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PlotWorkers.py
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PlotWorkers.py
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from PyQt5.QtGui import *
from PyQt5.QtWidgets import *
from PyQt5.QtCore import *
from FormattingStrings import *
from PlottingTools import *
import numpy as np
import AnalysisFunctions as af
import traceback
import math
from colorcet import cm
from sklearn.decomposition import PCA
from scipy.stats import norm
import time
import queue
import matplotlib.pyplot as plt
import sys
import os
from datetime import date
file_save_queue = queue.Queue()
atom_cmap = cm.blues
class PlotSaveWorker(QThread):
def __init__(self):
super(PlotSaveWorker, self).__init__()
self.running = True
return
def save_figure(self, fig, title, current_folder):
"""
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
Returns
-------
None.
"""
current_folder = current_folder.replace("/", "\\")
save_folder = f"{current_folder}"
folder_to_plot = current_folder.split("\\")[-2]
if not os.path.isdir(save_folder):
os.makedirs(save_folder)
fig.tight_layout(rect=[0, 0.03, 1, 0.95])
title_string = f"{folder_to_plot} | {str(date.today())}"
fig.suptitle(title_string)
print(f"{save_folder}{folder_to_plot}_{title}.png")
save_location = u'\\\\?\\' + \
f"{save_folder}{folder_to_plot}_{title}_{str(date.today())}.png"
fig.savefig(
save_location)
def run(self):
while self.running:
while file_save_queue.qsize() > 0:
try:
fig, fname, folder = file_save_queue.get()
self.save_figure(fig, fname, folder)
time.sleep(0.1)
except Exception as e:
self.stop()
(type, value, traceback) = sys.exc_info()
sys.excepthook(type, value, traceback)
print(e, "Line 35 in PlotWorkers")
traceback.print_exc()
QThread.sleep(1)
def stop(self):
file_save_queue.queue.clear()
self.running = False
# if self.folder_to_plot:
# self.signal_output.folder_output_signal.emit(self.folder_to_plot)
self.terminate()
class ProbePlotWorker(QRunnable):
def __init__(self, current_folder, fig, xlabel, units, keys_adjusted, bare_probes, rigol_probes):
"""
"""
return
def run(self, rigol_probes, bare_probes, keys_adjusted):
"""
"""
self.fig.clf()
##nrows, n_cols, index
ax_bare = self.fig.add_subplot(121)
ax_probe = self.fig.add_subplot(122)
probe_values = [np.mean(i) for i in rigol_probes]
transparent_edge_plot(ax_probe, keys_adjusted, rigol_probes)
ax_probe.set_xlabel(f"{self.xlabel} ({self.units})")
ax_probe.set_ylabel("Mean APD Voltage (from Rigol, without trimming)")
return
class PlotFitWorker(QRunnable):
def __init__(self, current_folder, fig, xlabel, units, fit_mean, fit_std, roi_labels, keys_adjusted, rois_to_exclude=[]):
"""
"""
super(PlotFitWorker, self).__init__()
self.current_folder = current_folder
self.fig = fig
self.xlabel = xlabel
self.units = units
self.fit_mean = fit_mean
self.fit_std = fit_std
self.roi_labels = roi_labels
self.keys_adjusted = keys_adjusted
self.rois_to_exclude = rois_to_exclude
class Plot2DWorker(PlotFitWorker):
def run(self):
try:
self.fig.clf()
rois_to_plot = [i for i in range(
len(self.roi_labels)) if self.roi_labels[i] not in self.rois_to_exclude]
n_rows, n_columns = 2, math.ceil(len(rois_to_plot) / 2)
if len(self.keys_adjusted) < 2:
return
extent = [-0.5, self.fit_mean.shape[2] - 0.5,
np.max(self.keys_adjusted) +
np.diff(self.keys_adjusted)[0],
np.min(self.keys_adjusted)]
for e, i in enumerate(rois_to_plot):
label = self.roi_labels[i]
ax = self.fig.add_subplot(n_rows, n_columns, e + 1)
cax = ax.imshow(self.fit_mean[i], aspect="auto",
cmap=atom_cmap, extent=extent, vmin=0)
af.save_array(self.fit_mean[i], label, self.current_folder)
self.fig.colorbar(cax, ax=ax, label="Atom Number")
if label in fancy_titles.keys():
ax.set_title(fancy_titles[label])
else:
ax.set_title(label)
ax.set_xlabel("Trap Index")
ax.set_ylabel(f"{self.xlabel} ({self.units})")
file_save_queue.put((self.fig, '2d_plot', self.current_folder))
except:
traceback.print_exc()
sys.stderr.write("Error in 2D plot")
(type, value, traceback) = sys.exc_info()
sys.excepthook(type, value, traceback)
class Plot1DWorker(PlotFitWorker):
"""
Worker Thread to make 1D plot
"""
def run(self):
try:
self.fig.clf()
axis = self.fig.add_subplot(111)
for state, state_std, label in zip(self.fit_mean, self.fit_std, self.roi_labels):
if label not in self.rois_to_exclude:
transparent_edge_plot(axis,
self.keys_adjusted,
np.mean(
state[:, self.traps], axis=1),
np.mean(state_std, axis=1),
label=fancy_titles[label])
af.save_array(np.mean(state, axis=1),
f"{label}_1d", self.current_folder)
af.save_array(np.mean(state, axis=1),
f"{label}_1d_std", self.current_folder)
axis.axhline(self.f2_threshold, color='r',
linestyle='--', label="F = 2 Threshold")
axis.legend()
axis.set_ylabel("Atoms")
axis.set_xlabel(f"{self.xlabel} ({self.units})")
# current_folder = self.current_folder.replace("/", "\\")
# folder_to_plot = current_folder.split("\\")[-2]
# self.fig.tight_layout(rect=[0, 0.03, 1, 0.95])
# title_string = f"{folder_to_plot}"
# self.fig.suptitle(title_string)
file_save_queue.put((self.fig, '1d_plot', self.current_folder))
except:
traceback.print_exc()
(type, value, traceback) = sys.exc_info()
sys.excepthook(type, value, traceback)
class Plot1DHistogramWorker(PlotFitWorker):
def run(self):
try:
colors = ["tab:blue", "tab:orange", "tab:green",
"tab:red", "tab:purple", "tab:brown", "tab:pink"]
self.fig.clf()
axis = self.fig.add_subplot(111)
for state, state_std, label, color in zip(self.fit_mean, self.fit_std, self.roi_labels, colors):
if label not in self.rois_to_exclude:
data = np.mean(state, axis=1)
mean, std = norm.fit(data)
out, bins, patches = axis.hist(data, bins='stone',
label=f"{fancy_titles[label]} - Mean: {mean:.0f}, Std.: {std:.1f}",
color=color)
x = np.linspace(np.min(data), np.max(data))
axis.plot(x, np.max(out) * norm.pdf(x, mean, std), c=color)
axis.legend()
axis.set_ylabel("Number of Shots")
axis.set_xlabel(f"Atoms")
# file_save_queue.put((self.fig, '1d_histplot', self.current_folder))
except:
traceback.print_exc()
class PlotPCAWorker(PlotFitWorker):
def run(self):
# HARD CODED, this is bad
self.fit_mean = self.fit_mean[..., [4, 6, 8, 10, 12, 14]]
if self.fit_mean.shape[0] < 8:
return
n_traps = self.fit_mean.shape[2]
self.roi_labels = list(self.roi_labels)
fit_1m1 = self.fit_mean[:, self.roi_labels.index('roi1-1')] # fit_sum
fit_1p1 = self.fit_mean[:, self.roi_labels.index('roi11')] # fit_sum
fit_10 = self.fit_mean[:, self.roi_labels.index('roi10')]
fit_sum = (fit_1m1 + fit_1p1 + fit_10)
fit_1m1, fit_1p1 = fit_1m1 / fit_sum, fit_1p1 / fit_sum
fit_side_mode = fit_1p1 - fit_1m1
n_components = 5
side_mode_pca = PCA(n_components=n_components)
side_mode_pca.fit(fit_side_mode)
variance_explanation = side_mode_pca.explained_variance_ratio_
components = side_mode_pca.components_
self.fig.clf()
ax_num = 1
for component, variance in zip(components, variance_explanation):
ax = self.fig.add_subplot(n_components, 1, ax_num)
c = component
mag = np.max(np.abs(c))
cax = ax.imshow([c], cmap=cm.coolwarm, vmin=-mag, vmax=mag)
ax.set_yticks([0.5])
ax.set_yticklabels([""])
ax.set_title(f"Variance explained: {variance * 100:.1f}%")
self.fig.colorbar(cax, ax=ax)
ax.set_xlabel("Trap Index")
ax_num += 1
# file_save_queue.put(
# (self.fig, 'pc_pca_components', self.current_folder))
class PlotXYWorker(PlotFitWorker):
def jackknife_error(self, arr, statistic):
n = np.arange(len(arr))
stats = np.array([statistic(np.delete(arr, i, axis=0)) for i in n])
print("STATS", stats.shape)
return np.std(stats, axis=0) * np.sqrt(len(arr))
def var(self, fit):
theta = np.linspace(0, np.pi, 1000)
fit_1m1 = fit[:, self.roi_labels.index('roi1-1')] # fit_sum
fit_1p1 = fit[:, self.roi_labels.index('roi11')] # fit_sum
fit_10 = fit[:, self.roi_labels.index('roi10')]
fit_sum = (fit_1m1 + fit_1p1 + fit_10)
fit_1m1, fit_1p1 = fit_1m1 / fit_sum, fit_1p1 / fit_sum
pol = fit_1p1 - fit_1m1
# shots X sites
c_nums = pol[:, ::2] + 1j * pol[:, 1::2]
# shots X sites/2
mags = np.abs(c_nums)
phases = np.angle(c_nums)
#phases = phases - phases[:, 0, np.newaxis]
adj_cnum = mags * np.exp(1j * phases)
x = np.real(adj_cnum)
y = np.imag(adj_cnum)
scaling = np.array([np.mean(
np.cos(t)**2 * fit_sum[:, 0] + np.sin(t)**2 * fit_sum[:, 1]) for t in theta])
var = np.array(
[np.var(np.real(mags * np.exp(1j * (phases + t)))) for t in theta])
return var * scaling
def run(self):
self.fit_mean = self.fit_mean[..., [8, 12]] # HARD CODED, this is bad
self.fit_mean = np.array([i for i in self.fit_mean if np.sum(i) > 600])
if self.fit_mean.shape[0] < 8:
return
n_traps = self.fit_mean.shape[2]
self.roi_labels = list(self.roi_labels)
fit_1m1 = self.fit_mean[:, self.roi_labels.index('roi1-1')] # fit_sum
fit_1p1 = self.fit_mean[:, self.roi_labels.index('roi11')] # fit_sum
fit_10 = self.fit_mean[:, self.roi_labels.index('roi10')]
fit_sum = (fit_1m1 + fit_1p1 + fit_10)
fit_1m1, fit_1p1 = fit_1m1 / fit_sum, fit_1p1 / fit_sum
pol = fit_1p1 - fit_1m1
# shots X sites
c_nums = pol[:, ::2] + 1j * pol[:, 1::2]
# shots X sites/2
mags = np.abs(c_nums)
phases = np.angle(c_nums)
# phases = phases - phases[:, 0, np.newaxis]
adj_cnum = mags * np.exp(1j * phases)
x = np.real(adj_cnum)
y = np.imag(adj_cnum)
self.fig.clf()
ax = (self.fig.add_subplot(2, 1, 1))
colors = ["tab:blue", "tab:green", "tab:red"]
cmap = plt.cm.cividis
tot = np.sum(fit_sum, axis=1)
try:
for e, (x_i, y_i) in enumerate(zip(x.T, y.T)):
cax = ax.scatter(x_i, y_i, c=self.xlabels,
cmap=cmap, alpha=0.7, )
theta = np.linspace(0, 2 * np.pi, 1000)
# ax.plot(theta, np.cos(theta) * np.var(x) + np.sin(theta) * np.var(y))
ax.set_aspect('equal')
ax.set_xlabel('$S_x$')
ax.set_ylabel('$Q_{yz}$')
self.fig.colorbar(
cax, ax=ax, label=f"{self.xlabel} ({self.units})")
except ValueError:
print("Waiting for the xlabels in XY plot")
ax = (self.fig.add_subplot(2, 1, 2))
theta = np.linspace(0, np.pi, 1000)
error = self.jackknife_error(self.fit_mean, self.var)
scaling = np.array([np.mean(
np.cos(t)**2 * fit_sum[:, 0] + np.sin(t)**2 * fit_sum[:, 1]) for t in theta])
var = np.array(
[np.var(np.real(mags * np.exp(1j * (phases + t)))) for t in theta])
# for i in np.linspace(0, 2, 10):
# ax.fill_between(theta/np.pi, self.var(self.fit_mean) - i * error, self.var(self.fit_mean) + i * error, color = "tab:blue", alpha = 0.2 * np.exp(- i**2),
# edgecolor = None)
ax.fill_between(theta / np.pi, self.var(self.fit_mean) - error, self.var(self.fit_mean) + error, color="tab:blue", alpha=0.3,
edgecolor=None)
ax.fill_between(theta / np.pi, self.var(self.fit_mean) - 2 * error, self.var(self.fit_mean) + 2 * error, color="tab:blue",
alpha=0.1,
edgecolor=None)
ax.plot(theta / np.pi, self.var(self.fit_mean), c="tab:blue")
ax.set_xlabel("Spinor Phase ($\pi$)")
ax.set_ylim(0.5, None)
ax.set_ylabel("Variance")
# file_save_queue.put((self.fig, 'pc_xy_plot', self.current_folder))
class PlotCorrelationWorker(PlotFitWorker):
def set_normalize(self, normalize):
self.normalize = normalize
def set_limits(self, correlation_low, correlation_high):
self.threshold_low = correlation_low
self.threshold_high = correlation_high
def run(self):
if self.fit_mean.shape[0] < 2:
return
self.roi_labels = list(self.roi_labels)
# HARD CODED, this is bad
self.fit_mean = self.fit_mean[..., [4, 6, 8, 10, 12, 14]]
fit_10 = self.fit_mean[:, self.roi_labels.index('roi10')]
fit_1m1 = self.fit_mean[:, self.roi_labels.index('roi1-1')]
fit_1p1 = self.fit_mean[:, self.roi_labels.index('roi11')]
n_traps = fit_10.shape[1]
sidemode = np.mean(
(fit_1m1 + fit_1p1) / (fit_10 + fit_1m1 + fit_1p1),
axis=1
)
threshold_index = np.where((sidemode >= self.threshold_low) &
(sidemode < self.threshold_high))
if self.threshold_low > self.threshold_high:
return
fit_1m1 = fit_1m1[threshold_index]
fit_1p1 = fit_1p1[threshold_index]
fit_10 = fit_10[threshold_index]
fit_sum = fit_10 + fit_1m1 + fit_1p1
fit_sidemode = fit_1m1 + fit_1p1
corr = np.corrcoef(
(fit_1m1 / fit_sum).T,
(fit_1p1 / fit_sum).T)[:n_traps, n_traps:]
pol = (fit_1p1 - fit_1m1) / fit_sum
if self.normalize:
corr_sidemode = np.corrcoef(pol, rowvar=False)
else:
corr_sidemode = np.cov(pol, rowvar=False)
self.fig.clf()
axes = (self.fig.add_subplot(2, 2, 1),
self.fig.add_subplot(2, 2, 2),
self.fig.add_subplot(2, 2, 3),
self.fig.add_subplot(2, 2, 4),
)
cax = axes[0].imshow(
corr,
aspect="equal",
interpolation="None",
vmin=-1,
vmax=1,
cmap=correlation_colormap)
axes[0].set_xlabel("1, -1 trap index")
axes[0].set_ylabel("1, 1 trap index")
axes[0].set_title("Total Atom Number Normalization")
self.fig.colorbar(cax, ax=axes[0])
af.save_array(corr, "total_norm_corr", self.current_folder)
pol_cov_mag = np.max(np.abs(corr_sidemode))
cax = axes[1].imshow(
corr_sidemode,
aspect="equal",
interpolation="None",
vmin=-pol_cov_mag,
vmax=pol_cov_mag,
cmap=correlation_colormap)
axes[1].set_xlabel("1, -1 trap index")
axes[1].set_ylabel("1, 1 trap index")
axes[1].set_title(
f"Polarization Covariance ({'' if self.normalize else 'not '}normalized)")
self.fig.colorbar(cax, ax=axes[1])
af.save_array(corr_sidemode, "pol_cov", self.current_folder)
positions = list(range(-n_traps + 1, n_traps))
total_diag = [np.mean(np.diagonal(corr, d)) for d in positions]
sidemode_diag = [np.mean(np.diagonal(corr_sidemode, d))
for d in positions]
axes_num = 2
for diag in [total_diag, sidemode_diag]:
axes[axes_num].plot(positions, diag, 'o--')
axes[axes_num].set_xlabel("Distance (sites)")
axes[axes_num].set_ylabel("Correlation")
axes[axes_num].set_ylim(-1, 1)
axes_num += 1
mag = np.max(np.abs(sidemode_diag))
axes[3].set_ylim(-mag, mag)
af.save_array(total_diag, "total_norm_corr_1d", self.current_folder)
af.save_array(sidemode_diag, "pol_cov_1d",
self.current_folder)
axes[2].set_title("Total atom number normalization")
axes[3].set_title("Sidemode normalization")
# file_save_queue.put((self.fig, 'correlations', self.current_folder))
# af.save_figure(self.fig, "correlations", self.current_folder)