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()
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)
So this is my code, it's written a little messy and my result is absolutely ridiculous. I have no idea how to fix it.
Also, the seaborn library does not work on my computer in any way.
.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
data=pd.read_csv('Data.csv',encoding="latin1",sep=";",engine="python")
table = data.replace(0, 0.1)
plt.plot(table["RMDM"], table["BSURF"], color="#03012d", marker=".", ls="None", markersize=3, label="")
data['RMDM'] = data['RMDM'].astype(float)
data['BSURF'] = data['BSURF'].astype(float)
fig, ax = plt.subplots()
x=data['BSURF']
y=data['RMDM']
ax.set_yscale('log')
ax.set_xscale('log')
plt.style.use('classic')
plt.xlabel('B_LC')
plt.ylabel('RM/DM')
plt.plot(x,y, 'og')
from scipy.stats import linregress
df = data.loc[(data['RMDM'] >0) & (data['BSURF'] >0)]
stats = linregress(np.log10(df["RMDM"]),np.log10(df["BSURF"]))
m = stats.slope
b = stats.intercept
r = stats.rvalue
x = np.logspace(-1, 5, base=10)
y = (m*x+b)
plt.plot(x, y, c='orange', label="fit")
plt.legend()
#m,c=np.polyfit(x,y,1)
#plt.plot(x,m*x+c)
plt.grid()
plt.show()
lmplot can be used to create a linear line through your data. you correctly used np.log for the linear regression data. keep x in terms of the log.
df['log_col1']=np.log(df['col1'])
sns.lmplot(x='log_col1','y='target', data=df, ci=None)
sns.scatterplot(y='target',x='log_col1',data=df)
plt.show()
I am trying to animate a simple line chart with 2 equal arrays. I've seen some guides online but honestly, I'm not sure what I'm doing here, am I close to the solution?
I'm looking for this: https://miro.medium.com/max/1126/1*j0LxVQPbwtQDpL17TH9gZw.gif
import numpy as np
from matplotlib import pyplot as plt
from matplotlib import animation
x = np.array([1,2,3,4,5,6,7,8])
y = np.array([6,9,12,42,50,62,76,82])
fig = plt.figure()
ax = plt.axes(xlim=(1, 8), ylim=(6, 82))
line, = ax.plot([], [], lw=2)
def init():
line.set_data([], [])
return line,
def animate(i):
line.set_ydata(x[i:])
line.set_xdata(y[i:])
return line,
anim = animation.FuncAnimation(fig, animate, init_func=init,
frames=200, interval=20, blit=True)
anim.save('basic_animation.mp4', fps=30, extra_args=['-vcodec', 'libx264'])
plt.show()
We are changing the way the data is set up. And I changed the number of frames to 9 because the number of data is 8. In addition, I used PIL to create GIF images, so please correct it.
import numpy as np
from matplotlib import pyplot as plt
from matplotlib import animation
from IPython.display import HTML
from matplotlib.animation import PillowWriter
x = np.array([1,2,3,4,5,6,7,8])
y = np.array([6,9,12,42,50,62,76,82])
fig = plt.figure()
ax = plt.axes(xlim=(1, 8), ylim=(6, 82))
line, = ax.plot([], [], lw=2)
def init():
line.set_data([], [])
return line,
def animate(i):
line.set_data(x[:i],y[:i])
return line,
anim = animation.FuncAnimation(fig, animate, init_func=init,
frames=9, interval=200, blit=True)
# anim.save('basic_animation.mp4', fps=30, extra_args=['-vcodec', 'libx264'])
# plt.show()
anim.save('plot_ani.gif', writer='pillow')
For example, this code works fine and saves the plt only when the plt.show() is present at the end. or else it just runs without saving any output.
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.animation
import numpy as np
def animate(i):
line.set_ydata(np.sin(2*np.pi*i / 50)*np.sin(x))
#fig.canvas.draw() not needed see comment by #tacaswell
plt.savefig(str(i)+".png")
return line,
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_xlim(0, 2*np.pi)
ax.set_ylim(-1,1)
x = np.linspace(0, 2*np.pi, 200)
line, = ax.plot(x, np.zeros_like(x))
plt.draw()
ani = matplotlib.animation.FuncAnimation(fig, animate, frames=5, repeat=False)
plt.show()
PS - I tried using ani.save(writer="ffmpeg",dpi=200) and it works fine, but later on as I am using ffmpeg commands to convert the image sequence to animation, it shows invalid PNG signature.
Also I am new in this field so my appoligies if i did not follow any proper method.
Thanks in advance.
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()