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Real-time plotting for monitoring data using plotly

I'm trying to plot data that is acquire in real-time by a set of sensors disposed in a test room. We succeed doing that using matplotlib, however it will be better to use plotly, since the graphs could be exported and visualized in other environments (considering our final goals). I saw some examples here and here but it does not fit what we have, because the data list is being constantly updated by the sensors data acquisition, not by a equation (depending on the time step set). The sensors are read by the nidaqmx package (National Instruments sensors) - but no problems with this part apparently. Here it is the working code that we used to plot using matplotlib (the sensors' name are stored in lists, not described here):
import nidaqmx
from nidaqmx.constantsimport(TerminalConfiguration,VoltageUnits,ThermocoupleType,CJCSource,TemperatureUnits,ResistanceConfiguration,ExcitationSource,RTDType)
from datetime import datetime,date
import time
import numpy as np
import pandas as pd
import plotly.express as px
import matplotlib.pyplot as plt
import matplotlib.animation as animation
sensor_name = "sensor_1"
list_voltage_rse = [...]
date_time = []
data = []
tempo = []
t_min = 2
t_max = 40
time_step = 10
N = 100
x_len = N
k = 1
i = 0
y_range = [t_min, t_max]
#read data from DAQ (specified sensor)
def readdaq():
with nidaqmx.Task() as task:
first_inicio = datetime.now()
if sensor_name in list_voltage_rse:
task.ai_channels.add_ai_voltage_chan(sensor_name,terminal_config=TerminalConfiguration.RSE,units=VoltageUnits.VOLTS)
first_fim = datetime.now()
tempo_leitura = (first_fim - first_inicio).total_seconds()
task.start()
value = task.read()
print(value)
task.stop()
#Write Data Function
def writefiledata(t, x):
# Open File
file = open("tempdata.txt", "a")
# Write Data
time = str(t)
value = str(round(x, 2))
file.write(time + "\t" + value)
file.write("\n")
# Close File
file.close()
#Create figure for plotting
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
xs = list(range(0, N))
ys = [0] * x_len
ax.set_ylim(y_range)
#Create a blank line. We will update the line in animate
line, = ax.plot(xs, ys)
#Configure Plot
plt.title('Temperature')
plt.xlabel('t [s]')
plt.ylabel('Temp [degC]')
plt.grid()
#Logging Temperature Data from DAQ Device
def logging(i, ys):
inicio = datetime.now()
value = readdaq()
print("T =", round(value,1), "[degC]")
data.append(value)
final = datetime.now()
tempo_leitura = (final - inicio).total_seconds()
print(tempo_leitura)
time.sleep(time_step - tempo_leitura)
global k
k = k + 1
writefiledata(k*time_step, value)
# Add y to list
ys.append(value)
# Limit y list to set number of items
ys = ys[-x_len:]
# Update line with new Y values
line.set_ydata(ys)
return line,
ani = animation.FuncAnimation(fig,logging,fargs=(ys,),interval=100,blit=True)
plt.show()
I hope this code helped to give an idea of what I'm looking for.

Wanted to partially remove items on x axis while using matplotlib.pyplot

I am designing a currency converter app and I had an idea to add graphical currency analysis to it.
for this I've started using matplotlib.pyplot . I am taking from date(i.e. date from which graph compares data ) as input from user.And using this data , i am taking real time currency data from certain sources.
But here came the main issue.When i drew the graph the x - axis is really bad😫.
Ill insert the output i am getting--> graph and a rough code of mine.The main isuue i want to eliminate is that i want only certain parts of x-axis visible.
import matplotlib.pyplot as plt
import requests
x = []
y = []
for i in range(fyear,tyear):
for j in range(fmonth,tmonth):
for k in range(fday,tday):
response = requests.get("https://api.ratesapi.io/api/{}-{}-{}?base={}&symbols{}".format(i,j,k,inp_curr,out_curr))
data = response.json()
rate = data['rates'][out_curr]
y.append(rate)
x.append("{}/{}/{}".format(j,i,k))
plt.plot(x,y)
OBTAINED OUTPUT:
enter image description here
need answer quickly.....

If for parts you mean to set only few labels along x axis you could use xticks and locator_params. See docs here: https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.xticks.html
import matplotlib.pyplot as plt
import numpy as np
import requests
# use some fake data for testing - use your params
fyear = 2019
tyear = 2020
fmonth = 1
tmonth = 13
fday=1
tday=28
inp_curr = "EUR"
out_curr = "GBP"
# init lists
x = []
y = []
for i in range(fyear,tyear):
for j in range(fmonth,tmonth):
for k in range(fday,tday):
response = requests.get("https://api.ratesapi.io/api/{}-{}-{}?base={}&symbols{}".format(i,j,k,inp_curr,out_curr))
data = response.json()
rate = data['rates'][out_curr]
y.append(rate)
x.append("{}/{}/{}".format(j,i,k))
# create subplot
fig, ax = plt.subplots(1,1, figsize=(20, 11))
# plot image
img = ax.plot(x, y)
# set the total number of x_ticks (the ticks on the x label)
ax.set_xticks(np.arange(len(x)))
# set the labels for each x_tick (actually is x list)
ax.set_xticklabels(x)
# set the number of ticks you want to visualize
# you can just select a number i.e. 10 and you will visualize onlu 10 ticks
# in order to visualize, say the first day of each month set this
n = round(len(x)/(tday-fday))
plt.locator_params(axis='x', nbins=n)
# change labels position to oblique
ax.get_figure().autofmt_xdate()
fig.tight_layout()
Remember to import numpy! Hope it helps you. Here you can see my output.

Making parts of a line graph a different colour depending on their y value in Matplotlib

I'm making a program which takes a random list of data and will plot it.
I want the colour of the graph to change if it goes above a certain value.
https://matplotlib.org/gallery/lines_bars_and_markers/multicolored_line.html
Matplotlib has an entry on doing just this but it seems to require using a function as input for the graph not using lists.
Does anyone know how to either convert this to work for lists or another way of doing so?
Here's my code so far (without my horrific failed attempts to colour code them)
from matplotlib import pyplot as plt
import random
import sys
import numpy as np
#setting the max and min values where I want the colour to change
A_min = 2
B_max = 28
#makes lists for later
A_min_lin = []
B_max_lin = []
#simulating a corruption of the data where it returns all zeros
sim_crpt = random.randint(0,10)
print(sim_crpt)
randomy = []
if sim_crpt == 0:
randomy = []
#making the empty lists for corrupted data
for i in range(0,20):
randomy.append(0)
print(randomy)
else:
#making a random set of values for the y axis
for i in range(0,20):
n = random.randint(0,30)
randomy.append(n)
print(randomy)
#making an x axis for time
time = t = np.arange(0, 20, 1)
#Making a list to plot a straight line showing where the maximum and minimum values
for i in range(0, len(time)):
A_min_lin.append(A_min)
B_max_lin.append(B_max)
#Testing to see if more than 5 y values are zero to return if it's corrupted
tracker = 0
for i in (randomy):
if i == 0:
tracker += 1
if tracker > 5:
sys.exit("Error, no data")
#ploting and showing the different graphs
plt.plot(time,randomy)
plt.plot(time,A_min_lin)
plt.plot(time,B_max_lin)
plt.legend(['Data', 'Minimum for linear', "Maximum for linear"])
plt.show

You can use np.interp to generate the fine-grain data to plot:
# fine grain time
new_time = np.linspace(time.min(), time.max(), 1000)
# interpolate the y values
new_randomy = np.interp(new_time, time, randomy)
# this is copied from the link with few modification
points = np.array([new_time, new_randomy]).T.reshape(-1, 1, 2)
segments = np.concatenate([points[:-1], points[1:]], axis=1)
fig, axs = plt.subplots()
norm = plt.Normalize(new_randomy.min(), new_randomy.max())
lc = LineCollection(segments, cmap='viridis', norm=norm)
# Set the values used for colormapping
lc.set_array(new_randomy[1:])
lc.set_linewidth(2)
line = axs.add_collection(lc)
fig.colorbar(line, ax=axs)
# set the limits
axs.set_xlim(new_time.min(), new_time.max())
axs.set_ylim(new_randomy.min(), new_randomy.max())
plt.show()
Output:

Using Python loop to compile single graph

I'm attempting to compile 12 boxplots on a single set of axes using a loop. However, when I run the code as show below, I end up with 12 different figures. Can anyone provide input on how to make sure they all stay on one figure?
import numpy as np
import matplotlib.pyplot as plt
mons =['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']
for i in range(len(mons)):
data = np.genfromtxt('Z:/Dan/SnowStatData/Errors/'+mons[i]+'ErrorStats.txt',skip_header=4)
lat = data[:,1]
lon = data[:,2]
corr = data[:,3]
corp = data[:,4]
RMSE = data[:,5]
MAE = data[:,6]
SDE = data[:,7]
sigcorrs = []
clat = []
clon = []
for j in range(len(lat)):
if corp[j] < 0.05:
sigcorrs = np.append(sigcorrs,corr[j])
clat = np.append(clat,lat[j])
clon = np.append(clon,lon[j])
else:
continue
plt.boxplot(sigcorrs,positions=[i])
plt.show()

The trick is to create the figure before you start looping:
fig = plt.figure()
ax = plt.subplot(111)
Now, in the loop don't use plt.boxplot which might create a new figure, but call the function directly from the axes object:
ax.boxplot(sigcorrs,positions=[i])
And then call plt.show() after the loop

It looks like you're calling plt.show() every time through the loop, so it's drawing the figure every time. Try putting it after the loop.

Matplotlib update a graph with new data, graph does not show

I have much the same problem as this guy: Dynamically updating plot in matplotlib. I want to update a graph with data from a serial port and I have been trying to implement this answer, but I can't get a MWE to work. The graph simply doesn't appear, but everything else seems to work. I have read about problems with the installation of Matplotlib causing similar symptoms.
Here is my Minimum Not Working Example (MNWE):
import numpy as np
import matplotlib.pyplot as plt
fig1 = plt.figure() #Create figure
l, = plt.plot([], [], 'r-') #What is the comma for? Is l some kind of struct/class?
plt.xlim(0, 1)
plt.ylim(0, 1)
plt.xlabel('x')
plt.title('test')
k = 5
xdata=[0.5 for i in range(k+1)] # Generate a list to hold data
ydata=[j for j in range(k+1)]
while True:
y = float(raw_input("y val :"))
xdata.append(y) # Append new data to list
k = k + 1 # inc x value
ydata.append(k)
l.set_data(xdata,ydata) # update data
print xdata # Print for debug perposes
print ydata
plt.draw # These seem to do nothing
plt.show # !?
Could someone please point me in the right direction / provide a link / tell me what to google? I'm lost. Thanks

As suggested by user #fhdrsdg I was missing brackets. Getting the rescale to work requires code from: set_data and autoscale_view matplotlib
A working MWE is provided below:
import numpy as np
import matplotlib.pyplot as plt
plt.ion() # Enable interactive mode
fig = plt.figure() # Create figure
axes = fig.add_subplot(111) # Add subplot (dont worry only one plot appears)
axes.set_autoscale_on(True) # enable autoscale
axes.autoscale_view(True,True,True)
l, = plt.plot([], [], 'r-') # Plot blank data
plt.xlabel('x') # Set up axes
plt.title('test')
k = 5
xdata=[0.5 for i in range(k+1)] # Generate a list to hold data
ydata=[j for j in range(k+1)]
while True:
y = float(raw_input("y val :")) #Get new data
xdata.append(y) # Append new data to list
k = k + 1 # inc x value
ydata.append(k)
l.set_data(ydata,xdata) # update data
print xdata # Print for debug perposes
print ydata
axes.relim() # Recalculate limits
axes.autoscale_view(True,True,True) #Autoscale
plt.draw() # Redraw