'''
PLEASE READ
To use this script you need to edit the user parameters section. 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.
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".
file_0 = The first (L) or only file to plot
file_1 = The second (R) file to plot. If only using one file change to = ''
plotstyle = 1 - traditional
2 - dual axis (twinx)
3 - dual plot FR and distortion
4 - dual plot FR zoom and dual axis (twinx)
5 - small plot FR only
riaamode = 0 - off
1 - bass emphasis
2 - trebel de-emphasis
3 - both
riaainv = 0 - disable
1 - inverse RIAA EQ per riaamode setting
str100 = 0 - disable
1 - enable 6dB/oct correction from 500Hz to 40Hz
xg7001 = 0 - disable
1 - custom bass filter for Denon XG-7001 sweep (@stereoplay filter)
normalize = Frequency in Hz to set as 0dB in the plot
file0norm = 0 - normalize both files independently
1 - normalize both files to file 0 level
onekfstart = 0 - disable
1 - start plot from 1kHz
endf = 0 - highest frequency to plot
'''
swversion = "17BETA"
from scipy import signal
from scipy.io.wavfile import read
from pathlib import Path
from matplotlib.legend_handler import HandlerBase
from matplotlib.offsetbox import AnchoredText
from itertools import chain
from datetime import datetime
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
import numpy as np
import os
import librosa
#edit user parameters
file_0 = 'wavfile0.wav'
file_1 = 'wavfile1.wav'
infoline = 'Cart / Load / Record'
equipinfo = 'Arm -> Phonostage -> ADC'
plotstyle = 4
plotdataout = 0
roundlvl = 1
riaamode = 1
riaainv = 0
str100 = 0
xg7001 = 0
normalize = 1000
file0norm = 0
onekfstart = 0
endf = 20000
ovdylim = 0
ovdylimvalue = [-35,5]
topdb = 100
framelength = 1024
hoplength = 256
#end Edit
fileopenidx = 0
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
return new_lim1, new_lim2
class AnyObjectHandler(HandlerBase):
def create_artists(self, legend, orig_handle,
x0, y0, width, height, fontsize, trans):
l1 = plt.Line2D([x0,y0+width], [0.7*height,0.7*height],
color=orig_handle[0], linestyle=orig_handle[1])
l2 = plt.Line2D([x0,y0+width], [0.3*height,0.3*height],
color=orig_handle[2], linestyle=orig_handle[3])
return [l1, l2]
def ft_window(n): #Matlab's flat top window
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
def find_nearest(array, value):
array = np.asarray(array)
idx = (np.abs(array - value)).argmin()
return idx
def createplotdata(insig, Fs):
fout = []
aout = []
foutx = []
aoutx = []
fout2 = []
aout2 = []
fout3 = []
aout3 = []
global norm
def interpolate(f, a, minf, maxf, fstep):
f_out = []
a_out = []
amp = 0
count = 0
for x in range(minf,(maxf)+1,fstep):
for y in range(0,len(f)):
if f[y] == x:
amp = amp + a[y]
count = count + 1
if count != 0:
f_out.append(x)
a_out.append(20*np.log10(amp/count))
amp = 0
count = 0
return f_out, a_out
def rfft(insig, Fs, minf, maxf, fstep):
freq = []
amp = []
freqx = []
ampx = []
freq2h = []
amp2h = []
freq3h = []
amp3h = []
F = int(Fs/fstep)
win = ft_window(F)
if chinfile == 1:
for x in range(0,len(insig)-F,F):
y = abs(np.fft.rfft(insig[x:x+F]*win))
f = np.argmax(y) #use largest bin
if f >=minf/fstep and f <=maxf/fstep:
freq.append(f*fstep)
amp.append(y[f])
if 2*f<F/2-2 and f > minf/fstep and f < maxf/fstep:
f2 = np.argmax(y[(2*f)-2:(2*f)+2])
freq2h.append(f*fstep)
amp2h.append(y[2*f-2+f2])
if 3*f<F/2-2 and f > minf/fstep and f < maxf/fstep:
f3 = np.argmax(y[(3*f)-2:(3*f)+2])
freq3h.append(f*fstep)
amp3h.append(y[3*f-2+f3])
else:
for x in range(0,len(insig[0])-F,F):
y0 = abs(np.fft.rfft(insig[0,x:x+F]*win))
y1 = abs(np.fft.rfft(insig[1,x:x+F]*win))
f0 = np.argmax(y0) #use largest bin
f1 = np.argmax(y1) #use largest bin
if f0 >=minf/fstep and f0 <=maxf/fstep:
freq.append(f0*fstep)
freqx.append(f1*fstep)
amp.append(y0[f0])
ampx.append(y1[f1])
if 2*f0<F/2-2 and f0 > minf/fstep and f0 < maxf/fstep:
f2 = np.argmax(y0[(2*f0)-2:(2*f0)+2])
freq2h.append(f0*fstep)
amp2h.append(y0[2*f0-2+f2])
if 3*f0<F/2-2 and f0 > minf/fstep and f0 < maxf/fstep:
f3 = np.argmax(y0[(3*f0)-2:(3*f0)+2])
freq3h.append(f0*fstep)
amp3h.append(y0[3*f0-2+f3])
return freq, amp, freqx, ampx, freq2h, amp2h, freq3h, amp3h
def normstr100(f, a):
fmin = 40
fmax = 500
slope = -6.02
for x in range(find_nearest(f, fmin), (find_nearest(f, fmax))):
a[x] = a[x] + 20*np.log10(1*((f[x])/fmax)**((slope/20)/np.log10(2)))
return a
def chunk(insig, Fs, fmin, fmax, step, offset):
f, a, fx, ax, f2, a2, f3, a3 = rfft(insig, Fs, fmin, fmax, step)
f, a = interpolate(f, a, fmin, fmax, step)
fx, ax = interpolate(fx, ax, fmin, fmax, step)
f2, a2 = interpolate(f2, a2, fmin, fmax, step)
f3, a3 = interpolate(f3, a3, fmin, fmax, step)
a = [x - offset for x in a]
ax = [x - offset for x in ax]
a2 = [x - offset for x in a2]
a3 = [x - offset for x in a3]
return f, a, fx, ax, f2, a2, f3, a3
def concat(f, a, fx, ax, f2, a2, f3, a3, fout, aout, foutx, aoutx, fout2, aout2, fout3, aout3):
fout = fout + f
aout = aout + a
foutx = foutx + fx
aoutx = aoutx + ax
fout2 = fout2 + f2
aout2 = aout2 + a2
fout3 = fout3 + f3
aout3 = aout3 + a3
return fout, aout, foutx, aoutx, fout2, aout2, fout3, aout3
if onekfstart == 0:
f, a, fx, ax, f2, a2, f3, a3 = chunk(insig, Fs, 20, 45, 5, 26.03)
fout, aout, foutx, aoutx, fout2, aout2, fout3, aout3 = concat(f, a, fx, ax, f2, a2, f3, a3, fout, aout, foutx, aoutx, fout2, aout2, fout3, aout3)
f, a, fx, ax, f2, a2, f3, a3 = chunk(insig, Fs, 50, 90, 10, 19.995)
fout, aout, foutx, aoutx, fout2, aout2, fout3, aout3 = concat(f, a, fx, ax, f2, a2, f3, a3, fout, aout, foutx, aoutx, fout2, aout2, fout3, aout3)
f, a, fx, ax, f2, a2, f3, a3 = chunk(insig, Fs, 100, 980, 20, 13.99)
fout, aout, foutx, aoutx, fout2, aout2, fout3, aout3 = concat(f, a, fx, ax, f2, a2, f3, a3, fout, aout, foutx, aoutx, fout2, aout2, fout3, aout3)
f, a, fx, ax, f2, a2, f3, a3 = chunk(insig, Fs, 1000, endf, 100, 0)
fout, aout, foutx, aoutx, fout2, aout2, fout3, aout3 = concat(f, a, fx, ax, f2, a2, f3, a3, fout, aout, foutx, aoutx, fout2, aout2, fout3, aout3)
if str100 == 1:
aout = normstr100(fout, aout)
aout2 = normstr100(fout2, aout2)
aout3 = normstr100(fout3, aout3)
if chinfile == 2:
aoutx = normstr100(foutx, aoutx)
if file0norm == 1 and fileopenidx == 1:
i = find_nearest(fout, normalize)
norm = aout[i]
elif file0norm == 0:
i = find_nearest(fout, normalize)
norm = aout[i]
aout = aout-norm #amplitude is in dB so normalize by subtraction at [i]
aoutx = aoutx-norm
aout2 = aout2-norm
aout3 = aout3-norm
sos = signal.iirfilter(3,.5, btype='lowpass', output='sos') #filter some noise
aout = signal.sosfiltfilt(sos,aout)
aout2 = signal.sosfiltfilt(sos,aout2)
aout3 = signal.sosfiltfilt(sos,aout3)
if chinfile == 2 and len(aoutx) >1:
aoutx = signal.sosfiltfilt(sos,aoutx)
return fout, aout, foutx, aoutx, fout2, aout2, fout3, aout3
def ordersignal(sig, Fs):
F = int(Fs/100)
win = ft_window(F)
if chinfile == 1:
y = abs(np.fft.rfft(sig[0:F]*win))
minf = np.argmax(y)
y = abs(np.fft.rfft(sig[len(sig)-F:len(sig)]*win))
maxf = np.argmax(y)
else:
y = abs(np.fft.rfft(sig[0,0:F]*win))
minf = np.argmax(y)
y = abs(np.fft.rfft(sig[0][len(sig[0])-F:len(sig[0])]*win))
maxf = np.argmax(y)
if maxf < minf:
maxf,minf = minf,maxf
sig = np.flipud(sig)
return sig, minf, maxf
def riaaiir(sig, Fs, mode, inv):
if Fs == 96000:
at = [1, -0.66168391, -0.18158841]
bt = [0.1254979638905360, 0.0458786797031512, 0.0018820452752401]
ars = [1, -0.60450091, -0.39094593]
brs = [0.90861261463964900, -0.52293147388301200, -0.34491369168550900]
if inv == 1:
at,bt = bt,at
ars,brs = brs,ars
if mode == 1:
sig = signal.lfilter(brs,ars,sig)
if mode == 2:
sig = signal.lfilter(bt,at,sig)
if mode == 3:
sig = signal.lfilter(bt,at,sig)
sig = signal.lfilter(brs,ars,sig)
return sig
def normxg7001(sig, Fs):
if Fs == 96000:
b = [1.0080900, -0.9917285, 0]
a = [1, -0.9998364, 0]
sig = signal.lfilter(b,a,sig)
return sig
def openaudio(_FILE):
global chinfile
global fileopenidx
chinfile = 1
srinfile = librosa.get_samplerate(_FILE)
audio, Fs = librosa.load(_FILE, sr=None, mono=False)
if len(audio.shape) == 2:
chinfile = 2
filelength = audio.shape[1] / Fs
else:
filelength = audio.shape[0] / Fs
print('Input File: ' + str(_FILE))
print('Sample Rate: ' + str("{:,}".format(srinfile) + 'Hz'))
if Fs <96000:
print(' Resampling to 96,000Hz')
audio = librosa.resample(audio, orig_sr=Fs, target_sr=96000)
Fs = 96000
print('Channels: ' + str(chinfile))
print(f"Length: {filelength}s")
if riaamode != 0:
audio = riaaiir(audio, Fs, riaamode, riaainv)
if xg7001 == 1:
audio = normxg7001(audio, Fs)
audio, index = librosa.effects.trim(audio, top_db=topdb, frame_length=framelength, hop_length=hoplength)
print(f"In/Out (s): {index / Fs}")
audio, minf, maxf = ordersignal(audio, Fs)
print('Min Freq: ' + str("{:,}".format(minf * 100) + 'Hz'))
print('Max Freq: ' + str("{:,}".format(maxf * 100) + 'Hz\n'))
fileopenidx +=1
return audio, Fs, minf, maxf
if __name__ == "__main__":
input_sig, Fs, minf, maxf = openaudio(file_0)
fo0, ao0, fox0, aox0, fo2h0, ao2h0, fo3h0, ao3h0 = createplotdata(input_sig, Fs)
deltaadj = ao0[find_nearest(fo0, normalize)]
deltah0 = round((max(ao0 - deltaadj)), roundlvl)
deltal0 = abs(round((min(ao0 - deltaadj)), roundlvl))
if aox0.size > 0:
print('X-talk @1kHz: ' + (str(round(aox0[find_nearest(fox0, 1000)], 2))) + 'dB\n\n')
if file_1:
input_sig, Fs, minf, maxf = openaudio(file_1)
fo1, ao1, fox1, aox1, fo2h1, ao2h1, fo3h1, ao3h1 = createplotdata(input_sig, Fs)
deltaadj = ao1[find_nearest(fo1, normalize)]
deltah1 = round((max(ao1 - deltaadj)), roundlvl)
deltal1 = abs(round((min(ao1 - deltaadj)), roundlvl))
if aox1.size > 0:
print('X-talk @1kHz: ' + (str(round(aox1[find_nearest(fox1, 1000)], 2))) + 'dB\n\n')
if plotdataout == 1:
dao0 = [*ao0, *[''] * (len(fo0) - len(ao0))]
daox0 = [*aox0, *[''] * (len(fo0) - len(aox0))]
dao2h0 = [*ao2h0, *[''] * (len(fo0) - len(ao2h0))]
dao3h0 = [*ao3h0, *[''] * (len(fo0) - len(ao3h0))]
print('\n\nFile 0 Plot Data: (freq, ampl, x-talk, 2h, 3h)\n\n')
dataout = list(zip(fo0, dao0, daox0, dao2h0, dao3h0))
for fo, ao, aox, ao2, ao3 in dataout:
print(fo, ao, aox, ao2, ao3, sep=', ')
if file_1:
dao1 = [*ao1, *[''] * (len(fo1) - len(ao1))]
daox1 = [*aox1, *[''] * (len(fo1) - len(aox1))]
dao2h1 = [*ao2h1, *[''] * (len(fo1) - len(ao2h1))]
dao3h1 = [*ao3h1, *[''] * (len(fo1) - len(ao3h1))]
print('\n\nFile 1 Plot Data: (freq, ampl, x-talk, 2h, 3h)\n\n')
dataout = list(zip(fo1, dao1, daox1, dao2h1, dao3h1))
for fo, ao, aox, ao2, ao3 in dataout:
print(fo, ao, aox, ao2, ao3, sep=', ')
plt.rcParams["xtick.minor.visible"] = True
plt.rcParams["ytick.minor.visible"] = True
if plotstyle == 1:
fig, axs = plt.subplots(1, 1, figsize=(14,6))
axs = np.ravel([axs])
axs[0].semilogx(fo0,ao0, color = '#0000ff', label = 'Freq Response')
axs[0].semilogx(fo2h0,ao2h0,color = '#0080ff', label = '2ⁿᵈ Harmonic', alpha = 1, linewidth = 0.75)
axs[0].semilogx(fo3h0,ao3h0,color = '#00dfff', label = '3ʳᵈ Harmonic', alpha = 1, linewidth = 0.75)
axs[0].semilogx(fox0,aox0,color = '#0000ff', linestyle = (0, (3, 1, 1, 1)), label = 'Crosstalk')
if file_1:
axs[0].semilogx(fo1,ao1, color = '#ff0000', label = 'Freq Response')
axs[0].semilogx(fo2h1,ao2h1,color = '#ff8000', label = '2ⁿᵈ Harmonic', alpha = 1, linewidth = 0.75)
axs[0].semilogx(fo3h1,ao3h1,color = '#ffdf00', label = '3ʳᵈ Harmonic', alpha = 1, linewidth = 0.75)
axs[0].semilogx(fox1,aox1,color = '#ff0000', linestyle = (0, (3, 1, 1, 1)), label = 'Crosstalk')
plt.legend([("#0000ff", "-", "#ff0000", "-"), ("#0000ff", (0, (3, 1, 1, 1)), "#ff0000", (0, (3, 1, 1, 1))),
("#0080ff", "-", "#ff8000", "-"), ("#00dfff", "-", "#ffdf00", "-")],
['Freq Response', 'Crosstalk', '2ⁿᵈ Harmonic', '3ʳᵈ Harmonic'],
handler_map={tuple: AnyObjectHandler()},loc=4)
axs[0].set_ylim((min(chain(aox0, aox1)) -2), (max(chain(ao0, ao1)) +2))
else:
plt.legend(loc=4)
axs[0].set_ylabel("Amplitude (dB)")
axs[0].set_xlabel("Frequency (Hz)")
plt.autoscale(enable=True, axis='y')
if ovdylim == 1:
axs[0].set_ylim(*ovdylimvalue)
if plotstyle == 2:
fig, axs = plt.subplots(1, sharex=True, figsize=(14,6))
axs = np.ravel([axs])
axtwin = axs[0].twinx()
axs[0].set_ylim(-5,5)
if max(ao0) <7:
axs[0].set_ylim(-25, 7)
if max(ao0) < 4:
axs[0].set_ylim(-25,5)
if max(ao0) < 2:
axs[0].set_ylim(-29,3)
if max(ao0) < 0.5:
axs[0].set_ylim(-30,2)
if aox0.size > 0:
if file_1:
axs[0].set_ylim((min(chain(aox0, aox1)) -2), (max(chain(ao0, ao1)) +2))
else:
axs[0].set_ylim((min(aox0) -2), (max(ao0) +2))
if ovdylim == 1:
axs[0].set_ylim(*ovdylimvalue)
axs[0].semilogx(fo0,ao0, color = '#0000ff', label = 'Freq Response')
axtwin.semilogx(fo2h0,ao2h0,color = '#0080ff', label = '2ⁿᵈ Harmonic', alpha = 1, linewidth = 0.75)
axtwin.semilogx(fo3h0,ao3h0,color = '#00dfff', label = '3ʳᵈ Harmonic', alpha = 1, linewidth = 0.75)
axs[0].semilogx(fox0,aox0,color = '#0000ff', linestyle = (0, (3, 1, 1, 1)), label = 'Crosstalk')
if file_1:
axs[0].semilogx(fo1,ao1, color = '#ff0000', label = 'Freq Response')
axtwin.semilogx(fo2h1,ao2h1,color = '#ff8000', label = '2ⁿᵈ Harmonic', alpha = 1, linewidth = 0.75)
axtwin.semilogx(fo3h1,ao3h1,color = '#ffdf00', label = '3ʳᵈ Harmonic', alpha = 1, linewidth = 0.75)
axs[0].semilogx(fox1,aox1,color = '#ff0000', linestyle = (0, (3, 1, 1, 1)), label = 'Crosstalk')
plt.legend([("#0000ff", "-", "#ff0000", "-"), ("#0000ff", (0, (3, 1, 1, 1)), "#ff0000", (0, (3, 1, 1, 1))),
("#0080ff", "-", "#ff8000", "-"), ("#00dfff", "-", "#ffdf00", "-")],
['Freq Response', 'Crosstalk', '2ⁿᵈ Harmonic', '3ʳᵈ Harmonic'],
handler_map={tuple: AnyObjectHandler()},loc=4)
if aox0.size > 0 and aox1.size > 0:
axs[0].set_ylim((min(chain(aox0, aox1)) -2), (max(chain(ao0, ao1)) +2))
else:
lines1, labels1 = axs[0].get_legend_handles_labels()
lines2, labels2 = axtwin.get_legend_handles_labels()
plt.legend(lines1 + lines2, labels1 + labels2, loc=4)
new_lim1, new_lim2 = align_yaxis(axs[0], axtwin)
axs[0].set_ylim(new_lim1)
axtwin.set_ylim(new_lim2)
axs[0].set_ylabel("Amplitude (dB)")
axtwin.set_ylabel("Distortion (dB)")
axs[0].set_xlabel("Frequency (Hz)")
if plotstyle == 3:
fig, axs = plt.subplots(2, 1, sharex=True, figsize=(14,6))
axs[0].set_ylim(-5,5)
if file_1:
if (min(chain(ao0, ao1)) <-5) or (max(chain(ao0, ao1)) >5):
axs[0].autoscale(enable=True, axis='y')
elif (min(ao0) <-5) or (max(ao0) >5):
axs[0].autoscale(enable=True, axis='y')
if ovdylim == 1:
axs[0].set_ylim(*ovdylimvalue)
axs[0].semilogx(fo0,ao0,color = '#0000ff', label = 'Freq Response')
axs[1].semilogx(fo2h0,ao2h0,color = '#0080ff', label = '2nd Harmonic')
axs[1].semilogx(fo3h0,ao3h0,color = '#00dfff', label = '3rd Harmonic')
if file_1:
axs[0].semilogx(fo1,ao1, color = '#ff0000', label = 'Freq Response')
axs[1].semilogx(fo2h1,ao2h1,color = '#ff8000', label = '2ⁿᵈ Harmonic')
axs[1].semilogx(fo3h1,ao3h1,color = '#ffdf00', label = '3ʳᵈ Harmonic')
axs[0].legend([("#0000ff", "-", "#ff0000", "-"),],
['Freq Response'],
handler_map={tuple: AnyObjectHandler()},loc=4)
axs[1].legend([("#0080ff", "-", "#ff8000", "-"), ("#00dfff", "-", "#ffdf00", "-")],
['2ⁿᵈ Harmonic', '3ʳᵈ Harmonic'],
handler_map={tuple: AnyObjectHandler()},loc=4)
else:
axs[0].legend(loc=4)
axs[1].legend(loc=4)
axs[0].set_ylabel("Amplitude (dB)")
axs[1].set_ylabel("Distortion (dB)")
axs[1].set_xlabel("Frequency (Hz)")
if plotstyle == 4:
fig, axs = plt.subplots(2, 1, sharex=True, figsize=(14,10))
axtwin = axs[1].twinx()
axs[0].set_ylim(-5,5)
if max(ao0) <7:
axs[1].set_ylim(-25, 7)
if max(ao0) < 4:
axs[1].set_ylim(-25,5)
if max(ao0) < 2:
axs[1].set_ylim(-29,3)
if max(ao0) < 0.5:
axs[1].set_ylim(-30,2)
if aox0.size > 0:
if file_1:
axs[1].set_ylim((min(chain(aox0, aox1)) -2), (max(chain(ao0, ao1)) +2))
else:
axs[1].set_ylim((min(aox0) -2), (max(ao0) +2))
if ovdylim == 1:
axs[1].set_ylim(*ovdylimvalue)
axs[1].semilogx(fo0,ao0, color = '#0000ff', label = 'Freq Response')
axtwin.semilogx(fo2h0,ao2h0,color = '#0080ff', label = '2ⁿᵈ Harmonic', alpha = 1, linewidth = 0.75)
axtwin.semilogx(fo3h0,ao3h0,color = '#00dfff', label = '3ʳᵈ Harmonic', alpha = 1, linewidth = 0.75)
axs[1].semilogx(fox0,aox0,color = '#0000ff', linestyle = (0, (3, 1, 1, 1)), label = 'Crosstalk')
if file_1:
axs[1].semilogx(fo1,ao1, color = '#ff0000', label = 'Freq Response')
axtwin.semilogx(fo2h1,ao2h1,color = '#ff8000', label = '2ⁿᵈ Harmonic', alpha = 1, linewidth = 0.75)
axtwin.semilogx(fo3h1,ao3h1,color = '#ffdf00', label = '3ʳᵈ Harmonic', alpha = 1, linewidth = 0.75)
axs[1].semilogx(fox1,aox1,color = '#ff0000', linestyle = (0, (3, 1, 1, 1)), label = 'Crosstalk')
plt.legend([("#0000ff", "-", "#ff0000", "-"), ("#0000ff", (0, (3, 1, 1, 1)), "#ff0000", (0, (3, 1, 1, 1))),
("#0080ff", "-", "#ff8000", "-"), ("#00dfff", "-", "#ffdf00", "-")],
['Freq Response', 'Crosstalk', '2ⁿᵈ Harmonic', '3ʳᵈ Harmonic'],
handler_map={tuple: AnyObjectHandler()},loc=4)
if aox0.size > 0 and aox1.size > 0:
axs[1].set_ylim((min(chain(aox0, aox1)) -2), (max(chain(ao0, ao1)) +2))
else:
lines1, labels1 = axs[1].get_legend_handles_labels()
lines2, labels2 = axtwin.get_legend_handles_labels()
plt.legend(lines1 + lines2, labels1 + labels2, loc=4)
new_lim1, new_lim2 = align_yaxis(axs[1], axtwin)
axs[1].set_ylim(new_lim1)
axtwin.set_ylim(new_lim2)
if file_1:
if (min(chain(ao0, ao1)) <-5) or (max(chain(ao0, ao1)) >5):
axs[0].autoscale(enable=True, axis='y')
elif (min(ao0) <-5) or (max(ao0) >5):
axs[0].autoscale(enable=True, axis='y')
if ovdylim == 1:
axs[0].set_ylim(*ovdylimvalue)
axs[0].semilogx(fo0,ao0,color = '#0000ff', label = 'Freq Response')
if file_1:
axs[0].semilogx(fo1,ao1, color = '#ff0000', label = 'Freq Response')
axs[0].legend([("#0000ff", "-", "#ff0000", "-"),],
['Freq Response'],
handler_map={tuple: AnyObjectHandler()},loc=4)
else:
axs[0].legend(loc=4)
axs[0].set_ylabel("Amplitude (dB)")
axs[1].set_ylabel("Amplitude (dB)")
axtwin.set_ylabel("Distortion (dB)")
axs[1].set_xlabel("Frequency (Hz)")
gs = GridSpec(2, 1, height_ratios=[1, 2])
axs[0].set_position(gs[0].get_position(fig))
axs[1].set_position(gs[1].get_position(fig))
if plotstyle == 5:
fig, axs = plt.subplots(1, 1, figsize=(14,3))
axs = np.ravel([axs])
axs[0].set_ylim(-5,5)
if file_1:
if (min(chain(ao0, ao1)) <-5) or (max(chain(ao0, ao1)) >5):
axs[0].autoscale(enable=True, axis='y')
elif (min(ao0) <-5) or (max(ao0) >5):
axs[0].autoscale(enable=True, axis='y')
if ovdylim == 1:
axs[0].set_ylim(*ovdylimvalue)
axs[0].semilogx(fo0,ao0,color = '#0000ff', label = 'Freq Response')
if file_1:
axs[0].semilogx(fo1,ao1, color = '#ff0000', label = 'Freq Response')
axs[0].legend([("#0000ff", "-", "#ff0000", "-"),],
['Freq Response'],
handler_map={tuple: AnyObjectHandler()},loc=4)
else:
axs[0].legend(loc=4)
axs[0].set_ylabel("Amplitude (dB)")
axs[0].set_xlabel("Frequency (Hz)")
for i, ax in enumerate(axs.flat):
if file0norm == 1:
if not (plotstyle == 3 and i != 0):
ax.axline((normalize, 0), (normalize, 1), color = 'm', lw = 1)
elif file0norm == 0:
if not (plotstyle == 3 and i != 0):
ax.plot(normalize, 0, marker = 'x', color = 'm')
anchored_text = AnchoredText('SJ',
frameon=False, borderpad=0, pad=0.03,
loc=1, bbox_transform=plt.gca().transAxes,
prop={'color':'m','fontsize':25,'alpha':.4,
'style':'oblique'})
ax.add_artist(anchored_text)
ax.grid(True, which="major", axis="both", ls="-", color="black")
ax.grid(True, which="minor", axis="both", ls="-", color="gainsboro")
ax.set_xticks([0,20,50,100,500,1000,5000,10000,20000,50000,100000])
ax.set_xticklabels(['0','20','50','100','500','1k','5k','10k','20k','50k','100k'])
bbox_args = dict(boxstyle="round", color='b', fc='w', ec='b', alpha=1, pad=.15)
axs[0].annotate('+' + str(deltah0) + ', ' + u"\u2212" + str(deltal0) + ' dB',color = 'b',\
xy=(fo0[0],(ao0[0]-1)), xycoords='data', \
xytext=(-10, -20), textcoords='offset points', \
ha="left", va="center", bbox=bbox_args)
if file_1:
bbox_args = dict(boxstyle="round", color='b', fc='w', ec='r', alpha=1, pad=.15)
axs[0].annotate('+' + str(deltah1) + ', ' + u"\u2212" + str(deltal1) + ' dB',color = 'r',\
xy=(fo0[0],(ao0[0]-1)), xycoords='data', \
xytext=(-10, -34.5), textcoords='offset points', \
ha="left", va="center", bbox=bbox_args)
plt.autoscale(enable=True, axis='x')
axs[0].set_title(infoline + "\n", fontsize=16)
now = datetime.now()
if file_1:
plt.figtext(.17, .118, "SJPlot v" + swversion + "\n" + file_0 + "\n" + file_1 + "\n" + \
now.strftime("%b %d, %Y %H:%M"), fontsize=6)
else:
plt.figtext(.17, .118, "SJPlot v" + swversion + "\n" + file_0 + "\n" + \
now.strftime("%b %d, %Y %H:%M"), fontsize=6)
plt.figtext(.125, 0, equipinfo, alpha=.75, fontsize=8)
plt.savefig(infoline.replace(' / ', '_') +'.png', bbox_inches='tight', pad_inches=.5, dpi=96)
plt.show()
print('\nDone!')
