import pyvisa import time import numpy as np # List of all valid timebases supported by OWON HDS2* series TIMEBASES = [ (2e-9, "2.0ns"), (5e-9, "5.0ns"), (10e-9, "10.0ns"), (20e-9, "20.0ns"), (50e-9, "50.0ns"), (100e-9, "100ns"), (200e-9, "200ns"), (500e-9, "500ns"), (1e-6, "1.0us"), (2e-6, "2.0us"), (5e-6, "5.0us"), (10e-6, "10us"), (20e-6, "20us"), (50e-6, "50us"), (100e-6, "100us"), (200e-6, "200us"), (500e-6, "500us"), (1e-3, "1.0ms"), (2e-3, "2.0ms"), (5e-3, "5.0ms"), (10e-3, "10ms"), (20e-3, "20ms"), (50e-3, "50ms"), (100e-3, "100ms"), (200e-3, "200ms"), (500e-3, "500ms"), (1.0, "1.0s"), (2.0, "2.0s"), (5.0, "5.0s"), (10.0, "10s"), (20.0, "20s"), (50.0, "50s"), (100.0, "100s"), (200.0, "200s"), (500.0, "500s"), (1000.0, "1000s") ] # Supported scale tables according to probe attenuation VOLT_SCALES = { "1X": [ (0.010, "10.0mV"), (0.020, "20.0mV"), (0.050, "50.0mV"), (0.100, "100mV"), (0.200, "200mV"), (0.500, "500mV"), (1.000, "1.00V"), (2.000, "2.00V"), (5.000, "5.00V"), (10.00, "10.0V") ], "10X": [ (0.100, "100mV"), (0.200, "200mV"), (0.500, "500mV"), (1.000, "1.00V"), (2.000, "2.00V"), (5.000, "5.00V"), (10.00, "10.0V"), (20.00, "20.0V"), (50.00, "50.0V"), (100.0, "100V") ], "100X": [ (1.000, "1.00V"), (2.000, "2.00V"), (5.000, "5.00V"), (10.00, "10.0V"), (20.00, "20.0V"), (50.00, "50.0V"), (100.0, "100V"), (200.0, "200V"), (500.0, "500V"), (1000.0, "1.00kV") ], "1000X": [ (10.00, "10.0V"), (20.00, "20.0V"), (50.00, "50.0V"), (100.0, "100V"), (200.0, "200V"), (500.0, "500V"), (1000.0, "1.00kV"),(2000.0, "2.00kV"),(5000.0, "5.00kV"), (10000.0, "10.0kV") ] } CSV_FILEPATH = "csv/bode_data.csv" LOG_ENABLE = False PROBE = "1X" GEN_PEAK = 0.10 # 2*GEN_PEAK peak to peak voltage FREQS_COUNT = 10 MEAN_SAMPLE_COUNT = 3 curr_scale_idx = 2 # 500 mV startup vertical height scales = VOLT_SCALES.get(PROBE) def LOG(msg): if LOG_ENABLE: print(f"[LOG] {msg}") def LOG_LOC(loc, msg): if LOG_ENABLE: print(f"[LOG]{loc} {msg}") def set_best_timebase(frequency): period = 1.0 / frequency target_scale = period / 2.0 # Display width is roughly 10 divisions # Pick the closest supported value in the list best_match = min(TIMEBASES, key=lambda x: abs(x[0] - target_scale)) LOG(f"HORizontal:SCALe {best_match[1]}") time.sleep(0.1) scope.write(f":HORizontal:SCALe {best_match[1]}") def set_best_vert_scale(): global curr_scale_idx read_val_str = scope.query(":MEASurement:CH1:MAX?").strip() LOG_LOC("[set_best_vert_scale]", f"read_val_str == {read_val_str}") while read_val_str.startswith(">"): LOG_LOC("[set_best_vert_scale]['>']", f"startswith('>')") if curr_scale_idx <= 9: curr_scale_idx = curr_scale_idx + 1 new_scale = scales[curr_scale_idx][1] LOG_LOC("[set_best_vert_scale]['>']", f"Changing vertical scale to {new_scale}") scope.write(f":CH1:SCALe {new_scale}") if freq < 10: # Roll mode time.sleep(10) else: time.sleep(1) # Read again with new scale read_val_str = scope.query(":MEASurement:CH1:MAX?").strip() LOG_LOC("[set_best_vert_scale]['>'][read again]", f"read_val_str == {read_val_str}") else: read_val_str.lstrip(">") while read_val_str.startswith("<"): LOG_LOC("[set_best_vert_scale]['<']", f"startswith('<')") if curr_scale_idx > 0: curr_scale_idx = curr_scale_idx - 1 new_scale = scales[curr_scale_idx][1] # print(f"Changing vertical scale to {new_scale}") scope.write(f":CH1:SCALe {new_scale}") if freq < 10: # Roll mode time.sleep(10) else: time.sleep(1) # Read again with new scale read_val_str = scope.query(":MEASurement:CH1:MAX?").strip() LOG_LOC("[set_best_vert_scale]['<']", f"read_val_str == {read_val_str}") else: read_val_str.lstrip("<") read_val = float(read_val_str) LOG_LOC("[set_best_vert_scale]", f"read_val == {read_val_str}") if curr_scale_idx > 0 and read_val < 2*scales[curr_scale_idx - 1][0]: curr_scale_idx = curr_scale_idx - 1 new_scale = scales[curr_scale_idx][1] # print(f"Changing vertical scale to {new_scale}") scope.write(f":CH1:SCALe {new_scale}") if freq < 10: # Roll mode time.sleep(10) # Read again with new scale read_val_str = scope.query(":MEASurement:CH1:MAX?").strip() LOG_LOC("[set_best_vert_scale][read_val < 2*next_scale", f"read_val_str == {read_val_str}") while read_val_str.startswith(">") or read_val_str.startswith("<"): read_val_str = scope.query(":MEASurement:CH1:MAX?").strip() read_val = float(read_val_str) # example: 5 decades (10^0 to 10^5) at 5 points/decade -> 5 * 5 + 1 = 26 points # freqs = np.concatenate((np.array([1]), np.logspace(4, 5, num=FREQS_COUNT))) freqs = np.logspace(0, 5, num=FREQS_COUNT) freqs = np.unique([int(f) for f in freqs]) print(freqs) amps = [] rm = pyvisa.ResourceManager('@py') try: scope = rm.open_resource("ASRL/dev/ttyUSB0::INSTR") except: print("[ERROR] Device not found") exit(0) try: data = scope.read_raw() print("Pending:", data) except pyvisa.errors.VisaIOError as e: print("No pending SCPI data") print("Identification: ", scope.query("*IDN?"), end="") # Configure scope scope.write(f":CH1:SCALe {scales[curr_scale_idx][1]}") scope.write(":CH1:COUPling AC") scope.write(f":CH1:PROBE {PROBE}") scope.write(f":FUNCtion:AMPLitude {GEN_PEAK*2}") scope.write(":FUNCtion SINE") print(f"Generator set to function SINE") # print(" freq, amp/peak") for freq in freqs: # print(f"Probing {freq:2.2f} Hz") scope.write(f":FUNCtion:FREQuency {freq:2.2f}") # print(scope.query(":FUNCtion:FREQuency?")) set_best_timebase(freq) # Roll mode if freq < 10: scope.write(":CH1:COUPling DC") time.sleep(10) else: scope.write(":CH1:COUPling AC") time.sleep(0.5) amp_arr = [] for i in range(MEAN_SAMPLE_COUNT): set_best_vert_scale() read_val_str = scope.query(":MEASurement:CH1:MAX?").strip() LOG_LOC(f"[main_loop]", f"read_val_str == {read_val_str}") while read_val_str.startswith(">") or read_val_str.startswith("<"): set_best_vert_scale() read_val_str = scope.query(":MEASurement:CH1:MAX?").strip() LOG_LOC(f"[main_loop]", f"read_val_str == {read_val_str}") read_val = float(read_val_str) # print(f"{read_val:<3.3f}") amp_arr.append(read_val) time.sleep(0.5) # time between mean samples amp_mean = (sum(amp_arr) / MEAN_SAMPLE_COUNT) amp_mean = 0.001 if amp_mean < 0.001 else amp_mean amp_norm = amp_mean / GEN_PEAK amps.append(amp_norm) print(f"{freq:>8}, {amp_norm:<3.3f}") scope.close() # print(f"freqs = [{' '.join(map(str, freqs))}]") # print(f"amps = [{' '.join(map(str, amps))}]") data = np.column_stack((freqs, amps)) np.savetxt(f"{CSV_FILEPATH}", data, delimiter=",", header="Frequency,Amplitude", comments="", fmt=["%d", "%.3f"])