[Python] How can I mask unwanted data for a display_bins() function (using pPXF data)?

first time posting, I am currently working on making a velocity and dispersion array for the galaxy NGC 4684. I have run through all the steps to get a pPXF data, but there is a star (I'm assuming) that is providing a massive discrepancy in my final output (as seen in the kinematics plots provided). Kinematics Plot (V and Sig)

For reference, this is what the velocity plots should look like (replicated using limits in vmin and vmax) Expected Velocity Plot

I want to completely mask out that section of data as I will be using this set extensively in further labs.

Here is my original code in full:

# Make 1x2 sub plot
fig, ax = plt.subplots(1,2,figsize=(10,10))

#
# Start first plot - Velocity
#============================
plt.subplot(121)


# Subtract off the median (systemic) velocity, so that the rotation is around zero
ax = display_bins(xpos, ypos, binNum, vpxf-numpy.median(vpxf), vmin=-160, vmax=160)

# Set labels
plt.title('Velocity') # Plot title
plt.ylabel('Arcsec') # y-axis label
plt.xlabel('Arcsec') # x-axis label

# Plot colour bar
cbar = plt.colorbar(ax, fraction=0.05, pad=0.04)
cbar.ax.get_yaxis().labelpad = 15
cbar.ax.set_ylabel('km/s', rotation=270)
#cbar.ax.set_clim(-200,200)

#cbar = plt.colorbar(fig, fraction=0.05, pad=0.04, vmin=-200, vmax=200)
#cbar.set_clim(-200, 200) # Set colorbar limits from 20 to 80

#
# Start 2nd plot - Dispersion
#============================
plt.subplot(122)

# Call display_bins for the dispersion values
ax = display_bins(xpos, ypos, binNum, spxf)

# Insert code to overplot contours here:


# Titles and colour bar
plt.title('Dispersion') # y-axis label
plt.ylabel('Arcsec') # y-axis label
plt.xlabel('Arcsec') # y-axis label
cbar = plt.colorbar(ax, fraction=0.05, pad=0.04)
cbar.ax.get_yaxis().labelpad = 15
cbar.ax.set_ylabel('km/s', rotation=270)

# Tweak the spacing
fig.tight_layout(pad=2.5)

# Show plots
plt.show()


SOLUTION ATTEMPT


The following is my code I have used to attempt a mask:


# Start first plot - Velocity
#============================
plt.subplot(121)

fixed_vel = []
fixed_binNum =[]
fixed_xpos =[]
fixed_ypos =[]
for i in binNum:
if vpxf-numpy.median(vpxf) > -500:
fixed_vel.append(vpxf)
fixed_binNum.append(binNum)
fixed_xpos.append(xpos)
fixed_ypos.append(ypos)
else:
print(i, xpos,ypos,vpxf)

fixed_vel = numpy.unique(numpy.array(fixed_vel))
fixed_vel_med = fixed_vel - numpy.median(fixed_vel)
fixed_binNum = numpy.array(fixed_binNum)
fixed_xpos =numpy.array(fixed_ypos)
fixed_ypos =numpy.array(fixed_xpos)



Output from else loop:

2 -20.8 12.0 -73.56390273059304
2 -20.8 12.0 -73.56390273059304
3 -20.8 12.8 -51.887747700703116
3 -20.8 12.8 -51.887747700703116
5 -20.8 14.4 -74.47643398005997
2 -20.8 12.0 -73.56390273059304
2 -20.8 12.0 -73.56390273059304
3 -20.8 12.8 -51.887747700703116
3 -20.8 12.8 -51.887747700703116
5 -20.8 14.4 -74.47643398005997
5 -20.8 14.4 -74.47643398005997
0 -21.6 14.4 -60.09910926725628
1 -21.6 15.2 -51.57607782626809
18 -19.2 8.8 -41.037816701653114
18 -19.2 8.8 -41.037816701653114
6 -20.8 15.2 -66.84680647265044
6 -20.8 15.2 -66.84680647265044
7 -20.0 9.6 -59.045807879638694
18 -19.2 8.8 -41.037816701653114
6 -20.8 15.2 -66.84680647265044
6 -20.8 15.2 -66.84680647265044
36 -18.4 11.2 -45.629206849673224
36 -18.4 11.2 -45.629206849673224
36 -18.4 11.2 -45.629206849673224


As far as I'm aware it does everything I want it to, except the binNum array isn't correct. The following is a readout for each variable used in the display_bins() function:

==============original==============
x 2045
y 2045
bin 2045
unique bin 293
vel 293
=============="fixed"==============
x 2021
y 2021
bin 2021
unique bin 14
vel 284


As you can see the unique bins has been reduced massively in the "fixed" values which is causing an error as unique bins !== vel. For some reason the bins getting appended is a massive amount of the same numbers, instead of 2-15 of the same number, as expected.

If anyone knows a way of masking while keeping the unique bins happy I would very much appreciate it.

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