MM vs MI vs MC

[JP]

This is pretty granular.

As a quick hack you can set ylim. For example, put this

plt.ylim(-15,15)

above the line

plt.semilogx(freqout,ampout,color = 'b')

 

[Thomas_A]

I don't know how to help answer your question as I've never seen a UP-4 in person and know almost nothing about it.

Maybe write to Nagaoka?

I could do that as well.

The size of the Moerch "headshell" (i.e. integrated in the tonearm) is 9 mm wide and 25 mm in length, and thus, the cartridge needs to mount flat on that area. If the top is not flat, it may or may not be a problem, depending on the dimensions of the recessed part.

 

[Thomas_A]

I have a Shure V15VxMR that I really like and it still sounds good but I know the stylus is approaching the end of its life. I've considered the Jico SAS replacement but others who've used it in the V15 indicate it raises the high frequencies and makes it sound very bright. I believe its because the boron (and now sapphire) cantilever has a resonance in the audio band whereas the original in the Shure is beryllium. Would changing the loading and capacitance reduce this effect? On the other hand the Jico stylus price is pretty steep and I wonder if sonically the Nagaoka MP500 is the best replacement for the Shure V15VxMR?

The JICO SAS boron sounds really good due to IMO a very nice symmetry of the stylus and diamond giving excellent crosstalk and channel balance. But it does have a peaking due to mechanical resonance. Compared to the beryllium with at 130 pF, the JICO is considerably brighter. The beryllium has a mechanical resonance >30 kHz.


Unfortunately it is difficult to fix the peak with loading unless you sacrifice the high frequencies. Depends of how high you hear...

For sapphire and z-version, the peaking is even worse. So if anything, try the boron. I think the price is ok. An MP-500 is more expensive.

 

[watchnerd]

As a quick hack you can set ylim. For example, put this

plt.ylim(-15,15)

above the line

plt.semilogx(freqout,ampout,color = 'b')

Is this what you expected to see?

Subjectively, it makes it look flatter, I guess.

 

[JP]

It's just a way for you to tune the scale to what looks reasonable to you. 1dB major ticks is hard for me to read as I'm not used to seeing a scale that exaggerated. You do you.

 

[watchnerd]

I wonder if sonically the Nagaoka MP500 is the best replacement for the Shure V15VxMR?

You can probably buy a NOS V15VxMR for cheaper than a new MP-500.

 

[JP]

Is it preferable to separate these in to two plots so that they can scale individually?

 

[watchnerd]

Is it preferable to separate these in to two plots so that they can scale individually?

View attachment 111569

That's neat.

I like that visualization.

 

[scott wurcer]

Is it preferable to separate these in to two plots so that they can scale individually?

View attachment 111569

The regular ripples on the top plot could be at the rotational frequency (LP eccentricity). This is an interesting artifact, you could verify this by taking the seconds per decade of the sweep and dividing by 1.8, I don't have a TRS1007.

BTW thanks for helping out here, I've lost my furnace and am distracted trying to keep any pipes from freezing.

 

[watchnerd]

The regular ripples on the top plot could be at the rotational frequency (LP eccentricity). This is an interesting artifact, you could verify this by taking the seconds per decade of the sweep and dividing by 1.8, I don't have a TRS1007.

BTW thanks for helping out here, I've lost my furnace and am distracted trying to keep any pipes from freezing.

Thank both of you for making it available, modding things and helping to troubleshoot!

The world really needs more cartridge measurements available to the public.

I've done nothing like the volume you guys have done, but even my pathetic measurement contribution is more than what I've seen from most major mags.

 

[watchnerd]

@scott wurcer @JPJ

Is there a way to set the x-axis to something less than 100kHz?

I tried playing with:

x = minf*100

but it didn't seem to do anything.

 

[JP]

As mentioned it should auto scale. Try updating matplotlib.

 

[JP]

The regular ripples on the top plot could be at the rotational frequency (LP eccentricity). This is an interesting artifact, you could verify this by taking the seconds per decade of the sweep and dividing by 1.8, I don't have a TRS1007.

BTW thanks for helping out here, I've lost my furnace and am distracted trying to keep any pipes from freezing.

Will check. Best of luck on the furnace!

 

[scott wurcer]

@scott wurcer @JPJ

Is there a way to set the x-axis to something less than 100kHz?

I tried playing with:

x = minf*100

but it didn't seem to do anything.

Try plt.xlim(1000.,20000.). It turns out the furnace had a transferable lifetime warrantee so only I'm only out $900 labor.

 

[JP]

Was going to finish the re-work of the plots today, but decided to go snowboarding instead. Will see where I get to on it tomorrow.

 

[JP]

Here's a bit of a 'pick your poison' version. Ex:

"""
PLEASE READ
To use this script you need to edit HOME to the directory where the .wav file is.
The .wav file can be any monotonic frequency sweep either up or down in frequency but
it must be trimmed at both ends to remove any leading silence. Frequencies below 1kHz are
ignored since virually all cartridges are well behaved below 1kHz.
Python does not read 24bit packed .wav's 16 bit is more than enough here.

The infoLine should be alpha-numeric with entries separated by " / " only.  The script
will save a .png file that is named from the info line, replacing " / " with "_".  As
example "this / is / a / test" will create a file named "this_is_a_test.png"

plotstyle =

                1 - traditional
                2 - dual axis (twinx)
                3 - dual plot


"""

from scipy import signal
from scipy.io.wavfile import read
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
import datetime
import os




#edit here to add a HOME directory, etc.

#HOME = '/Users/jjones/Documents/polar/' #THIS IS NOT NEEDED IF RAN FROM A FILE

_FILE = 'V15VMR350pF_TRS1007.wav'

infoLine =  'V15-VMR / 47K / 350pF / TRS-1007'

plotStyle = 3


#end Edit




def align_yaxis(ax1, ax2):
    y_lims = np.array([ax.get_ylim() for ax in [ax1, ax2]])

    # force 0 to appear on both axes, comment if don't need
    y_lims[:, 0] = y_lims[:, 0].clip(None, 0)
    y_lims[:, 1] = y_lims[:, 1].clip(0, None)

    # normalize both axes
    y_mags = (y_lims[:,1] - y_lims[:,0]).reshape(len(y_lims),1)
    y_lims_normalized = y_lims / y_mags

    # find combined range
    y_new_lims_normalized = np.array([np.min(y_lims_normalized), np.max(y_lims_normalized)])

    # denormalize combined range to get new axes
    new_lim1, new_lim2 = y_new_lims_normalized * y_mags
    ax1.set_ylim(new_lim1)
    ax2.set_ylim(new_lim2)



#Needed to steal Matlab's flat top window

def ft_window(n):
    w = []
    a0 = 0.21557895
    a1 = 0.41663158
    a2 = 0.277263158
    a3 = 0.083578947
    a4 = 0.006947368
    pi = np.pi

    for x in range(0,n):
        w.append(a0 - a1*np.cos(2*pi*x/(n-1)) + a2*np.cos(4*pi*x/(n-1)) - a3*np.cos(6*pi*x/(n-1)) + a4*np.cos(8*pi*x/(n-1)))
    return w

try:
    input = raw_input
except NameError:
    pass

#_FILE = input('Enter a .wav file to process ')
#File needs trimming with no leading or trailing silence (undefined f)

y = read(_FILE)
Fs = float(y[0])
input_sig = y[1]

frequency = []
amplitude = []

frequency2h = []
amplitude2h = []

frequency3h = []
amplitude3h = []

freqout = []
ampout = []

freqout2h = []
ampout2h = []

freqout3h = []
ampout3h = []

F = int(Fs/100)
win = ft_window(F)

#For Fs sampling use small Fs/100 FFT's so frequency is f/100
#All common sampling rates are divisible by 100
#First try, no overlap and flat top window

#Find the minimum and maximum frequencies and flip the data if the sweep
#is going down in frequency.

y = abs(np.fft.rfft(input_sig[0:F]*win))
minf = np.argmax(y)

y = abs(np.fft.rfft(input_sig[len(input_sig)-F:len(input_sig)]*win))
maxf = np.argmax(y)

if maxf < minf:
    maxf,minf = minf,maxf
    input_sig = np.flipud(input_sig)


for x in range(0,len(input_sig)-F,F):
    y = abs(np.fft.rfft(input_sig[x:x+F]*win))
    f = np.argmax(y) #use largest bin
    if f >=10:
        frequency.append(f*100)
        amplitude.append(y[f])
    if 2*f<F/2-2 and f >= 10:
        f2 = np.argmax(y[(2*f)-2:(2*f)+2])
        frequency2h.append(f*100)
        amplitude2h.append(y[2*f-2+f2])
    if 3*f<F/2-2 and f >= 10:
        f3 = np.argmax(y[(3*f)-2:(3*f)+2])
        frequency3h.append(f*100)
        amplitude3h.append(y[3*f-2+f3])

amp = 0
count = 0
x = minf*100

for x in range(minf*100,(maxf+1)*100,100):
    for y in range(0,len(frequency)):
        if frequency[y] == x:
            amp = amp + amplitude[y]
            count = count + 1
    if count != 0:
        freqout.append(x)
        ampout.append(20*np.log10(amp/count))
    amp = 0
    count = 0

amp = 0
count = 0
x = minf*100

for x in range(minf*100,(maxf+1)*100,100):
    for y in range(0,len(frequency2h)):
        if frequency2h[y] == x:
            amp = amp + amplitude2h[y]
            count = count + 1
    if count != 0:
        freqout2h.append(x)
        ampout2h.append(20*np.log10(amp/count))
    amp = 0
    count = 0
  

amp = 0
count = 0
x = minf*100

for x in range(minf*100,(maxf+1)*100,100):
    for y in range(0,len(frequency3h)):
        if frequency3h[y] == x:
            amp = amp + amplitude3h[y]
            count = count + 1
    if count != 0:
        freqout3h.append(x)
        ampout3h.append(20*np.log10(amp/count))
    amp = 0
    count = 0


b, a = signal.iirfilter(3,.5, btype='lowpass') #filter some noise
ampout = signal.filtfilt(b,a,ampout)
ampout2h = signal.filtfilt(b,a,ampout2h)
ampout3h = signal.filtfilt(b,a,ampout3h)

norm = ampout[0]
ampout = ampout-norm #amplitude is in dB so normalize by subtraction at [0]
ampout2h = ampout2h-norm
ampout3h = ampout3h-norm


plt.rcParams["xtick.minor.visible"] =  True
plt.rcParams["ytick.minor.visible"] =  True

if plotStyle == 1:
        fig, ax1 = plt.subplots(1, 1, figsize=(14,6))
        
        ax1.semilogx(freqout,ampout,color = 'b', label = 'Freq Response')
        ax1.semilogx(freqout2h,ampout2h,color = 'g', label = '2nd Harmonic')
        ax1.semilogx(freqout3h,ampout3h,color = 'r', label = '3rd Harmonic')

        ax1.set_ylabel("Amplitude (dB)")
        ax1.set_xlabel("Frequency (Hz)")

        ax1.legend(loc=4)
        

if plotStyle == 2:
        fig, ax1 = plt.subplots(1, 1, figsize=(14,6))
        ax2 = ax1.twinx()

        if min(ampout) >= -5:
                ax1.set_ylim(-10,)
         
        ax1.semilogx(freqout,ampout,color = 'b', label = 'Freq Response')
        ax2.semilogx(freqout2h,ampout2h,color = 'g', label = '2nd Harmonic')
        ax2.semilogx(freqout3h,ampout3h,color = 'r', label = '3rd Harmonic')

        align_yaxis(ax1, ax2)

        ax1.set_ylabel("Amplitude (dB)")
        ax2.set_ylabel("Distortion (dB)")
        ax1.set_xlabel("Frequency (Hz)")

        fig.legend(loc=4, bbox_to_anchor=(0.899, 0.11))
        

if plotStyle == 3:
        fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True, figsize=(14,6))

        ax2.grid(True, which="major", axis="both", ls="-", color="black")
        ax2.grid(True, which="minor", axis="both", ls="-", color="gainsboro")

        if max(ampout) <= 1.75 and min(ampout) >= -1.75:
                ax1.set_ylim(-2,2)

        ax1.semilogx(freqout,ampout,color = 'b', label = 'Freq Response')
        ax2.semilogx(freqout2h,ampout2h,color = 'g', label = '2nd Harmonic')
        ax2.semilogx(freqout3h,ampout3h,color = 'r', label = '3rd Harmonic')

        ax1.set_ylabel("Amplitude (dB)")
        ax2.set_ylabel("Distortion (dB)")
        ax2.set_xlabel("Frequency (Hz)")

        ax1.legend(loc=4)
        ax2.legend(loc=4)

        
ax1.grid(True, which="major", axis="both", ls="-", color="black")
ax1.grid(True, which="minor", axis="both", ls="-", color="gainsboro")
  
ax1.set_xticks([1000,10000])
ax1.set_xticklabels(['1k','10k'])

ax1.set_title(infoLine + "\n", fontsize=16)

mod_date = datetime.datetime.fromtimestamp(os.path.getmtime(_FILE))

plt.figtext(.17, .13, infoLine + "\n" + _FILE + "\n" + \
            mod_date.strftime("%b %d, %Y %H:%M:%S"), fontsize=8)
            
plt.savefig(infoLine.replace(' / ', '_') +'.png', bbox_inches='tight', pad_inches=.75, dpi=96)

plt.show()

 

[scott wurcer]

Here's a bit of a 'pick your poison' version. Ex:

View attachment 112043

View attachment 112044

View attachment 112045

"""
PLEASE READ
To use this script you need to edit HOME to the directory where the .wav file is.
The .wav file can be any monotonic frequency sweep either up or down in frequency but
it must be trimmed at both ends to remove any leading silence. Frequencies below 1kHz are
ignored since virually all cartridges are well behaved below 1kHz.
Python does not read 24bit packed .wav's 16 bit is more than enough here.

The info_line should be alpha-numeric with entries separated by " / " only.  The script
will save a .png file that is named from the info line, replacing " / " with "_".  As
example "this / is / a / test" will create a file named "this_is_a_test.png"

plotstyle =

                1 - traditional
                2 - dual axis (twinx)
                3 - dual plot


"""

from scipy import signal
from scipy.io.wavfile import read
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
import datetime
import os




#edit here to add a HOME directory, etc.

#HOME = '/Users/jjones/Documents/polar/' #THIS IS NOT NEEDED IF RAN FROM A FILE

_FILE = 'V15VMR350pF_TRS1007.wav'

infoLine =  'V15-VMR / 47K / 350pF / TRS-1007'

plotStyle = 3


#end Edit




def align_yaxis(ax1, ax2):
    y_lims = np.array([ax.get_ylim() for ax in [ax1, ax2]])

    # force 0 to appear on both axes, comment if don't need
    y_lims[:, 0] = y_lims[:, 0].clip(None, 0)
    y_lims[:, 1] = y_lims[:, 1].clip(0, None)

    # normalize both axes
    y_mags = (y_lims[:,1] - y_lims[:,0]).reshape(len(y_lims),1)
    y_lims_normalized = y_lims / y_mags

    # find combined range
    y_new_lims_normalized = np.array([np.min(y_lims_normalized), np.max(y_lims_normalized)])

    # denormalize combined range to get new axes
    new_lim1, new_lim2 = y_new_lims_normalized * y_mags
    ax1.set_ylim(new_lim1)
    ax2.set_ylim(new_lim2)



#Needed to steal Matlab's flat top window

def ft_window(n):
    w = []
    a0 = 0.21557895
    a1 = 0.41663158
    a2 = 0.277263158
    a3 = 0.083578947
    a4 = 0.006947368
    pi = np.pi

    for x in range(0,n):
        w.append(a0 - a1*np.cos(2*pi*x/(n-1)) + a2*np.cos(4*pi*x/(n-1)) - a3*np.cos(6*pi*x/(n-1)) + a4*np.cos(8*pi*x/(n-1)))
    return w

try:
    input = raw_input
except NameError:
    pass

#_FILE = input('Enter a .wav file to process ')
#File needs trimming with no leading or trailing silence (undefined f)

y = read(_FILE)
Fs = float(y[0])
input_sig = y[1]

frequency = []
amplitude = []

frequency2h = []
amplitude2h = []

frequency3h = []
amplitude3h = []

freqout = []
ampout = []

freqout2h = []
ampout2h = []

freqout3h = []
ampout3h = []

F = int(Fs/100)
win = ft_window(F)

#For Fs sampling use small Fs/100 FFT's so frequency is f/100
#All common sampling rates are divisible by 100
#First try, no overlap and flat top window

#Find the minimum and maximum frequencies and flip the data if the sweep
#is going down in frequency.

y = abs(np.fft.rfft(input_sig[0:F]*win))
minf = np.argmax(y)

y = abs(np.fft.rfft(input_sig[len(input_sig)-F:len(input_sig)]*win))
maxf = np.argmax(y)

if maxf < minf:
    maxf,minf = minf,maxf
    input_sig = np.flipud(input_sig)


for x in range(0,len(input_sig)-F,F):
    y = abs(np.fft.rfft(input_sig[x:x+F]*win))
    f = np.argmax(y) #use largest bin
    if f >=10:
        frequency.append(f*100)
        amplitude.append(y[f])
    if 2*f<F/2-2 and f >= 10:
        f2 = np.argmax(y[(2*f)-2:(2*f)+2])
        frequency2h.append(f*100)
        amplitude2h.append(y[2*f-2+f2])
    if 3*f<F/2-2 and f >= 10:
        f3 = np.argmax(y[(3*f)-2:(3*f)+2])
        frequency3h.append(f*100)
        amplitude3h.append(y[3*f-2+f3])
 
amp = 0
count = 0
x = minf*100

for x in range(minf*100,(maxf+1)*100,100):
    for y in range(0,len(frequency)):
        if frequency[y] == x:
            amp = amp + amplitude[y]
            count = count + 1
    if count != 0:
        freqout.append(x)
        ampout.append(20*np.log10(amp/count))
    amp = 0
    count = 0

amp = 0
count = 0
x = minf*100

for x in range(minf*100,(maxf+1)*100,100):
    for y in range(0,len(frequency2h)):
        if frequency2h[y] == x:
            amp = amp + amplitude2h[y]
            count = count + 1
    if count != 0:
        freqout2h.append(x)
        ampout2h.append(20*np.log10(amp/count))
    amp = 0
    count = 0
    

amp = 0
count = 0
x = minf*100

for x in range(minf*100,(maxf+1)*100,100):
    for y in range(0,len(frequency3h)):
        if frequency3h[y] == x:
            amp = amp + amplitude3h[y]
            count = count + 1
    if count != 0:
        freqout3h.append(x)
        ampout3h.append(20*np.log10(amp/count))
    amp = 0
    count = 0


b, a = signal.iirfilter(3,.5, btype='lowpass') #filter some noise
ampout = signal.filtfilt(b,a,ampout)
ampout2h = signal.filtfilt(b,a,ampout2h)
ampout3h = signal.filtfilt(b,a,ampout3h)

norm = ampout[0]
ampout = ampout-norm #amplitude is in dB so normalize by subtraction at [0]
ampout2h = ampout2h-norm
ampout3h = ampout3h-norm


plt.rcParams["xtick.minor.visible"] =  True
plt.rcParams["ytick.minor.visible"] =  True

if plotStyle == 1:
        fig, ax1 = plt.subplots(1, 1, figsize=(14,6))
     
        ax1.grid(True, which="major", axis="both", ls="-", color="black")
        ax1.grid(True, which="minor", axis="both", ls="-", color="gainsboro")

        ax1.semilogx(freqout,ampout,color = 'b', label = 'Freq Response')
        ax1.semilogx(freqout2h,ampout2h,color = 'g', label = '2nd Harmonic')
        ax1.semilogx(freqout3h,ampout3h,color = 'r', label = '3rd Harmonic')

        ax1.set_ylabel("Amplitude (dB)")
        ax1.set_xlabel("Frequency (Hz)")

        ax1.legend(loc=4)
     

if plotStyle == 2:
        fig, ax1 = plt.subplots(1, 1, figsize=(14,6))
        ax2 = ax1.twinx()

        ax1.grid(True, which="major", axis="both", ls="-", color="black")
        ax1.grid(True, which="minor", axis="both", ls="-", color="gainsboro")

        if min(ampout) >= -5:
                ax1.set_ylim(-10,)
      
        ax1.semilogx(freqout,ampout,color = 'b', label = 'Freq Response')
        ax2.semilogx(freqout2h,ampout2h,color = 'g', label = '2nd Harmonic')
        ax2.semilogx(freqout3h,ampout3h,color = 'r', label = '3rd Harmonic')

        align_yaxis(ax1, ax2)

        ax1.set_ylabel("Amplitude (dB)")
        ax2.set_ylabel("Distortion (dB)")
        ax1.set_xlabel("Frequency (Hz)")

        fig.legend(loc=4, bbox_to_anchor=(0.899, 0.11))
     

if plotStyle == 3:
        fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True, figsize=(14,6))

        ax1.grid(True, which="major", axis="both", ls="-", color="black")
        ax1.grid(True, which="minor", axis="both", ls="-", color="gainsboro")

        ax2.grid(True, which="major", axis="both", ls="-", color="black")
        ax2.grid(True, which="minor", axis="both", ls="-", color="gainsboro")

        if max(ampout) <= 1.75 and min(ampout) >= -1.75:
                ax1.set_ylim(-2,2)

        ax1.semilogx(freqout,ampout,color = 'b', label = 'Freq Response')
        ax2.semilogx(freqout2h,ampout2h,color = 'g', label = '2nd Harmonic')
        ax2.semilogx(freqout3h,ampout3h,color = 'r', label = '3rd Harmonic')

        ax1.set_ylabel("Amplitude (dB)")
        ax2.set_ylabel("Distortion (dB)")
        ax2.set_xlabel("Frequency (Hz)")

        ax1.legend(loc=4)
        ax2.legend(loc=4)
     

ax1.set_xticks([1000,10000])
ax1.set_xticklabels(['1K','10K'])

ax1.set_title(infoLine + "\n", fontsize=16)


mod_date = datetime.datetime.fromtimestamp(os.path.getmtime(_FILE))

plt.figtext(.17, .13, infoLine + "\n" + _FILE + "\n" + \
            mod_date.strftime("%b %d, %Y %H:%M:%S"), fontsize=8)
         
plt.savefig(infoLine.replace(' / ', '_') +'.png', bbox_inches='tight', pad_inches=.75, dpi=96)

plt.show()

Thanks for all the work on the plot formats, I suggest anyone interested keep JPJ's versions of these scripts I am most interested in the signal processing and data reduction end of things.

 

[JP]

You do the hard part and I’ll make ‘em pretty.

 

[levimax]

Where can I get the scripts and instructions? I looked back at the thread but only found bits and pieces and no link for the scripts... probably missed it.

 

[scott wurcer]

Where can I get the scripts and instructions? I looked back at the thread but only found bits and pieces and no link for the scripts... probably missed it.

Post #356 contains all you need with several Python packages. Some Python installs are not oriented to heavy duty math and signal processing so you have to load the libraries yourself but it's all pretty straight forward.