def test_do_plotting_warning(): test_plot = Display() with pytest.warns(UserWarning) as record: test_plot.do_plotting() # check that only one warning was raised assert len(record) == 1 # check that the message matches assert record[0].message.args[0] == """No plots have been created.
def test_do_plotting_warning():
test_plot = Display()
with pytest.warns(UserWarning) as record:
test_plot.do_plotting()
# check that only one warning was raised
assert len(record) == 1
# check that the message matches
args = record[0].message.args # type: ignore
expected = """No plots have been created.
Please call a plotting function before calling 'do_plotting()'"""
assert args[0] == expected
def test_Display(): no_args = Display() assert isinstance(no_args.fig, figure.Figure) assert isinstance(no_args.ax, axes.Axes) fig = plt.figure() fig_arg = Display(fig=fig) assert isinstance(fig_arg.fig, figure.Figure) assert isinstance(fig_arg.ax, axes.Axes) assert fig_arg.fig is fig fig = plt.figure() ax = fig.add_subplot(111) both_args = Display(fig=fig, ax=ax) assert isinstance(both_args.fig, figure.Figure) assert isinstance(both_args.ax, axes.Axes) assert both_args.fig is fig assert both_args.ax is ax for obj in [test_tuple, test_list_strs, test_list_ints, test_string, *test_numbers, test_dict]: with pytest.raises(TypeError): Display(fig=obj) with pytest.raises(TypeError): Display(fig=fig, ax=obj) fig = plt.figure() ax = fig.add_subplot(111) with pytest.raises(TypeError): Display(ax, fig)
def display(self):
"""
Display the Chromatogram
"""
self.constrain_zoom("x")
display = Display(self.fig, self.ax)
self.ax.clear()
# Read the TIC from the tic.dat file in the experiment tarfile
intensity_array, tic = self.experiment.tic_data
# peak_list = load_peaks(os.path.join(ExprDir, "{}_peaks.dat".format(self.experiment.name)))
display.plot_tic(tic, label=self.experiment.name, minutes=True)
# display.plot_peaks(filtered_peak_list, "Peaks")
# display.do_plotting('TIC and PyMS Detected Peaks')
# display.do_plotting(f'{self.experiment.name} TIC')
display.do_plotting('')
x = [time / 60 for time in tic.time_list]
self.ax.set_xlim(left=0, right=max(x))
self.ax.set_ylim(bottom=0)
self.ax.set_xlabel("Retention Time")
self.ax.set_ylabel("Intensity")
self.fig.subplots_adjust(left=0.1, bottom=0.125, top=0.9, right=0.97)
# figure.tight_layout()
self.canvas.draw()
self.setup_ylim_refresher(intensity_array, x)
area = peak_sum_area(im, peak)
peak.area = area
# print some details
UID = peak.UID
# height as sum of the intensities of the apexing ions
height = sum(peak.get_mass_spectrum().mass_spec.tolist())
print(UID + f", {rt:.2f}, {height:.2f}, {peak.area:.2f}")
# TIC from raw data
tic = data.get_tic()
# baseline correction for TIC
tic_bc = tophat(tic, struct="1.5m")
# Get Ion Chromatograms for all m/z channels
n_mz = len(im.get_mass_list())
ic_list = []
for m in range(n_mz):
ic_list.append(im.get_ic_at_index(m))
# Create a new display object, this time plot the ICs
# and the TIC, as well as the peak list
display = Display()
display.plot_tic(tic_bc, 'TIC BC')
for ic in ic_list:
display.plot_ic(ic)
display.plot_peaks(new_peak_list, 'Peaks')
display.do_plotting('TIC, and PyMassSpec Detected Peaks')
display.show_chart()
# real_peak_list is PyMassSpec' best guess at the true peak list
################## Run Simulator ######################
# Simulator takes a peak list, time_list and mass_list
# and returns an IntensityMatrix object.
# The mass_list and time_list are the same for the real
# data and the simulated data.
time_list = real_im.time_list
mass_list = real_im.mass_list
sim_im = gcms_sim(time_list, mass_list, real_peak_list)
# sim_im is an IntensityMatrix object
# select one ic to add noise to from this simulated intensity matrix
ic = sim_im.get_ic_at_mass(73)
ic_add_noise = copy.deepcopy(ic)
# Now add noise to the simulated intensity matrix object
scale = 1000
cutoff = 10000
prop = 0.0003
add_gaussv_noise_ic(ic_add_noise, scale, cutoff, prop)
display = Display()
display.plot_ics([ic, ic_add_noise], ['Without noise', 'With noise added'])
display.do_plotting('Simulated IC for m/z = 73, with and without noise')
peak.area = area
# real_peak_list is PyMassSpec' best guess at the true peak list
################## Run Simulator ######################
# Simulator takes a peak list, time_list and mass_list
# and returns an IntensityMatrix object.
# The mass_list and time_list are the same for the real
# data and the simulated data.
time_list = real_im.time_list
mass_list = real_im.mass_list
sim_im = gcms_sim(time_list, mass_list, real_peak_list)
# sim_im is an IntensityMatrix object
# Now add noise to the simulated intensity matrix object
scale = 1000
add_gaussc_noise(sim_im, scale)
### Now display the ics from the simulated data
ics = []
for i in range(n_mz):
ics.append(sim_im.get_ic_at_index(i))
display = Display()
for ic in ics:
display.plot_ic(ic)
display.do_plotting('ICs, and PyMassSpec Detected Peaks of Simulated Data')
display.show_chart()
# real_peak_list is PyMassSpec' best guess at the true peak list
################## Run Simulator ######################
# Simulator takes a peak list, time_list and mass_list
# and returns an IntensityMatrix object.
# The mass_list and time_list are the same for the real
# data and the simulated data.
time_list = real_im.get_time_list()
mass_list = real_im.get_mass_list()
sim_im = gcms_sim(time_list, mass_list, real_peak_list)
# sim_im is an IntensityMatrix object
# select one ic to add noise to from this simulated intensity matrix
ic = sim_im.get_ic_at_mass(73)
ic_add_noise = copy.deepcopy(ic)
# Now add noise to the simulated intensity matrix object
scale = 1000
add_gaussc_noise_ic(ic_add_noise, scale)
display = Display()
display.plot_ic(ic, label="Without Noise")
display.plot_ic(ic_add_noise, label="With Noise Added")
display.do_plotting('Simulated IC for m/z = 73, with and without noise')
display.show_chart()
def test_plot(): fig = plt.figure() ax = fig.add_subplot(111) test_plot = Display(fig, ax) return test_plot
# Set the peak areas
for peak in real_peak_list:
area = peak_sum_area(real_im, peak)
peak.area = area
# real_peak_list is PyMassSpec' best guess at the true peak list
################## Run Simulator ######################
# Simulator takes a peak list, time_list and mass_list
# and returns an IntensityMatrix object.
# The mass_list and time_list are the same for the real
# data and the simulated data.
time_list = real_im.get_time_list()
mass_list = real_im.get_mass_list()
sim_im = gcms_sim(time_list, mass_list, real_peak_list)
# sim_im is an IntensityMatrix object
### Now display the ics from the simulated data
ics = []
for i in range(n_mz):
ics.append(sim_im.get_ic_at_index(i))
display = Display()
for ic in ics:
display.plot_ic(ic)
display.do_plotting('ICs of Simulated Data')
display.show_chart()
