mpl-plotter 3.8.8

Matplotlib-based plotting library

MPL Plotter

Making plots for technical documents can be a time sink. At some point, I decided I might as well rid myself of that overhead, and learn some Python along the way! This library is the result of that. It does the job for me and I expand it when it can't. Some parts of the API are unstable (in those cases, you'll be warned in the section where the method is discussed) and the defaults might not be your cup of tea, but it might still do the trick! Hope you find some use in it.

Antonio Lopez Rivera, 2020

Table of Contents

1. Introduction

2. Install

3. Map of the library

4. Capabilities

5. Getting started

5.1 2D Lines

5.2 3D Lines

6. Base methods: examples, status

6.1 2D

6.2 3D

6.3 Plot combination examples

7. Matplotlib compatibility

7.1 Retrieving axes, figures

7.2 Using Matplotlib's axis tiling

8. Advanced plotting: Presets and custom_canvas

8.1 Custom presets

8.2 Standard presets

8.3 custom_canvas

9. Unstable functionality: panes

9.1 n_pane_single

9.2 n_pane_comparison

9.3 Bunch of panes

10. Contributing

11. All modifiable parameters

1. Introduction

MPL Plotter is a Matplotlib based Python plotting library built with the goals of achieving publication-quality plots in an efficient and comprehensive way.

---

The fundamental premise of MPL Plotter is to:

• Generate publication quality plots in a single function call
• Allow for any and all further customization with regular Matplotlib if needed

As a result, MPL Plotter is built with Matplotlib compatibility in mind: its capabilities expand when used in combination. Keep reading to see them in action!

---

There's three ways to use MPL Plotter:

• Calls to the 2D and 3D plotting classes.
• Using presets, either those shipped with the library, or custom ones.
• Calling the "decorator" custom_canvas class. This class won't plot anything, but rather allow you to create a customized canvas on which to plot using Matplotlib.

The first will be covered in Sections 4 and 5, from basic usage to in depth customization. The base output and API stability of all base methods can be seen in Section 6. The latter two, in Section 8.

Say goodbye to hours getting your plots in shape!

2. Install

pip install mpl_plotter

All dependencies will be checked for and installed automatically. They can be found in setup.py under install_requires.

PyPi

TROUBLESHOOTING: If you're upgrading to the latest version of MPL Plotter, please make sure to check your dependencies are up to date with the repo. To do so, download requirements.txt above, activate your virtual environment (if you work with one, otherwise ignore that), and

pip install -r requirements.txt

3. Map of the library

This is the map of the library. Mostly for import reference.

Entries in the map are in order of relevance (and in which they will be discussed).

• mpl_plotter
  • two_d
    • line
    • scatter
    • heatmap
    • quiver
    • streamline
    • fill_area
    • floating_text
  • three_d
    • line
    • scatter
    • surface
    • floating_text
  • setup
    • figure
    • custom_canvas
  • utilities
    • get_available_fonts
  • presets/
    • publication
      • two_d
      • three_d
    • precision
      • two_d
      • three_d
    • custom
      • two_d
      • three_d
      • generate_preset_2d
      • generate_preset_3d
    • panes
      • Lines
        • n_pane_single
        • n_pane_comparison
    • standard/
      • publication
      • precision
  • color/
    • maps
    • schemes

4. Capabilities

With a single call, you can generate the following plots:

• 2D
  • Line plots
  • Scatter plots
  • Heatmaps
  • Quiver plots
  • Streamline plots
  • Area fills
  • Floating text
• 3D
  • Line plots
  • Scatter plots
  • Surface plots
  • Floating text

Furthermore, MPL Plotter also allows to:

• Use a custom_canvas function to define a cusomized figure and axis on which to draw using Matplotlib
• Generate, customize and use 2D and 3D presets in one or many function calls
• Use the pre-made publication and precision presets to immediately obtain valuable plots
• Easily create custom linear segmented colormaps, so you can use any sequence of colors you fancy
• Custom colorschemes (currently only 1, as it's enough to fit my needs, perhaps more in the future)

---

Each plot has specific parameters which can be modified, plus general ones which apply for all 2D and 3D plots respectively. The specific parameters for each plotting class are available in the docstrings of their __init__ methods. It's comfortable to access them from the interactive Python terminal. Eg:

>>> from mpl_plotter.two_d import line
>>> help(line)

In Section 11 at the end of this README, all general parameters for 2D and 3D plots can be seen.

5. Getting started

In this section we'll go from the the most basic line plot to a fairly customized version in 2D, and similarly for 3D. The line demo scripts can be found in _demo/scripts/line_demos/.

The MPL Plotter workflow is simple by design: the walkthrough below is sufficient to acquaint you with all functionality, for line plots as well as all others.

The base output of all available 2D and 3D plot follow in Section 6. By then, you will be able to pick anyone up and do your thing.

5.1 2D Lines

As follows from the map above, the import to use the 2D line class is:

from mpl_plotter.two_d import line

And the following is the most basic MPL Plotter call, which will generate the image below (no input, and sin wave respectively).

line(show=True)	x = np.linspace(0, 2*np.pi, 100) y = np.sin(x) line(x=x, y=y, show=True)

Two important features are apparent:

1. MPL Plotter provides mock plots for every plotting class, so you can get straight into action and see what each does
2. MPL Plotter is somewhat "opinionated" and sets up quite a few parameters by default. This is based purely on my preference. You may not agree and you're more than welcome to play around with them!

---

Two more examples (result in the table below):

1. We can add some customization to make our line look a bit better:

    line(show=True, demo_pad_plot=True, spines_removed=None)
   

   Our line has now some margins to breathe while the ticks are placed at the maximum and minimums of our curve, and no spines are removed.

2. Lastly, an example using some of the parameters you can change:

    line(norm=True, line_width=4,
         aspect=1,
         show=True, demo_pad_plot=True,
         x_label="x", x_label_size=30, x_label_pad=-0.05,
         y_label="$\Psi$", y_label_size=30, y_label_rotation=0, y_label_pad=20,
         title="Custom Line", title_font="Pump Triline", title_size=40, title_color="orange",
         tick_color="darkgrey", workspace_color="darkred", tick_ndecimals=4,
         x_tick_number=12, y_tick_number=12,
         x_tick_rotation=35,
         color_bar=True, cb_tick_number=5, cb_pad=0.05,
         grid=True, grid_color="grey")
   

1. Somewhat customized	2. Customization example

---

5.2 3D Lines

Much of the same follows for 3D plots. In this case however customization is somewhat more limited. This is due to the fact that 1. 3D plots are less useful in general (in my experience, and thus I've spent less time on them) 2. Matplotlib support for 3D plots is more limited

Basic	Somewhat customized	Customization example

6. Base methods: examples, status

Below can be seen the base output of all methods, their input variables, and an indication of how stable each method is. In tests/test_minimal, base calls (no arguments besides show=True) for all methods are available. For real-world reference, tests/tests_2D and tests/tests_3D contain an example using various parameters for every single method.

For method-specific customization options (say, the line_width or point_size attributes for lines and scatter plots respectively), please check each method's docstring .

6.1 2D

All plots generated in tests/test_minimal.py.

Method	Status	Input	Base output
line	Stable	x, y
scatter	Stable	x, y
heatmap	Stable	x, y, z
quiver	Stable	x, y, u, v
streamline	Stable	x, y, u, v
fill	Stable	x, y, z

6.2 3D

Once more, all plots generated in tests/test_minimal.py. Wireframe is included: note it's not a method per se, but a setting of surface (hover over the image to see it).

Method	Status	Input	Base output
line	Stable	x, y, z
scatter	Stable	x, y, z
surface	Stable	x, y, z
Wireframe	Stable	x, y, z

6.3 Plot combination examples

7. Matplotlib compatibility

7.1 Retrieving axes, figures

The axis and figure on which each class draws are instance attributes. To retrieve them and continue modifications using standard Matplotlib:

from mpl_plotter.two_d import line

my_plot = line()
ax, fig = my_plot.ax, my_plot.fig

With the axis and figure, most Matplotlib functions out there can be used to further modify your plots.

7.2 Using Matplotlib's axis tiling

Matplotlib allows for subplot composition using subplot2grid. This can be used in combination with MPL Plotter:

Importantly:

• The auxiliary function figure (from mpl_plotter.setup import figure) sets up a figure in a chosen backend. This is convenient, as if the figure is created with plt.figure(), only the default non-interactive Matplotlib backend will be available, unless matplotlib.use(<backend>) is specified before importing pyplot.

    backend = "Qt5Agg"  # None -> regular non-interactive matplotlib output
  
    fig = figure(figsize=(10, 10), backend=backend)
  
    ax0 = plt.subplot2grid((2, 2), (0, 0), rowspan=1, aspect=1, fig=fig)
    ax1 = plt.subplot2grid((2, 2), (1, 0), rowspan=1, aspect=1, fig=fig)
    ax2 = plt.subplot2grid((2, 2), (0, 1), rowspan=1, aspect=1, fig=fig)
    ax3 = plt.subplot2grid((2, 2), (1, 1), rowspan=1, aspect=12, fig=fig)
  
    axes = [ax0, ax1, ax2, ax3]
    plots = [line, quiver, streamline, fill_area]
  
    for i in range(len(plots)):
        plots[i](fig=fig, ax=axes[i],
                 backend=backend
                 )
  
    plt.show()
  

8. Advanced plotting: Presets and custom_canvas

The following are alternative ways to use MPL Plotter. Presets are currently implemented for the 2D and 3D line and scatter plot classes. More might be implemented in the future.

8.1 Custom presets

Presets enable you to create plots without barely writing any code. An example workflow follows.

1. Use a preset creation function (generate_preset_2d or generate_preset_3d) to create a preset

    from mpl_plotter.presets.custom import generate_preset_2d
   
    generate_preset_2d(preset_dest="presets", preset_name="MYPRESET", disable_warning=True, overwrite=True)
   

   A MYPRESET.py file will be created in a new (or not) presets/ directory within your project's root directory.

   • If no preset_dest is provided, MYPRESET.py will be saved in your root directory.
   • If no preset_name is provided, the preset will be saved as preset_2d.py.
   • By setting disable_warning=True, console output reminding you of the risk of rewriting your preset will be suppressed.
   • By setting overwrite=True, every time your run the preset creation function, it will overwrite the previously created preset with the same name (rather inconvenient, but who knows when it can come in handy).

   This file has a preset dictionary inside, with all editable parameters inside it, and commented out. Eg:

    preset = { 
        ## Basic 
        # "plot_label": None, 
        ## Backend 
        # "backend": "Qt5Agg", 
        ## Fonts 
        # "font": "serif",
        ...
    }
   

   By uncommenting certain lines, those parameters will be read and used to shape your plots.

2. Modify MYPRESET.py according to your needs.

3. Import mpl_plotter.presets.custom.two_d (or three_d if working with a 3D preset) and initiate it with MYPRESET

    from mpl_plotter.presets.custom import two_d
   
    my_preset_plot_family = two_d(preset_dir="presets", preset_name="MYPRESET")
   
    my_preset_line = my_plot_family.line
   
    # You can create further plotting classes spawning from my_preset_plot_family:
    # Eg        --->        my_preset_scatter = my_plot_family.scatter
   

4. Call a plotting function child of two_d, setting any extra parameters appropriately (plot title, etc.)

    my_preset_line(show=True, demo_pad_plot=True, color="blue", title="TITLE", title_size=200, aspect=1)
   

   The result of this example plus the 3D version and those for all other available plots can be seen in the table below.

   2D
   3D

5. Make as many plots as you need. Tiling is supported as well (see panes in Section 9)

8.2 Standard presets

Standard presets are available to remove overhead. They're tailored for my needs and desires, but perhaps you find them useful too.

Publication

It is a common mistake to make a figure for a paper with unreadable labels. This preset tries to solve that, generating plots optimized to be printed on a small format, in side-by-side plots or embedded in a column of text.

from mpl_plotter.presets.precision import two_d
from mpl_plotter.color.schemes import one           # Custom colorscheme

x = np.linspace(0, 4, 1000)
y = np.exp(x)
z = abs(np.sin(x)*np.exp(x))

two_d.line(x, z, aspect=0.05, color=one()[-2], show=True)

Precision

Made to plot functions large on the screen, with equal x and y scales to avoid skewing the variables, and many ticks to visually inspect a signal.

from mpl_plotter.presets.precision import two_d

two_d.line(x, z, aspect=0.05, color=one()[-2], show=True)

Publication	Precision

And below, all remaining plots (publication preset above, precision below):

8.3 custom_canvas

Lastly, MPL Plotter can be used to create a "custom canvas" on which to draw with Matplotlib. - custom_canvas creates a figure and 1. By retrieving the figure, more axes may be created. - If you wish custom_canvas to resize your axes, it must be given the x and y of (one) of your plots

NOTE: functionality might not be at 100% yet when using custom_canvas+Matplotlib as compared to plotting with MPL Plotter directly.

from mpl_plotter.setup import custom_canvas

x = np.linspace(0, 2*np.pi, 100)
y = np.sin(x)

c = custom_canvas(x=x, y=y, spines_removed=None, font_color="darkred")  # x and y provided: axes are resized
ax, fig = c.ax, c.fig

# Regular Matplotlib stuff

plt.plot(x, y)

plt.show()

9. Unstable functionality: panes

Disclaimer: The following are utilities which combine presets and axis tiling to create n-pane plots. The API is very volatile, and flexibility must be improved. In any case, I find them practical from time to time, perhaps you too.

MPL Plotter includes a panes package for line plots, via the Lines class. The method "map" is as follows:

• mpl_plotter
  • panes
    • Lines
      • n_pane_single
      • n_pane_comparison

9.1 n_pane_single

This function takes in a number n of curves, and generates an n-pane panel plot with them.

 Lines(preset=preset).n_pane_single(x,                   # Horizontal vector
                                    [u, v, y],           # List of curves to be plotted
                                    ["u", "v", "y"],     # List of vertical axis labels
                                    ["a", "b", "c"]      # List of legend labels 
                                    )  

9.2 n_pane_comparison

In turn, this function takes in a number n of lists of m curves (where m=2 in the example below), to be plotted in the same pane for comparison.

Lines(preset=preset).n_pane_comparison(x,                               # Horizontal vector
                                       [[u, uu], [v, vv], [y, yy]],     # List of pairs of curves to be compared
                                       ["u", "v", "y"],                 # List of vertical axis labels
                                       ["a", "b"]                       # List of legend labels
                                       )

9.3 Bunch of panes

Cause why would you believe me otherwise.

And same for the n m-curve comparisons.

10. Contributing

There's much to be done yet. Feature suggestions or bug finds are welcome!

Backlog

Bugs

• 2D
  • Combination with python-control plots
    • Axis limits not working
• 3D
  • z label rotation not working

Documentation

• Color
• More examples
• Description of all parameters
• readthedocs?

Functionality

• 2D
  • Presets
    • Math - eg:
  • New plots
    • Financial
      • Bar charts
• 3D
  • New plots
    • tricontour
    • projections

11. All modifiable parameters

2D

Parameter	Default	Description
backend	Qt5Agg	-
font	serif	-
math_font	dejavuserif	-
font_color	black	-
font_size_increase	0	-
fig	None	-
ax	None	-
figsize	(6, 6)	-
shape_and_position	111	-
prune	None	-
resize_axes	True	-
aspect	None	-
workspace_color	None	-
workspace_color2	None	-
background_color_figure	white	-
background_color_plot	white	-
background_alpha	1	-
style	None	-
light	None	-
dark	None	-
spine_color	None	-
spines_removed	(0, 0, 1, 1)	-
x_upper_bound	None	-
x_lower_bound	None	-
y_upper_bound	None	-
y_lower_bound	None	-
x_bounds	None	-
y_bounds	None	-
demo_pad_plot	True	-
x_upper_resize_pad	0	-
x_lower_resize_pad	0	-
y_upper_resize_pad	0	-
y_lower_resize_pad	0	-
grid	True	-
grid_color	lightgrey	-
grid_lines	-.	-
color	darkred	-
cmap	RdBu_r	-
alpha	None	-
norm	None	-
title	None	-
title_size	12	-
title_y	1.025	-
title_weight	None	-
title_font	None	-
title_color	None	-
x_label	None	-
x_label_size	20	-
x_label_pad	10	-
x_label_rotation	None	-
x_label_weight	None	-
y_label	None	-
y_label_size	20	-
y_label_pad	10	-
y_label_rotation	None	-
y_label_weight	None	-
x_tick_number	3	-
x_tick_labels	None	-
y_tick_number	3	-
y_tick_labels	None	-
x_label_coords	None	-
y_label_coords	None	-
tick_color	None	-
tick_label_pad	5	-
ticks_where	(1, 1, 0, 0)	-
tick_label_size	None	-
x_tick_label_size	15	-
y_tick_label_size	15	-
x_custom_tick_locations	None	-
y_custom_tick_locations	None	-
fine_tick_locations	True	-
x_custom_tick_labels	None	-
y_custom_tick_labels	None	-
x_date_tick_labels	False	-
date_format	%Y-%m-%d	-
tick_ndecimals	1	-
x_tick_ndecimals	None	-
y_tick_ndecimals	3	-
x_tick_rotation	None	-
y_tick_rotation	None	-
tick_labels_where	(1, 1, 0, 0)	-
color_bar	False	-
cb_pad	0.2	-
cb_axis_labelpad	10	-
shrink	0.75	-
extend	neither	-
cb_title	None	-
cb_orientation	vertical	-
cb_title_rotation	None	-
cb_title_style	normal	-
cb_title_size	10	-
cb_top_title_y	1	-
cb_ytitle_labelpad	10	-
cb_title_weight	normal	-
cb_top_title	False	-
cb_y_title	False	-
cb_top_title_pad	None	-
x_cb_top_title	0	-
cb_vmin	None	-
cb_vmax	None	-
cb_hard_bounds	False	-
cb_outline_width	None	-
cb_tick_number	5	-
cb_ticklabelsize	10	-
tick_ndecimals_cb	None	-
plot_label	None	-
legend	False	-
legend_loc	upper right	-
legend_bbox_to_anchor	None	-
legend_size	15	-
legend_weight	normal	-
legend_style	normal	-
legend_handleheight	None	-
legend_ncol	1	-
show	False	-
zorder	None	-
filename	None	-
dpi	None	-
suppress	True	-

3D

Parameter	Default	Description
x_scale	1	-
y_scale	1	-
z_scale	1	-
backend	Qt5Agg	-
font	serif	-
math_font	dejavuserif	-
font_color	black	-
font_size_increase	0	-
fig	None	-
ax	None	-
figsize	None	-
shape_and_position	111	-
azim	-137	-
elev	26	-
prune	None	-
resize_axes	True	-
aspect	1	-
box_to_plot_pad	10	-
spines_juggled	(1,0,2)	-
spine_color	None	-
blend_edges	False	-
workspace_color	None	-
workspace_color2	None	-
background_color_figure	white	-
background_color_plot	white	-
background_alpha	1	-
style	None	-
light	None	-
dark	None	-
pane_fill	None	-
x_upper_bound	None	-
x_lower_bound	None	-
y_upper_bound	None	-
y_lower_bound	None	-
z_upper_bound	None	-
z_lower_bound	None	-
x_bounds	None	-
y_bounds	None	-
z_bounds	None	-
demo_pad_plot	False	-
x_upper_resize_pad	0	-
x_lower_resize_pad	0	-
y_upper_resize_pad	0	-
y_lower_resize_pad	0	-
z_upper_resize_pad	0	-
z_lower_resize_pad	0	-
show_axes	True	-
grid	True	-
grid_color	lightgrey	-
grid_lines	-.	-
color	darkred	-
cmap	RdBu_r	-
alpha	1	-
title	None	-
title_weight	normal	-
title_size	12	-
title_y	1.025	-
title_color	None	-
title_font	None	-
x_label	x	-
x_label_weight	normal	-
x_label_size	12	-
x_label_pad	7	-
x_label_rotation	None	-
y_label	y	-
y_label_weight	normal	-
y_label_size	12	-
y_label_pad	7	-
y_label_rotation	None	-
z_label	z	-
z_label_weight	normal	-
z_label_size	12	-
z_label_pad	7	-
z_label_rotation	None	-
x_tick_number	5	-
x_tick_labels	None	-
x_custom_tick_labels	None	-
x_custom_tick_locations	None	-
y_tick_number	5	-
y_tick_labels	None	-
y_custom_tick_labels	None	-
y_custom_tick_locations	None	-
z_tick_number	5	-
z_tick_labels	None	-
z_custom_tick_labels	None	-
z_custom_tick_locations	None	-
x_tick_rotation	None	-
y_tick_rotation	None	-
z_tick_rotation	None	-
tick_color	None	-
tick_label_pad	4	-
tick_ndecimals	1	-
tick_label_size	8.5	-
x_tick_label_size	None	-
y_tick_label_size	None	-
z_tick_label_size	None	-
color_bar	False	-
extend	neither	-
shrink	0.75	-
cb_title	None	-
cb_axis_labelpad	10	-
cb_tick_number	5	-
cb_outline_width	None	-
cb_title_rotation	None	-
cb_title_style	normal	-
cb_title_size	10	-
cb_top_title_y	1	-
cb_ytitle_labelpad	10	-
cb_title_weight	normal	-
cb_top_title	False	-
cb_y_title	False	-
cb_top_title_pad	None	-
x_cb_top_title	0	-
cb_vmin	None	-
cb_vmax	None	-
cb_ticklabelsize	10	-
plot_label	None	-
legend	False	-
legend_loc	upper right	-
legend_size	13	-
legend_weight	normal	-
legend_style	normal	-
legend_handleheight	None	-
legend_ncol	1	-
show	False	-
newplot	False	-
filename	None	-
dpi	None	-
suppress	True	-