def main():
print('Running!')
# Read in arguments from the command line
args = parser.parse_args()
# File to be plotted
input_path = args.file_path
path, file = os.path.split(input_path)
# Folder to which the graph should be saved
if args.output_directory == ' ':
output_directory = path
else:
output_directory = args.output_directory
# Assign variable with inputs from the command line
name = args.name
plot_type = args.plot_type
plot_th = input_boolean(args.plot_temp_and_hum)
plot_fit = input_boolean(args.plot_fits)
average = args.choose_average
remove = args.remove_anomalous
# Extract the data
data = Data()
data.name = name
data.type = plot_type
data.extract_data(input_path, average)
if remove is not None:
for i in range(len(remove)):
data.remove_anomalies(i)
# Plot the graph
plot = Plot()
plot.plot_graph(output_directory, plot_th, plot_fit, data)
def plot_graph(self, output_folder, th, fits, data=Data()):
# Create the axes
self.add_axes(th=th)
# Plot the data
if data.type == 'it':
self.plot_it(th, fits, data)
elif data.type == 'cv':
self.plot_cv(th, fits, data)
elif data.type == 'iv':
self.plot_iv(th, fits, data)
# Save the graph
self.save_graph(output_folder, data)
def main():
print('amw_2')
args = parser.parse_args()
input_directory = args.file_directory
if args.output_directory == ' ':
output_directory = args.file_directory
else:
output_directory = args.output_directory
plot_iv = input_boolean(args.plot_iv)
plot_cv = input_boolean(args.plot_cv)
plot_it = input_boolean(args.plot_it)
plot_ivth = input_boolean(args.plot_ivth)
plot_cvth = input_boolean(args.plot_cvth)
plot_itth = input_boolean(args.plot_itth)
plot_ivf = input_boolean(args.plot_ivf)
plot_cvf = input_boolean(args.plot_cvf)
plot_itf = input_boolean(args.plot_itf)
#plot_ivg = input_boolean(args.plot_ivg)
#plot_cvg = input_boolean(args.plot_cvg)
#plot_itg = input_boolean(args.plot_itg)
average_type = args.choose_mean
print(input_directory)
print(output_directory)
print(plot_cv)
print(plot_iv)
print(plot_it)
print(plot_itth)
print(plot_ivth)
print(plot_cvth)
print(plot_itf)
print(plot_ivf)
print(plot_cvf)
print(average_type)
files = []
#print(os.listdir(input_directory))
folder = os.listdir(input_directory)
"""
for Input in args.file_directory:
print("\tInput, ", Input)
OSInputs = sorted(glob.glob(Input+'/*.txt'))
print(OSInputs)
for OSInput in OSInputs:
if os.path.isfile(OSInput) and OSInput.endswith('.txt') and 'short' not in OSInput:
files.append(OSInput)
print("\t\t" + OSInput)
"""
for filename in folder:
if os.path.isfile(os.path.join(input_directory,
filename)) and filename.endswith(
'.txt') and 'short' not in filename:
files.append(os.path.join(input_directory, filename))
else:
continue
file_data = {}
#print(files)
for i in range(len(files)):
data = Data()
data.extract_data(files[i], average_type)
file_data[files[i]] = data
"""
for f in file_data.values():
print(f.v_mean)
continue
"""
iv_data = {}
cv_data = {}
it_data = {}
for f1, f2 in file_data.items():
if f2.type == 'iv':
iv_data[f1] = f2
elif f2.type == 'cv':
cv_data[f1] = f2
elif f2.type == 'it':
it_data[f1] = f2
if plot_iv:
#print('plotting iv')
for f1, f2 in iv_data.items():
graph = Plot()
graph.plot_graph(output_directory, plot_ivth, plot_ivf, f2)
if plot_cv:
for f1, f2 in cv_data.items():
graph = Plot()
graph.plot_graph(output_directory, plot_cvth, plot_cvf, f2)
if plot_it:
#print('plotting it')
for f1, f2 in it_data.items():
graph = Plot()
graph.plot_graph(output_directory, plot_itth, plot_itf, f2)
def plot_it(self, th, fits, it=Data()):
# Converts time from seconds to hours
it.time_to_hours()
"""
# A very rough method of trying to ignore anomalous points in the It data
mean_current= sum(it.i_mean)/len(it.i_mean)
mean_error = stats.tstd(it.i_mean)
main_data_i = []
main_data_t = []
main_data_ierror =[]
bad_data_i = []
bad_data_t = []
bad_data_ierror = []
for i in range(len(it.i_mean)):
if mean_current+(3 * mean_error) > it.i_mean[i] > mean_current - (3* mean_error):
main_data_t.append(it.time[i])
main_data_i.append(it.i_mean[i])
main_data_ierror.append(it.i_error[i])
else:
bad_data_t.append(it.time[i])
bad_data_i.append(it.i_mean[i])
bad_data_ierror.append(it.i_error[i])
current_line = self.fig.host.errorbar(x=main_data_t, y=main_data_i, yerr=main_data_ierror, fmt='r.',
label='Current')
current_line2 = self.fig.host.errorbar(x=bad_data_t, y=bad_data_i, yerr=bad_data_ierror, fmt='r.', alpha=0.5,
label='Current')
"""
# Plot the current data
current_line = self.fig.host.errorbar(x=it.time,
y=it.i_mean,
yerr=it.i_error,
fmt='r.',
label='Current')
# Set y axis range
self.fig.host.set_ylim([min(it.i_mean) * 1.1, 0])
# Label axis
self.fig.host.set_xlabel("Time (hours)")
self.fig.host.set_ylabel("Current ($\mu$A)")
# List of all the lines
lines = [current_line]
# Line ticks
tkw = dict(size=4, width=1.5)
self.fig.host.tick_params(axis='x', **tkw)
# If plotting temperature and humidity lines:
if th:
# Overall average temperature and humidity
temp_averages = it.average_temp()
hum_averages = it.average_hum()
# Plot temperature and humidity lines
temp_line, = self.fig.temp.plot(
it.time,
it.get_temperature(),
color='b',
alpha=0.4,
label='Temperature, $T_{Av}$ = %s$^\circ$C' % temp_averages[0])
hum_line, = self.fig.hum.plot(it.time,
it.get_humidity(),
color='g',
alpha=0.4,
label='Humidity, $H_{Av}$ = %s%%' %
hum_averages[0])
# Colour axis
self.fig.temp.yaxis.label.set_color(temp_line.get_color())
self.fig.hum.yaxis.label.set_color(hum_line.get_color())
# Uncomment if you want to set the temperature and humidity axis range
#self.fig.hum.set_ylim([30, 50])
#self.fig.temp.set_ylim([18, 22])
# Labels and tick sizes
self.fig.temp.set_ylabel('Temperature ($^\circ$C)')
self.fig.hum.set_ylabel('Humidity (%)')
self.fig.temp.tick_params(axis='y',
colors=temp_line.get_color(),
**tkw)
self.fig.hum.tick_params(axis='y',
colors=hum_line.get_color(),
**tkw)
# Add to list of plotted lines
lines.append(temp_line)
lines.append(hum_line)
# If testing the stability of the current
if fits:
fitting_data = Fitting.it_fits(it)
maximum_line = self.fig.host.axhline(
y=fitting_data[0],
color='r',
label='$I_{max}$ = %s$\mu$A' %
Fitting.round_sig(fitting_data[0], 2))
allowed_line = self.fig.host.axhline(
y=fitting_data[2],
color='k',
label='Allowed $I_{min}$ = %s$\mu$A, Result: %s' %
(Fitting.round_sig(fitting_data[2], 2), fitting_data[3]))
minimum_line = self.fig.host.axhline(
y=fitting_data[1],
color='r',
alpha=0.6,
label='$I_{min}$ = %s$\mu$A' %
Fitting.round_sig(fitting_data[1], 2))
# Add to list of plotted lines
lines.append(minimum_line)
lines.append(maximum_line)
lines.append(allowed_line)
# Create legend and title
self.fig.host.legend(lines, [l.get_label() for l in lines])
self.fig.suptitle(it.name)
def plot_cv(self, th, fits, cv=Data()):
#print('amw_3')
# Plot capacitance data
capacitance_line = self.fig.host.errorbar(
x=cv.v_mean,
y=cv.inverse_c_squared,
yerr=cv.inverse_c_squared_error,
fmt='r.',
label='Capacitance')
# Label axis
self.fig.host.set_xlabel("Voltage (V)")
self.fig.host.set_ylabel("$1/C^2$ ($1/pF^2$)")
# List of plotted lines
lines = [capacitance_line]
# Tick size and y axis limit
tkw = dict(size=4, width=1.5)
self.fig.host.tick_params(axis='x', **tkw)
self.fig.host.set_ylim([0, max(cv.inverse_c_squared) * 1.1])
# If plotting temperature and humidity:
if th:
# Overall average temperature and humidity
temp_averages = cv.average_temp()
hum_averages = cv.average_hum()
# Plot temperature and humidity
temp_line, = self.fig.temp.plot(
cv.v_mean,
cv.temperature,
color='b',
alpha=0.4,
label='Temperature, $T_{Av}$ = %s$^\circ$C' % temp_averages[0])
hum_line, = self.fig.hum.plot(cv.v_mean,
cv.humidity,
color='g',
alpha=0.4,
label='Humidity, $H_{Av}$ = %s%%' %
hum_averages[0])
# Colour axis
self.fig.temp.yaxis.label.set_color(temp_line.get_color())
self.fig.hum.yaxis.label.set_color(hum_line.get_color())
# Uncomment to constrain axis range
#self.fig.hum.set_ylim([30, 50])
#self.fig.temp.set_ylim([18, 22])
# Label axis and tick parameters
self.fig.temp.set_ylabel('Temperature ($^\circ$C)')
self.fig.hum.set_ylabel('Humidity (%)')
self.fig.temp.tick_params(axis='y',
colors=temp_line.get_color(),
**tkw)
self.fig.hum.tick_params(axis='y',
colors=hum_line.get_color(),
**tkw)
# Add to list of lines
lines.append(temp_line)
lines.append(hum_line)
# if finding the depletion voltage
if fits:
# Get data
fit_data = Fitting.cv_fits(cv)
# Plot two linear lines
self.fig.host.plot(cv.v_mean, fit_data[0], 'r')
self.fig.host.plot(cv.v_mean, fit_data[1], 'r')
# Plot line at full depletion
full_depletion = self.fig.host.axvline(
x=fit_data[2],
linestyle='--',
color='r',
label='$V_{Full \: Depletion}$ = %s $\pm$ %sV' %
(fit_data[2], fit_data[3]))
# Add to list of lines
lines.append(full_depletion)
# Create legend and title
self.fig.host.legend(lines, [l.get_label() for l in lines])
self.fig.suptitle(cv.name)
def plot_iv(self, th, fits, iv=Data()):
# Plot the current data
current_line = self.fig.host.errorbar(x=iv.v_mean,
y=iv.i_mean,
yerr=iv.i_error,
fmt='r.',
label='Current')
# Label axis
self.fig.host.set_xlabel("Voltage (V)")
self.fig.host.set_ylabel("Current ($\mu$A)")
# List of plotted lines
lines = [current_line]
# Tick size
tkw = dict(size=4, width=1.5)
self.fig.host.tick_params(axis='x', **tkw)
# If plotting temperature and humidity lines:
if th:
# Overall average temperature and humidity
temp_averages = iv.average_temp()
hum_averages = iv.average_hum()
# Plot temperature and humidity
temp_line, = self.fig.temp.plot(
iv.v_mean,
iv.temperature,
color='b',
alpha=0.4,
label='Temperature, $T_{Av}$ = %s$^\circ$C' % temp_averages[0])
hum_line, = self.fig.hum.plot(iv.v_mean,
iv.humidity,
color='g',
alpha=0.4,
label='Humidity, $H_{Av}$ = %s%%' %
hum_averages[0])
# Colour axis
self.fig.temp.yaxis.label.set_color(temp_line.get_color())
self.fig.hum.yaxis.label.set_color(hum_line.get_color())
# Uncomment to constrain axis range
#self.fig.hum.set_ylim([30, 50])
#self.fig.temp.set_ylim([18, 22])
# Label axis
self.fig.temp.set_ylabel("Temperature ($^\circ$C)")
self.fig.hum.set_ylabel("Humidity (%)")
self.fig.temp.tick_params(axis='y',
colors=temp_line.get_color(),
**tkw)
self.fig.hum.tick_params(axis='y',
colors=hum_line.get_color(),
**tkw)
# Add to list of lines
lines.append(temp_line)
lines.append(hum_line)
# If finding the breakdown voltage:
if fits:
bd_voltage = Fitting.breakdown_voltage(iv)[0]
bd_statement = Fitting.breakdown_voltage(iv)[1]
#print(bd_statement)
# Plot a vertical line at breakdown voltage
if bd_voltage is not None:
bd_line = self.fig.host.axvline(
x=bd_voltage,
color='r',
linestyle='--',
label='$V_{Breakdown}$ = %sV' %
Fitting.round_sig(bd_voltage, 3))
lines.append(bd_line)
else:
no_bd_line, = self.fig.host.plot([], [],
' ',
label=bd_statement)
lines.append(no_bd_line)
# Create legend and title
self.fig.host.legend(lines, [l.get_label() for l in lines])
self.fig.suptitle(iv.name)
def main():
print('Running!')
# Read in arguments from the command line
args = parser.parse_args()
# Folder containing the data
input_directory = args.file_directory
# Folder to which the graphs should be saved
if args.output_directory == ' ':
output_directory = args.file_directory
else:
output_directory = args.output_directory
# Assign variable with inputs from the command line
plot_iv = input_boolean(args.plot_iv)
plot_cv = input_boolean(args.plot_cv)
plot_it = input_boolean(args.plot_it)
plot_ivth = input_boolean(args.plot_ivth)
plot_cvth = input_boolean(args.plot_cvth)
plot_itth = input_boolean(args.plot_itth)
plot_ivf = input_boolean(args.plot_ivf)
plot_cvf = input_boolean(args.plot_cvf)
plot_itf = input_boolean(args.plot_itf)
#plot_ivg = input_boolean(args.plot_ivg)
#plot_cvg = input_boolean(args.plot_cvg)
#plot_itg = input_boolean(args.plot_itg)
average_type = args.choose_average
# Print inputs
print('Input Directory: ' + input_directory)
print('Output Directory: ' + output_directory)
print(f'Plot CV Graphs: {plot_cv}')
print(f'Plot IV Graphs: {plot_iv}')
print(f'Plot It Graphs: {plot_it}')
print(f'Plot CV Graphs with temperature and humidity: {plot_cvth}')
print(f'Plot IV Graphs with temperature and humidity: {plot_ivth}')
print(f'Plot It Graphs with temperature and humidity: {plot_itth}')
print(f'Plot IV Graphs with fits: {plot_ivf}')
print(f'Plot It Graphs with fits: {plot_itf}')
print('Calculate averages using ' + average_type)
# List of data files
files = []
#print(os.listdir(input_directory))
folder = os.listdir(input_directory)
# Struggled to get the code below to work
"""
for Input in args.file_directory:
print("\tInput, ", Input)
OSInputs = sorted(glob.glob(Input+'/*.txt'))
print(OSInputs)
for OSInput in OSInputs:
if os.path.isfile(OSInput) and OSInput.endswith('.txt') and 'short' not in OSInput:
files.append(OSInput)
print("\t\t" + OSInput)
"""
# Fill files list
for filename in folder:
if os.path.isfile(os.path.join(input_directory, filename)) and filename.endswith('.txt') and 'short' not in filename:
files.append(os.path.join(input_directory, filename))
else:
continue
file_data = {}
#print(files)
# Create a dictionary with all the data files and the corresponding data object
for i in range(len(files)):
data = Data()
data.extract_data(files[i], average_type)
file_data[files[i]] = data
"""
for f in file_data.values():
print(f.v_mean)
continue
"""
# Separate the data into IV, CV and It
iv_data = {}
cv_data = {}
it_data = {}
for f1, f2 in file_data.items():
if f2.type == 'iv':
iv_data[f1] = f2
elif f2.type == 'cv':
cv_data[f1] = f2
elif f2.type == 'it':
it_data[f1] = f2
# Plot all IV files
if plot_iv:
print('Plotting IV Graphs')
for f1, f2 in iv_data.items():
graph = Plot()
graph.plot_graph(output_directory, plot_ivth, plot_ivf, f2)
# Plot all CV files
if plot_cv:
print('Plotting CV Graphs')
for f1, f2 in cv_data.items():
graph = Plot()
graph.plot_graph(output_directory, plot_cvth, plot_cvf, f2)
# Plot all It files
if plot_it:
print('Plotting It Graphs')
for f1, f2 in it_data.items():
graph = Plot()
graph.plot_graph(output_directory, plot_itth, plot_itf, f2)