def t_dac_plot(h5_file_name):
with tb.open_file(h5_file_name, 'r') as in_file_h5:
t_dac = in_file_h5.root.scan_results.tdac_mask[:]
en_mask = in_file_h5.root.scan_results.en_mask[:]
shape = en_mask.shape
ges = 1
for i in range(2):
ges = ges * shape[i]
T_Dac_pure = ()
t_dac = t_dac.reshape(ges)
en_mask = en_mask.reshape(ges)
for i in range(ges):
if (str(en_mask[i]) == 'True'):
T_Dac_pure = np.append(T_Dac_pure, t_dac[i])
T_Dac_pure = T_Dac_pure.astype(int)
T_Dac_hist_y = np.bincount(T_Dac_pure)
T_Dac_hist_x = np.arange(0, T_Dac_hist_y.size, 1)
gauss_TDAC = analysis.fit_gauss(T_Dac_hist_x, T_Dac_hist_y)
plt_t_dac = figure(title='T-Dac-distribution ', x_axis_label="T-Dac", y_axis_label="#Pixel")
plt_t_dac.quad(top=T_Dac_hist_y, bottom=0, left=T_Dac_hist_x[:-1], right=T_Dac_hist_x[1:], fill_color="#036564",
line_color="#033649", legend="#{0:d} mean={1:.2f} std={2:.2f}".format(int(np.sum(T_Dac_hist_y[:])), gauss_TDAC[1] , gauss_TDAC[2] ))
plt_t_dac.line(T_Dac_hist_x, analysis.gauss(T_Dac_hist_x, gauss_TDAC[0], gauss_TDAC[1], gauss_TDAC[2]),
line_color="#D95B43", line_width=8, alpha=0.7)
return plt_t_dac
def t_dac_plot(h5_file_name):
with tb.open_file(h5_file_name, 'r') as in_file_h5:
t_dac = in_file_h5.root.scan_results.tdac_mask[:]
en_mask = in_file_h5.root.scan_results.en_mask[:]
shape = en_mask.shape
ges = 1
for i in range(2):
ges = ges * shape[i]
T_Dac_pure = ()
t_dac = t_dac.reshape(ges)
en_mask = en_mask.reshape(ges)
for i in range(ges):
if (str(en_mask[i]) == 'True'):
T_Dac_pure = np.append(T_Dac_pure, t_dac[i])
T_Dac_pure = T_Dac_pure.astype(int)
T_Dac_hist_y = np.bincount(T_Dac_pure)
T_Dac_hist_x = np.arange(0, T_Dac_hist_y.size, 1)
gauss_TDAC = analysis.fit_gauss(T_Dac_hist_x, T_Dac_hist_y)
plt_t_dac = figure(title='T-Dac-distribution ', x_axis_label="T-Dac", y_axis_label="#Pixel")
plt_t_dac.quad(top=T_Dac_hist_y, bottom=0, left=T_Dac_hist_x[:-1], right=T_Dac_hist_x[1:], fill_color="#036564",
line_color="#033649", legend="#{0:d} mean={1:.2f} std={2:.2f}".format(int(np.sum(T_Dac_hist_y[:])), gauss_TDAC[1] , gauss_TDAC[2] ))
plt_t_dac.line(T_Dac_hist_x, analysis.gauss(T_Dac_hist_x, gauss_TDAC[0], gauss_TDAC[1], gauss_TDAC[2]),
line_color="#D95B43", line_width=8, alpha=0.7)
return plt_t_dac
def scan_pix_hist(h5_file_name, scurve_sel_pix=200):
with tb.open_file(h5_file_name, 'r') as in_file_h5:
meta_data = in_file_h5.root.meta_data[:]
hit_data = in_file_h5.root.hit_data[:]
en_mask = in_file_h5.root.scan_results.en_mask[:]
Noise_gauss = in_file_h5.root.Noise_results.Noise_pure.attrs.fitdata_noise
Noise_pure = in_file_h5.root.Noise_results.Noise_pure[:]
Thresh_gauss = in_file_h5.root.Thresh_results.Threshold_pure.attrs.fitdata_thresh
Threshold_pure = in_file_h5.root.Thresh_results.Threshold_pure[:]
scan_args = yaml.load(in_file_h5.root.meta_data.attrs.kwargs)
scan_range = scan_args['scan_range']
scan_range_inx = np.arange(scan_range[0], scan_range[1], scan_range[2])
# np.set_printoptions(threshold=np.nan)
param = np.unique(meta_data['scan_param_id'])
ret = []
for i in param:
wh = np.where(hit_data['scan_param_id'] == i) # this can be faster and multi threaded
hd = hit_data[wh[0]]
hits = hd['col'].astype(np.uint16)
hits = hits * 64
hits = hits + hd['row']
value = np.bincount(hits)
value = np.pad(value, (0, 64 * 64 - value.shape[0]), 'constant')
if len(ret):
ret = np.vstack((ret, value))
else:
ret = value
repeat_command = max(ret[-3])
shape = en_mask.shape
ges = 1
for i in range(2):
ges = ges * shape[i]
ret_pure = ()
en_mask = en_mask.reshape(ges)
for n in range(param[-1]):
ret_pure1 = ()
for i in range(ges):
if (str(en_mask[i]) == 'True'):
ret_pure1 = np.append(ret_pure1, ret[n][i])
if n == 0:
ret_pure = ret_pure1
continue
ret_pure = np.vstack((ret_pure, ret_pure1))
ret_pure = ret_pure.astype(int)
s_hist = np.swapaxes(ret_pure, 0, 1)
pix_scan_hist = np.empty((s_hist.shape[1], repeat_command + 10))
for param in range(s_hist.shape[1]):
h_count = np.bincount(s_hist[:, param])
h_count = h_count[:repeat_command + 10]
pix_scan_hist[param] = np.pad(h_count, (0, (repeat_command + 10) - h_count.shape[0]), 'constant')
log_hist = np.log10(pix_scan_hist)
log_hist[~np.isfinite(log_hist)] = 0
data = {
'scan_param': np.ravel(np.indices(pix_scan_hist.shape)[0]),
'count': np.ravel(np.indices(pix_scan_hist.shape)[1]),
'value': np.ravel(log_hist)
}
x = scan_range_inx
px = scurve_sel_pix # 1110 #1539
single_scan = figure(title="Single pixel scan " + str(px), x_axis_label="Injection[V]", y_axis_label="Hits")
single_scan.diamond(x=x, y=s_hist[px], size=5, color="#1C9099", line_width=2)
yf = analysis.scurve(x, 100, Threshold_pure[px], Noise_pure[px])
single_scan.cross(x=x, y=yf, size=5, color="#E6550D", line_width=2)
hist, edges = np.histogram(Threshold_pure, density=False, bins=50)
hm1 = HeatMap(data, x='scan_param', y='count', values='value', title='Threshold Heatmap',
palette=Spectral11[::-1], stat=None, plot_width=1000) # , height=4100)
hm1.extra_x_ranges = {
"e": Range1d(start=edges[0] * 1000 * analysis.cap_fac(), end=edges[-1] * 1000 * analysis.cap_fac())}
hm_th = figure(title="Threshold", x_axis_label="pixel #", y_axis_label="threshold [V]",
y_range=(scan_range_inx[0], scan_range_inx[-1]), plot_width=1000)
hm_th.diamond(y=Threshold_pure, x=range(64 * 64), size=1, color="#1C9099", line_width=2)
hm_th.extra_y_ranges = {"e": Range1d(start=scan_range_inx[0] * 1000 * analysis.cap_fac(),
end=scan_range_inx[-1] * 1000 * analysis.cap_fac())}
hm_th.add_layout(LinearAxis(y_range_name="e"), 'right')
plt_th_dist = figure(title='Threshold Distribution ', x_axis_label="threshold [V]", y_axis_label="#Pixel",
y_range=(0, 1.1 * np.max(hist[1:])))
plt_th_dist.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:], fill_color="#036564",
line_color="#033649",
legend="# {0:d} mean={1:.2f} std={2:.2f}".format(int(np.sum(hist[:])), round(
Thresh_gauss['mu'] * 1000 * analysis.cap_fac(), 4),
round(Thresh_gauss[
'sigma'] * 1000 * analysis.cap_fac(),
4)))
plt_th_dist.extra_x_ranges = {"e": Range1d(start=edges[0] * 1000 * analysis.cap_fac(),
end=edges[-1] * 1000 * analysis.cap_fac())} # better 7.4?
plt_th_dist.add_layout(LinearAxis(x_range_name="e"), 'above')
plt_th_dist.line(np.arange(edges[1], edges[-1], 0.0001),
analysis.gauss(np.arange(edges[1], edges[-1], 0.0001), Thresh_gauss['height'],
Thresh_gauss['mu'], Thresh_gauss['sigma']), line_color="#D95B43", line_width=8,
alpha=0.7)
hist, edges = np.histogram(Noise_pure, density=False, bins=50)
hm_noise = figure(title="Noise", x_axis_label="pixel #", y_axis_label="noise [V]", y_range=(0, edges[-1]),
plot_width=1000)
hm_noise.diamond(y=Noise_pure, x=range(64 * 64), size=2, color="#1C9099", line_width=2)
hm_noise.extra_y_ranges = {"e": Range1d(start=0, end=edges[-1] * 1000 * analysis.cap_fac())} # default 7.6
hm_noise.add_layout(LinearAxis(y_range_name="e"), 'right')
plt_noise_dist = figure(title='Noise Distribution ', x_axis_label="noise [V]", y_axis_label="#Pixel",
y_range=(0, 1.1 * np.max(hist[1:])))
plt_noise_dist.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:], fill_color="#036564",
line_color="#033649",
legend="# {0:d} mean={1:.2f} std={2:.2f}".format(int(np.sum(hist[:])), round(
Noise_gauss['mu'] * 1000 * analysis.cap_fac(), 4), round(
Noise_gauss['sigma'] * 1000 * analysis.cap_fac(), 4)))
plt_noise_dist.extra_x_ranges = {
"e": Range1d(start=edges[0] * 1000 * analysis.cap_fac(), end=edges[-1] * 1000 * analysis.cap_fac())}
plt_noise_dist.add_layout(LinearAxis(x_range_name="e"), 'above')
plt_noise_dist.line(np.arange(edges[0], edges[-1], 0.0001),
analysis.gauss(np.arange(edges[0], edges[-1], 0.0001), Noise_gauss['height'],
Noise_gauss['mu'], Noise_gauss['sigma']), line_color="#D95B43", line_width=8,
alpha=0.7)
return vplot(hplot(hm_th, plt_th_dist), hplot(hm_noise, plt_noise_dist), hplot(hm1, single_scan)), s_hist
def scan_pix_hist(h5_file_name, scurve_sel_pix=200):
with tb.open_file(h5_file_name, 'r') as in_file_h5:
meta_data = in_file_h5.root.meta_data[:]
hit_data = in_file_h5.root.hit_data[:]
en_mask = in_file_h5.root.scan_results.en_mask[:]
Noise_gauss = in_file_h5.root.Noise_results.Noise_pure.attrs.fitdata_noise
Noise_pure = in_file_h5.root.Noise_results.Noise_pure[:]
Thresh_gauss = in_file_h5.root.Thresh_results.Threshold_pure.attrs.fitdata_thresh
Threshold_pure = in_file_h5.root.Thresh_results.Threshold_pure[:]
scan_args = yaml.load(in_file_h5.root.meta_data.attrs.kwargs)
scan_range = scan_args['scan_range']
scan_range_inx = np.arange(scan_range[0], scan_range[1], scan_range[2])
# np.set_printoptions(threshold=np.nan)
param = np.unique(meta_data['scan_param_id'])
ret = []
for i in param:
wh = np.where(hit_data['scan_param_id'] == i) # this can be faster and multi threaded
hd = hit_data[wh[0]]
hits = hd['col'].astype(np.uint16)
hits = hits * 64
hits = hits + hd['row']
value = np.bincount(hits)
value = np.pad(value, (0, 64 * 64 - value.shape[0]), 'constant')
if len(ret):
ret = np.vstack((ret, value))
else:
ret = value
repeat_command = max(ret[-3])
shape = en_mask.shape
ges = 1
for i in range(2):
ges = ges * shape[i]
ret_pure = ()
en_mask = en_mask.reshape(ges)
for n in range(param[-1]):
ret_pure1 = ()
for i in range(ges):
if (str(en_mask[i]) == 'True'):
ret_pure1 = np.append(ret_pure1, ret[n][i])
if n == 0:
ret_pure = ret_pure1
continue
ret_pure = np.vstack((ret_pure, ret_pure1))
ret_pure = ret_pure.astype(int)
s_hist = np.swapaxes(ret_pure, 0, 1)
pix_scan_hist = np.empty((s_hist.shape[1], repeat_command + 10))
for param in range(s_hist.shape[1]):
h_count = np.bincount(s_hist[:, param])
h_count = h_count[:repeat_command + 10]
pix_scan_hist[param] = np.pad(h_count, (0, (repeat_command + 10) - h_count.shape[0]), 'constant')
log_hist = np.log10(pix_scan_hist)
log_hist[~np.isfinite(log_hist)] = 0
data = {
'scan_param': np.ravel(np.indices(pix_scan_hist.shape)[0]),
'count': np.ravel(np.indices(pix_scan_hist.shape)[1]),
'value': np.ravel(log_hist)
}
x = scan_range_inx
px = scurve_sel_pix # 1110 #1539
single_scan = figure(title="Single pixel scan " + str(px), x_axis_label="Injection[V]", y_axis_label="Hits")
single_scan.diamond(x=x, y=s_hist[px], size=5, color="#1C9099", line_width=2)
yf = analysis.scurve(x, 100, Threshold_pure[px], Noise_pure[px])
single_scan.cross(x=x, y=yf, size=5, color="#E6550D", line_width=2)
hist, edges = np.histogram(Threshold_pure, density=False, bins=50)#50
hm1 = HeatMap(data, x='scan_param', y='count', values='value', title='Threshold Heatmap',
palette=Spectral11[::-1], stat=None, plot_width=1000) # , height=4100)
hm1.extra_x_ranges = {
"e": Range1d(start=edges[0] * 1000 * analysis.cap_fac(), end=edges[-1] * 1000 * analysis.cap_fac())}
hm_th = figure(title="Threshold", x_axis_label="pixel #", y_axis_label="threshold [V]",
y_range=(scan_range_inx[0], scan_range_inx[-1]), plot_width=1000)
hm_th.diamond(y=Threshold_pure, x=range(64 * 64), size=1, color="#1C9099", line_width=2)
hm_th.extra_y_ranges = {"e": Range1d(start=scan_range_inx[0] * 1000 * analysis.cap_fac(),
end=scan_range_inx[-1] * 1000 * analysis.cap_fac())}
hm_th.add_layout(LinearAxis(y_range_name="e"), 'right')
plt_th_dist = figure(title='Threshold Distribution ', x_axis_label="threshold [V]", y_axis_label="#Pixel",
y_range=(0, 1.1 * np.max(hist[1:])))
plt_th_dist.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:], fill_color="#036564",
line_color="#033649",
legend="# {0:d} mean={1:.2f} std={2:.2f}".format(int(np.sum(hist[:])), round(
Thresh_gauss['mu'] * 1000 * analysis.cap_fac(), 4),
round(Thresh_gauss[
'sigma'] * 1000 * analysis.cap_fac(),
4)))
plt_th_dist.extra_x_ranges = {"e": Range1d(start=edges[0] * 1000 * analysis.cap_fac(),
end=edges[-1] * 1000 * analysis.cap_fac())} # better 7.4?
plt_th_dist.add_layout(LinearAxis(x_range_name="e"), 'above')
plt_th_dist.line(np.arange(edges[1], edges[-1], 0.0001),
analysis.gauss(np.arange(edges[1], edges[-1], 0.0001), Thresh_gauss['height'],
Thresh_gauss['mu'], Thresh_gauss['sigma']), line_color="#D95B43", line_width=8,
alpha=0.7)
hist, edges = np.histogram(Noise_pure, density=False, bins=50) #50
hm_noise = figure(title="Noise", x_axis_label="pixel #", y_axis_label="noise [V]", y_range=(0, edges[-1]),
plot_width=1000)
hm_noise.diamond(y=Noise_pure, x=range(64 * 64), size=2, color="#1C9099", line_width=2)
hm_noise.extra_y_ranges = {"e": Range1d(start=0, end=edges[-1] * 1000 * analysis.cap_fac())} # default 7.6
hm_noise.add_layout(LinearAxis(y_range_name="e"), 'right')
plt_noise_dist = figure(title='Noise Distribution ', x_axis_label="noise [V]", y_axis_label="#Pixel",
y_range=(0, 1.1 * np.max(hist[1:])))
plt_noise_dist.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:], fill_color="#036564",
line_color="#033649",
legend="# {0:d} mean={1:.2f} std={2:.2f}".format(int(np.sum(hist[:])), round(
Noise_gauss['mu'] * 1000 * analysis.cap_fac(), 4), round(
Noise_gauss['sigma'] * 1000 * analysis.cap_fac(), 4)))
plt_noise_dist.extra_x_ranges = {
"e": Range1d(start=edges[0] * 1000 * analysis.cap_fac(), end=edges[-1] * 1000 * analysis.cap_fac())}
plt_noise_dist.add_layout(LinearAxis(x_range_name="e"), 'above')
plt_noise_dist.line(np.arange(edges[0], edges[-1], 0.0001),
analysis.gauss(np.arange(edges[0], edges[-1], 0.0001), Noise_gauss['height'],
Noise_gauss['mu'], Noise_gauss['sigma']), line_color="#D95B43", line_width=8,
alpha=0.7)
return vplot(hplot(hm_th, plt_th_dist), hplot(hm_noise, plt_noise_dist), hplot(hm1, single_scan)), s_hist
