plt.savefig(os.path.join(current_path+'/'+ runfile, runfile+"_std_interval"))
#A time series plot
plt.figure()
plt.plot(data.time-data.time[0],int_space_averaged)
plt.xlabel('Time since run start (s)')
plt.ylabel("Average amplitude (mV)")
plt.savefig(os.path.join(current_path+'/'+ runfile, runfile+"_space_averaged_over_time"))
#Different average camera images
camera = CameraImage(data.xpix, data.ypix, data.size)
camera.image = int_time_averaged
zmin_intspace = min(int_space_averaged) - 0.05*min(int_space_averaged)
zmax_intspace = max(int_space_averaged) + 0.05*max(int_space_averaged)
camera.set_limits_minmax(zmin=zmin_intspace,zmax = zmax_intspace)
camera.add_colorbar('Amplitdue (mV)')
camera.ax.set_title('Time averaged data')
plt.savefig(os.path.join(current_path+'/'+ runfile, runfile+"_camera_time_averaged"))
camera = CameraImage(data.xpix, data.ypix, data.size)
camera.image = calibrated_data[0,:]
camera.add_colorbar('Amplitdue (mV)')
zmin_calbdat = min(int_space_averaged) - 0.01*min(int_space_averaged)
#zmin_calbdat = 380
zmax_calbdat = max(int_space_averaged) + 0.01*max(int_space_averaged)
camera.set_limits_minmax(zmin=zmin_calbdat,zmax = zmax_calbdat)
camera.ax.set_title('Calibrated Data')
plt.savefig(os.path.join(current_path+'/'+ runfile, runfile+"_calibrated_data"))
camera = CameraImage(data.xpix, data.ypix, data.size)
def make_ssmovie(data,
red,
highlightpix=None,
minmax=(-10, 100),
path='movie',
dpi=800,
scale=0.2,
title="",
fps=25,
filename="out",
zlabel="Amplitude (mV)"):
"""Summary
Args:
data (TYPE): A SlowsignalData object
red (TYPE): A range object or a list of indices
highlightpix (None, optional): A list of pixel highlight index arrays
minmax (tuple, optional): min max for the z-scale
path (str, optional): The path from the current working directory where to store images for the movie
dpi (int, optional): Description
scale (float, optional): Description
title (str, optional): The title to be shown
fps (int, optional): Number of frames per second in the movie
filename (str, optional): output name of the movie
zlabel (str, optional): The colorbar label
"""
import os
import subprocess
import glob
impath = os.path.join(os.getcwd(), path)
if not os.path.isdir(impath):
os.mkdir(impath)
files = glob.glob(os.path.join(impath, "*"))
for f in files:
os.remove(f)
dpi -= int(scale * 19.20 * dpi) % 2
dpii = 400
scale = 0.70
fig, ax = plt.subplots(figsize=(1920 / dpii * scale, 1080 / dpii * scale),
dpi=dpii * scale)
camera = CameraImage(data.xpix, data.ypix, data.size, ax=ax)
camera.add_colorbar('Amplitdue (mV)')
camera.set_limits_minmax(*minmax)
im = copy.deepcopy(data.data[0])
camera.image = im
highl = None
for i in tqdm(red, total=len(red)):
im = copy.deepcopy(data.data[i])
im[np.isnan(im)] = np.nanmean(im)
camera.ax.set_title(title)
if highl is None:
highl = camera.highlight_pixels(highlightpix[i])
else:
lw_array = np.zeros(camera.image.shape[0])
lw_array[highlightpix[i]] = 0.5
highl.set_linewidth(lw_array)
camera.image = im
plt.savefig(os.path.join(path, "SlowSignalImage%.10d.png" % i),
dpi=dpi)
subprocess.check_call([
"ffmpeg", "-pattern_type", "glob", "-i", "{}".format(
os.path.join(impath, "SlowSignalImage*.png")), "-c:v", "libx264",
"-vf", " scale=iw:-2", "-vf", "fps={}".format(fps), "-pix_fmt",
"yuv420p", '-y', "{}.mp4".format(filename)
],
cwd=os.getcwd())
def cal_report(cal):
hist_a = dashi.histogram.hist1d(
np.linspace(np.nanmin(cal.a), np.nanmax(cal.a), 100))
hist_a.fill(cal.a)
hist_b = dashi.histogram.hist1d(
np.linspace(np.nanmin(cal.b), np.nanmax(cal.b), 100))
hist_b.fill(cal.b)
hist_c = dashi.histogram.hist1d(
np.linspace(np.nanmin(cal.c), np.nanmax(cal.c), 100))
hist_c.fill(cal.c)
thresh = (-cal.b + np.sqrt(cal.b**2 - 4 * cal.a * cal.c)) / (2 * cal.a)
hist_thresh = dashi.histogram.hist1d(
np.linspace(np.nanmin(thresh), np.nanmax(thresh), 100))
hist_thresh.fill(thresh)
plt.figure(figsize=(10, 8))
hist_a.line()
hist_a.statbox()
plt.title("a parameter")
plt.savefig("plots/cal_report_a_par_hist.png")
plt.figure(figsize=(10, 8))
hist_b.line()
hist_b.statbox()
plt.title("b parameter")
plt.savefig("plots/cal_report_b_par_hist.png")
plt.figure(figsize=(10, 8))
hist_c.line()
hist_c.statbox()
plt.title("c parameter")
plt.savefig("plots/cal_report_c_par_hist.png")
plt.figure(figsize=(10, 8))
hist_thresh.line()
hist_thresh.statbox()
plt.title("Threshold")
plt.savefig("plots/cal_report_threshold_hist.png")
from target_calib import CameraConfiguration
cam_config = CameraConfiguration("1.1.0")
mapping = cam_config.GetMapping()
pixsize = mapping.GetSize()
pix_posx = np.array(mapping.GetXPixVector())
pix_posy = np.array(mapping.GetYPixVector())
from CHECLabPy.plotting.camera import CameraImage
f, a = plt.subplots(figsize=(13, 8))
camera = CameraImage(pix_posx, pix_posy, pixsize, ax=a)
camera.image = cal.a
camera.add_colorbar("")
camera.ax.set_title("a parameter")
camera.highlight_pixels(list(cal.badpixs["unphysical_cal"]),
color="r",
linewidth=2)
# camera.highlight_pixels(np.where(unphysical_rate)[0],color='b',linewidth=1.4)
camera.highlight_pixels(list(cal.badpixs["no_cal"]), color="k")
camera.highlight_pixels(list(cal.badpixs["bad_fit"]),
color="b",
linewidth=1.4)
camera.set_limits_minmax(np.nanmin(cal.a), 0)
plt.savefig("plots/cal_report_a_par.png")
f, a = plt.subplots(figsize=(13, 8))
camera = CameraImage(pix_posx, pix_posy, pixsize, ax=a)
camera.image = cal.b
camera.add_colorbar("")
camera.ax.set_title("b parameter")
camera.highlight_pixels(list(cal.badpixs["unphysical_cal"]),
color="r",
linewidth=2)
# camera.highlight_pixels(np.where(unphysical_rate)[0],color='b',linewidth=1.4)
camera.highlight_pixels(list(cal.badpixs["no_cal"]), color="k")
camera.highlight_pixels(list(cal.badpixs["bad_fit"]),
color="b",
linewidth=1.4)
camera.set_limits_minmax(0, np.nanmax(cal.b))
plt.savefig("plots/cal_report_b_par.png")
f, a = plt.subplots(figsize=(13, 8))
camera = CameraImage(pix_posx, pix_posy, pixsize, ax=a)
camera.image = cal.c
camera.add_colorbar("")
camera.ax.set_title("c parameter")
camera.highlight_pixels(list(cal.badpixs["unphysical_cal"]),
color="r",
linewidth=2)
# camera.highlight_pixels(np.where(unphysical_rate)[0],color='b',linewidth=1.4)
camera.highlight_pixels(list(cal.badpixs["no_cal"]), color="k")
camera.highlight_pixels(list(cal.badpixs["bad_fit"]),
color="b",
linewidth=1.4)
camera.set_limits_minmax(np.nanmin(cal.c), 0)
plt.savefig("plots/cal_report_c_par.png")
f, a = plt.subplots(figsize=(13, 8))
camera = CameraImage(pix_posx, pix_posy, pixsize, ax=a)
camera.image = thresh
camera.add_colorbar("photon rate (MHz)")
camera.ax.set_title("NSB threshold")
camera.highlight_pixels(list(cal.badpixs["unphysical_cal"]),
color="r",
linewidth=2)
# camera.highlight_pixels(np.where(unphysical_rate)[0],color='b',linewidth=1.4)
camera.highlight_pixels(list(cal.badpixs["no_cal"]), color="k")
camera.highlight_pixels(list(cal.badpixs["bad_fit"]),
color="b",
linewidth=1.4)
# camera.set_limits_minmax(np.nanmin(cal.c),0)
plt.savefig("plots/cal_report_nsb_thres.png")
else:
frame_n = 1000
range_frames = 200
# plt.set_cmap('Greys_r')
#Different average camera images
camera = CameraImage(smooth_data.xpix, smooth_data.ypix, smooth_data.size)
im = copy.deepcopy(smooth_data.data[frame_n])
im[np.isnan(im)] = np.nanmean(im)
camera.image = im
camera.add_colorbar('Amplitude / MHz')
camera.highlight_pixels(
[item for sublist in clusters[frame_n] for item in sublist],
color='red')
camera.set_limits_minmax(zmin_calbat, zmax_calbat)
#for each frame put all the cluster pixels in one list
c = []
for cc in clusters:
c.append([item for sublist in cc for item in sublist])
#We only make the movie with every 200th frame
sel = range(0, len(smooth_data.data), range_frames)
#make_ssmovie(smooth_data,sel,c, minmax = (0,250))
read_from_path = os.path.join(os.getcwd(), path)
# moviefolder = os.path.join('cal_movie_'+read_from_path[-16:-5])
# if not os.path.isdir(moviefolder):
# os.mkdir('cal_movie_'+read_from_path[-16:-5])
#
new_good_pix[np.nan_to_num(data.data) == 0] = False
intensity = invf(data.data, a, b, d)
intensity[new_good_pix == 0] = 0 # Set bad pix to dummy intensity unequal zero
F_i = c_ * intensity
F_ioff = F_i - F_i[5000, :]
camera = CameraImage(data.xpix, data.ypix, data.size)
camera.image = F_i[0, :]
camera.add_colorbar('Intensity / MHz')
zmin_calbdat = min(int_space_averaged) - 0.01 * min(int_space_averaged)
#zmax_calbdat = 235
zmax_calbdat = max(int_space_averaged) + 0.01 * max(int_space_averaged)
#zmax_calbdat = 350
zmin_calbdat = 100
camera.set_limits_minmax(zmin=zmin_calbdat, zmax=zmax_calbdat)
camera.ax.set_title('Calibrated Data' + str(runfile))
def remback_int(calibrated_data, dt, new_good_pixel):
# Get stable interval from minimum of std, from calibrated data in 4 min measurement intervals
min_of_run = math.floor(dt[-1] / 60)
# min_of_run = 1
number_of_intervals = int(min_of_run / 1)
#Splits calibrated data into 4 min intervals
cal_interval_ = np.array_split(calibrated_data,
number_of_intervals,
axis=0)
cal_interval_std = [
np.std(cal_interval_[:][i], axis=0) for i in range(number_of_intervals)
fps = 25
filename = "out"
zlabel = "Amplitude / mHz"
highlightpix = c
dpi -= int(scale * 19.20 * dpi) % 2
dpii = 400
scale = 0.70
date = "2019-06-12 "
time_00 = 76920
fig, ax = plt.subplots(figsize=(1920 / dpii * scale, 1080 / dpii * scale),
dpi=dpii * scale)
camera = CameraImage(data.xpix, data.ypix, data.size, ax=ax)
camera.add_colorbar('Amplitude / MHz')
camera.set_limits_minmax(*minmax)
camera.ax.set_title(date + get_time(dt[0] + time_00))
im = copy.deepcopy(calibrated_data[0])
camera.image = im
highl = None
for i in tqdm(red, total=len(red)):
im = copy.deepcopy(calibrated_data[i])
im[np.isnan(im)] = np.nanmean(im)
camera.ax.set_title(date + get_time(dt[i] + time_00))
if highl is None:
highl = camera.highlight_pixels(highlightpix[i])
else:
lw_array = np.zeros(camera.image.shape[0])
lw_array[highlightpix[i]] = 0.5
def plot_calibration_properties(path_,
runfile_cal,
save=None,
camera_min=None,
camera_max=None,
cal_min=None,
cal_max=None):
#import data:
data, dt = get_your_data(runfile_cal)
#Import calibration properties
property_path = os.path.join(path_ + '/' + runfile_cal,
"calibration_properties")
with open(property_path, 'rb') as handle:
readout = pickle.load(handle)
int_begin, int_end = readout["Interval_offset"]
int_time_averaged = readout["Time_averaged_int"]
int_space_averaged = readout["Space_averaged"]
calibrated_data = readout["Calibrated_data"]
ff_c = readout["ff_coefficients_c"]
offset_calibrated_data = readout["Offset_calibrated_data"]
if camera_min == None:
zmin_intspace = min(
int_space_averaged) - 0.05 * min(int_space_averaged)
else:
zmin_intspace = camera_min
if camera_max == None:
zmax_intspace = max(
int_space_averaged) + 0.05 * max(int_space_averaged)
else:
zmax_intspace = camera_max
if cal_min == None:
zmin_calbdat = min(int_space_averaged) - 0.01 * min(int_space_averaged)
else:
zmin_calbdat = cal_min
if cal_max == None:
zmax_calbdat = max(int_space_averaged) + 0.01 * max(int_space_averaged)
else:
zmax_calbdat = cal_max
# Visualize for some pixels:
plt.figure()
plt.axvline(dt[int_begin], color="k")
plt.axvline(dt[int_end], color="k")
plt.xlabel("Time / s")
plt.ylabel("Amplitude / (mV)")
plt.title("Interval of offset")
for i in [1, 23, 600, 900, 1200]:
plt.plot(dt, calibrated_data[:, i])
if save != None:
plt.savefig(
os.path.join(current_path + '/' + runfile_cal,
runfile_cal + "_std_interval"))
plt.figure()
plt.plot(data.time - data.time[0], int_space_averaged)
plt.xlabel('Time since run start (s)')
plt.ylabel("Average amplitude (mV)")
if save != None:
plt.savefig(
os.path.join(current_path + '/' + runfile_cal,
runfile_cal + "_space_averaged_over_time"))
camera = CameraImage(data.xpix, data.ypix, data.size)
camera.image = int_time_averaged
camera.set_limits_minmax(zmin=zmin_intspace, zmax=zmax_intspace)
camera.add_colorbar('Amplitdue (mV)')
camera.ax.set_title('Time averaged data')
if save != None:
plt.savefig(
os.path.join(current_path + '/' + runfile_cal,
runfile_cal + "_camera_time_averaged"))
camera = CameraImage(data.xpix, data.ypix, data.size)
camera.image = calibrated_data[0, :]
camera.add_colorbar('Amplitdue (mV)')
zmin_calbdat = min(int_space_averaged) - 0.01 * min(int_space_averaged)
#zmin_calbdat = 380
zmax_calbdat = max(int_space_averaged) + 0.01 * max(int_space_averaged)
camera.set_limits_minmax(zmin=zmin_calbdat, zmax=zmax_calbdat)
camera.ax.set_title('Calibrated Data')
if save != None:
plt.savefig(
os.path.join(current_path + '/' + runfile_cal,
runfile_cal + "_calibrated_data"))
camera = CameraImage(data.xpix, data.ypix, data.size)
camera.image = ff_c
camera.add_colorbar('Amplitdue (mV)')
camera.ax.set_title('Flat field coefficents $ff_{c}$')
if save != None:
plt.savefig(
os.path.join(current_path + '/' + runfile_cal,
runfile_cal + "_flat_field_coeffs_c"))
camera = CameraImage(data.xpix, data.ypix, data.size)
camera.image = offset_calibrated_data[0, :]
camera.add_colorbar('Amplitdue (mV)')
camera.ax.set_title('Offset of calibrated data')
zmin_offset = None
zmax_offset = None
#np.where(offset_calibrated_data > 50)
#zmin_offset = 0
#zmax_offset = 60
camera.set_limits_minmax(zmin=zmin_offset, zmax=zmax_offset)
if save != None:
plt.savefig(
os.path.join(current_path + '/' + runfile_cal,
runfile_cal + "_offset_calibrated_data"))