def simple_heat():
"""Example heatmap of function z=z(x,y)"""
# Make dataset, with data provided [x,y,z]
dataset_a = DataSet([x_mesh, y_mesh, z], plot='heat')
# Make plot and add data
plot = Plot()
plot.set_axes(xlim=(-2, 2), ylim=(-2, 2))
plot.add_dataset(dataset_a)
# Plot graph and display
plot.plot()
plot.display()
def simple_scatter():
"""Example of two overlaid scatter plots"""
# Make two datasets specifying scatter graph
dataset_a = DataSet(random_2d_a, plot='scatter')
dataset_b = DataSet(random_2d_b, plot='scatter')
# Make plot object and add data sets
plot = Plot()
plot.add_dataset(dataset_a)
plot.add_dataset(dataset_b)
# Plot graph and display
plot.plot()
plot.save(name='./figures/2d_simple_scatter', fmt='png')
plot.display()
def simple_line():
"""Example of parametric line graph"""
# Make datasets
dataset_a = DataSet(heart, plot='line')
# Make plot and add data
plot = Plot(dim=3, elevation=115, angle=-90)
plot.set_axes(xlim=(-20, 20),
ylim=(-20, 20),
zlim=(-5, 35),
xticks=(10, 10),
yticks=())
plot.add_dataset(dataset_a)
# Plot graph and display
plot.plot()
plot.display()
def simple_scatter():
"""Example of two overlaid scatter plots"""
# Make two datasets specifying scatter graph, first coloured by random points, second sized by random points
dataset_a = DataSet(random_3d_a,
plot='scatter',
colour_map='Reds',
colour=random_1d_a)
dataset_b = DataSet(random_3d_b,
plot='scatter',
colour='blue',
marker_size=random_1d_b * 10)
# Make plot object, set as 3d and add data sets
plot = Plot()
plot.set_dimensions(dim=3)
plot.add_dataset(dataset_a)
plot.add_dataset(dataset_b)
# Plot graph and display
plot.plot()
plot.display()
def simple_contour():
"""Example contour plot of function z(x,y)"""
# Make dataset, with data provided [x,y,z]
dataset_a = DataSet([x_mesh, y_mesh, z],
plot='contour',
colour_map='Greys')
# Make plot and add data
plot = Plot()
plot.add_dataset(dataset_a)
# Plot graph and display
plot.plot()
plot.display()
random_colour = np.random.uniform(0, 1, 50)
# Make DataSet specifying properties on instantiation
dataset_a = DataSet(cosine,
plot='line',
label='cosine',
colour='black',
line_width=2)
# Make DataSet specifiying properties through setters
dataset_b = DataSet(random, plot='scatter', label='random')
dataset_b.set_marker(style='o', size=random_size)
dataset_b.set_colour(map='coolwarm', colour=random_colour)
# Make Plot class and add DataSets
plot = Plot()
plot.add_dataset(dataset_a)
plot.add_dataset(dataset_b)
# Adjust plot properties
plot.set_axes(xlim=(0, 10),
xticks=(1, 0.2),
yticks=(0.2, 0.05),
xlabel=r'$x$',
ylabel=r'$f\left(x\right)$') # Latex-style labels
# Plot graphs
plot.plot()
plot.save(fmt='png')
plot.display()
def simple_surface():
"""Example of surface plot, using either mesh or points"""
# Make dataset with data provided as [x,y,z], where x,y,z, are meshes
dataset_a = DataSet([mesh_x, mesh_y, func_1],
plot='surface_mesh',
colour_map='Blues')
# Make plot and add data
plot = Plot(dim=3, elevation=20, angle=130)
plot.add_dataset(dataset_a)
# Plot graph and display
plot.plot()
plot.set_axes(xlim=(-1.05, 1.05), ylim=(-2.05, 2.05), zlim=(-0.55, 0.55))
plot.display()
# Make dataset with data provided as (n,3)
dataset_b = DataSet(random_3d_b, plot='surface_points', colour_map='Reds')
# Make plot and add data
plot = Plot(dim=3, elevation=20, angle=130)
plot.add_dataset(dataset_a)
# Plot graph and display
plot.plot()
# plot.set_axes(xlim=(-1.05,1.05),ylim=(-2.05,2.05),zlim=(-0.55,0.55))
plot.display()
def advanced_contour():
"""Example contour plot of function z(x,y)"""
# Make two datasets, with same data provided [x,y,z], to highlight different contours
dataset_a = DataSet([x_mesh, y_mesh, z],
plot='contour',
colour_map='coolwarm',
colour_norm=(-0.5, 0.5),
line_width=1,
line_style='--')
dataset_a.set_contours(number=9,
limits=(-1, 1)) # Set number of contours and limits
dataset_b = DataSet([x_mesh, y_mesh, z],
plot='contour',
colour_map='Greys',
colour_norm=(-1, 0),
line_width=0.5,
line_style='-')
dataset_b.set_contours(levels=[0.0]) # Set specific contour values
# Make plot and add data
plot = Plot()
plot.add_dataset(dataset_a)
plot.add_dataset(dataset_b)
plot.set_axes(xlim=(-4, 4), ylim=(-4, 4))
# Plot graph and display
plot.plot()
plot.save(name='./figures/2d_contour', fmt='png')
plot.display()
def advanced_heat():
"""Example heatmap of function z=z(x,y) with additional options"""
# Make dataset, with data provided [x,y,z], colour normalisation conditions and interpolation method
dataset_a = DataSet([x_mesh, y_mesh, z],
plot='heat',
colour_map='Blues',
colour_norm=(-1, 1),
surface_interpolation='bilinear')
# Make plot and add data
plot = Plot()
plot.add_dataset(dataset_a)
plot.set_axes(xlim=(-4, 4), ylim=(-4, 4))
# Plot graph and display
plot.plot()
plot.save(name='./figures/2d_heat', fmt='png')
plot.display()
def advanced_line():
"""Example of line graphs demonstrating options"""
# Make dataset specifying arguments
dataset_a = DataSet(sine,
line_style='-',
line_width=1.5,
marker_style='o',
marker_size='4')
# Make dataset changing options using setters
dataset_b = DataSet(cosine)
dataset_b.set_line(style='--', width=1.5)
dataset_b.set_colour(colour='royalblue')
# Make plot object and adjust properties using setters
plot = Plot()
plot.set_text(latex=True, label=12)
plot.add_dataset(dataset_a)
plot.add_dataset(dataset_b)
plot.set_axes(xlim=(0, 8),
ylim=(-1.1, 1.1),
xlabel=r'$x$',
ylabel=r'$f\left(x\right)$',
xticks=(1.0, 0.2),
yticks=(0.2, 0.05))
# Plot graph and display
plot.plot()
plot.save(name='./figures/2d_advanced_line', fmt='png')
plot.display()
def simple_line():
"""Example of two overlaid line graphs"""
# Make two datasets
dataset_a = DataSet(sine)
dataset_b = DataSet(cosine)
# Make plot and add data
plot = Plot()
plot.set_text()
plot.add_dataset(dataset_a)
plot.add_dataset(dataset_b)
# Plot graph and display
plot.plot()
plot.save(name='./figures/2d_simple_line', fmt='png')
plot.display()
def error():
"""Example of a simple line graph with error bars, or line graph with shading"""
# Make data set using errors
dataset_a = DataSet(oscillating,
error_y=oscillating_error,
plot='error_bar',
label='Data and error')
dataset_a.set_error(interval=5, width=1, cap=2)
dataset_b = DataSet(oscillating,
plot='error_shade',
error_y=oscillating_error,
order=0,
colour='lightgrey',
label='Error')
dataset_c = DataSet(oscillating,
plot='line',
order=1,
colour='firebrick',
label='Data')
# Make line graph with error bars
plot_bar = Plot()
plot_bar.set_legend(legend=True)
plot_bar.add_dataset(dataset_a)
plot_bar.plot()
plot_bar.save(name='./figures/2d_error_bar', fmt='png')
plot_bar.display()
# Make line graph with shaded errors
plot_shade = Plot()
plot_shade.set_legend(legend=True, location='upper left')
plot_shade.add_dataset(dataset_b)
plot_shade.add_dataset(dataset_c)
plot_shade.plot()
plot_shade.save(name='./figures/2d_error_shade', fmt='png')
plot_shade.display()
def simple_bar():
"""Example of simple bar chart"""
# Make random discrete data
discrete_a = np.zeros((8, 2))
discrete_b = np.zeros((8, 2))
discrete_c = np.zeros((8, 2))
discrete_a[:, 0] = np.arange(8)
discrete_b[:, 0] = np.arange(8)
discrete_c[:, 0] = np.arange(8)
discrete_a[:, 1] = np.random.rand(8) * 10
discrete_b[:, 1] = np.random.rand(8) * 10
discrete_c[:, 1] = np.random.rand(8) * 10
# Make data sets, if using multiple bar_width must be the same
dataset_a = DataSet(discrete_a,
colour='pink',
bar_width=0.8,
plot='bar',
label='A')
dataset_b = DataSet(discrete_b,
colour='violet',
bar_width=0.8,
plot='bar',
label='B')
dataset_c = DataSet(discrete_c,
colour='darkviolet',
bar_width=0.8,
plot='bar',
label='C')
# Make plot object and add data sets
plot = Plot()
plot.add_dataset(dataset_a)
plot.add_dataset(dataset_b)
plot.add_dataset(dataset_c)
plot.set_axes(xticks=(1, 1), xlim=(-0.5, 7.5), ylim=(0, 12))
plot.set_legend(legend=True, location='upper right')
plot.set_text(legend=8)
# Plot graph and display
plot.plot()
plot.save(name='./figures/2d_simple_bar', fmt='png')
plot.display()
def scatter_and_line():
"""Example containing both a scatter and line graph"""
# Make random data points around straight line
random_linear = np.zeros((1000, 2))
random_linear[:, 0] = np.random.uniform(0, 10, 1000)
random_error = np.random.normal(0.0, 2.0, 1000)
random_linear[:, 1] = random_linear[:, 0] * 2.0 + 1.0 + random_error
# Make datasets, order determining line graph on top
dataset_a = DataSet(random_linear, plot='scatter', order=0, label='Random')
dataset_b = DataSet(linear,
plot='line',
colour='black',
order=1,
label='Linear')
# Colour scatter graph by error
dataset_a.set_colour(map='coolwarm', colour=random_error)
# Make plot object and add datasets
plot = Plot()
plot.set_text(latex=True)
plot.add_dataset(dataset_a)
plot.add_dataset(dataset_b)
plot.set_legend(legend=True)
# Plot graph and display
plot.plot()
plot.save(name='./figures/2d_scatter_and_line', fmt='png')
plot.display()