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Go back Launching Visual Studio If nothing happens, download the GitHub extension for Visual Studio and try again. Go back Latest commit @antonlopezr antonlopezr README: very last tweak ... d03f3c5 May 12, 2021 README: very last tweak d03f3c5 Git stats * 75 commits Files Permalink Failed to load latest commit information. Type Name Latest commit message Commit time _demo README: logo May 12, 2021 dev Ready to open source May 10, 2021 mpl_plotter More lots of panes May 11, 2021 tests README: input clarified, all method input parameters May 12, 2021 .gitattributes Initial commit Jul 17, 2020 .gitignore Major update Jan 1, 2021 LICENSE Major update Jan 1, 2021 README.md README: very last tweak May 12, 2021 setup.py MPL Plotter 3.8.3 May 11, 2021 View code MPL Plotter Table of Contents 1. Introduction 2. Install 3. Map of the library 4. Capabilities 5. Getting started 6. Base methods: examples, status 7. Matplotlib compatibility 8. Advanced plotting: Presets and custom_canvas 9. Unstable functionality: panes 10. Contributing 11. All modifiable parameters 1. Introduction 2. Install 3. Map of the library 4. Capabilities 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: 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 Publication Precision 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 Backlog Bugs Documentation Functionality 11. All modifiable parameters 2D 3D README.md MPL Plotter [logo] 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. The API might change, 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! alt text 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 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 o line o scatter o heatmap o quiver o streamline o fill_area o floating_text + three_d o line o scatter o surface o floating_text + setup o figure o custom_canvas + presets/ o publication # two_d # three_d o precision # two_d # three_d o custom # two_d # three_d # generate_preset_2d # generate_preset_3d o panes # Lines @ n_pane_single @ n_pane_comparison o standard/ # publication # precision + color/ o maps o 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). x = np.linspace(0, 2*np.pi, 100) line(show=True) y = np.sin(x) line(x=x, y=y, show=True) alt text alt text 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 alt text alt text --------------------------------------------------------------------- 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 alt text alt text alt text 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 alt text scatter Stable x, y alt text heatmap Stable x, y, z alt text quiver Stable x, y, u, v alt text streamline Stable x, y, u, v alt text fill Stable x, y, z alt text 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 alt text scatter Stable x, y, z alt text surface Stable x, y, z alt text Wireframe Stable x, y, z alt text 6.3 Plot combination examples alt text 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() 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() alt text 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) 2D 3D alt text alt text 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 aspect ratio of 1 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 alt text alt text 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() alt text 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 o 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 ) alt text 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 ) alt text 9.3 Bunch of panes Cause why would you believe me otherwise. alt text And same for the n m-curve comparisons. alt text 10. Contributing There's much to be done yet. Feature suggestions or bug finds are welcome! Backlog Bugs * 2D + prune o Tick number defaults to 10 + tick color overwritten by tick label color + Combination with python-control plots o Axis limits not working * 3D + z label rotation not working Documentation * Color * More examples * Description of all parameters * readthedocs? Functionality * 2D + Presets o Math - eg: image + New plots o Financial # Bar charts * 3D + New plots o tricontour o projections 11. All modifiable parameters 2D Parameter Default Description backend Qt5Agg - font serif - math_font dejavuserif - font_color black - 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 Pump Triline - title_color pink - 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 - 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 - 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 - About Matplotlib based plotting library. Simple and effective. Resources Readme License MIT License Releases 1 MPL Plotter 3.8.3 Latest May 11, 2021 Packages 0 No packages published Languages * Python 100.0% * (c) 2021 GitHub, Inc. * Terms * Privacy * Security * Status * Docs * Contact GitHub * Pricing * API * Training * Blog * About You can't perform that action at this time. You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session.