smallest_peak_idx = min(get_peaks(spectrum), key=spectrum.__getitem__)
ymin = (spectrum[smallest_peak_idx] - spectrum[0] - 5) // 10 * 10
fig, (time_ax, freq_ax) = plt.subplots(2, 1, num=name)
time_ax.vlines(np.arange(- size // 2, size // 2 + 1), 0, wnd_symm)
time_ax.set(xlim=(-(size // 2), size // 2), ylim=(-.1, 1.1),
xlabel="Time (samples)", title=name)
freq_ax.plot(list(line(full_size, -1, 1)), full_spectrum)
freq_ax.set(xlim=(-1, 1), ylim=(ymin, 0), ylabel="dB",
xlabel="Frequency (% of the Nyquist frequency)")
fig.tight_layout()
# Prints the table and other text contents
print(__doc__)
print("""
Schema
------
""")
for row in rst_table([(v, schema_full[k]) for k, v in iteritems(schema)],
["Column", "Description"]):
print(row)
print("""
Windows and Figures of Merit
----------------------------
""")
for row in rst_table(table, schema.values()):
print(row) # Some values aren't the same to the paper, though
plt.ioff()
plt.show()
fig, (time_ax, freq_ax) = plt.subplots(2, 1, num=name)
time_ax.vlines(np.arange(-size // 2, size // 2 + 1), 0, wnd_symm)
time_ax.set(xlim=(-(size // 2), size // 2),
ylim=(-.1, 1.1),
xlabel="Time (samples)",
title=name)
freq_ax.plot(list(line(full_size, -1, 1)), full_spectrum)
freq_ax.set(xlim=(-1, 1),
ylim=(ymin, 0),
ylabel="dB",
xlabel="Frequency (% of the Nyquist frequency)")
fig.tight_layout()
# Prints the table and other text contents
print(__doc__)
print("""
Schema
------
""")
for row in rst_table([(v, schema_full[k]) for k, v in iteritems(schema)],
["Column", "Description"]):
print(row)
print("""
Windows and Figures of Merit
----------------------------
""")
for row in rst_table(table, schema.values()):
print(row) # Some values aren't the same to the paper, though
plt.ioff()
plt.show()
wnd_full = wnd_func(full_size)
wnd_data = {
"name": name,
"cg": coherent_gain(wnd_full),
"enbw": enbw(wnd_full),
"bw3": 2 * find_xdb_bin(wnd, .5),
"scallop": scalloping_loss(wnd_full),
"wcpl": worst_case_processing_loss(wnd_full),
"bw6": 2 * find_xdb_bin(wnd, .25),
"ol75": overlap_correlation(wnd_full, .25 * full_size) * 100,
"ol50": overlap_correlation(wnd_full, .5 * full_size) * 100,
}
table.append([to_string(wnd_data[k]) for k in schema])
wnd_symm = wnd + [wnd[0]]
full_spectrum = np.hstack([spectrum[::-1], spectrum[1:-1]]) - spectrum[0]
fig, (time_ax, freq_ax) = plt.subplots(2, 1, num=name)
time_ax.vlines(np.arange(- size // 2, size // 2 + 1), 0, wnd_symm)
time_ax.set(xlim=(-(size // 2), size // 2), ylim=(-.1, 1.1),
xlabel="Time (samples)", title=name)
freq_ax.plot(list(line(full_size, -1, 1)), full_spectrum)
freq_ax.set(xlim=(-1, 1), ylim=(-90, 0), ylabel="dB",
xlabel="Frequency (% of the Nyquist frequency)")
fig.tight_layout()
print(__doc__)
for row in rst_table(table, schema.values()):
print(row) # Some values aren't the same to the paper, though
plt.show()
