt.Rectangle( (min(x1,x2),min(y1,y2)), np.abs(x1-x2), np.abs(y1-y2) )
print(rect)
ax.add_patch(rect)
fig.canvas.draw()
class Toolbar(NavigationToolbar2Tk):
def __init__(self, *args, **kwargs):
super(Toolbar, self).__init__(*args, **kwargs)
# ------------------------------- PySimpleGUI CODE
layout = [
[sg.B('start', key='start')],
[sg.Canvas(key='controls_cv')],
[sg.Column(
layout=[
[sg.Canvas(key='fig_cv',
# it's important that you set this size
size=(500 * 2, 700)
)]
],
background_color='#DAE0E6',
pad=(0, 0)
)],
]
window = sg.Window('Test', layout)
while True:
event, values = window.read()
print(event, values)
if event == sg.WIN_CLOSED:
break
elif event == 'start':
x = np.linspace(0, 2 * np.pi)
y = np.sin(x)
line, = ax.plot(x, y)
rs = RectangleSelector(ax, line_select_callback,
drawtype='box', useblit=False, button=[1],
minspanx=5, minspany=5, spancoords='pixels',
interactive=True)
draw_figure_w_toolbar(window['fig_cv'].TKCanvas, fig, window['controls_cv'].TKCanvas)
window.close()
If you want to insert a small plot inside a bigger one you can use Axes, like here.
The problem is that I don't know how to do the same inside a subplot.
I have several subplots and I would like to plot a small plot inside each subplot.
The example code would be something like this:
import numpy as np
import matplotlib.pyplot as plt
fig = plt.figure()
for i in range(4):
ax = fig.add_subplot(2,2,i)
ax.plot(np.arange(11),np.arange(11),'b')
#b = ax.axes([0.7,0.7,0.2,0.2])
#it gives an error, AxesSubplot is not callable
#b = plt.axes([0.7,0.7,0.2,0.2])
#plt.plot(np.arange(3),np.arange(3)+11,'g')
#it plots the small plot in the selected position of the whole figure, not inside the subplot
Any ideas?
I wrote a function very similar to plt.axes. You could use it for plotting yours sub-subplots. There is an example...
import matplotlib.pyplot as plt
import numpy as np
#def add_subplot_axes(ax,rect,facecolor='w'): # matplotlib 2.0+
def add_subplot_axes(ax,rect,axisbg='w'):
fig = plt.gcf()
box = ax.get_position()
width = box.width
height = box.height
inax_position = ax.transAxes.transform(rect[0:2])
transFigure = fig.transFigure.inverted()
infig_position = transFigure.transform(inax_position)
x = infig_position[0]
y = infig_position[1]
width *= rect[2]
height *= rect[3] # <= Typo was here
#subax = fig.add_axes([x,y,width,height],facecolor=facecolor) # matplotlib 2.0+
subax = fig.add_axes([x,y,width,height],axisbg=axisbg)
x_labelsize = subax.get_xticklabels()[0].get_size()
y_labelsize = subax.get_yticklabels()[0].get_size()
x_labelsize *= rect[2]**0.5
y_labelsize *= rect[3]**0.5
subax.xaxis.set_tick_params(labelsize=x_labelsize)
subax.yaxis.set_tick_params(labelsize=y_labelsize)
return subax
def example1():
fig = plt.figure(figsize=(10,10))
ax = fig.add_subplot(111)
rect = [0.2,0.2,0.7,0.7]
ax1 = add_subplot_axes(ax,rect)
ax2 = add_subplot_axes(ax1,rect)
ax3 = add_subplot_axes(ax2,rect)
plt.show()
def example2():
fig = plt.figure(figsize=(10,10))
axes = []
subpos = [0.2,0.6,0.3,0.3]
x = np.linspace(-np.pi,np.pi)
for i in range(4):
axes.append(fig.add_subplot(2,2,i))
for axis in axes:
axis.set_xlim(-np.pi,np.pi)
axis.set_ylim(-1,3)
axis.plot(x,np.sin(x))
subax1 = add_subplot_axes(axis,subpos)
subax2 = add_subplot_axes(subax1,subpos)
subax1.plot(x,np.sin(x))
subax2.plot(x,np.sin(x))
if __name__ == '__main__':
example2()
plt.show()
You can now do this with matplotlibs inset_axes method (see docs):
from mpl_toolkits.axes_grid.inset_locator import inset_axes
inset_axes = inset_axes(parent_axes,
width="30%", # width = 30% of parent_bbox
height=1., # height : 1 inch
loc=3)
Update: As Kuti pointed out, for matplotlib version 2.1 or above, you should change the import statement to:
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
There is now also a full example showing all different options available.
From matplotlib 3.0 on, you can use matplotlib.axes.Axes.inset_axes:
import numpy as np
import matplotlib.pyplot as plt
fig, axes = plt.subplots(2,2)
for ax in axes.flat:
ax.plot(np.arange(11),np.arange(11))
ins = ax.inset_axes([0.7,0.7,0.2,0.2])
plt.show()
The difference to mpl_toolkits.axes_grid.inset_locator.inset_axes mentionned in #jrieke's answer is that this is a lot easier to use (no extra imports etc.), but has the drawback of being slightly less flexible (no argument for padding or corner locations).
source: https://matplotlib.org/examples/pylab_examples/axes_demo.html
from mpl_toolkits.axes_grid.inset_locator import inset_axes
import matplotlib.pyplot as plt
import numpy as np
# create some data to use for the plot
dt = 0.001
t = np.arange(0.0, 10.0, dt)
r = np.exp(-t[:1000]/0.05) # impulse response
x = np.random.randn(len(t))
s = np.convolve(x, r)[:len(x)]*dt # colored noise
fig = plt.figure(figsize=(9, 4),facecolor='white')
ax = fig.add_subplot(121)
# the main axes is subplot(111) by default
plt.plot(t, s)
plt.axis([0, 1, 1.1*np.amin(s), 2*np.amax(s)])
plt.xlabel('time (s)')
plt.ylabel('current (nA)')
plt.title('Subplot 1: \n Gaussian colored noise')
# this is an inset axes over the main axes
inset_axes = inset_axes(ax,
width="50%", # width = 30% of parent_bbox
height=1.0, # height : 1 inch
loc=1)
n, bins, patches = plt.hist(s, 400, normed=1)
#plt.title('Probability')
plt.xticks([])
plt.yticks([])
ax = fig.add_subplot(122)
# the main axes is subplot(111) by default
plt.plot(t, s)
plt.axis([0, 1, 1.1*np.amin(s), 2*np.amax(s)])
plt.xlabel('time (s)')
plt.ylabel('current (nA)')
plt.title('Subplot 2: \n Gaussian colored noise')
plt.tight_layout()
plt.show()