def bias(path, show_plot, save_plot):
#BIAS
x = []
y = []
dy = []
line = 1
measured_values = []
markers = ['.', ',', 'x', '+', 'v', '^', '<', '>', 's', 'd']
plt.clf()
plt.cla()
with open(path + '/data/bias.csv', 'r') as csvfile:
plots = csv.reader(csvfile, delimiter=',')
next(csvfile)
for row in plots:
measured_values.append(float(row[2]))
if line % 3 == 0:
set_value = float(row[0])
x.append(set_value)
y.append(np.average(measured_values))
dy.append(np.std(measured_values, ddof=1) / 3**0.5)
#print([set_value,np.average(measured_values),np.std(measured_values,ddof=1)])
measured_values = []
line += 1
plt.scatter(x, y)
p, c = np.polyfit(x, y, 1, w=dy, cov=True)
e = np.sqrt(np.diag(c))
slope = sci_not(p[0], e[0])
offset = sci_not(p[1], e[1])
plt.plot(
np.array(x),
offset[0] * 10**offset[2] + np.array(x) * slope[0] * 10**slope[2])
slope = sci_not(p[0], e[0], True)
offset = sci_not(p[1], e[1], True)
slope_str = '(' + str(slope[0]) + '\\pm' + str(slope[1]) + ')e' + str(
slope[2]) if slope[2] != 0 else str(slope[0]) + '\\pm' + str(
slope[1])
offset_str = '(' + str(offset[0]) + '\\pm' + str(
offset[1]) + ')e' + str(offset[2]) if offset[2] != 0 else str(
offset[0]) + '\\pm' + str(offset[1])
med_std = sci_not(dy[2] * 3**0.5, dy[2] * 3**0.5, True)
std_str = str(med_std[0]) + 'e' + str(
med_std[2]) if med_std[2] != 0 else str(med_std[0])
out = '$' + slope_str + '$ & $' + offset_str + '$ & ' + std_str + '\\\\ \\hline'
#print(out)
plt.legend()
plt.xlabel('Set (V)')
plt.ylabel('Measurement (V)')
#plt.axis([-11, 11, -0.004, 0.003])
plt.title('Bias Output')
if show_plot == 'y':
plt.show()
if save_plot == 'y':
plt.savefig(path + '/plot/bias.png')
if show_plot == 'n':
print('Not showing plot')
if save_plot == 'n':
print('not saving plot')
return out
def current(path, ave_time, num_points, show_plot, save_plot):
out = ''
#CURRENT
range_values = [1, 10, 100, 1000, 50e3]
plt.clf()
plt.cla()
for range_value in range_values:
plt.close()
for channel in range(4):
#channel=2
x = []
xo = []
y = []
yo = []
dy = []
dyo = []
markers = [',', 'x', '+', 'v', '^', '<', '>', 's', 'd']
line = 0
with open(
path + '/data/' + ave_time + 'ms_current' + str(channel) +
'.csv', 'r') as csvfile:
plots = csv.reader(csvfile, delimiter=',')
next(csvfile)
line += 1
next(csvfile)
line += 1
for row in plots:
if len(row) > 1:
if row[5] == 'start':
input = float(row[0]) * (1e6)
mean = float(row[3])
std = float(row[4])
rng = float(row[1])
if rng == range_value:
if line % num_points < num_points:
x.append(input)
y.append(mean)
dy.append(std / num_points**0.5)
if range_value != 50e3:
if input < range_value:
xo.append(input)
yo.append(mean)
dyo.append(std / num_points**0.5)
else:
if input < range_value * 0.8:
xo.append(input)
yo.append(mean)
dyo.append(std / num_points**0.5)
line += 1
p, c = np.polyfit(xo, yo, 1, w=dyo, cov=True)
e = np.sqrt(np.diag(c))
slope = sci_not(p[0], e[0], True)
offset = sci_not(p[1], e[1], True)
#print([p[1],e[1]])
slope_str = '(' + str(slope[0]) + '\\pm' + str(
slope[1]) + ')e' + str(slope[2]) if slope[2] != 0 else str(
slope[0]) + '\\pm' + str(slope[1])
offset_str = '(' + str(offset[0]) + '\\pm' + str(
offset[1]) + ')e' + str(offset[2]) if offset[2] != 0 else str(
offset[0]) + '\\pm' + str(offset[1])
med_std = sci_not(dyo[round(len(dyo) / 2)] * num_points**0.5,
dyo[round(len(dyo) / 2)] * num_points**0.5, True)
std_str = str(med_std[0]) + 'e' + str(
med_std[2]) if med_std[2] != 0 else str(med_std[0])
if channel != 3:
temp_out = str(channel) + ' & ' + str(
range_value
) + ' & $' + slope_str + '$ & $' + offset_str + '$ & ' + std_str + ' \\\\ \\hline'
#print(temp_out)
out += temp_out + '\n'
else:
temp_out = str(channel) + ' & ' + str(
range_value
) + ' & $' + slope_str + '$ & $' + offset_str + '$ & ' + std_str + ' \\\\ \\Xhline{3\\arrayrulewidth}'
#print(temp_out)
out += temp_out + '\n'
slope = sci_not(p[0], e[0])
offset = sci_not(p[1], e[1])
plt.plot(
np.array(x), offset[0] * 10**offset[2] +
np.array(x) * slope[0] * 10**slope[2])
plt.scatter(x,
y,
marker=markers[channel],
label='channel' + str(channel))
plt.legend()
plt.xlabel('Input (\u03bcA)')
plt.ylabel('Measurement (\u03bcA)')
plt.title('Ave. Time ' + ave_time + 'ms, ' + 'Range ' +
str(int(range_value)))
if show_plot == 'y':
plt.show()
if save_plot == 'y':
plt.savefig(path + '/plot/' + ave_time + 'ms_range' +
str(int(range_value)) + '.png')
if show_plot == 'n':
print('Not showing plot')
if save_plot == 'n':
print('not saving plot')
return out
def scan(path, axis):
i = 0
x = 0
y = -1
out = ''
size = 401
fluxrr_pd = []
sensrr_d = []
epics_arr = []
Gs = []
contrasts = []
#colnames=['X', 'Y', 'Z','PD','A','B','C','D']
#data = pd.read_csv(path+'/scan_0.005mm.csv', names=colnames, header=None)
#print(data.describe())
with open(path + '/scan_0.005mm.csv', 'r') as csvfile:
plots = csv.reader(csvfile, delimiter=',')
for row in plots:
x_epics = float(row[0])
y_epics = float(row[1])
z_epics = float(row[2])
pd = float(row[3])
A = float(row[4]) / 1e6
B = float(row[5]) / 1e6
C = float(row[6]) / 1e6
D = float(row[7]) / 1e6
sum = A + B + C + D
#calculate the flux from the photodiode
flux_pd = pd * 1e9 / 266.4e-9
#append the flux measurement got from a pixel and append to a list
fluxrr_pd.append(flux_pd)
#calculate the sensitivity of diamond detector
sens_d = 0.99122 * sum * 266.4e-9 / pd
#append value to list
sensrr_d.append(sens_d)
#append all position data into a list and calculate the contrasts at each pixel
if axis == 'z':
epics_arr.append(z_epics)
G = (z_epics) * sum / ((C + D) - (A + B))
Gs.append(G)
contrasts.append(((C + D) - (A + B)) / (A + B + C + D))
elif axis == 'x':
epics_arr.append(x_epics)
G = (x_epics) * sum / ((A + D) - (B + C))
Gs.append(G)
contrasts.append(((A + D) - (B + C)) / sum)
i += 1
#calculate the mean flux from photodiode and sensitivty of diamond
flux_pd = np.mean(fluxrr_pd)
sens_d = np.mean(sensrr_d)
#print them out
print('pd flux: ' + str(flux_pd) + ' +/- ' +
str(np.std(fluxrr_pd) / (len(fluxrr_pd)**0.5)))
print('diamond sensitivity: ' + str(sens_d) + ' +/- ' +
str(np.std(sensrr_d) / (len(sensrr_d)**0.5)))
epics_sqr_arr = []
contrasts_sqr = []
#slice 1 dimensional array into 2 dimensional array by rows
for i in range(size):
epics_sqr_arr.append(epics_arr[i * size:(i + 1) * size])
contrasts_sqr.append(contrasts[i * size:(i + 1) * size])
if axis == 'z':
#rotate matrix so it's sliced by columns
epics_sqr_arr = np.rot90(epics_sqr_arr)
contrasts_sqr = np.rot90(contrasts_sqr)
#no need to do this for the x arrays because it's already sliced by rows which is what we want
epics_sqr_arr_cropped = []
contrasts_sqr_cropped = []
#set offset for every column
limits = 0.5
for i in range(size):
offset = 0
for j in range(size):
if contrasts_sqr[i][j] < limits and contrasts_sqr[i][
j] > -1 * limits:
offset = epics_sqr_arr[i][j] + 0.003
#print(offset)
break
epics_sqr_arr[i] = np.array(epics_sqr_arr[i]) - offset
#crop z data and align
for i in range(len(epics_sqr_arr)):
epics_sqr_arr_cropped.append([])
contrasts_sqr_cropped.append([])
count = 0
for j in range(len(epics_sqr_arr[i])):
#lim=0.1001
lim = 0.1001
if epics_sqr_arr[i][j] > -1 * lim and epics_sqr_arr[i][j] < 1 * lim:
epics_sqr_arr_cropped[i].append(epics_sqr_arr[i][j])
contrasts_sqr_cropped[i].append(contrasts_sqr[i][j])
epics_sqr_arr_cropped[i] = np.array(epics_sqr_arr_cropped[i])
contrasts_sqr[i] = np.array(contrasts_sqr[i])
epics_arr_average = []
contrasts_average = []
contrasts_stds = []
#create 1 dimensional array of each row averaged
epics_sqr_arr_cropped_temp = np.rot90(epics_sqr_arr_cropped, 3)
contrasts_sqr_cropped_temp = np.rot90(contrasts_sqr_cropped, 3)
for i in range(len(epics_sqr_arr_cropped_temp)):
limits = 0.015
if np.median(
epics_sqr_arr_cropped_temp[i]) >= -1 * limits and np.median(
epics_sqr_arr_cropped_temp[i]) <= limits:
epics_arr_average.append(np.median(epics_sqr_arr_cropped_temp[i]))
contrasts_average.append(np.mean(contrasts_sqr_cropped_temp[i]))
contrasts_stds.append(np.std(contrasts_sqr_cropped_temp[i]))
#create color gradient
color = []
half = int(size / 2)
for i in range(half):
color.append((i / half, 0, 50 / 255, 0.1))
for i in range(size - half):
color.append((1, float(i) / (size - half), 50 / 255, 0.1))
#plot each column
for i in range(size):
plt.scatter(np.array(epics_sqr_arr_cropped[i]),
contrasts_sqr_cropped[i],
c=color[i])
plt.scatter(epics_arr_average, contrasts_average, c='blue')
p, c = np.polyfit(epics_arr_average,
contrasts_average,
1,
w=contrasts_stds,
cov=True)
e = np.sqrt(np.diag(c))
#print([p,c,e])
#print([p[0],e[0]])
slope = sci_not(p[0], e[0], True)
offset = sci_not(p[1], e[1], True)
slope_str = '(' + str(slope[0]) + '+/-' + str(slope[1]) + ')e' + str(
slope[2]) if slope[2] != 0 else str(slope[0]) + '+/-' + str(slope[1])
slope = sci_not(p[0], e[0])
offset = sci_not(p[1], e[1])
plt.plot(np.array(epics_arr_average),
offset[0] * float(10)**offset[2] +
np.array(epics_arr_average) * slope[0] * float(10)**slope[2],
label='Gz: ' + slope_str)
#plt.scatter(np.array(x_epics_sqr_arr[i])-25.05,x_contrasts_sqr[i],c=color[i])
labels = None
if axis == 'z':
labels = ('Gz', 'Z')
elif axis == 'x':
labels = ('Gx', 'X')
print(labels[0] + ': ' + slope_str)
plt.xlabel(labels[1] + " position (mm)")
plt.ylabel(labels[1] + " contrast")
plt.legend()
plt.show()
measured_values.append(float(row[1]))
if line % 3 == 0:
set_value=float(row[0])
x.append(set_value)
y.append(np.average(measured_values))
dy.append(np.std(measured_values,ddof=1)/3**0.5)
#print([set_value,np.average(measured_values),np.std(measured_values,ddof=1)])
measured_values=[]
line+=1
plt.scatter(x,y, marker=markers[channel],label='channel'+str(channel))
#plt.errorbar(x,y,yerr=dy,fmt='none', marker=markers[channel],label='channel'+str(channel))
p, c = np.polyfit(x, y, 1, w=dy, cov=True)
e = np.sqrt(np.diag(c))
#print([p,c,e])
#print([p[0],e[0]])
slope=sci_not(p[0],e[0])
offset=sci_not(p[1],e[1])
plt.plot(np.array(x),offset[0]*float(10)**offset[2]+np.array(x)*slope[0]*float(10)**slope[2])
slope=sci_not(p[0],e[0],True)
offset=sci_not(p[1],e[1],True)
slope_str= '('+str(slope[0])+'\\pm'+str(slope[1])+')e'+str(slope[2]) if slope[2] != 0 else str(slope[0])+'\\pm'+str(slope[1])
offset_str= '('+str(offset[0])+'\\pm'+str(offset[1])+')e'+str(offset[2]) if offset[2] != 0 else str(offset[0])+'\\pm'+str(offset[1])
med_std=sci_not(dy[2]*3**0.5,dy[2]*3**0.5,True)
std_str= str(med_std[0])+'e'+str(med_std[2]) if med_std[2] != 0 else str(med_std[0])
print(str(channel)+' & $'+slope_str+'$ & $'+offset_str+'$ & '+std_str+'\\\\ \\hline')
plt.legend()
plt.xlabel('Set (V)')
plt.ylabel('Measurement (V)')
#plt.axis([-11, 11, -0.004, 0.003])
plt.title('DAC Output')
y.append(mean)
dy.append(std/num_points**0.5)
if range_value!=50e3:
if input < range_value:
xo.append(input)
yo.append(mean)
dyo.append(std/num_points**0.5)
else:
if input < range_value*0.8:
xo.append(input)
yo.append(mean)
dyo.append(std/num_points**0.5)
line+=1
p, c = np.polyfit(xo, yo, 1, w=dyo, cov=True)
e = np.sqrt(np.diag(c))
slope=sci_not(p[0],e[0],True)
offset=sci_not(p[1],e[1],True)
#print([p[1],e[1]])
slope_str= '('+str(slope[0])+'\\pm'+str(slope[1])+')e'+str(slope[2]) if slope[2] != 0 else str(slope[0])+'\\pm'+str(slope[1])
offset_str= '('+str(offset[0])+'\\pm'+str(offset[1])+')e'+str(offset[2]) if offset[2] != 0 else str(offset[0])+'\\pm'+str(offset[1])
med_std=sci_not(dyo[round(len(dyo)/2)]*num_points**0.5,dyo[round(len(dyo)/2)]*num_points**0.5,True)
std_str= str(med_std[0])+'e'+str(med_std[2]) if med_std[2] != 0 else str(med_std[0])
if channel!=3:
print(str(channel)+' & '+str(range_value)+' & $'+slope_str+'$ & $'+offset_str+'$ & '+std_str+' \\\\ \\hline')
else:
print(str(channel)+' & '+str(range_value)+' & $'+slope_str+'$ & $'+offset_str+'$ & '+std_str+' \\\\ \\Xhline{3\\arrayrulewidth}')
#\\\\ \\Xhline{3\\arrayrulewidth}
slope=sci_not(p[0],e[0])
offset=sci_not(p[1],e[1])