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AnalysisGUI.py
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AnalysisGUI.py
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# -*- coding: utf-8 -*-
"""
Created on Wed Aug 26 10:07:40 2020
@author: Quantum Engineer
"""
import csv
import time
import units
import sys
sys.path.append("Z://")
from runmanager.remote import Client
from scipy.optimize import curve_fit
from fit_functions import lorentzian
from datetime import datetime
import PlotWorkers
from PlotWorkers import Plot1DWorker, Plot2DWorker, Plot1DHistogramWorker, PlotPCAWorker, PlotCorrelationWorker, PlotXYWorker
from numpy import array
import numpy as np
from PyQt5.QtWidgets import *
from PyQt5 import QtCore
from PyQt5.QtCore import *
import AnalysisFunctions as af
from colorcet import cm
import matplotlib.pyplot as plt
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.backends.backend_qt5agg import NavigationToolbar2QT as NavigationToolbar
from AnalysisUI import AnalysisUI
from FileSorter import FileSorter
from plotformatting import *
from FormattingStrings import *
from units import unitsDef
import importlib
import collections
import math
import traceback
import json
import os
import AnalysisFunctions
pi = np.pi
select_traps = [10]
class AnalysisGUI(QMainWindow, AnalysisUI):
def __init__(self, app):
AnalysisUI.__init__(self)
QMainWindow.__init__(self)
app.aboutToQuit.connect(self.stop_sorting)
self.create_ui(self)
self.cb_script.currentIndexChanged.connect(self.set_script_folder)
self.cb_data.activated.connect(self.set_data_folder)
self.button_refresh_data.clicked.connect(self.set_data_folder)
self.picker_date.dateChanged.connect(self.set_date)
self.parameters_lineedit.returnPressed.connect(self.set_parameters)
self.index_lineedit.returnPressed.connect(self.set_list_index)
self.f2_threshold_input.returnPressed.connect(self.set_f2_threshold)
self.f2_threshold_checkbox.stateChanged.connect(
self.set_f2_threshold)
self.checkbox_imaging_calibration.stateChanged.connect(
self.set_imaging_calibration)
self.checkbox_adjust_amplitudes.stateChanged.connect(
self.set_amplitude_feedback)
self.checkbox_ignore_first_shot.stateChanged.connect(
self.set_ignore_first_shot
)
self.checkbox_adjust_probe.stateChanged.connect(
self.set_adjust_probe
)
self.checkbox_shot_alert.stateChanged.connect(
self.set_shot_alerts
)
self.button_reset_mail.clicked.connect(self.reset_probe_mails)
self.probe_threshold.sliderReleased.connect(self.set_probe_threshold)
self.date = QtCore.QDate.currentDate().toString(date_format_string)
self.traps = np.array([10])
self.script_folder = ""
self.shot_threshold_size = 2e6
self.data_folder_dict = {}
self.set_script_cb()
self.set_imaging_calibration()
self.probe_threshold_value = 0
self.amplitude_feedback = False
self.adjust_probe = False
self.rm_client = Client(host='171.64.56.36')
self.threadpool = QThreadPool()
self.parameters = ""
self.f2_threshold = 0
self.list_index = 0
self.ignore_first_shot = False
self.updated_folders = [] # BAD HACK BAD
self.plot_saver = PlotWorkers.PlotSaveWorker()
self.plot_saver.start()
def set_shot_alerts(self):
try:
self.worker.alert_system.do_alerts = self.checkbox_shot_alert.isChecked()
except Exception as e:
traceback.print_exc()
def reset_probe_mails(self):
try:
self.worker.alert_system.n_mails = 0
print('Congrats, you pushed me')
except Exception as e:
traceback.print_exc()
def set_date(self, date):
self.date = date.toString(date_format_string)
self.set_script_cb()
# self.script_folder = ""
def set_imaging_calibration(self):
self.imaging_calibration = self.checkbox_imaging_calibration.isChecked()
try:
self.worker.imaging_calibration = self.imaging_calibration
except Exception as e:
print(e, "trying to turn on imaging calibration")
def set_ignore_first_shot(self):
self.ignore_first_shot = self.checkbox_ignore_first_shot.isChecked()
try:
self.make_plots()
except:
print("Error making plots after setting ignore first shots")
traceback.print_exc()
def set_parameters(self):
parameter_text = self.parameters_lineedit.text()
parameter_list = [i.strip() for i in parameter_text.split(",")]
self.parameters = parameter_list
try:
self.worker.parameters = self.parameters
except AttributeError as e:
traceback.print_exc()
print(e, "No file sorter worker yet...")
def set_amplitude_feedback(self):
self.amplitude_feedback = self.checkbox_adjust_amplitudes.isChecked()
return
def set_adjust_probe(self):
self.adjust_probe = self.checkbox_adjust_probe.isChecked()
return
def set_script_cb(self):
self.cb_script.clear()
try:
folders = af.get_immediate_child_directories(
af.get_date_data_path(self.date))
folders = [af.get_folder_base(i) for i in folders]
self.cb_script.addItems(folders)
if len(folders) > 0:
self.script_folder = folders[0]
self.set_script_folder(0)
except FileNotFoundError:
print("Selected bad date!")
def set_script_folder(self, i):
self.script_folder = self.cb_script.currentText()
self.label_folder_name.setText(
f"{analysis_folder_string}: {self.script_folder}")
self.holding_folder = af.get_holding_folder(
self.script_folder, data_date=self.date)
self.set_data_cb()
try:
with open(self.holding_folder + "/folder_dict.json", 'r') as dict_file:
self.data_folder_dict = json.loads(dict_file.read())
except FileNotFoundError:
print("No dictionary file yet...")
def set_data_cb(self):
"""
Get list of folders
"""
self.cb_data.clear()
try:
folders = af.get_immediate_child_directories(self.holding_folder)
folders = [af.get_folder_base(i) for i in folders]
self.cb_data.addItems(folders)
except FileNotFoundError:
print("Selected bad date, or bad folder?")
return
def set_list_index(self):
try:
self.list_index = int(self.index_lineedit.text())
except:
self.list_index = 0
if self.worker:
self.worker.list_index = self.list_index
def set_f2_threshold(self):
try:
self.f2_threshold = self.f2_threshold_checkbox.isChecked() * \
float(self.f2_threshold_input.text())
except Exception as e:
print(e)
traceback.print_exc()
self.f2_threshold = 0
try:
self.make_plots(self.folder_to_plot)
except:
traceback.print_exc()
def set_probe_threshold(self):
try:
current_folder = current_folder = f"{self.holding_folder}/{self.folder_to_plot}/"
physics_probes = np.load(
current_folder + "/fzx_probe.npy", allow_pickle=True)
physics_means = np.array(
[self.__mean_probe_value__(i) for i in physics_probes]
)
self.probe_threshold_value = self.probe_threshold.value() / 100 * \
np.max(physics_means)
self.probe_threshold_value_label.setText(
f"{self.probe_threshold_value:.4f}")
self.make_plots(self.folder_to_plot)
except AttributeError:
print("Can't set probe threshold, because no folder to plot set yet.")
except Exception as e:
print(e)
traceback.print_exc()
# TODO: Error Handling
def set_data_folder(self):
folder_to_plot = self.cb_data.currentText()
try:
self.make_plots(folder_to_plot)
except AttributeError:
self.make_plots(folder_to_plot)
def stop_sorting(self):
try:
self.worker.folder_to_plot = False
self.worker.stop()
print("Stopping sorting thread...")
except Exception as e:
print(e)
print("TODO: Add nicer error handling")
def save_figure(self, fig, title, current_folder, extra_directory="",
extra_title=""):
"""
Save an figure at current_folder/extradirectory/title.png
Parameters
----------
fig : matplotlib figure
The figure to save. Hopefully all the axes are set up correctly.
title : String
file name.
current_folder : String
folder to save in
extra_directory : String, optional
Returns
-------
None.
"""
save_folder = f"{current_folder}\\{extra_directory}"
if not os.path.isdir(save_folder):
os.makedirs(save_folder)
fig.tight_layout(rect=[0, 0.03, 1, 0.95])
title_string = f"{self.folder_to_plot}"
if extra_title:
title_string += f" | {extra_title}"
fig.suptitle(title_string)
save_location = u'\\\\?\\' + \
f"{save_folder}{self.folder_to_plot}_{title}.png"
fig.savefig(save_location, dpi=200)
def save_array(self, data, title, current_folder, extra_directory=""):
"""
Save an array at current_folder/extradirectory/title.txt
Parameters
----------
data : array
Numpy to save.
title : String
file name.
current_folder : String
folder to save in
extra_directory : String, optional
Returns
-------
None.
"""
save_folder = f"{current_folder}"
if not os.path.isdir(save_folder):
os.makedirs(save_folder)
try:
np.savetxt(f"{save_folder}{self.folder_to_plot}_{title}.txt", data)
except OSError:
print("Problem saving")
# TODO: Error handling
def __seconds_since_midnight__(self) -> float:
"""
How many seconds has it been since midnight on the same day?
:returns seconds since midnight: float
"""
now = datetime.now()
seconds_since_midnight = (
now - now.replace(hour=0, minute=0, second=0, microsecond=0)
).total_seconds()
return seconds_since_midnight
def update_date(self):
"""
Update the date if it changed
"""
current_date = QtCore.QDate.currentDate().toString(date_format_string)
if current_date != self.date:
time.sleep(60 * 40)
self.stop_sorting()
self.date = current_date
self.picker_date.setDate(QtCore.QDate.currentDate())
self.set_date(QtCore.QDate.currentDate())
self.make_sorter_thread()
return
def make_plots(self, folder_to_plot):
"""
Make all the plots for some particular folder.
"""
import units
importlib.reload(units)
from units import unitsDef
import PlotWorkers
importlib.reload(PlotWorkers)
from PlotWorkers import Plot1DWorker, Plot2DWorker, Plot1DHistogramWorker, PlotPCAWorker, PlotCorrelationWorker, PlotXYWorker
if not self.plot_saver.running:
self.plot_saver.start()
self.check_roi_boxes()
self.folder_to_plot = folder_to_plot
current_folder = f"{self.holding_folder}/{self.folder_to_plot}/"
with open(current_folder + "/xlabel.txt", 'r') as xlabel_file:
xlabel = xlabel_file.read().strip()
sf, units = unitsDef(xlabel)
# fits = np.load(current_folder + "/all_rois.npy")
# fits = np.apply_along_axis(
# AnalysisFunctions.get_trap_counts_from_roi, 2, fits)
# print(fits.shape)
fits = np.load(current_folder + "/all_anums.npy")
print(fits.shape)
# fits = AnalysisFunctions.get_atom_number_from_fluorescence(fits)
xlabels = np.load(current_folder + "/xlabels.npy")
physics_probes = np.load(
current_folder + "/fzx_probe.npy", allow_pickle=True)
if self.ignore_first_shot:
fits = fits[1:]
xlabels = xlabels[1:]
physics_probes = physics_probes[1:]
fits, xlabels = self.select_probe_threshold(
fits, xlabels, physics_probes)
roi_labels = np.load(current_folder + "/state_labels.npy")
all_globals = np.load(
current_folder + "/globals.npy", allow_pickle=True)[0]
traps = all_globals['Tweezers_AOD1_Traps']
print("Expected Traps: ", traps)
# fits, xlabels = self.select_f2_threshold(fits, xlabels, roi_labels)
if len(fits) < 1:
return
fit_mean, fit_std, xlabels = self.group_shot(fits, xlabels)
# roi x Shots X Site
fit_mean, fit_std = np.swapaxes(
fit_mean, 0, 1), np.swapaxes(fit_std, 0, 1)
keys_adjusted = np.array(xlabels) * sf
plot1dworker = Plot1DWorker(current_folder, self.figure_1d, xlabel, units,
fit_mean, fit_std, roi_labels, keys_adjusted, rois_to_exclude=self.rois_to_exclude)
plot1dworker.f2_threshold = self.f2_threshold
plot1dworker.traps = traps
self.traps = traps
self.trap_selector_label.setText(
f"Traps: {' '.join(str(i) for i in self.traps)}")
plot2dworker = Plot2DWorker(current_folder, self.figure_2d, xlabel, units,
fit_mean, fit_std, roi_labels, keys_adjusted, rois_to_exclude=self.rois_to_exclude)
try:
self.threadpool.start(plot1dworker)
self.threadpool.start(plot2dworker)
if self.amplitude_feedback and ('reps' in folder_to_plot or 'iteration' in folder_to_plot):
n_traps = fit_mean.shape[-1]
print(fit_mean.shape)
self.adjust_amplitude_compensation(fit_mean, n_traps)
self.make_probe_plot()
plotXYWorker = PlotXYWorker(
current_folder, self.figure_6, xlabel, units, fits, fit_std, roi_labels, keys_adjusted, rois_to_exclude=self.rois_to_exclude
)
if "PairCreation" in self.folder_to_plot and 'time' not in self.folder_to_plot or 'iteration' in self.folder_to_plot:
self.canvas_corr.setFixedHeight(600)
plotPCAworker = PlotPCAWorker(
current_folder, self.figure_phase, xlabel, units, fits, fit_std, roi_labels, keys_adjusted, rois_to_exclude=self.rois_to_exclude
)
plotCorrelationWorker = PlotCorrelationWorker(
current_folder, self.figure_corr, xlabel, units, fits, fit_std, roi_labels, keys_adjusted, rois_to_exclude=self.rois_to_exclude
)
plotCorrelationWorker.set_limits(0, 1)
plotCorrelationWorker.set_normalize(
self.checkbox_normalize_correlations.isChecked())
plotXYWorker.xlabels = np.load(current_folder + "/xlabels.npy")
self.threadpool.start(plotXYWorker)
self.threadpool.start(plotPCAworker)
# self.threadpool.start(plotCorrelationWorker)
elif "Duration" in self.folder_to_plot or "OG_Duration" in self.folder_to_plot or "SpinExchange" in self.folder_to_plot or 'PhaseImprintPhase' in self.folder_to_plot or "PhaseTime" in self.folder_to_plot or "RamseyPhase" in self.folder_to_plot or "RamseyReadout" in self.folder_to_plot:
self.canvas_corr.setFixedHeight(600)
try:
self.make_phase_plot(sf, units)
except:
"Error Making Phase Plot"
traceback.print_exc()
self.make_magnetization_plot()
elif xlabel == no_xlabel_string:
self.canvas_corr.setFixedHeight(600)
plot1dhistworker = Plot1DHistogramWorker(
current_folder, self.figure_corr, xlabel, units,
fit_mean, fit_std, roi_labels, keys_adjusted, rois_to_exclude=self.rois_to_exclude)
self.threadpool.start(plot1dhistworker)
self.set_data_cb()
index = self.cb_data.findText(self.folder_to_plot)
self.cb_data.setCurrentIndex(index)
except:
traceback.print_exc()
if self.adjust_probe:
try:
self.adjust_probe_values()
except:
traceback.print_exc()
def select_probe_threshold(self, fits, xlabels, physics_probes):
if self.checkbox_probe_threhold.isChecked():
mean_probe = np.array([self.__mean_probe_value__(i)
for i in physics_probes])
indices_to_keep = mean_probe > self.probe_threshold_value
return fits[indices_to_keep], xlabels[indices_to_keep]
return fits, xlabels
def select_f2_threshold(self, fits, xlabels, roi_labels):
"""
Given a list of fits and xlabels, only return the fits + xlabels that
have F = 2 population > threshold set in the QtGui
Inputs:
fits: Shots x States x Traps array of data
xlabels: array of length shots with the appropriate vlaue of the xlabel
roi_labels: The order that the states in fits are.
Outputs:
fits: Thresholded Shots x States x Traps aray of data
xlabels: array of length (thresholded shots)
"""
fit2 = fits[:, list(roi_labels).index("roi2orOther")]
fit2 = np.mean(fit2, axis=1)
mask = fit2 > self.f2_threshold
return fits[mask], xlabels[mask]
def adjust_imaging_field(self, fits, xlabels):
"""
Adjust the frequencies of the magnetic fields to accound for slow drifts
in the magnetic field during the imaging portion of the sequence.
"""
current_folder = f"{self.holding_folder}/{self.folder_to_plot}/"
roi_labels = list(np.load(current_folder + "/roi_labels.npy"))
fit_10 = fits[roi_labels.index('roi10')]
fit_1m1 = fits[roi_labels.index('roi1-1')]
fit_1p1 = fits[roi_labels.index('roi11')]
fit_2 = fits[roi_labels.index('roi2orOther')]
if fit_2.shape[0] < 19:
return
fit_2 = fit_2[:, self.traps]
fit_1m1 = fit_1m1[:, self.traps]
pol = np.sum((fit_2 - fit_1m1), axis=1) / \
np.sum((fit_2 + fit_1m1), axis=1)
# pol has shape n_shots X n_traps
if np.mean(fit_2 + fit_1m1) < 200:
return
print("Adjusting B Field")
optimal_detuning = xlabels[np.argmax(pol)]
print(f"optimal_detuning: {optimal_detuning}")
globals = self.rm_client.get_globals(raw=True)
globals['CheckMagneticField'] = "False"
detuning_global = globals['MS_KPDetuning']
previous_detuning = eval(detuning_global)
new_freq = previous_detuning + optimal_detuning
new_freq_str = repr(new_freq)
new_globals = {
'MS_KPDetuning': new_freq_str
}
with open(f'{self.holding_folder}/b_field.csv', 'a+') as f:
writer = csv.writer(f)
writer.writerow(
[datetime.now().strftime('%Y-%m-%d %H:%M:%S'), new_freq])
globals['MS_KPDetuning'] = new_freq_str
self.updated_folders.append(self.folder_to_plot[-6:])
# self.rm_client.set_globals(new_globals, raw = True)
# Get Current B Field with self.rm_client
def adjust_cleaning_field(self, fits, xlabels):
"""
Adjust the frequencies of the magnetic fields to accound for slow drifts
in the magnetic field during the cleaning portion of the sequence.
"""
current_folder = f"{self.holding_folder}/{self.folder_to_plot}/"
roi_labels = list(np.load(current_folder + "/roi_labels.npy"))
fit_10 = fits[roi_labels.index('roi10')]
fit_1m1 = fits[roi_labels.index('roi1-1')]
fit_1p1 = fits[roi_labels.index('roi11')]
fit_2 = fits[roi_labels.index('roi2orOther')]
if fit_2.shape[0] < 21:
return
globals = self.rm_client.get_globals(raw=True)
if len(self.traps) > 1:
population_1m1 = np.sum(fit_1m1[:, self.traps], axis=1)
else:
population_1m1 = np.squeeze(fit_1m1[:, self.traps])
filter_f1 = population_1m1 < 1000
if np.sum(filter_f1) < 5:
print('MOT seems to be out for the cleaning adjustment')
return
print("Adjusting cleaning B Field")
optimal_detuning = xlabels[np.argmin(population_1m1)]
print(f"optimal_detuning: {optimal_detuning}")
freq_global = globals['CK_MWCleanPrep_Freq']
previous_freq = eval(freq_global)
new_freq = previous_freq + optimal_detuning
new_freq_str = repr(new_freq)
new_globals = {
'CK_MWCleanPrep_Freq': new_freq_str,
'CheckMagneticFieldCleaning': 'False'
}
with open(f'{self.holding_folder}/b_field.csv', 'a+') as f:
writer = csv.writer(f)
writer.writerow(
[datetime.now().strftime('%Y-%m-%d %H:%M:%S'), 'Cleaning', new_freq])
print(
f"Adjusted cleaning field from {freq_global} to {new_freq_str}")
self.rm_client.set_globals(new_globals, raw=True)
def adjust_raman_field_ramsey(self, fits, xlabels, n_shots=4):
current_folder = f"{self.holding_folder}/{self.folder_to_plot}/"
roi_labels = list(np.load(current_folder + "/roi_labels.npy"))
fit_10 = fits[roi_labels.index('roi10')]
fit_1m1 = fits[roi_labels.index('roi1-1')]
fit_1p1 = fits[roi_labels.index('roi11')]
fit_2 = fits[roi_labels.index('roi2orOther')]
print('Starting magnetic field analysis')
pol = np.sum((fit_1p1 - fit_1m1), axis=0) / \
np.sum((fit_1p1 + fit_1m1 + fit_10), axis=0)
print(pol.shape)
pol = pol[[10, ]] # TODO: Fix
current_globals = np.load(
current_folder + "globals.npy", allow_pickle=True)[0]
ramsey_time = current_globals['Raman_CheckMagTime']
if fit_1p1.shape[0] < n_shots or np.max(fit_1p1) < 100:
return
print("Adjusting B field via Ramsey: ")
adjusted_delta = np.round(
np.mean(np.arcsin(pol) / (2 * pi * ramsey_time) * 1e-6), 4)
detuning_global = current_globals['Raman_BareFreq']
new_freq = np.array([detuning_global + adjusted_delta])
new_freq_str = repr(new_freq)
new_globals = {
'Raman_BareFreq': new_freq_str,
'CheckMagneticField': 'False'
}
with open(f'{self.holding_folder}/b_field.csv', 'a+') as f:
writer = csv.writer(f)
writer.writerow([datetime.now().strftime(
'%Y-%m-%d %H:%M:%S'), 'Raman', detuning_global + adjusted_delta])
print(f"Adjusted Raman field from {detuning_global} to {new_freq_str}")
self.rm_client.set_globals(new_globals, raw=True)
# self.rm_client.engage()
# self.rm_client.set_globals(
# {'CheckSimultaneousReadout': 'False'}, raw=True)
self.updated_folders.append(self.folder_to_plot[-6:])
return
def adjust_imaging_field_ramsey(self, fits, xlabels, n_shots=4):
current_folder = f"{self.holding_folder}/{self.folder_to_plot}/"
roi_labels = list(np.load(current_folder + "/roi_labels.npy"))
fit_10 = fits[roi_labels.index('roi10')]
fit_1m1 = fits[roi_labels.index('roi1-1')]
fit_1p1 = fits[roi_labels.index('roi11')]
fit_2 = fits[roi_labels.index('roi2orOther')]
print('Starting imaging magnetic field analysis')
pol = np.sum((fit_2 * 1.1 - fit_1m1), axis=0) / \
np.sum((fit_2 * 1.1 + fit_1m1), axis=0)
print(pol.shape)
pol = pol[[10, ]] # TODO: Fix
current_globals = np.load(
current_folder + "globals.npy", allow_pickle=True)[0]
ramsey_time = current_globals['MS_CheckFieldWaitTime']
ramsey_detuning = current_globals['MS_CheckFieldDetuning']
if fit_2.shape[0] < n_shots or np.max(fit_1m1 + fit_2) < 1000:
return
print("Adjusting B field via Ramsey: ")
adjusted_delta = np.round(
np.mean(ramsey_detuning - np.arccos(pol) / (2 * pi * ramsey_time) * 1e-6), 4)
if np.abs(adjusted_delta) > 5e-3 or np.isnan(adjusted_delta):
return
detuning_global = current_globals['MS_KPDetuning']
new_freq = np.array([detuning_global + adjusted_delta])
new_freq_str = repr(new_freq)
new_globals = {
'MS_KPDetuning': new_freq_str
}
with open(f'{self.holding_folder}/b_field.csv', 'a+') as f:
writer = csv.writer(f)
writer.writerow([datetime.now().strftime(
'%Y-%m-%d %H:%M:%S'), 'Imaging', detuning_global + adjusted_delta])
print(
f"Adjusted imaging field from {detuning_global} to {new_freq_str}")
self.rm_client.set_globals(new_globals, raw=True)
self.updated_folders.append(self.folder_to_plot[-6:])
return
def run_spectroscopy(self, type="imaging"):
new_globals = {
'CheckMagneticField': 'True',
'MeasurePairCreation': 'False',
'PR_WaitTime': '0',
'Tweezers_AOD0_LoadAmp': '21',
'Tweezers_AOD0_ImageAmp': 'Tweezers_AOD0_LoadAmp + 3',
'MS_CheckFieldWaitTime': '0',
'MS_CheckFieldDetuning': '1e-3 * np.concatenate([arange(-40, 50, 10), arange(-5, 5, 1)])',
'Descriptor': "'CheckSpectroscopy'",
'iteration': 'arange(1)'
}
# self.rm_client.set_globals(new_globals, raw = True)
# self.rm_client.engage()
def check_field(self, fits, xlabel, type="imaging", n_shots=2):
current_folder = f"{self.holding_folder}/{self.folder_to_plot}/"
roi_labels = list(np.load(current_folder + "/roi_labels.npy"))
fit_10 = fits[roi_labels.index('roi10')]
fit_1m1 = fits[roi_labels.index('roi1-1')]
fit_1p1 = fits[roi_labels.index('roi11')]
fit_2 = fits[roi_labels.index('roi2orOther')]
if fit_2.shape[0] < n_shots or np.max(fit_2) < 100:
return
pol = np.sum((fit_2 - fit_1m1), axis=0) / \
np.sum((fit_2 + fit_1m1), axis=0)
pol = pol[[10, ]] # TODO: Fix
print("Adjusting B field via Ramsey: ")
print(pol)
# If the pi pulse is not really a pi pulse, then do it with spectroscopy
if pol < 0.7:
self.run_spectroscopy(type)
self.updated_folders.append(self.folder_to_plot[-6:])
return
def adjust_amplitude_compensation(self, fits, n_traps):
"""
Look at the current atom uniformity, and update the relative trap powers
to optimize uniformity. This function engages with the runmanager remote
client to automatically engage the next set.
Inputs: n_traps
"""
current_folder = f"{self.holding_folder}/{self.folder_to_plot}/"
print("Amplitude Compensation")
roi_labels = list(np.load(current_folder + "/roi_labels.npy"))
fit_10 = fits[roi_labels.index('roi10')]
fit_1m1 = fits[roi_labels.index('roi1-1')]
fit_1p1 = fits[roi_labels.index('roi11')]
fit_sum = fit_10 # + fit_1m1 + fit_1p1
print(fit_sum.shape)
trap_values = np.mean(fit_sum, axis=0)
assert n_traps == len(trap_values), f"{n_traps} {len(trap_values)}"
# Reverse, since the frequency -> trap ordering is reversed
if len(fit_sum) > 2 and len(fit_sum) % 4 == 0:
sites = self.traps
saved_path = compensation_path(len(sites))
print(f"Attempting to load from {saved_path}")
try:
current_compensation = np.load(saved_path)
except Exception as e:
print(e)
current_compensation = np.ones(n_traps)
trap_values = np.mean(fit_sum, axis=0)
# compensation = trap_values[::-1] ** (-1 / 3)
compensation = trap_values ** (-1 / 3)
compensation[np.delete(np.arange(n_traps), sites)] = 0
new_compensation = current_compensation * compensation
new_compensation = new_compensation / \
np.linalg.norm(new_compensation)
print(f"compensation calculated: {compensation}")
print(f"New compensation = {new_compensation}")
print(f"saving new compensation to {saved_path}")
np.save(saved_path, new_compensation)
print("Engaging new scan")
self.rm_client.engage()
return
def __bare_probe_filter__(self, bare_probe):
if np.max(bare_probe) < 0.12:
return False
return True
def __fit_filter__(self, popt, pstd):
print(f"POPT: {popt}")
if popt[0] > 4 or popt[0] < 0.03:
return False
if popt[1] < 0.15 or popt[1] > 0.3:
return False
return True
def adjust_probe_values(self):
print("Adjusting probe values")
current_folder = f"{self.holding_folder}/{self.folder_to_plot}/"
bare_probes = np.load(
current_folder + "/bare_probe.npy", allow_pickle=True)
bare_probes = np.array(bare_probes)
current_globals = np.load(
current_folder + "globals.npy", allow_pickle=True)[0]
current_offset = current_globals[agilent_offset_string]
current_physics_freq = current_globals[agilent_physics_string]
offset = current_offset
physics_freq = current_physics_freq
#A, full_width, x0, offset
for i in bare_probes[-6:]:
i = i - np.mean(i[-50:])
if self.__bare_probe_filter__(i):
freq = np.linspace(-4, 4, len(i))
guess = [np.max(i), 0.25, freq[np.argmax(i)], 0]
popt, pcov = curve_fit(lorentzian, freq, i,
bounds=([0, 0, -np.inf, -np.inf],
[2 * np.max(i), np.inf, np.inf, np.inf]),
p0=guess, ftol=1e-4, xtol=1e-3)
pstd = np.diag(np.sqrt(np.abs(pcov)))
if self.__fit_filter__(popt, pstd):
_, _, center, _ = popt
offset = np.round(current_offset - center, 1)
physics_freq = np.round(current_physics_freq - center, 1)
self.rm_client.set_globals({agilent_offset_string: offset}) # ,
# agilent_physics_string: physics_freq})
print("Adjusted globals to", {agilent_offset_string: offset,
}, f"from {current_offset}")
def make_probe_plot(self):
current_folder = f"{self.holding_folder}/{self.folder_to_plot}/"
with open(current_folder + "/xlabel.txt", 'r') as xlabel_file:
xlabel = xlabel_file.read().strip()
sf, units = unitsDef(xlabel)
xlabels = np.load(current_folder + "/xlabels.npy")
keys_adjusted = sf * np.array(xlabels)
bare_probes = np.load(
current_folder + "/bare_probe.npy", allow_pickle=True)
physics_probes = np.load(
current_folder + "/fzx_probe.npy", allow_pickle=True)
bare_probes = np.array(bare_probes)
self.figure_probe.clf()
# Bare probe scan
sorting_order = np.argsort(xlabels)
bare_probes = bare_probes[sorting_order]
ax = self.figure_probe.add_subplot(1, 2, 1)
try:
extent = [-4, 4, np.max(keys_adjusted) + np.diff(
keys_adjusted[sorting_order])[0], np.min(keys_adjusted)]
cax = ax.imshow(bare_probes, aspect="auto", extent=extent)
ax.set_ylabel(f"{xlabel} ({units})")
ax.set_xlabel(f"Frequency (MHz)")
self.figure_probe.colorbar(cax, ax=ax)
except TypeError:
print("Type Error in Probe plot")
"""
TODO: Add in error handling
"""
traceback.print_exc()
except IndexError:
print("IndexError in Probe Plot")
traceback.print_exc()
# Physics probe
physics_means = np.array(
[self.__mean_probe_value__(i) for i in physics_probes]
)
print(physics_means)
ax = self.figure_probe.add_subplot(1, 2, 2)
transparent_edge_plot(
ax, keys_adjusted[sorting_order], physics_means[sorting_order])
ax.axhline(float(self.probe_threshold_value),
ls='--', c="r", label="Probe Threshold")
ax.legend()
ax.set_ylabel("Mean APD Voltage")
ax.set_xlabel(f"{xlabel} ({units})")
af.save_array(physics_means, "mean_probe_physics", current_folder)
if "cavity_shift" in self.folder_to_plot:
try:
#A, full_width, x0, offset
popt, pcov = curve_fit(lorentzian,
keys_adjusted[sorting_order],
physics_means[sorting_order],
bounds=([0, 0.05, min(keys_adjusted), -0.01],
[0.5, 0.5, max(keys_adjusted), 0.01]),
p0=[0.06, 0.2,
keys_adjusted[np.argmax(
physics_means[sorting_order])],
0
])
pstd = np.sqrt(np.diag(pcov))
if popt[0] > 0.03 and len(keys_adjusted) > 14:
self.rm_client.set_globals(
{shifted_resonance_string: f"{popt[2]:.4g}"},
raw=True)
key_fine = np.linspace(np.min(keys_adjusted),
np.max(keys_adjusted), 1000)
ax.plot(key_fine, lorentzian(
key_fine, *popt), '--', c="tab:blue")
except:
traceback.print_exc()
#af.save_figure(self.figure_probe, "probe", current_folder)
print("Putting Probe figure in queue")
PlotWorkers.file_save_queue.put(
(self.figure_probe, "probe", current_folder))
self.canvas_probe.draw()
def __mean_probe_value__(self, probe):
print(len(probe))
if len(probe) > 65:
bg = probe[:65]
trans = probe[65:-35]
print(len(trans))
return np.mean(trans) - np.mean(bg)
return 0
def __get_magnetization__(self, mean, roi_labels):
print(np.max(mean))
mag = np.zeros((len(mean), mean.shape[2]))
plus = mean[:, roi_labels.index("roi11"), self.traps]
minus = mean[:, roi_labels.index("roi1-1"), self.traps]
zero = mean[:, roi_labels.index("roi10"), self.traps]
mag[:, self.traps] = (
(plus - minus)
/ (plus + minus + zero)
)
return mag
def make_magnetization_plot(self):
current_folder = f"{self.holding_folder}/{self.folder_to_plot}/"
with open(current_folder + "/xlabel.txt", 'r') as xlabel_file:
xlabel = xlabel_file.read().strip()
sf, units = unitsDef(xlabel)
fits = np.load(current_folder + "/all_anums.npy")
if len(fits) < 2:
return
xlabels = np.load(current_folder + "/xlabels.npy")
physics_probes = np.load(
current_folder + "/fzx_probe.npy", allow_pickle=True)
fits, xlabels = self.select_probe_threshold(
fits, xlabels, physics_probes)
if len(xlabels) < 2:
return
roi_labels = list(np.load(current_folder + "/state_labels.npy"))
fit_mean, fit_std, xlabels = self.group_shot(fits, xlabels)
keys_adjusted = np.array(xlabels) * sf
self.figure_corr.clf()
ax = self.figure_corr.add_subplot(1, 1, 1)
if len(keys_adjusted) < 2:
return
pol = self.__get_magnetization__(fit_mean, roi_labels)
extent = [-0.5, fit_mean.shape[2] - 0.5,
np.max(keys_adjusted) + np.diff(keys_adjusted)[0],
np.min(keys_adjusted)]
mag = min(np.max(np.abs(pol)), 10)
cax = ax.imshow(
pol, aspect="auto", cmap=magnetization_colormap, extent=extent, vmin=-mag, vmax=mag)
self.figure_corr.colorbar(cax, ax=ax)
ax.set_ylabel(f"{xlabel} ({units})")
ax.set_xlabel(f"Trap Index")
af.save_figure(self.figure_corr, "magnetization", current_folder)
self.canvas_corr.draw()
def make_phase_plot(self, sf, units):
print("Making Phase Plot")
current_folder = f"{self.holding_folder}/{self.folder_to_plot}/"
with open(current_folder + "/xlabel.txt", 'r') as xlabel_file:
xlabel = xlabel_file.read().strip()
# sf, units = unitsDef(xlabel)
# if xlabel != "PR_IntDuration" and xlabel != "OG_Duration":
# return
globals_list = np.load(current_folder + "/globals.npy",
allow_pickle=True)
fits = np.load(current_folder + "/all_anums.npy")
fits = np.apply_along_axis(
AnalysisFunctions.get_trap_counts_from_roi, 2, fits)
fits = AnalysisFunctions.get_atom_number_from_fluorescence(fits)
if len(fits) < 2:
return
roi_labels = list(np.load(current_folder + "/state_labels.npy"))
phase_dict = self.group_shot_globals(fits,
globals_list,
"Raman_RamseyPhase")
if len(phase_dict.keys()) < 2:
return
fits_x, xlabels = self.sort_phase_list(phase_dict[0], xlabel)
fits_y, _ = self.sort_phase_list(phase_dict[90], xlabel)
print(np.max(fits_x))
if len(fits_x) != len(fits_y):
print(f"Incompatible x/y lengths {len(fits_x)} and {len(fits_y)}")
return
keys_adjusted = np.array(xlabels) * sf
print(f"max x:{np.max(fits_x)}")
x_pol = self.__get_magnetization__(fits_x, roi_labels)
y_pol = self.__get_magnetization__(fits_y, roi_labels)
n_traps = fits_x.shape[2]
if len(keys_adjusted) < 2:
return
extent = [-0.5, n_traps - 0.5, np.max(keys_adjusted) + np.diff(keys_adjusted)[0],
np.min(keys_adjusted)]
c_num = x_pol + 1j * y_pol
if len(c_num) == 1:
c_num = np.array([c_num])
phase = np.angle(c_num)
contrast = np.abs(c_num)
contrast[:, np.delete(np.arange(n_traps), self.traps)] = 0
phase[:, np.delete(np.arange(n_traps), self.traps)] = 0
print("Contrast", contrast.dtype)
print("phase", phase)
self.save_array(phase, "phase", current_folder)
self.save_array(contrast, "contrast", current_folder)
self.save_array(keys_adjusted, "keys_adjusted", current_folder)
nrows, ncolumns = 2, 2
self.figure_phase.clf()
ax_phase = self.figure_phase.add_subplot(nrows, ncolumns, 1)
ax_phase.set_title("Phase")
ax_phase.set_ylabel(f"{xlabel} ({units})")
cax = ax_phase.imshow(
phase, cmap=phase_colormap, aspect="auto", extent=extent,
vmin=-np.pi, vmax=np.pi, interpolation=None)
self.figure_phase.colorbar(cax)
ax_contrast = self.figure_phase.add_subplot(nrows, ncolumns, 2)
ax_contrast.set_title("Contrast")
ax_contrast.set_ylabel(f"{xlabel} ({units})")
cax = ax_contrast.imshow(contrast, cmap=contrast_colormap, aspect="auto", extent=extent,
vmin=0, vmax=1, interpolation=None)
self.figure_phase.colorbar(cax, ax=ax_contrast)
ax_x = self.figure_phase.add_subplot(nrows, ncolumns, 3)
ax_x.set_title("Phase = 0")
ax_x.set_ylabel(f"{xlabel} ({units})")
cax = ax_x.imshow(x_pol, cmap=magnetization_colormap, aspect="auto", extent=extent,
vmin=-1, vmax=1)
self.figure_phase.colorbar(cax, ax=ax_x)
ax_y = self.figure_phase.add_subplot(nrows, ncolumns, 4)
ax_y.set_title("Phase = 90")
ax_y.set_ylabel(f"{xlabel} ({units})")
cax = ax_y.imshow(y_pol, cmap=magnetization_colormap, aspect="auto", extent=extent,
vmin=-1, vmax=1)
self.figure_phase.colorbar(cax, ax=ax_y)
af.save_figure(self.figure_phase, "phase_plot", current_folder)
self.canvas_phase.draw()
def sort_phase_list(self, fits, parameter):
"""
Parameters
----------
fits : list of (fit, globals)
DESCRIPTION.