Example #1
0
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.
Example #2
0
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
Example #3
0
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)
Example #5
0
    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()
Example #6
0
# 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')
Example #7
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    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()
Example #8
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# 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()
Example #9
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def test_plot():
	fig = plt.figure()
	ax = fig.add_subplot(111)
	test_plot = Display(fig, ax)
	return test_plot
Example #10
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# 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()