<jupyter_start><jupyter_text>Tests<jupyter_code>buff2 = tds2002b.ask('CURVE?')
dataPointsCharacters= int(buff2[1:2])
dataOffset= 2+ dataPointsCharacters
dataCount= int(buff2[2:2+ dataPointsCharacters])
res2 = np.frombuffer(buff2, dtype = np.dtype('int8').newbyteorder('<'), count= dataCount, offset= dataOffset)
np.size(res2)
# vertical axis
tds2002b.scale = tds2002b.get_out_waveform_vertical_scale_factor()
tds2002b.offset = tds2002b.get_out_waveform_vertical_position()
yAxisLimits= ([tds2002b.offset* tds2002b.scale, (-2* tds2002b.offset+50)* tds2002b.scale])
tds2002b.offset, tds2002b.scale, yAxisLimits
plt.plot(X_axis)
X_axis[0], X_axis[-1], plt.show()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.14658925979680695, 'fraction_numerical': 0.08853410740203194, 'mean_word_length': 9.952380952380953, 'pattern_counts': {'":': 0, '<': 6, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '331452', 'license': 'BSD-3-Clause', 'n_tokens_mistral': 314, 'n_tokens_neox': 278, 'n_words': 38, 'path': 'pyTektronixScopeUSBTMC/example.ipynb', 'repo_name': 'bettachini/pyTektronixScopeUSBTMC', 'size': '689'}