I am currently writing a program where I can project a hologram video on my computer screen, I had written the code below and I do not know how to specifically rotate a subplot, I had created a 3*3 subplot and I need to rotate subplot 4 by 270 clockwise, subplot 6 by 90 clockwise and subplot 8 by 180.
Second question is how to get rid of all of the axis label... So that the hologram projected will be nice and neatly....
import pandas as pd
import serial
import numpy as np
import matplotlib.pyplot as plt
ser = serial.Serial("COM5", 115200) # define the serial port that we are communicating to and also the baud rate
plt.style.use('dark_background') #define the black background
plt.ion() # tell pyplot we need live data
fig,[[ax1,ax2,ax3],[ax4,ax5,ax6],[ax7,ax8,ax9]] = plt.subplots(3,3) # plotting a figure with 9 subplot
Xplot = []
Yplot = []
Zplot = []
blankx = []
blanky = []
fig = [ax1,ax2,ax3,ax4,ax5,ax6,ax7,ax8,ax9]
while True: #always looping this sequence
while(ser.inWaiting()==0): #if no input from the serial, wait and do nothing
pass
data = ser.readline() #obtain the input from COM 5
data_processed = data.decode('utf-8') #to get rid of the unnecessary string part
data_split = data_processed.split(",") # split the incoming string into a list
x = float(data_split[0]) #to obtain seperate float values for x,y,z
y = float(data_split[1])
z = float(data_split[2])
reset = int(data_split[3]) # reset will output 1
draw = int(data_split[4]) # draw will output 2
if(draw == 2):
Xplot.append(x) #if draw is given instruction, add the x,y,z value into the list to be plot on the graph
Yplot.append(y)
Zplot.append(z)
ax1.plot(blankx,blanky) # subplotting
ax2.plot(Xplot,Yplot,"ro")
ax3.plot(blankx,blank)
ax4.plot(Xplot,Yplot,"ro")
ax5.plot(blankx,blank)
ax6.plot(Xplot,Yplot,"ro")
ax7.plot(blankx,blanky)
ax8.plot(Xplot,Yplot,"ro")
ax9.plot(blankx,blanky)
if(reset == 1):
for f in fig: #if reset is given instruction, clear all figure and clear the elements in the plotting list
f.clear()
Xplot = []
Yplot = []
Zplot = []
plt.pause(.000001)
I might have found a solution, but not a perfect one, I use math instead of code to rotate the plotting, just multiple it by negative value to flip at x and y axis, I have also added a denoiser function to lower the deviation, here is the code that I use, if anyone had any idea about how to rotate a subplot freely, please enlight me.
import pandas as pd
import serial
import matplotlib.pyplot as plt
ser = serial.Serial("COM5", 115200) # define the serial port that we are communicating to and also the baud rate
plt.style.use('dark_background') #define the black background
plt.ion() # tell pyplot we need live data
fig,[[ax1,ax2,ax3],[ax4,ax5,ax6],[ax7,ax8,ax9]] = plt.subplots(3,3) # plotting a figure with 9 subplot
rx = [0]
ry = [0]
rz = [0]
Xplot2 = []
Xplot4 = []
Xplot6 = []
Xplot8 = []
Zplot2 = []
Zplot4 = []
Zplot6 = []
Zplot8 = []
blankx = []
blankz = []
fig = [ax1,ax2,ax3,ax4,ax5,ax6,ax7,ax8,ax9]
def switch(x):
return x*-1
def denoiser(x):
return (x[-1] +x[-2])/4
while True: #always looping this sequence
while(ser.inWaiting()==0): #if no input from the serial, wait and do nothing
pass
data = ser.readline() #obtain the input from COM 5
data_processed = data.decode('utf-8') #to get rid of the unnecessary string part
data_split = data_processed.split(",") # split the incoming string into a list
rx.append(float(data_split[0])) #to obtain seperate float values for x,y,z
ry.append(float(data_split[1]))
rz.append(float(data_split[2]))
reset = int(data_split[3]) # reset will output 1
draw = int(data_split[4]) # draw will output 2
x = denoiser(rx)
y = denoiser(ry)
z = denoiser(rz)
if(draw == 2):
Xplot8.append(x) #if draw is given instruction, add the x,y,z value into the list to be plot on the graph
Zplot8.append(z)
Xplot2.append(switch(x))
Zplot2.append(switch(z))
Xplot4.append(x)
Zplot4.append(switch(z))
Xplot6.append(switch(x))
Zplot6.append(z)
ax1.plot(blankx,blankz) # subplotting
ax1.axis("off")
ax2.plot(Xplot2,Zplot2,"ro")
ax2.axis("off")
ax3.plot(blankx,blankz)
ax3.axis("off")
ax4.plot(Xplot4,Zplot4,"ro")
ax4.axis("off")
ax5.plot(blankx,blankz)
ax5.axis("off")
ax6.plot(Xplot6,Zplot6,"ro")
ax6.axis("off")
ax7.plot(blankx,blankz)
ax7.axis("off")
ax8.plot(Xplot8,Zplot8,"ro")
ax8.axis("off")
ax9.plot(blankx,blankz)
ax9.axis("off")
if(reset == 1):
for f in fig: #if reset is given instruction, clear all figure and clear the elements in the plotting list
f.clear()
Xplot2 = []
Xplot4 = []
Xplot6 = []
Xplot8 = []
Zplot2 = []
Zplot4 = []
Zplot6 = []
Zplot8 = []
plt.pause(.000001)
Related
I'm just trying to graph some simple data and whether I try to do it with plot or subplot it comes out the same. All values in my lists are positive but the y axis is acting like a number line with only positives.
import matplotlib.pyplot as plt
xVal = []
yVal1 = []
yVal2 = []
yVal3 = []
data = []
# load data
with open(r"path", 'r') as f:
data = f.readlines()
yVal1 = data[0].split(",")
yVal2 = data[1].split(",")
yVal3 = data[2].split(",")
del yVal1[-1]
del yVal2[-1]
del yVal3[-1]
print(yVal1)
print(yVal2)
print(yVal3)
# graph dem bois
xVal = [*range(0, len(yVal1))]
'''fig, ax = plt.subplots(3)
ax[0].plot(xVal, yVal1)
ax[0].set_title("pm5")
ax[1].plot(xVal, yVal2)
ax[1].set_title("pm7.5")
ax[2].plot(xVal, yVal3)
ax[2].set_title("pm10")
fig.suptitle("Particulate Levels over time")'''
plt.plot(xVal, yVal3)
plt.show()
As per the comment by Jody Klymak I converted the string lists into float lists and it worked.
fyVal1 = [float(x) for x in yVal1]
I have the following code below:
import time,board,busio
import numpy as np
import adafruit_mlx90640
import matplotlib.pyplot as plt
print("Initializing MLX90640")
i2c = busio.I2C(board.SCL, board.SDA, frequency=800000) # setup I2C
mlx = adafruit_mlx90640.MLX90640(i2c) # begin MLX90640 with I2C comm
mlx.refresh_rate = adafruit_mlx90640.RefreshRate.REFRESH_2_HZ # set refresh rate
mlx_shape = (24,32)
print("Initialized")
# setup the figure for plotting
plt.ion() # enables interactive plotting
fig,ax = plt.subplots(figsize=(12,7))
therm1 = ax.imshow(np.zeros(mlx_shape),vmin=0,vmax=60) #start plot with zeros
cbar = fig.colorbar(therm1) # setup colorbar for temps
cbar.set_label('Temperature [$^{\circ}$C]',fontsize=14) # colorbar label
frame = np.zeros((24*32,)) # setup array for storing all 768 temperatures
t_array = []
print("Starting loop")
while True:
t1 = time.monotonic()
try:
mlx.getFrame(frame) # read MLX temperatures into frame var
data_array = (np.reshape(frame,mlx_shape)) # reshape to 24x32
therm1.set_data(np.fliplr(data_array)) # flip left to right
therm1.set_clim(vmin=np.min(data_array),vmax=np.max(data_array)) # set bounds
cbar.update_normal(therm1) # update colorbar range
plt.title(f"Max Temp: {np.max(data_array):.1f}C")
plt.pause(0.001) # required
#fig.savefig('mlx90640_test_fliplr.png',dpi=300,facecolor='#FCFCFC', bbox_inches='tight') # comment out to speed up
t_array.append(time.monotonic()-t1)
print('Sample Rate: {0:2.1f}fps'.format(len(t_array)/np.sum(t_array)))
except ValueError:
continue # if error, just read again
And my output is shown below:
Image of output
What I'm trying to do is output the x and y coordinates of the max heat that is detected and store them as variables. They are stored in data_array. I know it should be simple code, but I'm confused. Could someone help me?
I have a dataset that is a list of lists.
Each list is a category to be plotted as a box plot.
Each list has a list of up to 9 components to be plotted into subplots.
The functions I am using is below was based on this answer. I pulled it out of my work and added some mock data. Should be a minimal example below.
neonDict = {
0:0, 1:1, 2:2, 3:3, 4:4, 5:5, 6:6, 7:7, 8:8
}
import matplotlib as mpl
import matplotlib.pyplot as plt
def coloredBoxPlot(axis, data,edgeColor,fillColor):
bp = axis.boxplot(data,vert=False,patch_artist=True)
for element in ['boxes', 'whiskers', 'fliers', 'means', 'medians', 'caps']:
plt.setp(bp[element], color=edgeColor)
for patch in bp['boxes']:
patch.set(facecolor=fillColor)
return bp
def plotCalStats(data, prefix='Channel', savedir=None,colors=['#00597c','#a8005c','#00aeea','#007d50','#400080','#e07800'] ):
csize = mpl.rcParams['figure.figsize']
cdpi = mpl.rcParams['figure.dpi']
mpl.rcParams['figure.figsize'] = (12,8)
mpl.rcParams['figure.dpi'] = 1080
pkdata = []
labels = []
lstyles = []
fg, ax = plt.subplots(3,3)
for pk in range(len(neonDict)):
px = pk // 3
py = pk % 3
ax[px,py].set_xlabel('Max Pixel')
ax[px,py].set_ylabel('')
ax[px,py].set_title(str(neonDict[pk]) + ' nm')
pkdata.append([])
for cat in range(len(data)):
bp = ''
for acal in data[cat]:
for apeak in acal.peaks:
pkdata[apeak].append(acal.peaks[apeak][0])
for pk in range(9):
px = pk // 3
py = pk % 3
bp = coloredBoxPlot(ax[px,py], pkdata[pk], colors[cat], '#ffffff')
if len(data[cat]) > 0:
#print(colors[cat])
#print(bp['boxes'][0].get_edgecolor())
labels.append(prefix+' '+str(cat))
lstyles.append(bp['boxes'][0])
fg.legend(lstyles,labels)
fg.suptitle('Calibration Summary by '+prefix)
fg.tight_layout()
if savedir is not None:
plt.savefig(savedir + 'Boxplots.png')
plt.show()
mpl.rcParams['figure.figsize'] = csize
mpl.rcParams['figure.dpi'] = cdpi
return
class acal:
def __init__(self):
self.peaks = {}
for x in range(9):
self.peaks[x] = (np.random.randint(20*x,20*(x+1)),)
mockData = [[acal() for y in range(100)] for x in range(6)]
#Some unused channels
mockData[2] = []
mockData[3] = []
mockData[4] = []
plotCalStats(mockData)
So the issue is that the plot colors do not match the legend. Even if I restrict the data to only add a label if data exists (ensuring thus there is no issue with calling boxplots with an empty data set and not getting an appropriate PathPatch.
The printouts verify the colors are correctly stored in the PathPatch. (I can add my digits -> hex converter) if that is questioned.
Attached is the output. One can see I get a purple box but no purple in the legend. Purple is the 4th category which is empty.
Any ideas why the labels don't match the actual style? Thanks much!
EDITS:
To address question on 'confusing'.
I have six categories of data, each category is coming from a single event. Each event has 9 components. I want to compare all events, for each individual component, for each category on a single plot as shown below.
Each subplot is a individual component comprised from the series of data for each categorical (Channel).
So the link I have provided, (like I said, is adapted from) shows how to create a single box plot on one axis for 2 data sets. I've basically done the same thing for 6 data sets on 9 axis, where 3 data sets are empty (but don't have to be, I did it to illustrate the issue. If I have all 6 data sets there, how can you tell the colors are messed up?????)
Regarding the alpha:
The alphas are always 'ff' when giving only RGB data to matplotlib. If I call get_edgecolors, it will return a tuple (RGBA) where A = 1.0.
See commented out print statement.
EDIT2:
If I restrict it down to a single category, it makes the box plot view less confusing.
Single Example (see how box plot color is orange, figure says it's blue)
All colors off
Feel like this used to work....
Uncertain how the error presented as it did, but the issue has to do with reformatting the data before creating the box plot.
By removing pkdata.append([]) during the creation of the subplots before looping the categories and adding:
pkdata = [[],[],[],[],[],[],[],[],[]] during each iteration of the category loop fixed the issue. The former was sending in all previous channel data...
Output is now better. Full sol attached.
Likely, since the plot uses data from pkdata, the empty channel (data[cat]) plotted previous data (from data[cat-1]) as that was still in pkdata (actually, all previous data[cat] was still in pkdata) which was then plotted. I only check data[cat] for data on each loop to add to the legend. The legend was set up for channels 0,1,5, for example.. but we saw data for channel: 0 as 0, 0+1 as 1, 0+1 as 2, 0+1 as 3, 0+1 as 4, 0+1+5 as 5... thus channel 4 (purple) had data to plot but wasn't added to the legend. Giving the impression of 'misaligned' legends but rather unlegend data...
The single channel data is actually all 6 channels overlapping, the final channel 5 color being orange, overlapping all previous, namely the original channel 0 data to whom the data belongs and was properly added to the legend.
neonDict = {
0:0, 1:1, 2:2, 3:3, 4:4, 5:5, 6:6, 7:7, 8:8
}
import matplotlib as mpl
import matplotlib.pyplot as plt
def getHex(r,g,b,a=1.0):
colors = [int(r * 255 ),int(g * 255 ),int(b * 255 ),int(a * 255) ]
s = '#'
for x in range(4):
cs = hex(colors[x])
if len(cs) == 3:
cs = cs + '0'
s += cs.replace('0x','')
return s
def getRGB(colstr):
try:
a = ''
r = int(colstr[1:3],16) / 255
g = int(colstr[3:5],16) / 255
b = int(colstr[5:7],16) / 255
if len (colstr) == 7:
a = 1.0
else:
a = int(colstr[7:],16) / 255
return (r,g,b,a)
except Exception as e:
print(e)
raise e
return
def compareHexColors(col1,col2):
try:
## ASSUME #RBG or #RBGA
## If less than 7, append the ff for the colors
if len(col1) < 9:
col1 += 'ff'
if len(col2) < 9:
col2 += 'ff'
return col1.lower() == col2.lower()
except Exception as e:
raise e
return False
def coloredBoxPlot(axis, data,edgeColor,fillColor):
bp = axis.boxplot(data,vert=False,patch_artist=True)
for element in ['boxes', 'whiskers', 'fliers', 'means', 'medians', 'caps']:
plt.setp(bp[element], color=edgeColor)
for patch in bp['boxes']:
patch.set(facecolor=fillColor)
return bp
def plotCalStats(data, prefix='Channel', savedir=None,colors=['#00597c','#a8005c','#00aeea','#007d50','#400080','#e07800'] ):
csize = mpl.rcParams['figure.figsize']
cdpi = mpl.rcParams['figure.dpi']
mpl.rcParams['figure.figsize'] = (12,8)
mpl.rcParams['figure.dpi'] = 1080
pkdata = []
labels = []
lstyles = []
fg, ax = plt.subplots(3,3)
for pk in range(len(neonDict)):
px = pk // 3
py = pk % 3
ax[px,py].set_xlabel('Max Pixel')
ax[px,py].set_ylabel('')
ax[px,py].set_title(str(neonDict[pk]) + ' nm')
for cat in range(len(data)):
bp = ''
pkdata = [[],[],[],[],[],[],[],[],[]]
for acal in data[cat]:
for apeak in acal.peaks:
pkdata[apeak].append(acal.peaks[apeak][0])
for pk in range(9):
px = pk // 3
py = pk % 3
bp = coloredBoxPlot(ax[px,py], pkdata[pk], colors[cat], '#ffffff')
if len(data[cat]) > 0:
print(compareHexColors(colors[cat],getHex(*bp['boxes'][0].get_edgecolor())))
labels.append(prefix+' '+str(cat))
lstyles.append(bp['boxes'][0])
fg.legend(lstyles,labels)
fg.suptitle('Calibration Summary by '+prefix)
fg.tight_layout()
if savedir is not None:
plt.savefig(savedir + 'Boxplots.png')
plt.show()
mpl.rcParams['figure.figsize'] = csize
mpl.rcParams['figure.dpi'] = cdpi
return
class acal:
def __init__(self,center):
self.peaks = {}
for x in range(9):
self.peaks[x] = [10*x + (center) + (np.random.randint(10)-1)/2.0,0,0]
mockData = [[acal(x) for y in range(1000)] for x in range(6)]
#Some unused channels
mockData[2] = []
mockData[3] = []
mockData[4] = []
plotCalStats(mockData)
I'm making a plot to compare band structure calculations from two different methods. This means plotting multiple lines for each set of data. I want to have a set of widgets that controls each set of data separately. The code below works if I only plot one set of data, but I can't get the widgets to work properly for two sets of data.
#!/usr/bin/env python3
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.widgets import Slider, TextBox
#cols = ['blue', 'red', 'green', 'purple']
cols = ['#3f54bf','#c14142','#59bf3f','#b83fbf']
finam = ['wan_band.dat','wan_band.pwx.dat']
#finam = ['wan_band.dat'] # this works
lbot = len(finam)*0.09 + 0.06
fig, ax = plt.subplots()
plt.subplots_adjust(bottom=lbot)
ax.margins(x=0) # lines go to the edge of the horizontal axes
def setlines(lines, txbx1, txbx2):
''' turn lines on/off based on text box values '''
try:
mn = int(txbx1) - 1
mx = int(txbx2) - 1
for ib in range(len(lines)):
if (ib<mn) or (ib>mx):
lines[ib].set_visible(False)
else :
lines[ib].set_visible(True)
plt.draw()
except ValueError as err:
print('Invalid range')
#end def setlines(cnt, lines, txbx1, txbx2):
def alphalines(lines, valin):
''' set lines' opacity '''
maxval = int('ff',16)
maxval = hex(int(valin*maxval))[2:]
for ib in range(bcnt):
lines[ib].set_color(cols[cnt]+maxval)
plt.draw()
#end def alphalines(lines, valtxt):
lines = [0]*len(finam) # 2d list to hold Line2Ds
txbox1 = [0]*len(finam) # list of Lo Band TextBoxes
txbox2 = [0]*len(finam) # lsit of Hi Band TextBoxes
alslid = [0]*len(finam) # list of Line Opacity Sliders
for cnt, fnam in enumerate(finam):
ptcnt = 0 # point count
fid = open(fnam, 'r')
fiit = iter(fid)
for line in fiit:
if line.strip() == '' :
break
ptcnt += 1
fid.close()
bandat_raw = np.loadtxt(fnam)
bcnt = int(np.round((bandat_raw.shape[0] / (ptcnt))))
print(ptcnt)
print(bcnt)
# get views of the raw data that are easier to work with
kbandat = bandat_raw[:ptcnt,0] # k point length along path
ebandat = bandat_raw.reshape((bcnt,ptcnt,2))[:,:,1] # band energy # k-points
lines[cnt] = [0]*bcnt # point this list element to another list
for ib in range(bcnt):
#l, = plt.plot(kbandat, ebandat[ib], c=cols[cnt],lw=1.0)
l, = ax.plot(kbandat, ebandat[ib], c=cols[cnt],lw=1.0)
lines[cnt][ib] = l
y0 = 0.03 + 0.07*cnt
bxht = 0.035
axbox1 = plt.axes([0.03, y0, 0.08, bxht]) # x0, y0, width, height
axbox2 = plt.axes([0.13, y0, 0.08, bxht])
txbox1[cnt] = TextBox(axbox1, '', initial=str(1))
txbox2[cnt] = TextBox(axbox2, '', initial=str(bcnt))
txbox1[cnt].on_submit( lambda x: setlines(lines[cnt], x, txbox2[cnt].text) )
txbox2[cnt].on_submit( lambda x: setlines(lines[cnt], txbox1[cnt].text, x) )
axalpha = plt.axes([0.25, y0, 0.65, bxht])
alslid[cnt] = Slider(axalpha, '', 0.1, 1.0, valinit=1.0)
salpha = alslid[cnt]
alslid[cnt].on_changed( lambda x: alphalines(lines[cnt], x) )
#end for cnt, fnam in enumerate(finam):
plt.text(0.01, 1.2, 'Lo Band', transform=axbox1.transAxes)
plt.text(0.01, 1.2, 'Hi Band', transform=axbox2.transAxes)
plt.text(0.01, 1.2, 'Line Opacity', transform=axalpha.transAxes)
plt.show()
All the widgets only control the last data set plotted instead of the individual data sets I tried to associate with each widget. Here is a sample output:
Here the bottom slider should be changing the blue lines' opacity, but instead it changes the red lines' opacity. Originally the variables txbox1, txbox2, and alslid were not lists. I changed them to lists though to ensure they weren't garbage collected but it didn't change anything.
Here is the test data set1 and set2 I've been using. They should be saved as files 'wan_band.dat' and 'wan_band.pwx.dat' as per the hard coded list finam in the code.
I figured it out, using a lambda to partially execute some functions with an iterator value meant they were always being evaluated with the last value of the iterator. Switching to functools.partial fixed the issue.
I'm having an issue exactly like this post, but slightly more frustrating.
I'm using matplotlib to read from a file that is being fed data from another application. For some reason, the ends of the data only connect after the animation (animation.FuncAnimation) has completed its first refresh. Here are some images:
This is before the refresh:
And this is after the refresh:
Any ideas as to why this could be happening? Here is my code:
import json
import itertools
import dateutil.parser
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from matplotlib import style
import scipy.signal as sig
import numpy as np
import pylab as plt
sensors = {}
data = []
lastLineReadNum = 0
class Sensor:
def __init__(self, name, points = 0, lastReading = 0):
self.points = points
self.lastReading = lastReading
self.name = name
self.x = []
self.y = []
class ScanResult:
def __init__(self, name, id, rssi, macs, time):
self.name = name
self.id = id
self.rssi = rssi
self.macs = macs
# Is not an integer, but a datetime.datetime
self.time = time
def readJSONFile(filepath):
with open(filepath, "r") as read_file:
global lastLineReadNum
# Load json results into an object that holds important info
for line in itertools.islice(read_file, lastLineReadNum, None):
temp = json.loads(line)
# Only reads the most recent points...
data.append(ScanResult(name = temp["dev_id"],
id = temp["hardware_serial"],
rssi = temp["payload_fields"]["rssis"],
macs = temp["payload_fields"]["macs"],
time = dateutil.parser.parse(temp["metadata"]["time"])))
lastLineReadNum += 1
return data
style.use('fivethirtyeight')
fig = plt.figure()
ax1 = fig.add_subplot(1, 1, 1)
def smooth(y, box_pts):
box = np.ones(box_pts)/box_pts
y_smooth = np.convolve(y, box, mode='same')
return y_smooth
def determineClosestSensor():
global sensors
#sensors.append(Sensor(time = xs3, rssi = ys3))
def determineXAxisTime(scanresult):
return ((scanresult.time.hour * 3600) + (scanresult.time.minute * 60) + (scanresult.time.second)) / 1000.0
def animate(i):
data = readJSONFile(filepath = "C:/python_testing/rssi_logging_json.json")
for scan in data:
sensor = sensors.get(scan.name)
# First time seeing the sensor
if(sensor == None):
sensors[scan.name] = Sensor(scan.name)
sensor = sensors.get(scan.name)
sensor.name = scan.name
sensor.x.append(determineXAxisTime(scan))
sensor.y.append(scan.rssi)
else:
sensor.x.append(determineXAxisTime(scan))
sensor.y.append(scan.rssi)
ax1.clear()
#basic smoothing using nearby averages
#y_smooth3 = smooth(np.ndarray.flatten(np.asarray(sensors.get("sentrius_sensor_3").y)), 1)
for graphItem in sensors.itervalues():
smoothed = smooth(np.ndarray.flatten(np.asarray(graphItem.y)), 1)
ax1.plot(graphItem.x, smoothed, label = graphItem.name, linewidth = 2.0)
ax1.legend()
determineClosestSensor()
fig.suptitle("Live RSSI Graph from Sentrius Sensors", fontsize = 14)
def main():
ani = animation.FuncAnimation(fig, animate, interval = 15000)
plt.show()
if __name__ == "__main__":
main()
As far as I can tell you are regenerating your data in each animation step by appending to the existing datasets, but then this means that your last x point from the first step is followed by the first x point in the second step, leading to a rewind in the plot. This appears as the line connecting the last datapoint with the first one; the rest of the data is unchanged.
The relevant part of animate:
def animate(i):
data = readJSONFile(filepath = "C:/python_testing/rssi_logging_json.json")
for scan in data:
sensor = sensors.get(scan.name)
# First time seeing the sensor
if(sensor is None): # always check for None with `is`!
... # stuff here
else:
sensor.x.append(determineXAxisTime(scan)) # always append!
sensor.y.append(scan.rssi) # always append!
... # rest of the stuff here
So, in each animation step you
1. load the same JSON file
2. append the same data to an existing sensor identified by sensors.get(scan.name)
3. plot stuff without ever using i.
Firstly, your animate should naturally make use of the index i: you're trying to do something concerning step i. I can't see i being used anywhere.
Secondly, your animate should be as lightweigh as possible in order to get a smooth animation. Load your data once before plotting, and only handle the drawing differences in animate. This will involve slicing or manipulating your data as a function of i.
Of course if the file really does change from step to step, and this is the actual dynamics in the animation (i.e. i is a dummy variable that is never used), all you need to do is zero-initialize all the plotting data in each step. Start with a clean slate. Then you'll stop seeing the lines corresponding to these artificial jumps in the data. But again, if you want a lightweigh animate, you should look into manipulating the underlying data of existing plots rather than replotting everything all the time (especially since calls to ax1.plot will keep earlier points on the canvas, which is not what you usually want in an animation).
try changing :
ani = animation.FuncAnimation(fig, animate, interval = 15000)
to :
ani = animation.FuncAnimation(fig, animate, interval = 15000, repeat = False)