forked from awendt/pyrelacs
272 lines
8.7 KiB
Python
272 lines
8.7 KiB
Python
import faulthandler
|
|
import time
|
|
|
|
import nixio as nix
|
|
import uldaq
|
|
from IPython import embed
|
|
import numpy as np
|
|
import matplotlib.pyplot as plt
|
|
from scipy.signal import welch
|
|
from scipy.signal import find_peaks
|
|
|
|
from pyrelacs.devices.mccdac import MccDac
|
|
from pyrelacs.util.logging import config_logging
|
|
|
|
log = config_logging()
|
|
# for more information on seg faults
|
|
faulthandler.enable()
|
|
|
|
|
|
class Calibration(MccDac):
|
|
def __init__(self) -> None:
|
|
super().__init__()
|
|
self.SAMPLERATE = 40_000.0
|
|
self.DURATION = 5
|
|
self.AMPLITUDE = 1
|
|
self.SINFREQ = 750
|
|
|
|
@staticmethod
|
|
def run(nix_file: nix.File):
|
|
calb = Calibration()
|
|
calb.check_beat(nix_file)
|
|
|
|
def check_amplitude(self):
|
|
db_values = [0.0, -5.0, -10.0, -20.0, -50.0]
|
|
colors = ["red", "green", "blue", "black", "yellow"]
|
|
self.set_attenuation_level(db_channel1=0.0, db_channel2=0.0)
|
|
# write to ananlog 1
|
|
t = np.arange(0, self.DURATION, 1 / self.SAMPLERATE)
|
|
data = self.AMPLITUDE * np.sin(2 * np.pi * self.SINFREQ * t)
|
|
fig, ax = plt.subplots()
|
|
|
|
for i, db_value in enumerate(db_values):
|
|
self.set_attenuation_level(db_channel1=db_value, db_channel2=db_value)
|
|
log.debug(f"{db_value}")
|
|
|
|
stim = self.write_analog(
|
|
data,
|
|
[0, 0],
|
|
self.SAMPLERATE,
|
|
ScanOption=uldaq.ScanOption.EXTTRIGGER,
|
|
)
|
|
|
|
data_channel_one = self.read_analog(
|
|
[0, 0],
|
|
self.DURATION,
|
|
self.SAMPLERATE,
|
|
ScanOption=uldaq.ScanOption.EXTTRIGGER,
|
|
)
|
|
time.sleep(1)
|
|
|
|
log.debug("Starting the Scan")
|
|
self.digital_trigger()
|
|
|
|
try:
|
|
self.ao_device.scan_wait(uldaq.WaitType.WAIT_UNTIL_DONE, 15)
|
|
log.debug("Scan finished")
|
|
self.write_bit(channel=0, bit=0)
|
|
time.sleep(1)
|
|
self.set_analog_to_zero()
|
|
except uldaq.ul_exception.ULException:
|
|
log.debug("Operation timed out")
|
|
# reset the diggital trigger
|
|
self.write_bit(channel=0, bit=0)
|
|
time.sleep(1)
|
|
self.set_analog_to_zero()
|
|
self.disconnect_dac()
|
|
|
|
if i == 0:
|
|
ax.plot(t, stim, label=f"Input_{db_value}", color=colors[i])
|
|
ax.plot(t, data_channel_one, label=f"Reaout {db_value}", color=colors[i])
|
|
|
|
ax.legend()
|
|
plt.show()
|
|
|
|
self.disconnect_dac()
|
|
|
|
def check_beat(self, nix_file: nix.File):
|
|
self.set_attenuation_level(db_channel1=-10.0, db_channel2=0.0)
|
|
t = np.arange(0, self.DURATION, 1 / self.SAMPLERATE)
|
|
data = self.AMPLITUDE * np.sin(2 * np.pi * self.SINFREQ * t)
|
|
# data = np.concatenate((data, data))
|
|
db_values = [0.0, -5.0, -8.5, -10.0]
|
|
colors = ["red", "blue", "black", "green"]
|
|
colors_in = ["lightcoral", "lightblue", "grey", "lightgreen"]
|
|
block = nix_file.create_block("Calibration", "data")
|
|
# fig, axes = plt.subplots(2, 2, sharex="col")
|
|
for i, db_value in enumerate(db_values):
|
|
self.set_attenuation_level(db_channel1=db_value)
|
|
stim = self.write_analog(
|
|
data,
|
|
[0, 0],
|
|
self.SAMPLERATE,
|
|
ScanOption=uldaq.ScanOption.EXTTRIGGER,
|
|
)
|
|
readout = self.read_analog(
|
|
[0, 1],
|
|
self.DURATION,
|
|
self.SAMPLERATE,
|
|
ScanOption=uldaq.ScanOption.EXTTRIGGER,
|
|
)
|
|
self.digital_trigger()
|
|
log.info(self.ao_device)
|
|
ai_status = uldaq.ScanStatus.RUNNING
|
|
ao_status = uldaq.ScanStatus.RUNNING
|
|
|
|
log.debug(
|
|
f"Status Analog_output {ao_status}\n, Status Analog_input {ai_status}"
|
|
)
|
|
while (ai_status != uldaq.ScanStatus.IDLE) and (
|
|
ao_status != uldaq.ScanStatus.IDLE
|
|
):
|
|
# log.debug("Scanning")
|
|
time.time_ns()
|
|
ai_status = self.ai_device.get_scan_status()[0]
|
|
ao_status = self.ao_device.get_scan_status()[0]
|
|
|
|
self.write_bit(channel=0, bit=0)
|
|
log.debug(
|
|
f"Status Analog_output {ao_status}\n, Status Analog_input {ai_status}"
|
|
)
|
|
|
|
channel1 = np.array(readout[::2])
|
|
channel2 = np.array(readout[1::2])
|
|
|
|
block.create_data_array(
|
|
f"stimulus_{db_value}",
|
|
"Array",
|
|
shape=data.shape,
|
|
data=channel1,
|
|
label="Voltage",
|
|
unit="V",
|
|
)
|
|
block.create_data_array(
|
|
f"fish_{db_value}",
|
|
"Array",
|
|
shape=data.shape,
|
|
data=channel2,
|
|
label="Voltage",
|
|
unit="V",
|
|
)
|
|
|
|
beat = channel1 + channel2
|
|
beat_square = beat**2
|
|
|
|
f, powerspec = welch(beat, fs=self.SAMPLERATE)
|
|
powerspec = decibel(powerspec)
|
|
|
|
f_sq, powerspec_sq = welch(beat_square, fs=self.SAMPLERATE)
|
|
powerspec_sq = decibel(powerspec_sq)
|
|
peaks = find_peaks(powerspec_sq, prominence=20)[0]
|
|
|
|
f_stim, powerspec_stim = welch(channel1, fs=self.SAMPLERATE)
|
|
powerspec_stim = decibel(powerspec_stim)
|
|
|
|
f_in, powerspec_in = welch(channel2, fs=self.SAMPLERATE)
|
|
powerspec_in = decibel(powerspec_in)
|
|
|
|
# axes[0, 0].plot(
|
|
# t,
|
|
# channel1,
|
|
# label=f"{db_value} Readout Channel0",
|
|
# color=colors[i],
|
|
# )
|
|
# axes[0, 0].plot(
|
|
# t,
|
|
# channel2,
|
|
# label=f"{db_value} Readout Channel1",
|
|
# color=colors_in[i],
|
|
# )
|
|
#
|
|
# axes[0, 1].plot(
|
|
# f_stim,
|
|
# powerspec_stim,
|
|
# label=f"{db_value} powerspec Channel0",
|
|
# color=colors[i],
|
|
# )
|
|
# axes[0, 1].plot(
|
|
# f_in,
|
|
# powerspec_in,
|
|
# label=f"{db_value} powerspec Channel2",
|
|
# color=colors_in[i],
|
|
# )
|
|
# axes[0, 1].set_xlabel("Freq [HZ]")
|
|
# axes[0, 1].set_ylabel("dB")
|
|
#
|
|
# axes[1, 0].plot(
|
|
# t,
|
|
# beat,
|
|
# label="Beat",
|
|
# color=colors[i],
|
|
# )
|
|
# axes[1, 0].plot(
|
|
# t,
|
|
# beat**2,
|
|
# label="Beat squared",
|
|
# color=colors_in[i],
|
|
# )
|
|
# axes[1, 0].legend()
|
|
#
|
|
# axes[1, 1].plot(
|
|
# f,
|
|
# powerspec,
|
|
# color=colors[i],
|
|
# )
|
|
# axes[1, 1].plot(
|
|
# f_sq,
|
|
# powerspec_sq,
|
|
# color=colors_in[i],
|
|
# label=f"dB {db_value}, first peak {np.min(f_sq[peaks])}",
|
|
# )
|
|
# axes[1, 1].scatter(
|
|
# f_sq[peaks],
|
|
# powerspec_sq[peaks],
|
|
# color="maroon",
|
|
# )
|
|
# axes[1, 1].set_xlabel("Freq [HZ]")
|
|
# axes[1, 1].set_ylabel("dB")
|
|
# axes[0, 0].legend()
|
|
# axes[1, 1].legend()
|
|
# plt.show()
|
|
self.set_analog_to_zero()
|
|
self.disconnect_dac()
|
|
|
|
|
|
def decibel(power, ref_power=1.0, min_power=1e-20):
|
|
"""Transform power to decibel relative to ref_power.
|
|
|
|
\\[ decibel = 10 \\cdot \\log_{10}(power/ref\\_power) \\]
|
|
Power values smaller than `min_power` are set to `-np.inf`.
|
|
|
|
Parameters
|
|
----------
|
|
power: float or array
|
|
Power values, for example from a power spectrum or spectrogram.
|
|
ref_power: float or None or 'peak'
|
|
Reference power for computing decibel.
|
|
If set to `None` or 'peak', the maximum power is used.
|
|
min_power: float
|
|
Power values smaller than `min_power` are set to `-np.inf`.
|
|
|
|
Returns
|
|
-------
|
|
decibel_psd: array
|
|
Power values in decibel relative to `ref_power`.
|
|
"""
|
|
if np.isscalar(power):
|
|
tmp_power = np.array([power])
|
|
decibel_psd = np.array([power])
|
|
else:
|
|
tmp_power = power
|
|
decibel_psd = power.copy()
|
|
if ref_power is None or ref_power == "peak":
|
|
ref_power = np.max(decibel_psd)
|
|
decibel_psd[tmp_power <= min_power] = float("-inf")
|
|
decibel_psd[tmp_power > min_power] = 10.0 * np.log10(
|
|
decibel_psd[tmp_power > min_power] / ref_power
|
|
)
|
|
if np.isscalar(power):
|
|
return decibel_psd[0]
|
|
else:
|
|
return decibel_psd
|