def test_plot_pandas_default(data):
default_kw = {'xlabel': data['xlabel'], 'ylabel': data['ylabel']}
desired_fig = Figure()
desired_ax = desired_fig.subplots(subplot_kw=default_kw)
desired_ax.plot(data['xdata'], data['ydata'])
actual_fig, actual_ax = default_plotter(data['data'])
assert_figures_equal(actual_fig, desired_fig)
def test_plot_pandas_log(data):
desired_fig = Figure()
log_kw= {
'xlabel': data['xlabel'], 'ylabel': data['ylabel'],
'xscale': 'log', 'yscale': 'log'}
desired_ax = desired_fig.subplots(subplot_kw=log_kw)
desired_ax.plot(data['xdata'], data['ydata'])
actual_fig, actual_ax = default_plotter(data['data'],
subplot_kw=log_kw)
assert_figures_equal(actual_fig, desired_fig)
def test_plot_pandas_twinx(data):
"""
Tests that we can generate a plot with multiple axes when we pass in different y-valued data.
"""
second_data = data['data'].copy()
second_data['Frequency (Hz)'] *= 2
second_data.rename(columns={'Frequency (Hz)': 'Frequency (kHz)'},
inplace=True)
default_kw = {'xlabel': data['xlabel'], 'ylabel': data['ylabel']}
desired_fig = Figure()
desired_ax = desired_fig.subplots(subplot_kw=default_kw)
desired_ax.plot(data['xdata'], data['ydata'])
new_ax = desired_ax.twinx()
new_ax.plot(second_data['Time (ms)'], second_data['Frequency (kHz)'])
new_ax.set_xlabel('Time (ms)')
new_ax.set_ylabel('Frequency (kHz)')
actual_fig, ax = default_plotter(data['data'])
actual_fig, _ = default_plotter(second_data, fig=actual_fig)
assert_figures_equal(actual_fig, desired_fig)
def test_plot_linear_fit(impedance_data_complex):
x_data = np.array([0, 1, 2, 3, 4, 5])
y_data = np.array([0, 1, 2, 3, 4, 5])
def linear_fit(x, offset, slope):
return offset + x * slope
fig_actual, ax_actual = default_plotter(
x_data, y_data,
fit_func=linear_fit)
fig_desired = Figure()
ax_desired = fig_desired.subplots()
ax_desired.plot(x_data, y_data)
ax_desired.plot(x_data, y_data, linestyle='dashed')
assert_figures_equal(fig_actual, fig_desired,
rtol=1e-8, atol=1e-8)
def test_plot_pandas_theory(data):
def gaussian(x, a=1, mu=0, sigma=1):
return a*np.exp(-np.square((x - mu)/(np.sqrt(2)*sigma)))
subplot_kw= {'xlabel': data['xlabel'], 'ylabel': data['ylabel']}
line_kw = {'linestyle': 'dashed'}
theory_kw = {'a': 2, 'mu': 1, 'sigma': 3}
theory_data = gaussian(data['xdata'], a=2, mu=1, sigma=3)
desired_fig = Figure()
desired_ax = desired_fig.subplots(subplot_kw=subplot_kw)
desired_ax.plot(data['xdata'], data['ydata'])
desired_ax.plot(data['xdata'], theory_data, **line_kw)
actual_fig, actual_ax = default_plotter(data['data'],
theory_func=gaussian, theory_kw=theory_kw)
assert_figures_equal(actual_fig, desired_fig)
def test_plot_pandas_theory_data(data):
default_kw = {'xlabel': data['xlabel'], 'ylabel': data['ylabel']}
desired_fig = Figure()
desired_ax = desired_fig.subplots(subplot_kw=default_kw)
theory_line_kw = {'linestyle': 'dashed'}
theory_data=pd.DataFrame({
data['xlabel']: [0, 1, 2, 3],
data['ylabel']: [2, 3, 4, 5],
})
desired_ax.plot(data['xdata'], data['ydata'])
desired_ax.plot(theory_data[data['xlabel']],
theory_data[data['ylabel']], **theory_line_kw)
desired_ax.set_xlim(0.9, 3.3)
actual_fig, actual_ax = default_plotter(data['data'],
theory_data=theory_data)
assert_figures_equal(actual_fig, desired_fig)